2023-01-11T20:35:39.9400694Z Requested labels: linux.2xlarge
2023-01-11T20:35:39.9400798Z Job defined at: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/91627/merge
2023-01-11T20:35:39.9400995Z Reusable workflow chain:
2023-01-11T20:35:39.9401034Z pytorch/pytorch/.github/workflows/pull.yml@refs/pull/91627/merge (57fc38f02f250896a12b32cfa200a6105a03d09c)
2023-01-11T20:35:39.9401085Z -> pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/91627/merge (57fc38f02f250896a12b32cfa200a6105a03d09c)
2023-01-11T20:35:39.9401137Z Waiting for a runner to pick up this job...
2023-01-11T20:35:40.4008905Z Job is about to start running on the runner: i-05ef50dfe628429d7 (organization)
2023-01-11T20:35:47.3085258Z Current runner version: '2.300.2'
2023-01-11T20:35:47.3090546Z Runner name: 'i-05ef50dfe628429d7'
2023-01-11T20:35:47.3090994Z Runner group name: 'Default'
2023-01-11T20:35:47.3091557Z Machine name: 'ip-10-0-2-51'
2023-01-11T20:35:47.3093273Z ##[group]GITHUB_TOKEN Permissions
2023-01-11T20:35:47.3093741Z Actions: read
2023-01-11T20:35:47.3094011Z Checks: read
2023-01-11T20:35:47.3094307Z Contents: read
2023-01-11T20:35:47.3094541Z Deployments: read
2023-01-11T20:35:47.3094810Z Discussions: read
2023-01-11T20:35:47.3095074Z Issues: read
2023-01-11T20:35:47.3095291Z Metadata: read
2023-01-11T20:35:47.3095641Z Packages: read
2023-01-11T20:35:47.3095905Z Pages: read
2023-01-11T20:35:47.3096129Z PullRequests: read
2023-01-11T20:35:47.3096428Z RepositoryProjects: read
2023-01-11T20:35:47.3096750Z SecurityEvents: read
2023-01-11T20:35:47.3096980Z Statuses: read
2023-01-11T20:35:47.3097244Z ##[endgroup]
2023-01-11T20:35:47.3100362Z Secret source: None
2023-01-11T20:35:47.3101120Z Prepare workflow directory
2023-01-11T20:35:47.9366238Z Prepare all required actions
2023-01-11T20:35:47.9527145Z Getting action download info
2023-01-11T20:35:48.2017180Z Download action repository 'pytorch/test-infra@main' (SHA:2c225610d00fb13c04fcd60389d3e4d8326167c3)
2023-01-11T20:35:48.4746089Z Download action repository 'pytorch/pytorch@master' (SHA:c5836153f5332ca83d5cacde38f2829a4d54793e)
2023-01-11T20:35:51.5136225Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2023-01-11T20:35:51.8025747Z Getting action download info
2023-01-11T20:35:52.7243931Z Download action repository 'malfet/checkout@silent-checkout' (SHA:c7b8fef48edfe1bca0044a44b1f7f7c4318a3076)
2023-01-11T20:35:52.9090710Z Getting action download info
2023-01-11T20:35:53.7875341Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2023-01-11T20:35:53.9224820Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml
2023-01-11T20:35:53.9226492Z ##[group] Inputs
2023-01-11T20:35:53.9226785Z   build-environment: linux-focal-py3.7-clang7-asan
2023-01-11T20:35:53.9227434Z   test-matrix: { include: [
  { config: "default", shard: 1, num_shards: 5, runner: "linux.2xlarge" },
  { config: "default", shard: 2, num_shards: 5, runner: "linux.2xlarge" },
  { config: "default", shard: 3, num_shards: 5, runner: "linux.2xlarge" },
  { config: "default", shard: 4, num_shards: 5, runner: "linux.4xlarge" },
  { config: "default", shard: 5, num_shards: 5, runner: "linux.4xlarge" },
  { config: "functorch", shard: 1, num_shards: 1, runner: "linux.2xlarge" },
]}

2023-01-11T20:35:53.9228156Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T20:35:53.9228506Z   sync-tag: 
2023-01-11T20:35:53.9229180Z   timeout-minutes: 240
2023-01-11T20:35:53.9229355Z   use-gha: 
2023-01-11T20:35:53.9229542Z ##[endgroup]
2023-01-11T20:35:53.9230012Z Complete job name: linux-focal-py3.7-clang7-asan / test (default, 3, 5, linux.2xlarge)
2023-01-11T20:35:53.9897475Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2023-01-11T20:35:53.9897773Z with:
2023-01-11T20:35:53.9898257Z   github-secret: ***
2023-01-11T20:35:53.9898604Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2023-01-11T20:35:53.9898931Z   activate-with-label: false
2023-01-11T20:35:53.9899124Z   label: with-ssh
2023-01-11T20:35:53.9899317Z   remove-existing-keys: true
2023-01-11T20:35:53.9899490Z env:
2023-01-11T20:35:53.9899665Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:35:53.9899853Z ##[endgroup]
2023-01-11T20:35:54.4111838Z Grabbing public ssh keys from https://github.com/LucaLumetti.keys
2023-01-11T20:35:54.4893174Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh
2023-01-11T20:35:54.4905870Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys
2023-01-11T20:35:54.4930123Z Login using: ssh ec2-user@ec2-3-82-96-97.compute-1.amazonaws.com
2023-01-11T20:35:54.4930691Z All testing is done inside the container, to start an interactive session run:
2023-01-11T20:35:54.4931081Z   docker exec -it $(docker container ps --format '{{.ID}}') bash
2023-01-11T20:35:54.5142401Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@master
2023-01-11T20:35:54.5142665Z with:
2023-01-11T20:35:54.5142840Z   submodules: recursive
2023-01-11T20:35:54.5143014Z   fetch-depth: 0
2023-01-11T20:35:54.5143181Z env:
2023-01-11T20:35:54.5143353Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:35:54.5143525Z ##[endgroup]
2023-01-11T20:35:54.5351520Z ##[group]Run retry () {
2023-01-11T20:35:54.5351779Z retry () {
2023-01-11T20:35:54.5352007Z   $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
2023-01-11T20:35:54.5352206Z }
2023-01-11T20:35:54.5352397Z echo "${GITHUB_WORKSPACE}"
2023-01-11T20:35:54.5352620Z if [ -z "${NO_SUDO}" ]; then
2023-01-11T20:35:54.5352844Z   retry sudo rm -rf "${GITHUB_WORKSPACE}"
2023-01-11T20:35:54.5353031Z else
2023-01-11T20:35:54.5353225Z   retry rm -rf "${GITHUB_WORKSPACE}"
2023-01-11T20:35:54.5353415Z fi
2023-01-11T20:35:54.5353615Z mkdir "${GITHUB_WORKSPACE}"
2023-01-11T20:35:54.5369698Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:35:54.5369934Z env:
2023-01-11T20:35:54.5370112Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:35:54.5370294Z   NO_SUDO: 
2023-01-11T20:35:54.5370448Z ##[endgroup]
2023-01-11T20:35:54.5464723Z /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T20:35:56.8965515Z ##[group]Run malfet/checkout@silent-checkout
2023-01-11T20:35:56.8965794Z with:
2023-01-11T20:35:56.8966028Z   ref: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T20:35:56.8966273Z   fetch-depth: 0
2023-01-11T20:35:56.8966472Z   submodules: recursive
2023-01-11T20:35:56.8966688Z   quiet-checkout: true
2023-01-11T20:35:56.8966926Z   repository: pytorch/pytorch
2023-01-11T20:35:56.8967233Z   token: ***
2023-01-11T20:35:56.8967435Z   ssh-strict: true
2023-01-11T20:35:56.8967657Z   persist-credentials: true
2023-01-11T20:35:56.8967877Z   clean: true
2023-01-11T20:35:56.8968064Z   lfs: false
2023-01-11T20:35:56.8968275Z   set-safe-directory: true
2023-01-11T20:35:56.8968484Z env:
2023-01-11T20:35:56.8968669Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:35:56.8968884Z ##[endgroup]
2023-01-11T20:35:57.0054593Z Syncing repository: pytorch/pytorch
2023-01-11T20:35:57.0056123Z ##[group]Getting Git version info
2023-01-11T20:35:57.0056692Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2023-01-11T20:35:57.0057202Z [command]/usr/bin/git version
2023-01-11T20:35:57.0057418Z git version 2.38.1
2023-01-11T20:35:57.0058119Z ##[endgroup]
2023-01-11T20:35:57.0070553Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/0a03262f-1133-4a94-b9ee-d0253620e387' before making global git config changes
2023-01-11T20:35:57.0071014Z Adding repository directory to the temporary git global config as a safe directory
2023-01-11T20:35:57.0071506Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T20:35:57.0101458Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2023-01-11T20:35:57.0105592Z ##[group]Initializing the repository
2023-01-11T20:35:57.0108175Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T20:35:57.0357508Z hint: Using 'master' as the name for the initial branch. This default branch name
2023-01-11T20:35:57.0358222Z hint: is subject to change. To configure the initial branch name to use in all
2023-01-11T20:35:57.0358853Z hint: of your new repositories, which will suppress this warning, call:
2023-01-11T20:35:57.0359343Z hint: 
2023-01-11T20:35:57.0359891Z hint: 	git config --global init.defaultBranch <name>
2023-01-11T20:35:57.0360400Z hint: 
2023-01-11T20:35:57.0360874Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2023-01-11T20:35:57.0361284Z hint: 'development'. The just-created branch can be renamed via this command:
2023-01-11T20:35:57.0361535Z hint: 
2023-01-11T20:35:57.0361853Z hint: 	git branch -m <name>
2023-01-11T20:35:57.0362260Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/
2023-01-11T20:35:57.0369362Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch
2023-01-11T20:35:57.0401520Z ##[endgroup]
2023-01-11T20:35:57.0402175Z ##[group]Disabling automatic garbage collection
2023-01-11T20:35:57.0405517Z [command]/usr/bin/git config --local gc.auto 0
2023-01-11T20:35:57.0433197Z ##[endgroup]
2023-01-11T20:35:57.0433728Z ##[group]Setting up auth
2023-01-11T20:35:57.0440466Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2023-01-11T20:35:57.0469818Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :
2023-01-11T20:35:57.0707869Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2023-01-11T20:35:57.0740903Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :
2023-01-11T20:35:57.0984759Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2023-01-11T20:35:57.1025728Z ##[endgroup]
2023-01-11T20:35:57.1026196Z ##[group]Fetching the repository
2023-01-11T20:35:57.1031828Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --quiet --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2023-01-11T20:36:50.2980676Z [command]/usr/bin/git rev-parse --verify --quiet 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e^{object}
2023-01-11T20:36:50.3006556Z 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T20:36:50.3010493Z ##[endgroup]
2023-01-11T20:36:50.3010841Z ##[group]Determining the checkout info
2023-01-11T20:36:50.3011747Z ##[endgroup]
2023-01-11T20:36:50.3012072Z ##[group]Checking out the ref
2023-01-11T20:36:50.3015367Z [command]/usr/bin/git checkout --quiet --force 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T20:36:51.5775431Z ##[endgroup]
2023-01-11T20:36:51.5775926Z ##[group]Setting up auth for fetching submodules
2023-01-11T20:36:51.5780986Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2023-01-11T20:36:51.5832743Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2023-01-11T20:36:51.5861139Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2023-01-11T20:36:51.5889780Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2023-01-11T20:36:51.5915467Z ##[endgroup]
2023-01-11T20:36:51.5916145Z ##[group]Fetching submodules
2023-01-11T20:36:51.5920243Z [command]/usr/bin/git submodule sync --recursive
2023-01-11T20:36:51.6170426Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2023-01-11T20:36:51.6415927Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni'
2023-01-11T20:36:51.6418036Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16'
2023-01-11T20:36:51.6418788Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv'
2023-01-11T20:36:51.6420971Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK'
2023-01-11T20:36:51.6423213Z Submodule 'third_party/QNNPACK' (https://github.com/pytorch/QNNPACK) registered for path 'third_party/QNNPACK'
2023-01-11T20:36:51.6425709Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator'
2023-01-11T20:36:51.6427994Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK'
2023-01-11T20:36:51.6430475Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark'
2023-01-11T20:36:51.6432970Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo'
2023-01-11T20:36:51.6435643Z Submodule 'third_party/cub' (https://github.com/NVlabs/cub.git) registered for path 'third_party/cub'
2023-01-11T20:36:51.6438531Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend'
2023-01-11T20:36:51.6442309Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass'
2023-01-11T20:36:51.6445592Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen'
2023-01-11T20:36:51.6448745Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm'
2023-01-11T20:36:51.6451775Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers'
2023-01-11T20:36:51.6455032Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'
2023-01-11T20:36:51.6458426Z Submodule 'third_party/foxi' (https://github.com/houseroad/foxi.git) registered for path 'third_party/foxi'
2023-01-11T20:36:51.6462166Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp'
2023-01-11T20:36:51.6465595Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo'
2023-01-11T20:36:51.6469465Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest'
2023-01-11T20:36:51.6473187Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep'
2023-01-11T20:36:51.6477155Z Submodule 'third_party/ios-cmake' (https://github.com/Yangqing/ios-cmake.git) registered for path 'third_party/ios-cmake'
2023-01-11T20:36:51.6481260Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi'
2023-01-11T20:36:51.6485395Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto'
2023-01-11T20:36:51.6489571Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl'
2023-01-11T20:36:51.6493927Z Submodule 'third_party/neon2sse' (https://github.com/intel/ARM_NEON_2_x86_SSE.git) registered for path 'third_party/neon2sse'
2023-01-11T20:36:51.6498381Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann'
2023-01-11T20:36:51.6503014Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx'
2023-01-11T20:36:51.6507622Z Submodule 'third_party/onnx-tensorrt' (https://github.com/onnx/onnx-tensorrt) registered for path 'third_party/onnx-tensorrt'
2023-01-11T20:36:51.6512328Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft'
2023-01-11T20:36:51.6517087Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf'
2023-01-11T20:36:51.6522206Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd'
2023-01-11T20:36:51.6527202Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool'
2023-01-11T20:36:51.6532365Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11'
2023-01-11T20:36:51.6537639Z Submodule 'third_party/python-enum' (https://github.com/PeachPy/enum34.git) registered for path 'third_party/python-enum'
2023-01-11T20:36:51.6542914Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy'
2023-01-11T20:36:51.6548306Z Submodule 'third_party/python-six' (https://github.com/benjaminp/six.git) registered for path 'third_party/python-six'
2023-01-11T20:36:51.6553661Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef'
2023-01-11T20:36:51.6559229Z Submodule 'third_party/tbb' (https://github.com/01org/tbb) registered for path 'third_party/tbb'
2023-01-11T20:36:51.6565648Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe'
2023-01-11T20:36:51.6571362Z Submodule 'third_party/zstd' (https://github.com/facebook/zstd.git) registered for path 'third_party/zstd'
2023-01-11T20:36:51.6594491Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'...
2023-01-11T20:36:51.9505941Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'...
2023-01-11T20:36:52.1533108Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'...
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2023-01-11T20:37:44.3887430Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch'
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2023-01-11T20:37:47.3370073Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81'
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2023-01-11T20:38:07.1643911Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11'
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2023-01-11T20:38:08.6422114Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx-tensorrt/third_party/onnx'
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2023-01-11T20:38:10.5858099Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark'
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2023-01-11T20:38:11.8767522Z Submodule path 'third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark': checked out 'e776aa0275e293707b6a0901e0e8d8a8a3679508'
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2023-01-11T20:38:12.4279825Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest'
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2023-01-11T20:38:14.1267187Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081'
2023-01-11T20:38:14.1341996Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900'
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2023-01-11T20:38:14.1729950Z Submodule path 'third_party/pybind11': checked out '80dc998efced8ceb2be59756668a7e90e8bef917'
2023-01-11T20:38:14.1806472Z Submodule path 'third_party/python-enum': checked out '4cfedc426c4e2fc52e3f5c2b4297e15ed8d6b8c7'
2023-01-11T20:38:14.2056114Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67'
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2023-01-11T20:38:14.3739047Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest'
2023-01-11T20:38:14.3741377Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop'
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2023-01-11T20:38:18.9432132Z ##[endgroup]
2023-01-11T20:38:18.9432522Z ##[group]Persisting credentials for submodules
2023-01-11T20:38:18.9438042Z [command]/usr/bin/git submodule foreach --recursive git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :
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2023-01-11T20:38:19.1765727Z Entering 'third_party/zstd'
2023-01-11T20:38:19.1810953Z [command]/usr/bin/git submodule foreach --recursive git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url
2023-01-11T20:38:19.2058768Z Entering 'android/libs/fbjni'
2023-01-11T20:38:19.2090479Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2023-01-11T20:38:19.2104036Z Entering 'third_party/FP16'
2023-01-11T20:38:19.2135239Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2023-01-11T20:38:19.2148967Z Entering 'third_party/FXdiv'
2023-01-11T20:38:19.2180085Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2023-01-11T20:38:19.2194029Z Entering 'third_party/NNPACK'
2023-01-11T20:38:19.2225594Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2023-01-11T20:38:19.2240140Z Entering 'third_party/QNNPACK'
2023-01-11T20:38:19.2270259Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/QNNPACK/config	remote.origin.url
2023-01-11T20:38:19.2284821Z Entering 'third_party/VulkanMemoryAllocator'
2023-01-11T20:38:19.2315485Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2023-01-11T20:38:19.2330197Z Entering 'third_party/XNNPACK'
2023-01-11T20:38:19.2362728Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
2023-01-11T20:38:19.2384703Z Entering 'third_party/benchmark'
2023-01-11T20:38:19.2416196Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
2023-01-11T20:38:19.2430128Z Entering 'third_party/cpuinfo'
2023-01-11T20:38:19.2461006Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
2023-01-11T20:38:19.2475334Z Entering 'third_party/cub'
2023-01-11T20:38:19.2508199Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cub/config	remote.origin.url
2023-01-11T20:38:19.2522013Z Entering 'third_party/cudnn_frontend'
2023-01-11T20:38:19.2552960Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
2023-01-11T20:38:19.2571249Z Entering 'third_party/cutlass'
2023-01-11T20:38:19.2602964Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
2023-01-11T20:38:19.2622369Z Entering 'third_party/eigen'
2023-01-11T20:38:19.2654909Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
2023-01-11T20:38:19.2671161Z Entering 'third_party/fbgemm'
2023-01-11T20:38:19.2702554Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2023-01-11T20:38:19.2716095Z Entering 'third_party/fbgemm/third_party/asmjit'
2023-01-11T20:38:19.2747073Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
2023-01-11T20:38:19.2760423Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2023-01-11T20:38:19.2791172Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
2023-01-11T20:38:19.2806963Z Entering 'third_party/fbgemm/third_party/googletest'
2023-01-11T20:38:19.2837862Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
2023-01-11T20:38:19.2851135Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2023-01-11T20:38:19.2881655Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
2023-01-11T20:38:19.2896179Z Entering 'third_party/flatbuffers'
2023-01-11T20:38:19.2927807Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2023-01-11T20:38:19.2943421Z Entering 'third_party/fmt'
2023-01-11T20:38:19.2975374Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
2023-01-11T20:38:19.2989591Z Entering 'third_party/foxi'
2023-01-11T20:38:19.3020064Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/foxi/config	remote.origin.url
2023-01-11T20:38:19.3033892Z Entering 'third_party/gemmlowp/gemmlowp'
2023-01-11T20:38:19.3065018Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2023-01-11T20:38:19.3078798Z Entering 'third_party/gloo'
2023-01-11T20:38:19.3111897Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
2023-01-11T20:38:19.3125917Z Entering 'third_party/googletest'
2023-01-11T20:38:19.3157395Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2023-01-11T20:38:19.3172304Z Entering 'third_party/ideep'
2023-01-11T20:38:19.3203892Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2023-01-11T20:38:19.3217267Z Entering 'third_party/ideep/mkl-dnn'
2023-01-11T20:38:19.3248206Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2023-01-11T20:38:19.3264419Z Entering 'third_party/ideep/mkl-dnn/third_party/oneDNN'
2023-01-11T20:38:19.3296009Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/modules/third_party/oneDNN/config	remote.origin.url
2023-01-11T20:38:19.3315722Z Entering 'third_party/ios-cmake'
2023-01-11T20:38:19.3347126Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ios-cmake/config	remote.origin.url
2023-01-11T20:38:19.3361206Z Entering 'third_party/ittapi'
2023-01-11T20:38:19.3392833Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
2023-01-11T20:38:19.3407241Z Entering 'third_party/kineto'
2023-01-11T20:38:19.3438820Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2023-01-11T20:38:19.3452483Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2023-01-11T20:38:19.3485028Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config	remote.origin.url
2023-01-11T20:38:19.3498848Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2023-01-11T20:38:19.3531754Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config	remote.origin.url
2023-01-11T20:38:19.3546764Z Entering 'third_party/nccl/nccl'
2023-01-11T20:38:19.3577635Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config	remote.origin.url
2023-01-11T20:38:19.3591513Z Entering 'third_party/neon2sse'
2023-01-11T20:38:19.3623001Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/neon2sse/config	remote.origin.url
2023-01-11T20:38:19.3636366Z Entering 'third_party/nlohmann'
2023-01-11T20:38:19.3668086Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config	remote.origin.url
2023-01-11T20:38:19.3683869Z Entering 'third_party/onnx'
2023-01-11T20:38:19.3714877Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config	remote.origin.url
2023-01-11T20:38:19.3740190Z Entering 'third_party/onnx/third_party/benchmark'
2023-01-11T20:38:19.3772616Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/benchmark/config	remote.origin.url
2023-01-11T20:38:19.3786889Z Entering 'third_party/onnx/third_party/pybind11'
2023-01-11T20:38:19.3818640Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T20:38:19.3834235Z Entering 'third_party/onnx-tensorrt'
2023-01-11T20:38:19.3866441Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/config	remote.origin.url
2023-01-11T20:38:19.3880289Z Entering 'third_party/onnx-tensorrt/third_party/onnx'
2023-01-11T20:38:19.3912007Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/config	remote.origin.url
2023-01-11T20:38:19.3930359Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark'
2023-01-11T20:38:19.3962743Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/benchmark/config	remote.origin.url
2023-01-11T20:38:19.3976270Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11'
2023-01-11T20:38:19.4008884Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T20:38:19.4022304Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang'
2023-01-11T20:38:19.4054379Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx-tensorrt/modules/third_party/onnx/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
2023-01-11T20:38:19.4073208Z Entering 'third_party/pocketfft'
2023-01-11T20:38:19.4104430Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config	remote.origin.url
2023-01-11T20:38:19.4119320Z Entering 'third_party/protobuf'
2023-01-11T20:38:19.4151103Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config	remote.origin.url
2023-01-11T20:38:19.4169171Z Entering 'third_party/protobuf/third_party/benchmark'
2023-01-11T20:38:19.4200747Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config	remote.origin.url
2023-01-11T20:38:19.4214841Z Entering 'third_party/protobuf/third_party/googletest'
2023-01-11T20:38:19.4245963Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config	remote.origin.url
2023-01-11T20:38:19.4261516Z Entering 'third_party/psimd'
2023-01-11T20:38:19.4293307Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config	remote.origin.url
2023-01-11T20:38:19.4307415Z Entering 'third_party/pthreadpool'
2023-01-11T20:38:19.4340118Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config	remote.origin.url
2023-01-11T20:38:19.4353806Z Entering 'third_party/pybind11'
2023-01-11T20:38:19.4385256Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T20:38:19.4398826Z Entering 'third_party/python-enum'
2023-01-11T20:38:19.4430448Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-enum/config	remote.origin.url
2023-01-11T20:38:19.4443906Z Entering 'third_party/python-peachpy'
2023-01-11T20:38:19.4475503Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config	remote.origin.url
2023-01-11T20:38:19.4489561Z Entering 'third_party/python-six'
2023-01-11T20:38:19.4520803Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-six/config	remote.origin.url
2023-01-11T20:38:19.4535293Z Entering 'third_party/sleef'
2023-01-11T20:38:19.4565979Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config	remote.origin.url
2023-01-11T20:38:19.4580317Z Entering 'third_party/tbb'
2023-01-11T20:38:19.4611102Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tbb/config	remote.origin.url
2023-01-11T20:38:19.4627272Z Entering 'third_party/tensorpipe'
2023-01-11T20:38:19.4661112Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config	remote.origin.url
2023-01-11T20:38:19.4675351Z Entering 'third_party/tensorpipe/third_party/googletest'
2023-01-11T20:38:19.4706298Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config	remote.origin.url
2023-01-11T20:38:19.4719692Z Entering 'third_party/tensorpipe/third_party/libnop'
2023-01-11T20:38:19.4750942Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config	remote.origin.url
2023-01-11T20:38:19.4764753Z Entering 'third_party/tensorpipe/third_party/libuv'
2023-01-11T20:38:19.4795215Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config	remote.origin.url
2023-01-11T20:38:19.4810184Z Entering 'third_party/tensorpipe/third_party/pybind11'
2023-01-11T20:38:19.4840503Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config	remote.origin.url
2023-01-11T20:38:19.4853775Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2023-01-11T20:38:19.4884559Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
2023-01-11T20:38:19.4900749Z Entering 'third_party/zstd'
2023-01-11T20:38:19.4933211Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/zstd/config	remote.origin.url
2023-01-11T20:38:19.5628925Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
2023-01-11T20:38:19.5892994Z Entering 'android/libs/fbjni'
2023-01-11T20:38:19.5928347Z Entering 'third_party/FP16'
2023-01-11T20:38:19.5964862Z Entering 'third_party/FXdiv'
2023-01-11T20:38:19.5999941Z Entering 'third_party/NNPACK'
2023-01-11T20:38:19.6035615Z Entering 'third_party/QNNPACK'
2023-01-11T20:38:19.6072875Z Entering 'third_party/VulkanMemoryAllocator'
2023-01-11T20:38:19.6108649Z Entering 'third_party/XNNPACK'
2023-01-11T20:38:19.6155595Z Entering 'third_party/benchmark'
2023-01-11T20:38:19.6191609Z Entering 'third_party/cpuinfo'
2023-01-11T20:38:19.6229784Z Entering 'third_party/cub'
2023-01-11T20:38:19.6265657Z Entering 'third_party/cudnn_frontend'
2023-01-11T20:38:19.6305707Z Entering 'third_party/cutlass'
2023-01-11T20:38:19.6346802Z Entering 'third_party/eigen'
2023-01-11T20:38:19.6385160Z Entering 'third_party/fbgemm'
2023-01-11T20:38:19.6420414Z Entering 'third_party/fbgemm/third_party/asmjit'
2023-01-11T20:38:19.6454543Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2023-01-11T20:38:19.6489382Z Entering 'third_party/fbgemm/third_party/googletest'
2023-01-11T20:38:19.6522639Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2023-01-11T20:38:19.6556573Z Entering 'third_party/flatbuffers'
2023-01-11T20:38:19.6592800Z Entering 'third_party/fmt'
2023-01-11T20:38:19.6627177Z Entering 'third_party/foxi'
2023-01-11T20:38:19.6661044Z Entering 'third_party/gemmlowp/gemmlowp'
2023-01-11T20:38:19.6695264Z Entering 'third_party/gloo'
2023-01-11T20:38:19.6729320Z Entering 'third_party/googletest'
2023-01-11T20:38:19.6763712Z Entering 'third_party/ideep'
2023-01-11T20:38:19.6796547Z Entering 'third_party/ideep/mkl-dnn'
2023-01-11T20:38:19.6832643Z Entering 'third_party/ideep/mkl-dnn/third_party/oneDNN'
2023-01-11T20:38:19.6873962Z Entering 'third_party/ios-cmake'
2023-01-11T20:38:19.6907376Z Entering 'third_party/ittapi'
2023-01-11T20:38:19.6941758Z Entering 'third_party/kineto'
2023-01-11T20:38:19.6975743Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2023-01-11T20:38:19.7010972Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2023-01-11T20:38:19.7046264Z Entering 'third_party/nccl/nccl'
2023-01-11T20:38:19.7080389Z Entering 'third_party/neon2sse'
2023-01-11T20:38:19.7114514Z Entering 'third_party/nlohmann'
2023-01-11T20:38:19.7149522Z Entering 'third_party/onnx'
2023-01-11T20:38:19.7195874Z Entering 'third_party/onnx/third_party/benchmark'
2023-01-11T20:38:19.7230170Z Entering 'third_party/onnx/third_party/pybind11'
2023-01-11T20:38:19.7266364Z Entering 'third_party/onnx-tensorrt'
2023-01-11T20:38:19.7299729Z Entering 'third_party/onnx-tensorrt/third_party/onnx'
2023-01-11T20:38:19.7338127Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark'
2023-01-11T20:38:19.7372267Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11'
2023-01-11T20:38:19.7406920Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang'
2023-01-11T20:38:19.7445441Z Entering 'third_party/pocketfft'
2023-01-11T20:38:19.7480250Z Entering 'third_party/protobuf'
2023-01-11T20:38:19.7517736Z Entering 'third_party/protobuf/third_party/benchmark'
2023-01-11T20:38:19.7552070Z Entering 'third_party/protobuf/third_party/googletest'
2023-01-11T20:38:19.7587222Z Entering 'third_party/psimd'
2023-01-11T20:38:19.7621321Z Entering 'third_party/pthreadpool'
2023-01-11T20:38:19.7656375Z Entering 'third_party/pybind11'
2023-01-11T20:38:19.7690746Z Entering 'third_party/python-enum'
2023-01-11T20:38:19.7724631Z Entering 'third_party/python-peachpy'
2023-01-11T20:38:19.7758271Z Entering 'third_party/python-six'
2023-01-11T20:38:19.7792264Z Entering 'third_party/sleef'
2023-01-11T20:38:19.7825432Z Entering 'third_party/tbb'
2023-01-11T20:38:19.7861465Z Entering 'third_party/tensorpipe'
2023-01-11T20:38:19.7895887Z Entering 'third_party/tensorpipe/third_party/googletest'
2023-01-11T20:38:19.7928881Z Entering 'third_party/tensorpipe/third_party/libnop'
2023-01-11T20:38:19.7961713Z Entering 'third_party/tensorpipe/third_party/libuv'
2023-01-11T20:38:19.7994576Z Entering 'third_party/tensorpipe/third_party/pybind11'
2023-01-11T20:38:19.8027791Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2023-01-11T20:38:19.8063266Z Entering 'third_party/zstd'
2023-01-11T20:38:19.8106834Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:'
2023-01-11T20:38:19.8354354Z Entering 'android/libs/fbjni'
2023-01-11T20:38:19.8387431Z Entering 'third_party/FP16'
2023-01-11T20:38:19.8420947Z Entering 'third_party/FXdiv'
2023-01-11T20:38:19.8454986Z Entering 'third_party/NNPACK'
2023-01-11T20:38:19.8489047Z Entering 'third_party/QNNPACK'
2023-01-11T20:38:19.8522327Z Entering 'third_party/VulkanMemoryAllocator'
2023-01-11T20:38:19.8556558Z Entering 'third_party/XNNPACK'
2023-01-11T20:38:19.8599682Z Entering 'third_party/benchmark'
2023-01-11T20:38:19.8633194Z Entering 'third_party/cpuinfo'
2023-01-11T20:38:19.8667125Z Entering 'third_party/cub'
2023-01-11T20:38:19.8701986Z Entering 'third_party/cudnn_frontend'
2023-01-11T20:38:19.8740158Z Entering 'third_party/cutlass'
2023-01-11T20:38:19.8779261Z Entering 'third_party/eigen'
2023-01-11T20:38:19.8815159Z Entering 'third_party/fbgemm'
2023-01-11T20:38:19.8849698Z Entering 'third_party/fbgemm/third_party/asmjit'
2023-01-11T20:38:19.8882494Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2023-01-11T20:38:19.8915807Z Entering 'third_party/fbgemm/third_party/googletest'
2023-01-11T20:38:19.8948666Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2023-01-11T20:38:19.8982421Z Entering 'third_party/flatbuffers'
2023-01-11T20:38:19.9017669Z Entering 'third_party/fmt'
2023-01-11T20:38:19.9052293Z Entering 'third_party/foxi'
2023-01-11T20:38:19.9087315Z Entering 'third_party/gemmlowp/gemmlowp'
2023-01-11T20:38:19.9120363Z Entering 'third_party/gloo'
2023-01-11T20:38:19.9154234Z Entering 'third_party/googletest'
2023-01-11T20:38:19.9188337Z Entering 'third_party/ideep'
2023-01-11T20:38:19.9222987Z Entering 'third_party/ideep/mkl-dnn'
2023-01-11T20:38:19.9257979Z Entering 'third_party/ideep/mkl-dnn/third_party/oneDNN'
2023-01-11T20:38:19.9297659Z Entering 'third_party/ios-cmake'
2023-01-11T20:38:19.9332695Z Entering 'third_party/ittapi'
2023-01-11T20:38:19.9366167Z Entering 'third_party/kineto'
2023-01-11T20:38:19.9400034Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2023-01-11T20:38:19.9432994Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2023-01-11T20:38:19.9467569Z Entering 'third_party/nccl/nccl'
2023-01-11T20:38:19.9501301Z Entering 'third_party/neon2sse'
2023-01-11T20:38:19.9535177Z Entering 'third_party/nlohmann'
2023-01-11T20:38:19.9570705Z Entering 'third_party/onnx'
2023-01-11T20:38:19.9615315Z Entering 'third_party/onnx/third_party/benchmark'
2023-01-11T20:38:19.9648710Z Entering 'third_party/onnx/third_party/pybind11'
2023-01-11T20:38:19.9683944Z Entering 'third_party/onnx-tensorrt'
2023-01-11T20:38:19.9717818Z Entering 'third_party/onnx-tensorrt/third_party/onnx'
2023-01-11T20:38:19.9755484Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark'
2023-01-11T20:38:19.9788672Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11'
2023-01-11T20:38:19.9821773Z Entering 'third_party/onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang'
2023-01-11T20:38:19.9859999Z Entering 'third_party/pocketfft'
2023-01-11T20:38:19.9892825Z Entering 'third_party/protobuf'
2023-01-11T20:38:19.9930022Z Entering 'third_party/protobuf/third_party/benchmark'
2023-01-11T20:38:19.9963986Z Entering 'third_party/protobuf/third_party/googletest'
2023-01-11T20:38:19.9998308Z Entering 'third_party/psimd'
2023-01-11T20:38:20.0032401Z Entering 'third_party/pthreadpool'
2023-01-11T20:38:20.0066947Z Entering 'third_party/pybind11'
2023-01-11T20:38:20.0101543Z Entering 'third_party/python-enum'
2023-01-11T20:38:20.0135285Z Entering 'third_party/python-peachpy'
2023-01-11T20:38:20.0170453Z Entering 'third_party/python-six'
2023-01-11T20:38:20.0203956Z Entering 'third_party/sleef'
2023-01-11T20:38:20.0237759Z Entering 'third_party/tbb'
2023-01-11T20:38:20.0273639Z Entering 'third_party/tensorpipe'
2023-01-11T20:38:20.0308262Z Entering 'third_party/tensorpipe/third_party/googletest'
2023-01-11T20:38:20.0342021Z Entering 'third_party/tensorpipe/third_party/libnop'
2023-01-11T20:38:20.0374106Z Entering 'third_party/tensorpipe/third_party/libuv'
2023-01-11T20:38:20.0406483Z Entering 'third_party/tensorpipe/third_party/pybind11'
2023-01-11T20:38:20.0439126Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2023-01-11T20:38:20.0474919Z Entering 'third_party/zstd'
2023-01-11T20:38:20.0517321Z ##[endgroup]
2023-01-11T20:38:20.0557199Z [command]/usr/bin/git log -1 --format='%H'
2023-01-11T20:38:20.0583262Z '8419ddda87c8a47eacc63b54bc7ec98c1f27c26e'
2023-01-11T20:38:20.0699700Z Prepare all required actions
2023-01-11T20:38:20.0725565Z ##[group]Run ./.github/actions/setup-linux
2023-01-11T20:38:20.0725768Z env:
2023-01-11T20:38:20.0725928Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:38:20.0726115Z ##[endgroup]
2023-01-11T20:38:20.0740989Z ##[group]Run set -euo pipefail
2023-01-11T20:38:20.0741221Z set -euo pipefail
2023-01-11T20:38:20.0741428Z function get_ec2_metadata() {
2023-01-11T20:38:20.0741662Z   # Pulled from instance metadata endpoint for EC2
2023-01-11T20:38:20.0742017Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2023-01-11T20:38:20.0742321Z   category=$1
2023-01-11T20:38:20.0742563Z   curl -fsSL "http://169.254.169.254/latest/meta-data/${category}"
2023-01-11T20:38:20.0742783Z }
2023-01-11T20:38:20.0742983Z echo "ami-id: $(get_ec2_metadata ami-id)"
2023-01-11T20:38:20.0743262Z echo "instance-id: $(get_ec2_metadata instance-id)"
2023-01-11T20:38:20.0743526Z echo "instance-type: $(get_ec2_metadata instance-type)"
2023-01-11T20:38:20.0743771Z echo "system info $(uname -a)"
2023-01-11T20:38:20.0754702Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:38:20.0754902Z env:
2023-01-11T20:38:20.0755075Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:38:20.0755259Z ##[endgroup]
2023-01-11T20:38:20.0829923Z ami-id: ami-096198a0bccc6bad4
2023-01-11T20:38:20.0880191Z instance-id: i-05ef50dfe628429d7
2023-01-11T20:38:20.0925210Z instance-type: c5.2xlarge
2023-01-11T20:38:20.0931510Z system info Linux ip-10-0-2-51.ec2.internal 4.14.252-195.483.amzn2.x86_64 #1 SMP Mon Nov 1 20:58:46 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
2023-01-11T20:38:20.0947163Z ##[group]Run if systemctl is-active --quiet docker; then
2023-01-11T20:38:20.0947448Z if systemctl is-active --quiet docker; then
2023-01-11T20:38:20.0947693Z     echo "Docker daemon is running...";
2023-01-11T20:38:20.0947903Z else
2023-01-11T20:38:20.0948124Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2023-01-11T20:38:20.0948352Z fi
2023-01-11T20:38:20.0958819Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:38:20.0959019Z env:
2023-01-11T20:38:20.0959192Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:38:20.0959375Z ##[endgroup]
2023-01-11T20:38:20.1074957Z Docker daemon is running...
2023-01-11T20:38:20.1090536Z ##[group]Run AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2023-01-11T20:38:20.1090893Z AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
2023-01-11T20:38:20.1091174Z retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T20:38:20.1091550Z retry aws ecr get-login*** "$AWS_DEFAULT_REGION" | docker login --username AWS \
2023-01-11T20:38:20.1091899Z     --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
2023-01-11T20:38:20.1102255Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:38:20.1102470Z env:
2023-01-11T20:38:20.1102644Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:38:20.1102826Z   AWS_RETRY_MODE: standard
2023-01-11T20:38:20.1103011Z   AWS_MAX_ATTEMPTS: 5
2023-01-11T20:38:20.1103204Z   AWS_DEFAULT_REGION: us-east-1
2023-01-11T20:38:20.1103394Z ##[endgroup]
2023-01-11T20:38:21.3040017Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2023-01-11T20:38:21.3040544Z Configure a credential helper to remove this warning. See
2023-01-11T20:38:21.3041289Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2023-01-11T20:38:21.3041554Z 
2023-01-11T20:38:21.3041637Z Login Succeeded
2023-01-11T20:38:21.3069954Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T20:38:21.3070258Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T20:38:21.3070605Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2023-01-11T20:38:21.3081923Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:38:21.3082125Z env:
2023-01-11T20:38:21.3082300Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:38:21.3082483Z ##[endgroup]
2023-01-11T20:38:21.3154380Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2023-01-11T20:38:21.3154632Z with:
2023-01-11T20:38:21.3154971Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T20:38:21.3155308Z env:
2023-01-11T20:38:21.3155484Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:38:21.3155658Z ##[endgroup]
2023-01-11T20:38:21.3169051Z ##[group]Run retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T20:38:21.3169328Z retry () { "$@"  || (sleep 1 && "$@") || (sleep 2 && "$@") }
2023-01-11T20:38:21.3169590Z # ignore output since only exit code is used for conditional
2023-01-11T20:38:21.3169869Z # only pull docker image if it's not available locally
2023-01-11T20:38:21.3170164Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2023-01-11T20:38:21.3170454Z   retry docker pull "${DOCKER_IMAGE}"
2023-01-11T20:38:21.3170648Z fi
2023-01-11T20:38:21.3180979Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:38:21.3181192Z env:
2023-01-11T20:38:21.3181363Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:38:21.3181719Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T20:38:21.3182057Z ##[endgroup]
2023-01-11T20:38:21.5266828Z fd224c2e6c79d7fdec6408da598bf52bc5b201dd: Pulling from pytorch/pytorch-linux-focal-py3-clang7-asan
2023-01-11T20:38:21.5274339Z 846c0b181fff: Pulling fs layer
2023-01-11T20:38:21.5277308Z 21fdbe34507b: Pulling fs layer
2023-01-11T20:38:21.5277589Z 15f2f231937c: Pulling fs layer
2023-01-11T20:38:21.5277886Z a1e39d4ff4de: Pulling fs layer
2023-01-11T20:38:21.5278186Z dac676e29afb: Pulling fs layer
2023-01-11T20:38:21.5278529Z b6a2ffe2f948: Pulling fs layer
2023-01-11T20:38:21.5278872Z 49efecb612e9: Pulling fs layer
2023-01-11T20:38:21.5279159Z 613971da6ac7: Pulling fs layer
2023-01-11T20:38:21.5279411Z 8fb6475afaf3: Pulling fs layer
2023-01-11T20:38:21.5279688Z 435d0e36d6a5: Pulling fs layer
2023-01-11T20:38:21.5279885Z 3b4e6eac36dd: Pulling fs layer
2023-01-11T20:38:21.5282221Z aea18031c0c7: Pulling fs layer
2023-01-11T20:38:21.5282675Z 49efecb612e9: Waiting
2023-01-11T20:38:21.5283009Z a1e39d4ff4de: Waiting
2023-01-11T20:38:21.5283362Z 4604cf003b36: Pulling fs layer
2023-01-11T20:38:21.5283628Z 613971da6ac7: Waiting
2023-01-11T20:38:21.5283824Z 4e981f6dbb89: Pulling fs layer
2023-01-11T20:38:21.5284007Z 435d0e36d6a5: Waiting
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2023-01-11T20:38:21.5284359Z 8fb6475afaf3: Waiting
2023-01-11T20:38:21.5284542Z 620feb79e578: Pulling fs layer
2023-01-11T20:38:21.5284716Z 3b4e6eac36dd: Waiting
2023-01-11T20:38:21.5284922Z a3dbac233a42: Pulling fs layer
2023-01-11T20:38:21.5285176Z dac676e29afb: Waiting
2023-01-11T20:38:21.5285376Z aea18031c0c7: Waiting
2023-01-11T20:38:21.5285684Z 4a56f6ee78f6: Pulling fs layer
2023-01-11T20:38:21.5285997Z 50d1e6404d82: Pulling fs layer
2023-01-11T20:38:21.5286303Z 4604cf003b36: Waiting
2023-01-11T20:38:21.5286624Z 2f0941e33bd6: Pulling fs layer
2023-01-11T20:38:21.5286978Z 7743227009bb: Waiting
2023-01-11T20:38:21.5287259Z eb18bead7c47: Pulling fs layer
2023-01-11T20:38:21.5287632Z 4e981f6dbb89: Waiting
2023-01-11T20:38:21.5287927Z 7b8ecfd940e0: Pulling fs layer
2023-01-11T20:38:21.5288182Z 57ce702c520c: Pulling fs layer
2023-01-11T20:38:21.5288538Z f998920cae3e: Pulling fs layer
2023-01-11T20:38:21.5288918Z 2d4cddbabc89: Pulling fs layer
2023-01-11T20:38:21.5289434Z c536fd508674: Pulling fs layer
2023-01-11T20:38:21.5289764Z c9a786894499: Pulling fs layer
2023-01-11T20:38:21.5290115Z e8764e64bb1c: Pulling fs layer
2023-01-11T20:38:21.5290479Z a3dbac233a42: Waiting
2023-01-11T20:38:21.5290796Z 4d1e09a08f81: Pulling fs layer
2023-01-11T20:38:21.5291139Z 4a56f6ee78f6: Waiting
2023-01-11T20:38:21.5291384Z bdc8f587ef89: Pulling fs layer
2023-01-11T20:38:21.5291682Z 50d1e6404d82: Waiting
2023-01-11T20:38:21.5291932Z 70704e78fced: Pulling fs layer
2023-01-11T20:38:21.5292194Z 2f0941e33bd6: Waiting
2023-01-11T20:38:21.5292430Z 1aa09a70183a: Pulling fs layer
2023-01-11T20:38:21.5292630Z f998920cae3e: Waiting
2023-01-11T20:38:21.5292866Z c536fd508674: Waiting
2023-01-11T20:38:21.5293105Z f369cb0dac39: Pulling fs layer
2023-01-11T20:38:21.5293353Z eb18bead7c47: Waiting
2023-01-11T20:38:21.5293586Z 2d4cddbabc89: Waiting
2023-01-11T20:38:21.5293835Z 9a83edd28cbc: Pulling fs layer
2023-01-11T20:38:21.5294054Z ebecc6d269f2: Pulling fs layer
2023-01-11T20:38:21.5294293Z 7b8ecfd940e0: Waiting
2023-01-11T20:38:21.5294544Z 57ce702c520c: Waiting
2023-01-11T20:38:21.5294740Z 296146b0eae7: Pulling fs layer
2023-01-11T20:38:21.5295060Z 7d5d82a88c1b: Pulling fs layer
2023-01-11T20:38:21.5295446Z 667f5a34e1ef: Pulling fs layer
2023-01-11T20:38:21.5295806Z e8764e64bb1c: Waiting
2023-01-11T20:38:21.5296220Z 75eb61e130a0: Pulling fs layer
2023-01-11T20:38:21.5296532Z 620feb79e578: Waiting
2023-01-11T20:38:21.5296823Z 8a92db3dc3eb: Pulling fs layer
2023-01-11T20:38:21.5297198Z 296146b0eae7: Waiting
2023-01-11T20:38:21.5297574Z b97379cf7644: Pulling fs layer
2023-01-11T20:38:21.5297782Z 1aa09a70183a: Waiting
2023-01-11T20:38:21.5298009Z 667f5a34e1ef: Waiting
2023-01-11T20:38:21.5298259Z 4d1e09a08f81: Waiting
2023-01-11T20:38:21.5298441Z 7d5d82a88c1b: Waiting
2023-01-11T20:38:21.5298733Z 9a83edd28cbc: Waiting
2023-01-11T20:38:21.5298974Z 68e7cd28096c: Pulling fs layer
2023-01-11T20:38:21.5299179Z ebecc6d269f2: Waiting
2023-01-11T20:38:21.5299419Z 99a0e1297615: Pulling fs layer
2023-01-11T20:38:21.5299801Z b97379cf7644: Waiting
2023-01-11T20:38:21.5300016Z 2d59c2783db2: Pulling fs layer
2023-01-11T20:38:21.5300253Z 68e7cd28096c: Waiting
2023-01-11T20:38:21.5300495Z e02c9601b85c: Pulling fs layer
2023-01-11T20:38:21.5300824Z 2d59c2783db2: Waiting
2023-01-11T20:38:21.5301046Z 99a0e1297615: Waiting
2023-01-11T20:38:21.5301303Z 36ecb985ca81: Pulling fs layer
2023-01-11T20:38:21.5301517Z 40a91c7bbcdd: Pulling fs layer
2023-01-11T20:38:21.5301775Z 5b3f4c615eba: Pulling fs layer
2023-01-11T20:38:21.5302421Z a8e823303f62: Pulling fs layer
2023-01-11T20:38:21.5302642Z 03b9596d9ece: Pulling fs layer
2023-01-11T20:38:21.5302928Z 342326185042: Pulling fs layer
2023-01-11T20:38:21.5303186Z 9229b7372ee6: Pulling fs layer
2023-01-11T20:38:21.5303392Z 794d48fd071f: Pulling fs layer
2023-01-11T20:38:21.5303674Z 7b6b0b10b9c6: Pulling fs layer
2023-01-11T20:38:21.5303951Z c1ec5d2760ef: Pulling fs layer
2023-01-11T20:38:21.5304201Z e02c9601b85c: Waiting
2023-01-11T20:38:21.5304399Z 099631a27757: Pulling fs layer
2023-01-11T20:38:21.5304646Z ca880176f07f: Pulling fs layer
2023-01-11T20:38:21.5304913Z 8507d046f7f4: Pulling fs layer
2023-01-11T20:38:21.5305105Z 342326185042: Waiting
2023-01-11T20:38:21.5305338Z c1ec5d2760ef: Waiting
2023-01-11T20:38:21.5305596Z a8e823303f62: Waiting
2023-01-11T20:38:21.5305778Z 7b6b0b10b9c6: Waiting
2023-01-11T20:38:21.5306020Z ca880176f07f: Waiting
2023-01-11T20:38:21.5306255Z 8507d046f7f4: Waiting
2023-01-11T20:38:21.5306433Z 099631a27757: Waiting
2023-01-11T20:38:21.5306657Z 36ecb985ca81: Waiting
2023-01-11T20:38:21.5306887Z 40a91c7bbcdd: Waiting
2023-01-11T20:38:21.6103688Z 21fdbe34507b: Download complete
2023-01-11T20:38:21.6868747Z a1e39d4ff4de: Download complete
2023-01-11T20:38:21.8696891Z 846c0b181fff: Verifying Checksum
2023-01-11T20:38:21.8697321Z 846c0b181fff: Download complete
2023-01-11T20:38:21.9552902Z b6a2ffe2f948: Verifying Checksum
2023-01-11T20:38:21.9553400Z b6a2ffe2f948: Download complete
2023-01-11T20:38:22.0464180Z 49efecb612e9: Verifying Checksum
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2023-01-11T20:38:22.1931542Z 8fb6475afaf3: Verifying Checksum
2023-01-11T20:38:22.1931986Z 8fb6475afaf3: Download complete
2023-01-11T20:38:22.2690114Z 435d0e36d6a5: Download complete
2023-01-11T20:38:22.3521213Z 3b4e6eac36dd: Verifying Checksum
2023-01-11T20:38:22.3521657Z 3b4e6eac36dd: Download complete
2023-01-11T20:38:22.3799747Z dac676e29afb: Verifying Checksum
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2023-01-11T20:38:22.4577334Z 4604cf003b36: Verifying Checksum
2023-01-11T20:38:22.4577600Z 4604cf003b36: Download complete
2023-01-11T20:38:22.5597801Z 7743227009bb: Verifying Checksum
2023-01-11T20:38:22.5598269Z 7743227009bb: Download complete
2023-01-11T20:38:22.5823279Z 846c0b181fff: Pull complete
2023-01-11T20:38:22.6296600Z 620feb79e578: Download complete
2023-01-11T20:38:22.6814277Z 21fdbe34507b: Pull complete
2023-01-11T20:38:22.7176591Z a3dbac233a42: Verifying Checksum
2023-01-11T20:38:22.7176998Z a3dbac233a42: Download complete
2023-01-11T20:38:22.7947726Z 4a56f6ee78f6: Download complete
2023-01-11T20:38:22.8757139Z 50d1e6404d82: Verifying Checksum
2023-01-11T20:38:22.8757440Z 50d1e6404d82: Download complete
2023-01-11T20:38:22.9493771Z 2f0941e33bd6: Download complete
2023-01-11T20:38:23.0304013Z eb18bead7c47: Verifying Checksum
2023-01-11T20:38:23.0304653Z eb18bead7c47: Download complete
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2023-01-11T20:38:23.1101854Z 7b8ecfd940e0: Download complete
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2023-01-11T20:38:23.2534499Z f998920cae3e: Download complete
2023-01-11T20:38:24.5590474Z 2d4cddbabc89: Verifying Checksum
2023-01-11T20:38:24.5590760Z 2d4cddbabc89: Download complete
2023-01-11T20:38:24.6345539Z c536fd508674: Download complete
2023-01-11T20:38:24.6836255Z 15f2f231937c: Verifying Checksum
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2023-01-11T20:38:24.7659418Z e8764e64bb1c: Download complete
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2023-01-11T20:38:24.8316020Z bdc8f587ef89: Verifying Checksum
2023-01-11T20:38:24.8316388Z bdc8f587ef89: Download complete
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2023-01-11T20:38:24.9634778Z f369cb0dac39: Verifying Checksum
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2023-01-11T20:38:25.2721844Z 7d5d82a88c1b: Verifying Checksum
2023-01-11T20:38:25.2722249Z 7d5d82a88c1b: Download complete
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2023-01-11T20:38:25.4372951Z 75eb61e130a0: Verifying Checksum
2023-01-11T20:38:25.4373360Z 75eb61e130a0: Download complete
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2023-01-11T20:38:25.5209664Z 8a92db3dc3eb: Download complete
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2023-01-11T20:38:26.2171554Z 2d59c2783db2: Verifying Checksum
2023-01-11T20:38:26.2171985Z 2d59c2783db2: Download complete
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2023-01-11T20:38:26.5559240Z 5b3f4c615eba: Verifying Checksum
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2023-01-11T20:38:27.0199835Z 342326185042: Verifying Checksum
2023-01-11T20:38:27.0200319Z 342326185042: Download complete
2023-01-11T20:38:27.0404226Z 70704e78fced: Verifying Checksum
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2023-01-11T20:39:25.8077910Z Digest: sha256:6dd98a84a12a3a3be24bbc7c3112415c10051ad261832daa2e17a60a48fce645
2023-01-11T20:39:25.8117542Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T20:39:25.8156123Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T20:39:25.8207087Z ##[group]Run python3 -m pip install psutil==5.9.1
2023-01-11T20:39:25.8207371Z python3 -m pip install psutil==5.9.1
2023-01-11T20:39:25.8207595Z python3 -m pip install pynvml==11.4.1
2023-01-11T20:39:25.8207854Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2023-01-11T20:39:25.8208138Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2023-01-11T20:39:25.8219136Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:39:25.8219354Z env:
2023-01-11T20:39:25.8219531Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:39:25.8219702Z ##[endgroup]
2023-01-11T20:39:26.6626061Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T20:39:26.6899214Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.7/site-packages (5.9.1)
2023-01-11T20:39:27.1315093Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T20:39:27.1493112Z Requirement already satisfied: pynvml==11.4.1 in /home/ec2-user/.local/lib/python3.7/site-packages (11.4.1)
2023-01-11T20:39:27.3588981Z Prepare all required actions
2023-01-11T20:39:27.3589242Z Getting action download info
2023-01-11T20:39:27.5478510Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:4a8bfae15cc25cc0785c1603ee87a9da8fd442ea)
2023-01-11T20:39:27.7117576Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a)
2023-01-11T20:39:27.8440523Z ##[group]Run ./.github/actions/download-build-artifacts
2023-01-11T20:39:27.8440893Z with:
2023-01-11T20:39:27.8441090Z   name: linux-focal-py3.7-clang7-asan
2023-01-11T20:39:27.8441271Z env:
2023-01-11T20:39:27.8441445Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:39:27.8441642Z ##[endgroup]
2023-01-11T20:39:27.8466864Z ##[group]Run seemethere/download-artifact-s3@v4
2023-01-11T20:39:27.8467077Z with:
2023-01-11T20:39:27.8467258Z   name: linux-focal-py3.7-clang7-asan
2023-01-11T20:39:27.8467473Z   s3-bucket: gha-artifacts
2023-01-11T20:39:27.8467660Z   region: us-east-1
2023-01-11T20:39:27.8467844Z env:
2023-01-11T20:39:27.8468002Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:39:27.8468182Z ##[endgroup]
2023-01-11T20:39:28.2590840Z Found 1 objects with prefix pytorch/pytorch/3896099317/linux-focal-py3.7-clang7-asan/
2023-01-11T20:39:28.2591299Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2023-01-11T20:39:44.9789038Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2023-01-11T20:39:44.9789288Z 
2023-01-11T20:39:44.9804523Z ##[warning]The `set-output` command is deprecated and will be disabled soon. Please upgrade to using Environment Files. For more information see: https://github.blog/changelog/2022-10-11-github-actions-deprecating-save-state-and-set-output-commands/
2023-01-11T20:39:44.9812459Z Artifact download has finished successfully
2023-01-11T20:39:44.9899580Z ##[group]Run unzip -o artifacts.zip
2023-01-11T20:39:44.9899815Z unzip -o artifacts.zip
2023-01-11T20:39:44.9911481Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:39:44.9911705Z env:
2023-01-11T20:39:44.9911881Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:39:44.9912057Z ##[endgroup]
2023-01-11T20:39:44.9975747Z Archive:  artifacts.zip
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2023-01-11T20:39:57.5622527Z    creating: build/bin/
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2023-01-11T20:39:59.4091216Z   inflating: build/bin/operator_name_test  
2023-01-11T20:39:59.4240021Z   inflating: build/bin/operators_test  
2023-01-11T20:39:59.4394123Z   inflating: build/bin/packedtensoraccessor_test  
2023-01-11T20:39:59.4595115Z   inflating: build/bin/pow_test      
2023-01-11T20:39:59.4758548Z   inflating: build/bin/quantized_test  
2023-01-11T20:39:59.7960567Z   inflating: build/bin/test_api      
2023-01-11T20:39:59.8110676Z   inflating: build/bin/reportMemoryUsage_test  
2023-01-11T20:39:59.8257711Z   inflating: build/bin/reduce_ops_test  
2023-01-11T20:39:59.8431236Z   inflating: build/bin/scalar_tensor_test  
2023-01-11T20:39:59.8599316Z   inflating: build/bin/scalar_test   
2023-01-11T20:39:59.8753800Z   inflating: build/bin/stride_properties_test  
2023-01-11T20:39:59.8993147Z   inflating: build/bin/tensor_iterator_test  
2023-01-11T20:39:59.9152770Z   inflating: build/bin/test_parallel  
2023-01-11T20:39:59.9158832Z   inflating: build/bin/thread_init_test  
2023-01-11T20:39:59.9312554Z   inflating: build/bin/type_ptr_test  
2023-01-11T20:39:59.9496213Z   inflating: build/bin/type_test     
2023-01-11T20:39:59.9642816Z   inflating: build/bin/variant_test  
2023-01-11T20:39:59.9797446Z   inflating: build/bin/undefined_tensor_test  
2023-01-11T20:39:59.9949275Z   inflating: build/bin/weakref_test  
2023-01-11T20:39:59.9951489Z   inflating: build/bin/verify_api_visibility  
2023-01-11T20:40:00.0153235Z   inflating: build/bin/legacy_vmap_test  
2023-01-11T20:40:00.0304283Z   inflating: build/bin/wrapdim_test  
2023-01-11T20:40:00.0450340Z   inflating: build/bin/xla_tensor_test  
2023-01-11T20:40:00.0622331Z   inflating: build/bin/IListRef_test  
2023-01-11T20:40:00.0871866Z   inflating: build/bin/List_test     
2023-01-11T20:40:00.1232080Z   inflating: build/bin/kernel_function_legacy_test  
2023-01-11T20:40:00.1421195Z   inflating: build/bin/KernelFunction_test  
2023-01-11T20:40:00.1695341Z   inflating: build/bin/kernel_function_test  
2023-01-11T20:40:00.2070698Z   inflating: build/bin/kernel_lambda_legacy_test  
2023-01-11T20:40:00.2250673Z   inflating: build/bin/kernel_stackbased_test  
2023-01-11T20:40:00.2552242Z   inflating: build/bin/kernel_lambda_test  
2023-01-11T20:40:00.2831769Z   inflating: build/bin/make_boxed_from_unboxed_functor_test  
2023-01-11T20:40:00.2979964Z   inflating: build/bin/CppSignature_test  
2023-01-11T20:40:00.3123819Z   inflating: build/bin/op_allowlist_test  
2023-01-11T20:40:00.3277910Z   inflating: build/bin/inline_container_test  
2023-01-11T20:40:00.3443971Z   inflating: build/bin/backend_fallback_test  
2023-01-11T20:40:00.4342187Z   inflating: build/bin/op_registration_test  
2023-01-11T20:40:00.4516470Z   inflating: build/bin/test_edge_op_registration  
2023-01-11T20:40:00.4560222Z   inflating: build/bin/tutorial_tensorexpr  
2023-01-11T20:40:00.4576137Z   inflating: build/bin/torch_shm_manager  
2023-01-11T20:40:00.6431341Z   inflating: build/bin/test_tensorexpr  
2023-01-11T20:40:00.8204792Z   inflating: build/bin/test_jit      
2023-01-11T20:40:00.8205682Z   inflating: .pytorch-test-times.json  
2023-01-11T20:40:00.8228129Z ##[group]Run df -H
2023-01-11T20:40:00.8228322Z df -H
2023-01-11T20:40:00.8239385Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T20:40:00.8239606Z env:
2023-01-11T20:40:00.8239783Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:40:00.8239961Z ##[endgroup]
2023-01-11T20:40:00.8272491Z Filesystem      Size  Used Avail Use% Mounted on
2023-01-11T20:40:00.8272929Z devtmpfs        8.2G     0  8.2G   0% /dev
2023-01-11T20:40:00.8273286Z tmpfs           8.2G  2.1M  8.2G   1% /dev/shm
2023-01-11T20:40:00.8273477Z tmpfs           8.2G  431k  8.2G   1% /run
2023-01-11T20:40:00.8273684Z tmpfs           8.2G     0  8.2G   0% /sys/fs/cgroup
2023-01-11T20:40:00.8274101Z /dev/nvme0n1p1  162G   18G  144G  11% /
2023-01-11T20:40:00.8391649Z ##[group]Run .github/scripts/parse_ref.py
2023-01-11T20:40:00.8391913Z .github/scripts/parse_ref.py
2023-01-11T20:40:00.8402566Z shell: /usr/bin/bash -e {0}
2023-01-11T20:40:00.8402750Z env:
2023-01-11T20:40:00.8402913Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:40:00.8403100Z ##[endgroup]
2023-01-11T20:40:00.8652138Z ##[group]Run set -x
2023-01-11T20:40:00.8652420Z set -x
2023-01-11T20:40:00.8652586Z 
2023-01-11T20:40:00.8652769Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2023-01-11T20:40:00.8653026Z   TEST_COMMAND=.jenkins/pytorch/multigpu-test.sh
2023-01-11T20:40:00.8653283Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2023-01-11T20:40:00.8653504Z   TEST_COMMAND=.jenkins/onnx/test.sh
2023-01-11T20:40:00.8653699Z else
2023-01-11T20:40:00.8653901Z   TEST_COMMAND=.jenkins/pytorch/test.sh
2023-01-11T20:40:00.8654088Z fi
2023-01-11T20:40:00.8654243Z 
2023-01-11T20:40:00.8654477Z COMMIT_MESSAGES=$(git cherry -v "origin/${GIT_DEFAULT_BRANCH:-master}")
2023-01-11T20:40:00.8654706Z 
2023-01-11T20:40:00.8654922Z # sanitize the input commit message and PR body here:
2023-01-11T20:40:00.8655135Z #
2023-01-11T20:40:00.8655403Z # trim all new lines from commit messages + PR_BODY to avoid issues with batch environment
2023-01-11T20:40:00.8655782Z # variable copying. see https://github.com/pytorch/pytorch/pull/80043#issuecomment-1167796028
2023-01-11T20:40:00.8656093Z COMMIT_MESSAGES="${COMMIT_MESSAGES//[$'\n\r']}"
2023-01-11T20:40:00.8656322Z PR_BODY="${PR_BODY//[$'\n\r']}"
2023-01-11T20:40:00.8656493Z 
2023-01-11T20:40:00.8656752Z # then trim all special characters like single and double quotes to avoid unescaped inputs to
2023-01-11T20:40:00.8657028Z # wreak havoc internally
2023-01-11T20:40:00.8657248Z export COMMIT_MESSAGES="${COMMIT_MESSAGES//[\'\"]}"
2023-01-11T20:40:00.8657484Z export PR_BODY="${PR_BODY//[\'\"]}"
2023-01-11T20:40:00.8657674Z 
2023-01-11T20:40:00.8657898Z # detached container should get cleaned up by teardown_ec2_linux
2023-01-11T20:40:00.8658179Z # TODO: Stop building test binaries as part of the build phase
2023-01-11T20:40:00.8658451Z # Used for GPU_FLAG since that doesn't play nice
2023-01-11T20:40:00.8658689Z # shellcheck disable=SC2086,SC2090
2023-01-11T20:40:00.8658897Z container_name=$(docker run \
2023-01-11T20:40:00.8659097Z   ${GPU_FLAG:-} \
2023-01-11T20:40:00.8659294Z   -e BUILD_ENVIRONMENT \
2023-01-11T20:40:00.8659481Z   -e PR_NUMBER \
2023-01-11T20:40:00.8659674Z   -e GITHUB_ACTIONS \
2023-01-11T20:40:00.8659862Z   -e BASE_SHA \
2023-01-11T20:40:00.8660041Z   -e BRANCH \
2023-01-11T20:40:00.8660291Z   -e SHA1 \
2023-01-11T20:40:00.8660482Z   -e AWS_DEFAULT_REGION \
2023-01-11T20:40:00.8660678Z   -e IN_WHEEL_TEST \
2023-01-11T20:40:00.8660951Z   -e SHARD_NUMBER \
2023-01-11T20:40:00.8661140Z   -e TEST_CONFIG \
2023-01-11T20:40:00.8661329Z   -e NUM_TEST_SHARDS \
2023-01-11T20:40:00.8661503Z   -e PR_BODY \
2023-01-11T20:40:00.8661693Z   -e COMMIT_MESSAGES \
2023-01-11T20:40:00.8661899Z   -e CONTINUE_THROUGH_ERROR \
2023-01-11T20:40:00.8662106Z   -e PYTORCH_RETRY_TEST_CASES \
2023-01-11T20:40:00.8662332Z   -e PYTORCH_OVERRIDE_FLAKY_SIGNAL \
2023-01-11T20:40:00.8662539Z   -e PR_LABELS \
2023-01-11T20:40:00.8662732Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2023-01-11T20:40:00.8662940Z   -e SCCACHE_BUCKET \
2023-01-11T20:40:00.8663138Z   -e SCCACHE_S3_KEY_PREFIX \
2023-01-11T20:40:00.8663329Z   -e XLA_CUDA \
2023-01-11T20:40:00.8663521Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2023-01-11T20:40:00.8663750Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2023-01-11T20:40:00.8663986Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2023-01-11T20:40:00.8664229Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2023-01-11T20:40:00.8664461Z   --ulimit stack=10485760:83886080 \
2023-01-11T20:40:00.8664727Z   --security-opt seccomp=unconfined \
2023-01-11T20:40:00.8664937Z   --cap-add=SYS_PTRACE \
2023-01-11T20:40:00.8665129Z   --ipc=host \
2023-01-11T20:40:00.8665320Z   --shm-size="${SHM_SIZE}" \
2023-01-11T20:40:00.8665503Z   --tty \
2023-01-11T20:40:00.8665661Z   --detach \
2023-01-11T20:40:00.8665856Z   --name="${container_name}" \
2023-01-11T20:40:00.8666051Z   --user jenkins \
2023-01-11T20:40:00.8666271Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2023-01-11T20:40:00.8666519Z   -w /var/lib/jenkins/workspace \
2023-01-11T20:40:00.8666721Z   "${DOCKER_IMAGE}"
2023-01-11T20:40:00.8666881Z )
2023-01-11T20:40:00.8667098Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2023-01-11T20:40:00.8667428Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2023-01-11T20:40:00.8678208Z shell: /usr/bin/bash -e {0}
2023-01-11T20:40:00.8678390Z env:
2023-01-11T20:40:00.8678561Z   GIT_DEFAULT_BRANCH: master
2023-01-11T20:40:00.8678792Z   BUILD_ENVIRONMENT: linux-focal-py3.7-clang7-asan
2023-01-11T20:40:00.8679021Z   PR_NUMBER: 91627
2023-01-11T20:40:00.8679197Z   BRANCH: pull/91627
2023-01-11T20:40:00.8679411Z   SHA1: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T20:40:00.8679644Z   BASE_SHA: db2a237763eb8693a20788be94f8c192e762baa8
2023-01-11T20:40:00.8679865Z   PYTORCH_RETRY_TEST_CASES: 1
2023-01-11T20:40:00.8680070Z   PYTORCH_OVERRIDE_FLAKY_SIGNAL: 1
2023-01-11T20:40:00.8680253Z   TEST_CONFIG: default
2023-01-11T20:40:00.8680428Z   SHARD_NUMBER: 3
2023-01-11T20:40:00.8680783Z   NUM_TEST_SHARDS: 5
2023-01-11T20:40:00.8680986Z   PR_BODY: Fixes #91003 


cc @ezyang @gchanan
2023-01-11T20:40:00.8681203Z   CONTINUE_THROUGH_ERROR: False
2023-01-11T20:40:00.8681447Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2023-01-11T20:40:00.8681674Z   SCCACHE_S3_KEY_PREFIX: pull
2023-01-11T20:40:00.8681858Z   SHM_SIZE: 1g
2023-01-11T20:40:00.8682210Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T20:40:00.8682541Z   XLA_CUDA: 
2023-01-11T20:40:00.8682786Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2023-01-11T20:40:00.8683066Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2023-01-11T20:40:00.8683284Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2023-01-11T20:40:00.8683465Z ##[endgroup]
2023-01-11T20:40:00.8707289Z + [[ default == \m\u\l\t\i\g\p\u ]]
2023-01-11T20:40:00.8707977Z + [[ linux-focal-py3.7-clang7-asan == *onnx* ]]
2023-01-11T20:40:00.8708226Z + TEST_COMMAND=.jenkins/pytorch/test.sh
2023-01-11T20:40:00.8710064Z ++ git cherry -v origin/master
2023-01-11T20:40:00.9152692Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into '\''input'\''
2023-01-11T20:40:00.9153082Z + 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch
2023-01-11T20:40:00.9153523Z + 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e '\''other'\'' instead of '\''output'\'' in documentation'
2023-01-11T20:40:00.9154300Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into '\''input'\''+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e '\''other'\'' instead of '\''output'\'' in documentation'
2023-01-11T20:40:00.9154821Z + PR_BODY='Fixes #91003 cc @ezyang @gchanan'
2023-01-11T20:40:00.9155948Z + export 'COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation'
2023-01-11T20:40:00.9157001Z + COMMIT_MESSAGES='+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation'
2023-01-11T20:40:00.9157506Z + export 'PR_BODY=Fixes #91003 cc @ezyang @gchanan'
2023-01-11T20:40:00.9157784Z + PR_BODY='Fixes #91003 cc @ezyang @gchanan'
2023-01-11T20:40:00.9163507Z +++ nproc --ignore=2
2023-01-11T20:40:00.9174508Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e PR_BODY -e COMMIT_MESSAGES -e CONTINUE_THROUGH_ERROR -e PYTORCH_RETRY_TEST_CASES -e PYTORCH_OVERRIDE_FLAKY_SIGNAL -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS --env-file=/tmp/github_env_3896099317 --ulimit stack=10485760:83886080 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T20:40:15.5999130Z + container_name=bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T20:40:15.5999535Z + echo DOCKER_CONTAINER_ID=bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T20:40:15.6003436Z ++ echo dist/torch-2.0.0a0+git8419ddd-cp37-cp37m-linux_x86_64.whl
2023-01-11T20:40:15.6005195Z + docker exec -t bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187 sh -c 'pip install dist/torch-2.0.0a0+git8419ddd-cp37-cp37m-linux_x86_64.whl[opt-einsum] && .jenkins/pytorch/test.sh'
2023-01-11T20:40:16.0106742Z Processing ./dist/torch-2.0.0a0+git8419ddd-cp37-cp37m-linux_x86_64.whl
2023-01-11T20:40:16.8551478Z Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch==2.0.0a0+git8419ddd) (4.4.0)
2023-01-11T20:40:16.8554251Z Requirement already satisfied: sympy in /opt/conda/lib/python3.7/site-packages (from torch==2.0.0a0+git8419ddd) (1.10.1)
2023-01-11T20:40:16.8557793Z Requirement already satisfied: networkx in /opt/conda/lib/python3.7/site-packages (from torch==2.0.0a0+git8419ddd) (2.6.3)
2023-01-11T20:40:16.8570913Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/lib/python3.7/site-packages (from torch==2.0.0a0+git8419ddd) (3.3.0)
2023-01-11T20:40:16.8632529Z Requirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.7/site-packages (from opt-einsum>=3.3->torch==2.0.0a0+git8419ddd) (1.18.5)
2023-01-11T20:40:16.8800952Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.7/site-packages (from sympy->torch==2.0.0a0+git8419ddd) (1.2.1)
2023-01-11T20:40:17.6042615Z Installing collected packages: torch
2023-01-11T20:40:26.9502120Z Successfully installed torch-2.0.0a0+git8419ddd
2023-01-11T20:40:27.0923948Z + echo 'Environment variables:'
2023-01-11T20:40:27.0924232Z Environment variables:
2023-01-11T20:40:27.0925724Z + env
2023-01-11T20:40:27.0960343Z INSTALLED_DB=yes
2023-01-11T20:40:27.0961081Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T20:40:27.0962286Z CONTINUE_THROUGH_ERROR=False
2023-01-11T20:40:27.0962806Z BUILD_ENVIRONMENT=linux-focal-py3.7-clang7-asan
2023-01-11T20:40:27.0963072Z PYTORCH_OVERRIDE_FLAKY_SIGNAL=1
2023-01-11T20:40:27.0963275Z HOSTNAME=bc317e370d4c
2023-01-11T20:40:27.0963687Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.0963990Z GITHUB_ACTION=__self
2023-01-11T20:40:27.0964204Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2023-01-11T20:40:27.0964532Z GITHUB_RUN_NUMBER=77928
2023-01-11T20:40:27.0964777Z TEST_CONFIG=default
2023-01-11T20:40:27.0965014Z GITHUB_REPOSITORY_OWNER_ID=21003710
2023-01-11T20:40:27.0965713Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2023-01-11T20:40:27.0966126Z GITHUB_TRIGGERING_ACTOR=albanD
2023-01-11T20:40:27.0966485Z GITHUB_REF_TYPE=branch
2023-01-11T20:40:27.0966826Z TORCH_CUDA_ARCH_LIST=Maxwell
2023-01-11T20:40:27.0967242Z BASE_SHA=db2a237763eb8693a20788be94f8c192e762baa8
2023-01-11T20:40:27.1001269Z XLA_CUDA=
2023-01-11T20:40:27.1003523Z ***
2023-01-11T20:40:27.1003739Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2023-01-11T20:40:27.1003955Z GITHUB_REPOSITORY_ID=65600975
2023-01-11T20:40:27.1004136Z GITHUB_ACTIONS=true
2023-01-11T20:40:27.1004362Z SHA1=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T20:40:27.1004609Z GITHUB_SHA=57fc38f02f250896a12b32cfa200a6105a03d09c
2023-01-11T20:40:27.1004894Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/91627/merge
2023-01-11T20:40:27.1005153Z UCC_HOME=/usr
2023-01-11T20:40:27.1005337Z GITHUB_REF=refs/pull/91627/merge
2023-01-11T20:40:27.1005510Z SHARD_NUMBER=3
2023-01-11T20:40:27.1005692Z GITHUB_REF_PROTECTED=false
2023-01-11T20:40:27.1005885Z HOME=/var/lib/jenkins
2023-01-11T20:40:27.1006100Z GITHUB_API_URL=https://api.github.com
2023-01-11T20:40:27.1006317Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2023-01-11T20:40:27.1006513Z INSTALLED_THRIFT=
2023-01-11T20:40:27.1006682Z UCX_COMMIT=
2023-01-11T20:40:27.1006847Z SCCACHE_S3_KEY_PREFIX=pull
2023-01-11T20:40:27.1007032Z NUM_TEST_SHARDS=5
2023-01-11T20:40:27.1007198Z UCX_HOME=/usr
2023-01-11T20:40:27.1014834Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1015288Z PYTORCH_RETRY_TEST_CASES=1
2023-01-11T20:40:27.1016074Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1016517Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2023-01-11T20:40:27.1016801Z GITHUB_EVENT_NAME=pull_request
2023-01-11T20:40:27.1016987Z GITHUB_RUN_ID=3896099317
2023-01-11T20:40:27.1017415Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1017727Z GITHUB_ACTOR=LucaLumetti
2023-01-11T20:40:27.1017897Z PR_NUMBER=91627
2023-01-11T20:40:27.1018065Z DESIRED_CUDA=
2023-01-11T20:40:27.1018239Z GITHUB_RUN_ATTEMPT=2
2023-01-11T20:40:27.1018466Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2023-01-11T20:40:27.1018679Z TERM=xterm
2023-01-11T20:40:27.1018846Z INSTALLED_VISION=yes
2023-01-11T20:40:27.1019012Z BRANCH=pull/91627
2023-01-11T20:40:27.1019198Z OPENSSL_ROOT_DIR=/opt/openssl
2023-01-11T20:40:27.1019393Z CUDA_PATH=/usr/local/cuda
2023-01-11T20:40:27.1019744Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2023-01-11T20:40:27.1020264Z GITHUB_SERVER_URL=https://github.com
2023-01-11T20:40:27.1020466Z UCC_COMMIT=
2023-01-11T20:40:27.1020624Z INSTALLED_ANDROID=
2023-01-11T20:40:27.1020792Z SHLVL=1
2023-01-11T20:40:27.1020949Z MAX_JOBS=6
2023-01-11T20:40:27.1021109Z GITHUB_ACTOR_ID=7543386
2023-01-11T20:40:27.1021560Z COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation
2023-01-11T20:40:27.1022125Z GITHUB_WORKFLOW_SHA=57fc38f02f250896a12b32cfa200a6105a03d09c
2023-01-11T20:40:27.1022352Z GITHUB_REF_NAME=91627/merge
2023-01-11T20:40:27.1022696Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2023-01-11T20:40:27.1022941Z GITHUB_JOB=test
2023-01-11T20:40:27.1023139Z GITHUB_REPOSITORY=pytorch/pytorch
2023-01-11T20:40:27.1023347Z GITHUB_RETENTION_DAYS=90
2023-01-11T20:40:27.1023525Z OPENSSL_DIR=/opt/openssl
2023-01-11T20:40:27.1023721Z GITHUB_ACTION_REPOSITORY=
2023-01-11T20:40:27.1024031Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T20:40:27.1024378Z GITHUB_BASE_REF=master
2023-01-11T20:40:27.1024555Z CI=true
2023-01-11T20:40:27.1024737Z GITHUB_REPOSITORY_OWNER=pytorch
2023-01-11T20:40:27.1024927Z INSTALLED_PROTOBUF=yes
2023-01-11T20:40:27.1025117Z GITHUB_HEAD_REF=master
2023-01-11T20:40:27.1025299Z GITHUB_ACTION_REF=
2023-01-11T20:40:27.1025567Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2023-01-11T20:40:27.1025800Z GITHUB_WORKFLOW=pull
2023-01-11T20:40:27.1025999Z DEBIAN_FRONTEND=noninteractive
2023-01-11T20:40:27.1026410Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1026730Z PR_BODY=Fixes #91003 cc @ezyang @gchanan
2023-01-11T20:40:27.1026925Z _=/usr/bin/env
2023-01-11T20:40:27.1027209Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2023-01-11T20:40:27.1123539Z + TORCH_INSTALL_DIR=/opt/conda/lib/python3.7/site-packages/torch
2023-01-11T20:40:27.1124000Z + TORCH_BIN_DIR=/opt/conda/lib/python3.7/site-packages/torch/bin
2023-01-11T20:40:27.1124498Z + TORCH_LIB_DIR=/opt/conda/lib/python3.7/site-packages/torch/lib
2023-01-11T20:40:27.1124949Z + TORCH_TEST_DIR=/opt/conda/lib/python3.7/site-packages/torch/test
2023-01-11T20:40:27.1125185Z + BUILD_DIR=build
2023-01-11T20:40:27.1125385Z + BUILD_RENAMED_DIR=build_renamed
2023-01-11T20:40:27.1125601Z + BUILD_BIN_DIR=build/bin
2023-01-11T20:40:27.1125792Z + export VALGRIND=ON
2023-01-11T20:40:27.1125975Z + VALGRIND=ON
2023-01-11T20:40:27.1126171Z + export TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T20:40:27.1126374Z + TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T20:40:27.1126666Z + [[ linux-focal-py3.7-clang7-asan == *clang9* ]]
2023-01-11T20:40:27.1126978Z + [[ linux-focal-py3.7-clang7-asan != *bazel* ]]
2023-01-11T20:40:27.1128747Z ++ realpath build/custom_test_artifacts
2023-01-11T20:40:27.1163106Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2023-01-11T20:40:27.1165289Z ++ dirname .jenkins/pytorch/test.sh
2023-01-11T20:40:27.1206914Z + source .jenkins/pytorch/common.sh
2023-01-11T20:40:27.1209357Z +++ dirname .jenkins/pytorch/common.sh
2023-01-11T20:40:27.1214482Z ++ source .jenkins/pytorch/common_utils.sh
2023-01-11T20:40:27.1216123Z +++ declare -f -t trap_add
2023-01-11T20:40:27.1221330Z ++ set -ex
2023-01-11T20:40:27.1222126Z ++ [[ linux-focal-py3.7-clang7-asan == *rocm* ]]
2023-01-11T20:40:27.1222374Z ++ BUILD_TEST_LIBTORCH=0
2023-01-11T20:40:27.1222614Z + echo 'Environment variables'
2023-01-11T20:40:27.1222836Z Environment variables
2023-01-11T20:40:27.1223106Z + env
2023-01-11T20:40:27.1229072Z INSTALLED_DB=yes
2023-01-11T20:40:27.1230447Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2023-01-11T20:40:27.1231372Z CONTINUE_THROUGH_ERROR=False
2023-01-11T20:40:27.1231933Z BUILD_ENVIRONMENT=linux-focal-py3.7-clang7-asan
2023-01-11T20:40:27.1233051Z PYTORCH_OVERRIDE_FLAKY_SIGNAL=1
2023-01-11T20:40:27.1233430Z HOSTNAME=bc317e370d4c
2023-01-11T20:40:27.1234020Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1234324Z GITHUB_ACTION=__self
2023-01-11T20:40:27.1234518Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2023-01-11T20:40:27.1234719Z GITHUB_RUN_NUMBER=77928
2023-01-11T20:40:27.1234902Z TEST_CONFIG=default
2023-01-11T20:40:27.1235089Z GITHUB_REPOSITORY_OWNER_ID=21003710
2023-01-11T20:40:27.1235351Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2023-01-11T20:40:27.1235571Z GITHUB_TRIGGERING_ACTOR=albanD
2023-01-11T20:40:27.1235768Z GITHUB_REF_TYPE=branch
2023-01-11T20:40:27.1235950Z TORCH_CUDA_ARCH_LIST=Maxwell
2023-01-11T20:40:27.1236171Z BASE_SHA=db2a237763eb8693a20788be94f8c192e762baa8
2023-01-11T20:40:27.1236371Z XLA_CUDA=
2023-01-11T20:40:27.1236612Z ***
2023-01-11T20:40:27.1236787Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2023-01-11T20:40:27.1236996Z GITHUB_REPOSITORY_ID=65600975
2023-01-11T20:40:27.1237174Z GITHUB_ACTIONS=true
2023-01-11T20:40:27.1237391Z SHA1=8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T20:40:27.1237740Z GITHUB_SHA=57fc38f02f250896a12b32cfa200a6105a03d09c
2023-01-11T20:40:27.1238023Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/91627/merge
2023-01-11T20:40:27.1238272Z UCC_HOME=/usr
2023-01-11T20:40:27.1238461Z GITHUB_REF=refs/pull/91627/merge
2023-01-11T20:40:27.1238637Z SHARD_NUMBER=3
2023-01-11T20:40:27.1238821Z GITHUB_REF_PROTECTED=false
2023-01-11T20:40:27.1239014Z HOME=/var/lib/jenkins
2023-01-11T20:40:27.1239216Z GITHUB_API_URL=https://api.github.com
2023-01-11T20:40:27.1239445Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2023-01-11T20:40:27.1239645Z INSTALLED_THRIFT=
2023-01-11T20:40:27.1239801Z UCX_COMMIT=
2023-01-11T20:40:27.1239980Z SCCACHE_S3_KEY_PREFIX=pull
2023-01-11T20:40:27.1240171Z NUM_TEST_SHARDS=5
2023-01-11T20:40:27.1240325Z UCX_HOME=/usr
2023-01-11T20:40:27.1241003Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1241592Z TORCH_INDUCTOR_INSTALL_GXX=ON
2023-01-11T20:40:27.1242661Z PYTORCH_RETRY_TEST_CASES=1
2023-01-11T20:40:27.1243343Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1244061Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2023-01-11T20:40:27.1244600Z GITHUB_EVENT_NAME=pull_request
2023-01-11T20:40:27.1244859Z GITHUB_RUN_ID=3896099317
2023-01-11T20:40:27.1245307Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1245622Z GITHUB_ACTOR=LucaLumetti
2023-01-11T20:40:27.1245805Z PR_NUMBER=91627
2023-01-11T20:40:27.1245963Z DESIRED_CUDA=
2023-01-11T20:40:27.1246139Z GITHUB_RUN_ATTEMPT=2
2023-01-11T20:40:27.1246312Z VALGRIND=ON
2023-01-11T20:40:27.1246529Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2023-01-11T20:40:27.1246743Z TERM=xterm
2023-01-11T20:40:27.1246911Z INSTALLED_VISION=yes
2023-01-11T20:40:27.1247076Z BRANCH=pull/91627
2023-01-11T20:40:27.1247265Z OPENSSL_ROOT_DIR=/opt/openssl
2023-01-11T20:40:27.1247460Z CUDA_PATH=/usr/local/cuda
2023-01-11T20:40:27.1247812Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2023-01-11T20:40:27.1248112Z GITHUB_SERVER_URL=https://github.com
2023-01-11T20:40:27.1248309Z UCC_COMMIT=
2023-01-11T20:40:27.1248465Z INSTALLED_ANDROID=
2023-01-11T20:40:27.1248631Z SHLVL=1
2023-01-11T20:40:27.1248782Z MAX_JOBS=6
2023-01-11T20:40:27.1248938Z GITHUB_ACTOR_ID=7543386
2023-01-11T20:40:27.1249383Z COMMIT_MESSAGES=+ 52a16ce42647731c772e14e7175afa40fda07b3d make torchgen rename also Number arguments into input+ 87db01a53ecb702267ec36787654e418a52f8e93 fix torch.where signature mismatch+ 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e other instead of output in documentation
2023-01-11T20:40:27.1249972Z GITHUB_WORKFLOW_SHA=57fc38f02f250896a12b32cfa200a6105a03d09c
2023-01-11T20:40:27.1250198Z GITHUB_REF_NAME=91627/merge
2023-01-11T20:40:27.1250525Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2023-01-11T20:40:27.1250788Z GITHUB_JOB=test
2023-01-11T20:40:27.1250984Z GITHUB_REPOSITORY=pytorch/pytorch
2023-01-11T20:40:27.1251179Z GITHUB_RETENTION_DAYS=90
2023-01-11T20:40:27.1251371Z OPENSSL_DIR=/opt/openssl
2023-01-11T20:40:27.1251566Z GITHUB_ACTION_REPOSITORY=
2023-01-11T20:40:27.1251865Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T20:40:27.1252150Z GITHUB_BASE_REF=master
2023-01-11T20:40:27.1252319Z CI=true
2023-01-11T20:40:27.1252497Z GITHUB_REPOSITORY_OWNER=pytorch
2023-01-11T20:40:27.1252680Z INSTALLED_PROTOBUF=yes
2023-01-11T20:40:27.1252865Z GITHUB_HEAD_REF=master
2023-01-11T20:40:27.1253042Z GITHUB_ACTION_REF=
2023-01-11T20:40:27.1253306Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2023-01-11T20:40:27.1253534Z GITHUB_WORKFLOW=pull
2023-01-11T20:40:27.1253732Z DEBIAN_FRONTEND=noninteractive
2023-01-11T20:40:27.1254193Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_aebc1c07-f0ee-465f-aa66-d5d1c8691acb
2023-01-11T20:40:27.1254512Z PR_BODY=Fixes #91003 cc @ezyang @gchanan
2023-01-11T20:40:27.1254705Z _=/usr/bin/env
2023-01-11T20:40:27.1254902Z + echo 'Testing pytorch'
2023-01-11T20:40:27.1255086Z Testing pytorch
2023-01-11T20:40:27.1255282Z + export LANG=C.UTF-8
2023-01-11T20:40:27.1255458Z + LANG=C.UTF-8
2023-01-11T20:40:27.1339233Z + PR_NUMBER=91627
2023-01-11T20:40:27.1339546Z + [[ default == \d\e\f\a\u\l\t ]]
2023-01-11T20:40:27.1339867Z + export CUDA_VISIBLE_DEVICES=0
2023-01-11T20:40:27.1340238Z + CUDA_VISIBLE_DEVICES=0
2023-01-11T20:40:27.1340537Z + export HIP_VISIBLE_DEVICES=0
2023-01-11T20:40:27.1340787Z + HIP_VISIBLE_DEVICES=0
2023-01-11T20:40:27.1341068Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]]
2023-01-11T20:40:27.1341392Z + [[ default == \s\l\o\w ]]
2023-01-11T20:40:27.1341939Z + [[ linux-focal-py3.7-clang7-asan == *slow-gradcheck* ]]
2023-01-11T20:40:27.1342533Z + [[ linux-focal-py3.7-clang7-asan == *cuda* ]]
2023-01-11T20:40:27.1342872Z + [[ linux-focal-py3.7-clang7-asan == *rocm* ]]
2023-01-11T20:40:27.1343097Z + [[ default == *crossref* ]]
2023-01-11T20:40:27.1343275Z + [[ default == *dynamo* ]]
2023-01-11T20:40:27.1343465Z + [[ default == *inductor* ]]
2023-01-11T20:40:27.1343734Z + [[ linux-focal-py3.7-clang7-asan == *rocm* ]]
2023-01-11T20:40:27.1344022Z + [[ linux-focal-py3.7-clang7-asan != *-bazel-* ]]
2023-01-11T20:40:27.1344296Z + pip_install --user ninja==1.10.2
2023-01-11T20:40:27.1344585Z + pip install --progress-bar off --user ninja==1.10.2
2023-01-11T20:40:27.5773157Z Collecting ninja==1.10.2
2023-01-11T20:40:27.5950256Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2023-01-11T20:40:28.2914838Z Installing collected packages: ninja
2023-01-11T20:40:28.3000512Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2023-01-11T20:40:28.3001162Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2023-01-11T20:40:28.3046220Z Successfully installed ninja-1.10.2
2023-01-11T20:40:28.3693063Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T20:40:28.3693813Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2023-01-11T20:40:28.3694539Z + [[ linux-focal-py3.7-clang7-asan == *asan* ]]
2023-01-11T20:40:28.3695063Z + export ASAN_OPTIONS=detect_leaks=0:symbolize=1:detect_stack_use_after_return=1:strict_init_order=true:detect_odr_violation=0
2023-01-11T20:40:28.3696039Z + ASAN_OPTIONS=detect_leaks=0:symbolize=1:detect_stack_use_after_return=1:strict_init_order=true:detect_odr_violation=0
2023-01-11T20:40:28.3696533Z + export UBSAN_OPTIONS=print_stacktrace=1
2023-01-11T20:40:28.3696917Z + UBSAN_OPTIONS=print_stacktrace=1
2023-01-11T20:40:28.3697237Z + export PYTORCH_TEST_WITH_ASAN=1
2023-01-11T20:40:28.3697536Z + PYTORCH_TEST_WITH_ASAN=1
2023-01-11T20:40:28.3697850Z + export PYTORCH_TEST_WITH_UBSAN=1
2023-01-11T20:40:28.3698187Z + PYTORCH_TEST_WITH_UBSAN=1
2023-01-11T20:40:28.3698738Z + export ASAN_SYMBOLIZER_PATH=/usr/lib/llvm-7/bin/llvm-symbolizer
2023-01-11T20:40:28.3699260Z + ASAN_SYMBOLIZER_PATH=/usr/lib/llvm-7/bin/llvm-symbolizer
2023-01-11T20:40:28.3699633Z + export TORCH_USE_RTLD_GLOBAL=1
2023-01-11T20:40:28.3699931Z + TORCH_USE_RTLD_GLOBAL=1
2023-01-11T20:40:28.3700563Z + export LD_PRELOAD=/usr/lib/llvm-7/lib/clang/7.0.1/lib/linux/libclang_rt.asan-x86_64.so
2023-01-11T20:40:28.3701090Z + LD_PRELOAD=/usr/lib/llvm-7/lib/clang/7.0.1/lib/linux/libclang_rt.asan-x86_64.so
2023-01-11T20:40:28.3701364Z + ulimit -s 81920
2023-01-11T20:40:28.3701540Z + cd test
2023-01-11T20:40:28.3701833Z + python -c 'import torch; print(torch.__version__, torch.version.git_version)'
2023-01-11T20:40:32.3598028Z 2.0.0a0+git8419ddd 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T20:40:32.8390206Z + echo 'The next four invocations are expected to crash; if they don'\''t that means ASAN/UBSAN is misconfigured'
2023-01-11T20:40:32.8390986Z The next four invocations are expected to crash; if they don't that means ASAN/UBSAN is misconfigured
2023-01-11T20:40:32.8391363Z + cd test
2023-01-11T20:40:32.8391774Z + get_exit_code python -c 'import torch; torch._C._crash_if_csrc_asan(3)'
2023-01-11T20:40:32.8392008Z + set +e
2023-01-11T20:40:32.8392276Z + python -c 'import torch; torch._C._crash_if_csrc_asan(3)'
2023-01-11T20:40:36.5009099Z /var/lib/jenkins/workspace/torch/csrc/Module.cpp:176:3: runtime error: index 3 out of bounds for type 'volatile char [3]'
2023-01-11T20:40:37.6654487Z     #0 0x7fc0df3fe127 in THPModule_crashIfCsrcASAN(_object*, _object*) (/opt/conda/lib/python3.7/site-packages/torch/lib/libtorch_python.so+0x2d72127)
2023-01-11T20:40:37.7621791Z warning: parsing line table prologue at 0x00000000 should have ended at 0x0000046f but it ended at 0x0000046e
2023-01-11T20:40:37.7622320Z     #1 0x4ba813 in _PyMethodDef_RawFastCallKeywords (/opt/conda/bin/python3.7+0x4ba813)
2023-01-11T20:40:37.7624593Z     #2 0x4ba34f in _PyCFunction_FastCallKeywords (/opt/conda/bin/python3.7+0x4ba34f)
2023-01-11T20:40:37.7625263Z     #3 0x4ba34f in call_function /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:4568:9
2023-01-11T20:40:37.7625739Z     #4 0x4b6cc1 in _PyEval_EvalFrameDefault (/opt/conda/bin/python3.7+0x4b6cc1)
2023-01-11T20:40:37.7626562Z     #5 0x4b1410 in PyEval_EvalFrameEx (/opt/conda/bin/python3.7+0x4b1410)
2023-01-11T20:40:37.7627242Z     #6 0x4b1410 in _PyEval_EvalCodeWithName /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3930:14
2023-01-11T20:40:37.7627716Z     #7 0x4b1208 in PyEval_EvalCodeEx (/opt/conda/bin/python3.7+0x4b1208)
2023-01-11T20:40:37.7628417Z     #8 0x54f39a in PyEval_EvalCode (/opt/conda/bin/python3.7+0x54f39a)
2023-01-11T20:40:37.7630273Z     #9 0x56a262 in run_mod (/opt/conda/bin/python3.7+0x56a262)
2023-01-11T20:40:37.7630559Z     #10 0x56692a in PyRun_StringFlags (/opt/conda/bin/python3.7+0x56692a)
2023-01-11T20:40:37.7631289Z     #11 0x56679a in PyRun_SimpleStringFlags (/opt/conda/bin/python3.7+0x56679a)
2023-01-11T20:40:37.7633592Z     #12 0x544966 in pymain_run_command (/opt/conda/bin/python3.7+0x544966)
2023-01-11T20:40:37.7634165Z     #13 0x544966 in pymain_run_python /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:2964:26
2023-01-11T20:40:37.7634667Z     #14 0x544966 in pymain_main /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3517:5
2023-01-11T20:40:37.7634998Z     #15 0x54487b in _Py_UnixMain (/opt/conda/bin/python3.7+0x54487b)
2023-01-11T20:40:37.7728183Z     #16 0x7fc0e7c72082 in __libc_start_main (/lib/x86_64-linux-gnu/libc.so.6+0x24082)
2023-01-11T20:40:37.7728671Z     #17 0x54472d in _start (/opt/conda/bin/python3.7+0x54472d)
2023-01-11T20:40:37.7728846Z 
2023-01-11T20:40:37.7729173Z SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /var/lib/jenkins/workspace/torch/csrc/Module.cpp:176:3 in 
2023-01-11T20:40:37.8254094Z + retcode=1
2023-01-11T20:40:37.8254422Z + set -e
2023-01-11T20:40:37.8254588Z + return 1
2023-01-11T20:40:37.8257932Z + cd test
2023-01-11T20:40:37.8258364Z + get_exit_code python -c 'import torch; torch._C._crash_if_csrc_ubsan(0)'
2023-01-11T20:40:37.8258599Z + set +e
2023-01-11T20:40:37.8258871Z + python -c 'import torch; torch._C._crash_if_csrc_ubsan(0)'
2023-01-11T20:40:41.3420058Z /var/lib/jenkins/workspace/torch/csrc/Module.cpp:188:18: runtime error: division by zero
2023-01-11T20:40:41.4765435Z     #0 0x7f484d3fe375 in THPModule_crashIfCsrcUBSAN(_object*, _object*) (/opt/conda/lib/python3.7/site-packages/torch/lib/libtorch_python.so+0x2d72375)
2023-01-11T20:40:41.5529806Z warning: parsing line table prologue at 0x00000000 should have ended at 0x0000046f but it ended at 0x0000046e
2023-01-11T20:40:41.5530690Z     #1 0x4ba813 in _PyMethodDef_RawFastCallKeywords (/opt/conda/bin/python3.7+0x4ba813)
2023-01-11T20:40:41.5532242Z     #2 0x4ba34f in _PyCFunction_FastCallKeywords (/opt/conda/bin/python3.7+0x4ba34f)
2023-01-11T20:40:41.5533066Z     #3 0x4ba34f in call_function /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:4568:9
2023-01-11T20:40:41.5533653Z     #4 0x4b6cc1 in _PyEval_EvalFrameDefault (/opt/conda/bin/python3.7+0x4b6cc1)
2023-01-11T20:40:41.5536084Z     #5 0x4b1410 in PyEval_EvalFrameEx (/opt/conda/bin/python3.7+0x4b1410)
2023-01-11T20:40:41.5536644Z     #6 0x4b1410 in _PyEval_EvalCodeWithName /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3930:14
2023-01-11T20:40:41.5536988Z     #7 0x4b1208 in PyEval_EvalCodeEx (/opt/conda/bin/python3.7+0x4b1208)
2023-01-11T20:40:41.5538399Z     #8 0x54f39a in PyEval_EvalCode (/opt/conda/bin/python3.7+0x54f39a)
2023-01-11T20:40:41.5539979Z     #9 0x56a262 in run_mod (/opt/conda/bin/python3.7+0x56a262)
2023-01-11T20:40:41.5540449Z     #10 0x56692a in PyRun_StringFlags (/opt/conda/bin/python3.7+0x56692a)
2023-01-11T20:40:41.5541107Z     #11 0x56679a in PyRun_SimpleStringFlags (/opt/conda/bin/python3.7+0x56679a)
2023-01-11T20:40:41.5543125Z     #12 0x544966 in pymain_run_command (/opt/conda/bin/python3.7+0x544966)
2023-01-11T20:40:41.5543691Z     #13 0x544966 in pymain_run_python /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:2964:26
2023-01-11T20:40:41.5544123Z     #14 0x544966 in pymain_main /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3517:5
2023-01-11T20:40:41.5544657Z     #15 0x54487b in _Py_UnixMain (/opt/conda/bin/python3.7+0x54487b)
2023-01-11T20:40:41.5558349Z     #16 0x7f4855c72082 in __libc_start_main (/lib/x86_64-linux-gnu/libc.so.6+0x24082)
2023-01-11T20:40:41.5558850Z     #17 0x54472d in _start (/opt/conda/bin/python3.7+0x54472d)
2023-01-11T20:40:41.5559038Z 
2023-01-11T20:40:41.5559588Z SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /var/lib/jenkins/workspace/torch/csrc/Module.cpp:188:18 in 
2023-01-11T20:40:41.5999905Z + retcode=1
2023-01-11T20:40:41.6000734Z + set -e
2023-01-11T20:40:41.6001032Z + return 1
2023-01-11T20:40:41.6004703Z + cd test
2023-01-11T20:40:41.6005291Z + get_exit_code python -c 'import torch; torch._C._crash_if_vptr_ubsan()'
2023-01-11T20:40:41.6005538Z + set +e
2023-01-11T20:40:41.6005802Z + python -c 'import torch; torch._C._crash_if_vptr_ubsan()'
2023-01-11T20:40:44.9600782Z /var/lib/jenkins/workspace/torch/csrc/Module.cpp:207:16: runtime error: member call on address 0x7ffb35ce34a0 which does not point to an object of type 'Foo'
2023-01-11T20:40:44.9614069Z 0x7ffb35ce34a0: note: object is of type 'THPModule_crashIfvptrUBSAN(_object*, _object*)::Baz'
2023-01-11T20:40:44.9614686Z  ff 7f 00 00  f0 48 9b 33 fb 7f 00 00  04 00 00 00 00 00 00 00  00 00 00 00 00 00 00 00  92 c8 bf 35
2023-01-11T20:40:44.9615029Z               ^~~~~~~~~~~~~~~~~~~~~~~
2023-01-11T20:40:44.9615427Z               vptr for 'THPModule_crashIfvptrUBSAN(_object*, _object*)::Baz'
2023-01-11T20:40:45.0929988Z     #0 0x7ffb30c2659c in THPModule_crashIfvptrUBSAN(_object*, _object*) (/opt/conda/lib/python3.7/site-packages/torch/lib/libtorch_python.so+0x2d7259c)
2023-01-11T20:40:45.1704167Z warning: parsing line table prologue at 0x00000000 should have ended at 0x0000046f but it ended at 0x0000046e
2023-01-11T20:40:45.1704563Z     #1 0x4ba799 in _PyMethodDef_RawFastCallKeywords (/opt/conda/bin/python3.7+0x4ba799)
2023-01-11T20:40:45.1706010Z     #2 0x4ba34f in _PyCFunction_FastCallKeywords (/opt/conda/bin/python3.7+0x4ba34f)
2023-01-11T20:40:45.1706569Z     #3 0x4ba34f in call_function /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:4568:9
2023-01-11T20:40:45.1707012Z     #4 0x4b6cc1 in _PyEval_EvalFrameDefault (/opt/conda/bin/python3.7+0x4b6cc1)
2023-01-11T20:40:45.1708246Z     #5 0x4b1410 in PyEval_EvalFrameEx (/opt/conda/bin/python3.7+0x4b1410)
2023-01-11T20:40:45.1709096Z     #6 0x4b1410 in _PyEval_EvalCodeWithName /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3930:14
2023-01-11T20:40:45.1709445Z     #7 0x4b1208 in PyEval_EvalCodeEx (/opt/conda/bin/python3.7+0x4b1208)
2023-01-11T20:40:45.1709736Z     #8 0x54f39a in PyEval_EvalCode (/opt/conda/bin/python3.7+0x54f39a)
2023-01-11T20:40:45.1711076Z     #9 0x56a262 in run_mod (/opt/conda/bin/python3.7+0x56a262)
2023-01-11T20:40:45.1711875Z     #10 0x56692a in PyRun_StringFlags (/opt/conda/bin/python3.7+0x56692a)
2023-01-11T20:40:45.1713155Z     #11 0x56679a in PyRun_SimpleStringFlags (/opt/conda/bin/python3.7+0x56679a)
2023-01-11T20:40:45.1715115Z     #12 0x544966 in pymain_run_command (/opt/conda/bin/python3.7+0x544966)
2023-01-11T20:40:45.1716217Z     #13 0x544966 in pymain_run_python /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:2964:26
2023-01-11T20:40:45.1716979Z     #14 0x544966 in pymain_main /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3517:5
2023-01-11T20:40:45.1717476Z     #15 0x54487b in _Py_UnixMain (/opt/conda/bin/python3.7+0x54487b)
2023-01-11T20:40:45.1730904Z     #16 0x7ffb3949a082 in __libc_start_main (/lib/x86_64-linux-gnu/libc.so.6+0x24082)
2023-01-11T20:40:45.1731416Z     #17 0x54472d in _start (/opt/conda/bin/python3.7+0x54472d)
2023-01-11T20:40:45.1731660Z 
2023-01-11T20:40:45.1732412Z SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /var/lib/jenkins/workspace/torch/csrc/Module.cpp:207:16 in 
2023-01-11T20:40:45.2168012Z + retcode=1
2023-01-11T20:40:45.2168503Z + set -e
2023-01-11T20:40:45.2168671Z + return 1
2023-01-11T20:40:45.2171797Z + cd test
2023-01-11T20:40:45.2172262Z + get_exit_code python -c 'import torch; torch._C._crash_if_aten_asan(3)'
2023-01-11T20:40:45.2172485Z + set +e
2023-01-11T20:40:45.2172898Z + python -c 'import torch; torch._C._crash_if_aten_asan(3)'
2023-01-11T20:40:48.5954296Z /var/lib/jenkins/workspace/aten/src/ATen/Utils.cpp:20:3: runtime error: index 3 out of bounds for type 'volatile char [3]'
2023-01-11T20:40:49.2447336Z     #0 0x7f846bac1ec4 in at::_crash_if_asan(int) (/opt/conda/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so+0xd7c3ec4)
2023-01-11T20:40:49.3074559Z     #1 0x7f8495aca6db in THPModule_crashIfATenASAN(_object*, _object*) (/opt/conda/lib/python3.7/site-packages/torch/lib/libtorch_python.so+0x2d726db)
2023-01-11T20:40:49.3842149Z warning: parsing line table prologue at 0x00000000 should have ended at 0x0000046f but it ended at 0x0000046e
2023-01-11T20:40:49.3842559Z     #2 0x4ba813 in _PyMethodDef_RawFastCallKeywords (/opt/conda/bin/python3.7+0x4ba813)
2023-01-11T20:40:49.3844517Z     #3 0x4ba34f in _PyCFunction_FastCallKeywords (/opt/conda/bin/python3.7+0x4ba34f)
2023-01-11T20:40:49.3845478Z     #4 0x4ba34f in call_function /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:4568:9
2023-01-11T20:40:49.3845942Z     #5 0x4b6cc1 in _PyEval_EvalFrameDefault (/opt/conda/bin/python3.7+0x4b6cc1)
2023-01-11T20:40:49.3846595Z     #6 0x4b1410 in PyEval_EvalFrameEx (/opt/conda/bin/python3.7+0x4b1410)
2023-01-11T20:40:49.3847292Z     #7 0x4b1410 in _PyEval_EvalCodeWithName /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3930:14
2023-01-11T20:40:49.3847778Z     #8 0x4b1208 in PyEval_EvalCodeEx (/opt/conda/bin/python3.7+0x4b1208)
2023-01-11T20:40:49.3848079Z     #9 0x54f39a in PyEval_EvalCode (/opt/conda/bin/python3.7+0x54f39a)
2023-01-11T20:40:49.3849692Z     #10 0x56a262 in run_mod (/opt/conda/bin/python3.7+0x56a262)
2023-01-11T20:40:49.3850235Z     #11 0x56692a in PyRun_StringFlags (/opt/conda/bin/python3.7+0x56692a)
2023-01-11T20:40:49.3851065Z     #12 0x56679a in PyRun_SimpleStringFlags (/opt/conda/bin/python3.7+0x56679a)
2023-01-11T20:40:49.3853226Z     #13 0x544966 in pymain_run_command (/opt/conda/bin/python3.7+0x544966)
2023-01-11T20:40:49.3854049Z     #14 0x544966 in pymain_run_python /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:2964:26
2023-01-11T20:40:49.3854685Z     #15 0x544966 in pymain_main /tmp/build/80754af9/python_1669321496543/work/build-static/<invalid>:3517:5
2023-01-11T20:40:49.3854996Z     #16 0x54487b in _Py_UnixMain (/opt/conda/bin/python3.7+0x54487b)
2023-01-11T20:40:49.3867502Z     #17 0x7f849e33e082 in __libc_start_main (/lib/x86_64-linux-gnu/libc.so.6+0x24082)
2023-01-11T20:40:49.3867876Z     #18 0x54472d in _start (/opt/conda/bin/python3.7+0x54472d)
2023-01-11T20:40:49.3868077Z 
2023-01-11T20:40:49.3868545Z SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /var/lib/jenkins/workspace/aten/src/ATen/Utils.cpp:20:3 in 
2023-01-11T20:40:49.4299629Z + retcode=1
2023-01-11T20:40:49.4300167Z + set -e
2023-01-11T20:40:49.4300414Z + return 1
2023-01-11T20:40:49.4302490Z + [[ linux-focal-py3.7-clang7-asan == *-tsan* ]]
2023-01-11T20:40:49.4302982Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2023-01-11T20:40:49.4303284Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2023-01-11T20:40:49.4309581Z + [[ linux-focal-py3.7-clang7-asan == *tbb* ]]
2023-01-11T20:40:49.4321116Z + [[ linux-focal-py3.7-clang7-asan == *libtorch* ]]
2023-01-11T20:40:49.4321706Z + [[ linux-focal-py3.7-clang7-asan == *-bazel-* ]]
2023-01-11T20:40:49.4322257Z + [[ linux-focal-py3.7-clang7-asan == *-tsan* ]]
2023-01-11T20:40:49.4323958Z + cd test
2023-01-11T20:40:49.4325063Z + python -c 'import torch; print(torch.__config__.show())'
2023-01-11T20:40:52.8567618Z PyTorch built with:
2023-01-11T20:40:52.8568022Z   - GCC 4.2
2023-01-11T20:40:52.8568282Z   - C++ Version: 201703
2023-01-11T20:40:52.8568473Z   - clang 7.0.1
2023-01-11T20:40:52.8568934Z   - Intel(R) oneAPI Math Kernel Library Version 2022.0-Product Build 20211112 for Intel(R) 64 architecture applications
2023-01-11T20:40:52.8569297Z   - LAPACK is enabled (usually provided by MKL)
2023-01-11T20:40:52.8569558Z   - NNPACK is enabled
2023-01-11T20:40:52.8569832Z   - CPU capability usage: AVX2
2023-01-11T20:40:52.8573294Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS=-fsanitize=undefined -fsanitize=address  -fno-sanitize-recover=all -fsanitize-address-use-after-scope -shared-libasan -Wno-deprecated -fvisibility-inlines-hidden -fsanitize=address -fPIE -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Winconsistent-missing-override -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wvla-extension -Wno-range-loop-analysis -Wno-pass-failed -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -Wconstant-conversion -Wno-invalid-partial-specialization -Wno-typedef-redefinition -Wno-unused-private-field -Wno-inconsistent-missing-override -Wno-constexpr-not-const -Wno-missing-braces -Wunused-lambda-capture -Wunused-local-typedef -Qunused-arguments -fcolor-diagnostics -fdiagnostics-color=always -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=0, USE_CUDNN=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=0, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=OFF, USE_ROCM=OFF, 
2023-01-11T20:40:52.8576042Z 
2023-01-11T20:40:53.3245264Z + cd test
2023-01-11T20:40:53.3245713Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2023-01-11T20:40:56.7605934Z ATen/Parallel:
2023-01-11T20:40:56.7606366Z 	at::get_num_threads() : 4
2023-01-11T20:40:56.7606645Z 	at::get_num_interop_threads() : 4
2023-01-11T20:40:56.7606856Z OpenMP not found
2023-01-11T20:40:56.7607564Z Intel(R) oneAPI Math Kernel Library Version 2022.0-Product Build 20211112 for Intel(R) 64 architecture applications
2023-01-11T20:40:56.7607843Z 	mkl_get_max_threads() : 1
2023-01-11T20:40:56.7608025Z MKLDNN not found
2023-01-11T20:40:56.7608224Z std::thread::hardware_concurrency() : 8
2023-01-11T20:40:56.7608421Z Environment variables:
2023-01-11T20:40:56.7608614Z 	OMP_NUM_THREADS : [not set]
2023-01-11T20:40:56.7608806Z 	MKL_NUM_THREADS : [not set]
2023-01-11T20:40:56.7609006Z ATen parallel backend: native thread pool
2023-01-11T20:40:56.7609143Z 
2023-01-11T20:40:57.2310895Z + [[ default == *backward* ]]
2023-01-11T20:40:57.2311176Z + [[ default == *xla* ]]
2023-01-11T20:40:57.2311366Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]]
2023-01-11T20:40:57.2311795Z + [[ linux-focal-py3.7-clang7-asan == *libtorch* ]]
2023-01-11T20:40:57.2312034Z + [[ default == distributed ]]
2023-01-11T20:40:57.2312226Z + [[ default == deploy ]]
2023-01-11T20:40:57.2312433Z + [[ default == *inductor_distributed* ]]
2023-01-11T20:40:57.2312635Z + [[ default == *dynamo* ]]
2023-01-11T20:40:57.2312806Z + [[ default == *dynamo* ]]
2023-01-11T20:40:57.2313011Z + [[ default == *inductor_huggingface* ]]
2023-01-11T20:40:57.2313221Z + [[ default == *inductor_timm* ]]
2023-01-11T20:40:57.2313430Z + [[ default == *inductor_torchbench* ]]
2023-01-11T20:40:57.2313621Z + [[ default == *inductor* ]]
2023-01-11T20:40:57.2313829Z + [[ 3 == 1 ]]
2023-01-11T20:40:57.2313991Z + [[ 3 == 2 ]]
2023-01-11T20:40:57.2314204Z + [[ 3 -gt 2 ]]
2023-01-11T20:40:57.2314368Z + install_triton
2023-01-11T20:40:57.2314565Z + local commit
2023-01-11T20:40:57.2314823Z + [[ default == *rocm* ]]
2023-01-11T20:40:57.2315027Z ++ get_pinned_commit triton
2023-01-11T20:40:57.2315234Z ++ cat .github/ci_commit_pins/triton.txt
2023-01-11T20:40:57.2387802Z + commit=0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T20:40:57.2388649Z + pip_install --user git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T20:40:57.2389219Z + pip install --progress-bar off --user git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T20:40:57.6952962Z Collecting git+https://github.com/openai/triton@0d7e7532279e45672555e344646f5c19c3972331#subdirectory=python
2023-01-11T20:40:57.6959016Z   Cloning https://github.com/openai/triton (to revision 0d7e7532279e45672555e344646f5c19c3972331) to /tmp/pip-req-build-6zzd5eir
2023-01-11T20:40:57.7315593Z   Running command git clone --filter=blob:none --quiet https://github.com/openai/triton /tmp/pip-req-build-6zzd5eir
2023-01-11T20:40:59.0012240Z   Running command git rev-parse -q --verify 'sha^0d7e7532279e45672555e344646f5c19c3972331'
2023-01-11T20:40:59.0105006Z   Running command git fetch -q https://github.com/openai/triton 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T20:40:59.8793133Z   Running command git checkout -q 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T20:41:00.2715751Z   Resolved https://github.com/openai/triton to commit 0d7e7532279e45672555e344646f5c19c3972331
2023-01-11T20:41:00.2717821Z   Running command git submodule update --init --recursive -q
2023-01-11T20:41:01.1161163Z   Preparing metadata (setup.py) ... [?25l- done
2023-01-11T20:41:01.3138084Z [?25hCollecting cmake
2023-01-11T20:41:01.3434835Z   Downloading cmake-3.25.0-py2.py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.7 MB)
2023-01-11T20:41:01.7871667Z Collecting filelock
2023-01-11T20:41:01.7911676Z   Downloading filelock-3.9.0-py3-none-any.whl (9.7 kB)
2023-01-11T20:41:01.7961110Z Requirement already satisfied: torch in /opt/conda/lib/python3.7/site-packages (from triton==2.0.0) (2.0.0a0+git8419ddd)
2023-01-11T20:41:01.8209053Z Requirement already satisfied: sympy in /opt/conda/lib/python3.7/site-packages (from torch->triton==2.0.0) (1.10.1)
2023-01-11T20:41:01.8212898Z Requirement already satisfied: networkx in /opt/conda/lib/python3.7/site-packages (from torch->triton==2.0.0) (2.6.3)
2023-01-11T20:41:01.8216940Z Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch->triton==2.0.0) (4.4.0)
2023-01-11T20:41:01.8419914Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.7/site-packages (from sympy->torch->triton==2.0.0) (1.2.1)
2023-01-11T20:41:01.8492984Z Building wheels for collected packages: triton
2023-01-11T20:42:16.0839740Z   Building wheel for triton (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done
2023-01-11T20:42:16.1382801Z [?25h  Created wheel for triton: filename=triton-2.0.0-cp37-cp37m-linux_x86_64.whl size=15415081 sha256=e266aebd4110ff868f3451cf209106d1aa27093d5471282ff9314fda38c3f629
2023-01-11T20:42:16.1383851Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/aa/c5/3a/263a5ba27ed44796dab5f19b43135afb68beabac5d137652f7
2023-01-11T20:42:16.1427468Z Successfully built triton
2023-01-11T20:42:17.0336622Z Installing collected packages: cmake, filelock, triton
2023-01-11T20:42:18.7982885Z Successfully installed cmake-3.25.0 filelock-3.9.0 triton-2.0.0
2023-01-11T20:42:19.1229102Z + pip_install --user jinja2
2023-01-11T20:42:19.1229494Z + pip install --progress-bar off --user jinja2
2023-01-11T20:42:19.6858978Z Collecting jinja2
2023-01-11T20:42:19.7136373Z   Downloading Jinja2-3.1.2-py3-none-any.whl (133 kB)
2023-01-11T20:42:19.7325439Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.7/site-packages (from jinja2) (2.1.1)
2023-01-11T20:42:20.6237875Z Installing collected packages: jinja2
2023-01-11T20:42:20.7271990Z Successfully installed jinja2-3.1.2
2023-01-11T20:42:20.8198985Z + test_python_shard 3
2023-01-11T20:42:20.8199306Z + [[ -z 5 ]]
2023-01-11T20:42:20.8199691Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 3 5 --verbose
2023-01-11T20:42:24.7787169Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:42:25.0421179Z Ignoring disabled issues:  ['91003']
2023-01-11T20:42:25.1912161Z Found test time stats from artifacts
2023-01-11T20:42:25.1969158Z Selected tests:
2023-01-11T20:42:25.1969485Z  test_ops_gradients
2023-01-11T20:42:25.1969762Z  test_sparse_csr
2023-01-11T20:42:25.1970031Z  distributions/test_distributions
2023-01-11T20:42:25.1970509Z  test_torch
2023-01-11T20:42:25.1970745Z  nn/test_convolution
2023-01-11T20:42:25.1971086Z  test_jit_cuda_fuser
2023-01-11T20:42:25.1971345Z  test_show_pickle
2023-01-11T20:42:25.1971635Z  test_cpp_extensions_aot_ninja
2023-01-11T20:42:25.1971935Z  distributions/test_constraints
2023-01-11T20:42:25.1972222Z  inductor/test_perf
2023-01-11T20:42:25.1972450Z  nn/test_init
2023-01-11T20:42:25.1972675Z  test_cuda_sanitizer
2023-01-11T20:42:25.1972896Z  test_matmul_cuda
2023-01-11T20:42:25.1973144Z  inductor/test_torchinductor
2023-01-11T20:42:25.1973445Z  dynamo/test_minifier
2023-01-11T20:42:25.1973716Z  test_view_ops
2023-01-11T20:42:25.1974341Z  test_tensorboard
2023-01-11T20:42:25.1974624Z  nn/test_parametrization
2023-01-11T20:42:25.1974900Z  test_function_schema
2023-01-11T20:42:25.1975160Z  dynamo/test_verify_correctness
2023-01-11T20:42:25.1975444Z  test_native_mha
2023-01-11T20:42:25.1975727Z  dynamo/test_python_autograd
2023-01-11T20:42:25.1976018Z  test_futures
2023-01-11T20:42:25.1976247Z  test_fx_reinplace_pass
2023-01-11T20:42:25.1976506Z  test_model_dump
2023-01-11T20:42:25.1976768Z  test_datapipe
2023-01-11T20:42:25.1976997Z  test_autocast
2023-01-11T20:42:25.1977213Z  test_native_functions
2023-01-11T20:42:25.1977487Z  benchmark_utils/test_benchmark_utils
2023-01-11T20:42:25.1977765Z  test_fx_passes
2023-01-11T20:42:25.1978018Z  dynamo/test_optimizations
2023-01-11T20:42:25.1978289Z  test_monitor
2023-01-11T20:42:25.1978508Z  dynamo/test_global
2023-01-11T20:42:25.1978779Z  dynamo/test_comptime
2023-01-11T20:42:25.1979038Z  profiler/test_profiler_tree
2023-01-11T20:42:25.1979308Z  dynamo/test_skip_non_tensor
2023-01-11T20:42:25.1979552Z  test_tensorexpr_pybind
2023-01-11T20:42:25.1979862Z  dynamo/test_export_mutations
2023-01-11T20:42:25.1980145Z  dynamo/test_nops
2023-01-11T20:42:25.1980380Z  test_pytree
2023-01-11T20:42:25.1980731Z  nn/test_module_hooks
2023-01-11T20:42:25.1981166Z  test_per_overload_api
2023-01-11T20:42:25.1981436Z  test_license
2023-01-11T20:42:25.1981722Z  test_set_default_mobile_cpu_allocator
2023-01-11T20:42:25.1982016Z  test_type_info
2023-01-11T20:42:25.1982257Z  test_itt
2023-01-11T20:42:25.1982495Z  test_numpy_interop
2023-01-11T20:42:25.1982724Z  nn/test_pruning
2023-01-11T20:42:25.1982930Z  test_decomp
2023-01-11T20:42:25.1983171Z  test_dlpack
2023-01-11T20:42:25.1983439Z  test_jit_llga_fuser
2023-01-11T20:42:25.1983695Z  test_mkldnn
2023-01-11T20:42:25.1983940Z  test_nvfuser_frontend
2023-01-11T20:42:25.1984201Z  test_mkldnn_fusion
2023-01-11T20:42:25.1984446Z  test_numba_integration
2023-01-11T20:42:25.1984695Z  dynamo/test_aot_cudagraphs
2023-01-11T20:42:25.1984977Z  dynamo/test_replay_record
2023-01-11T20:42:25.1985298Z  dynamo/test_torchxla_integration
2023-01-11T20:42:25.1985587Z  test_complex
2023-01-11T20:42:25.1985868Z  test_nvfuser_dynamo
2023-01-11T20:42:25.1986117Z  dynamo/test_model_output
2023-01-11T20:42:25.1986375Z  test_vulkan
2023-01-11T20:42:25.1986635Z  test_type_hints
2023-01-11T20:42:25.1986872Z  test_openmp
2023-01-11T20:42:25.9022422Z Prioritized test from test file changes.
2023-01-11T20:42:25.9022841Z reordering tests for PR:
2023-01-11T20:42:25.9023859Z prioritized: ['test_sparse_csr', 'test_torch', 'inductor/test_torchinductor', 'test_function_schema', 'dynamo/test_optimizations', 'profiler/test_profiler_tree', 'dynamo/test_torchxla_integration']
2023-01-11T20:42:25.9028203Z the rest: ['test_ops_gradients', 'distributions/test_distributions', 'nn/test_convolution', 'test_jit_cuda_fuser', 'test_show_pickle', 'test_cpp_extensions_aot_ninja', 'distributions/test_constraints', 'inductor/test_perf', 'nn/test_init', 'test_cuda_sanitizer', 'test_matmul_cuda', 'dynamo/test_minifier', 'test_view_ops', 'test_tensorboard', 'nn/test_parametrization', 'dynamo/test_verify_correctness', 'test_native_mha', 'dynamo/test_python_autograd', 'test_futures', 'test_fx_reinplace_pass', 'test_model_dump', 'test_datapipe', 'test_autocast', 'test_native_functions', 'benchmark_utils/test_benchmark_utils', 'test_fx_passes', 'test_monitor', 'dynamo/test_global', 'dynamo/test_comptime', 'dynamo/test_skip_non_tensor', 'test_tensorexpr_pybind', 'dynamo/test_export_mutations', 'dynamo/test_nops', 'test_pytree', 'nn/test_module_hooks', 'test_per_overload_api', 'test_license', 'test_set_default_mobile_cpu_allocator', 'test_type_info', 'test_itt', 'test_numpy_interop', 'nn/test_pruning', 'test_decomp', 'test_dlpack', 'test_jit_llga_fuser', 'test_mkldnn', 'test_nvfuser_frontend', 'test_mkldnn_fusion', 'test_numba_integration', 'dynamo/test_aot_cudagraphs', 'dynamo/test_replay_record', 'test_complex', 'test_nvfuser_dynamo', 'dynamo/test_model_output', 'test_vulkan', 'test_type_hints', 'test_openmp']
2023-01-11T20:42:25.9030834Z 
2023-01-11T20:42:25.9031602Z Downloading https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json
2023-01-11T20:42:25.9304618Z Downloading https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2023-01-11T20:42:25.9531496Z parallel (file granularity) tests:
2023-01-11T20:42:25.9531830Z  inductor/test_torchinductor
2023-01-11T20:42:25.9532057Z  test_function_schema
2023-01-11T20:42:25.9532254Z  dynamo/test_optimizations
2023-01-11T20:42:25.9532446Z  profiler/test_profiler_tree
2023-01-11T20:42:25.9532656Z  dynamo/test_torchxla_integration
2023-01-11T20:42:25.9532868Z  distributions/test_constraints
2023-01-11T20:42:25.9533050Z  inductor/test_perf
2023-01-11T20:42:25.9533222Z  nn/test_init
2023-01-11T20:42:25.9533389Z  test_cuda_sanitizer
2023-01-11T20:42:25.9533550Z  test_matmul_cuda
2023-01-11T20:42:25.9533733Z  dynamo/test_minifier
2023-01-11T20:42:25.9533905Z  test_view_ops
2023-01-11T20:42:25.9534064Z  test_tensorboard
2023-01-11T20:42:25.9534244Z  nn/test_parametrization
2023-01-11T20:42:25.9534441Z  dynamo/test_verify_correctness
2023-01-11T20:42:25.9534786Z  test_native_mha
2023-01-11T20:42:25.9534974Z  dynamo/test_python_autograd
2023-01-11T20:42:25.9535158Z  test_futures
2023-01-11T20:42:25.9535321Z  test_fx_reinplace_pass
2023-01-11T20:42:25.9535501Z  test_model_dump
2023-01-11T20:42:25.9535670Z  test_datapipe
2023-01-11T20:42:25.9535823Z  test_autocast
2023-01-11T20:42:25.9535996Z  test_native_functions
2023-01-11T20:42:25.9536199Z  benchmark_utils/test_benchmark_utils
2023-01-11T20:42:25.9536381Z  test_fx_passes
2023-01-11T20:42:25.9536549Z  test_monitor
2023-01-11T20:42:25.9536720Z  dynamo/test_global
2023-01-11T20:42:25.9536885Z  dynamo/test_comptime
2023-01-11T20:42:25.9537077Z  dynamo/test_skip_non_tensor
2023-01-11T20:42:25.9537273Z  test_tensorexpr_pybind
2023-01-11T20:42:25.9537459Z  dynamo/test_export_mutations
2023-01-11T20:42:25.9537651Z  dynamo/test_nops
2023-01-11T20:42:25.9537826Z  test_pytree
2023-01-11T20:42:25.9537985Z  nn/test_module_hooks
2023-01-11T20:42:25.9538167Z  test_per_overload_api
2023-01-11T20:42:25.9538347Z  test_license
2023-01-11T20:42:25.9538525Z  test_set_default_mobile_cpu_allocator
2023-01-11T20:42:25.9538721Z  test_type_info
2023-01-11T20:42:25.9538886Z  test_itt
2023-01-11T20:42:25.9539038Z  test_numpy_interop
2023-01-11T20:42:25.9539212Z  nn/test_pruning
2023-01-11T20:42:25.9539382Z  test_decomp
2023-01-11T20:42:25.9539534Z  test_dlpack
2023-01-11T20:42:25.9539702Z  test_jit_llga_fuser
2023-01-11T20:42:25.9539873Z  test_mkldnn
2023-01-11T20:42:25.9540031Z  test_nvfuser_frontend
2023-01-11T20:42:25.9540212Z  test_mkldnn_fusion
2023-01-11T20:42:25.9540393Z  test_numba_integration
2023-01-11T20:42:25.9540656Z  dynamo/test_aot_cudagraphs
2023-01-11T20:42:25.9540854Z  dynamo/test_replay_record
2023-01-11T20:42:25.9541036Z  test_complex
2023-01-11T20:42:25.9541212Z  test_nvfuser_dynamo
2023-01-11T20:42:25.9541384Z  dynamo/test_model_output
2023-01-11T20:42:25.9541562Z  test_vulkan
2023-01-11T20:42:25.9541730Z  test_type_hints
2023-01-11T20:42:25.9541885Z  test_openmp
2023-01-11T20:42:25.9542071Z serial (file granularity) tests:
2023-01-11T20:42:25.9542260Z  test_sparse_csr
2023-01-11T20:42:25.9542412Z  test_torch
2023-01-11T20:42:25.9542579Z  test_ops_gradients
2023-01-11T20:42:25.9542773Z  distributions/test_distributions
2023-01-11T20:42:25.9542959Z  nn/test_convolution
2023-01-11T20:42:25.9543137Z  test_jit_cuda_fuser
2023-01-11T20:42:25.9543306Z  test_show_pickle
2023-01-11T20:42:25.9543478Z  test_cpp_extensions_aot_ninja
2023-01-11T20:42:30.0068008Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:42:30.0842023Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:42:30.1726766Z Ignoring disabled issues:  ['91003']
2023-01-11T20:42:30.1919043Z Running inductor/test_torchinductor ... [2023-01-11 20:42:30.191541]
2023-01-11T20:42:30.1920557Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_torchinductor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:42:30.191820]
2023-01-11T20:42:30.2323390Z Ignoring disabled issues:  ['91003']
2023-01-11T20:42:30.2516476Z Running test_function_schema ... [2023-01-11 20:42:30.251275]
2023-01-11T20:42:30.2518213Z Executing ['/opt/conda/bin/python', '-bb', 'test_function_schema.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:42:30.251538]
2023-01-11T20:42:36.7956885Z 
2023-01-11T20:42:36.7957466Z Expand the folded group to see the log file of test_function_schema
2023-01-11T20:42:36.7964308Z ##[group]PRINTING LOG FILE of test_function_schema (/var/lib/jenkins/workspace/test/test-reports/test_function_schema_4owpcnjl)
2023-01-11T20:42:36.7965349Z Test results will be stored in test-reports/python-unittest/test_function_schema
2023-01-11T20:42:36.7965713Z 
2023-01-11T20:42:36.7965851Z Running tests...
2023-01-11T20:42:36.7966433Z ----------------------------------------------------------------------
2023-01-11T20:42:36.7967037Z   test_backward_compatible_arguments (__main__.TestFunctionSchema) ... ok (1.165s)
2023-01-11T20:42:36.7970153Z   test_backward_compatible_outputs (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T20:42:36.7970884Z   test_backward_compatible_structure (__main__.TestFunctionSchema) ... ok (0.002s)
2023-01-11T20:42:36.7971611Z   test_backward_compatible_with_smart_serialization (__main__.TestFunctionSchema) ... ok (0.003s)
2023-01-11T20:42:36.7972387Z   test_forward_compatible_arguments_real_use_case (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T20:42:36.7973127Z   test_forward_compatible_arguments_with_out (__main__.TestFunctionSchema) ... ok (0.002s)
2023-01-11T20:42:36.7976325Z   test_forward_compatible_arguments_without_out (__main__.TestFunctionSchema) ... ok (0.003s)
2023-01-11T20:42:36.7976909Z   test_out_schema (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T20:42:36.7977411Z   test_schema_error (__main__.TestFunctionSchema) ... ok (0.002s)
2023-01-11T20:42:36.7977735Z   test_serialize_and_deserialize (__main__.TestFunctionSchema) ... ok (0.918s)
2023-01-11T20:42:36.7978052Z   test_string_optional_parameter_default_value (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T20:42:36.7978455Z   test_tensor_list_alias_annotation_properly_parsed (__main__.TestFunctionSchema) ... ok (0.001s)
2023-01-11T20:42:36.7978996Z   test_tensor_option_arguments_properly_parsed (__main__.TestFunctionSchema) ... ok (0.002s)
2023-01-11T20:42:36.7979337Z 
2023-01-11T20:42:36.7979785Z ----------------------------------------------------------------------
2023-01-11T20:42:36.7980018Z Ran 13 tests in 2.102s
2023-01-11T20:42:36.8002876Z 
2023-01-11T20:42:36.8003087Z OK
2023-01-11T20:42:36.8003283Z 
2023-01-11T20:42:36.8003432Z Generating XML reports...
2023-01-11T20:42:36.8004209Z Generated XML report: test-reports/python-unittest/test_function_schema/TEST-TestFunctionSchema-20230111204234.xml
2023-01-11T20:42:36.8004604Z 
2023-01-11T20:42:36.8005047Z ##[endgroup]
2023-01-11T20:42:36.8005692Z FINISHED PRINTING LOG FILE of test_function_schema (/var/lib/jenkins/workspace/test/test-reports/test_function_schema_4owpcnjl)
2023-01-11T20:42:36.8006015Z 
2023-01-11T20:42:41.6487380Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:42:41.8162001Z Ignoring disabled issues:  ['91003']
2023-01-11T20:42:41.8358125Z Running dynamo/test_optimizations ... [2023-01-11 20:42:41.835382]
2023-01-11T20:42:41.8359757Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_optimizations.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:42:41.835739]
2023-01-11T20:42:47.5212200Z 
2023-01-11T20:42:47.5212754Z Expand the folded group to see the log file of dynamo/test_optimizations
2023-01-11T20:42:47.5213917Z ##[group]PRINTING LOG FILE of dynamo/test_optimizations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizations_mkjtp290)
2023-01-11T20:42:47.5215178Z Test results will be stored in test-reports/python-unittest/dynamo.test_optimizations
2023-01-11T20:42:47.5215506Z 
2023-01-11T20:42:47.5215634Z Running tests...
2023-01-11T20:42:47.5216185Z ----------------------------------------------------------------------
2023-01-11T20:42:47.5216713Z   test_inplace_normalize (__main__.NormalizeIRTests) ... ok (0.402s)
2023-01-11T20:42:47.5217213Z frames [('total', 1), ('ok', 1)]
2023-01-11T20:42:47.5217761Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:42:47.5218275Z aot_autograd [('total', 1), ('ok', 1)]
2023-01-11T20:42:47.5219016Z   test_example_inputs (__main__.TestOptimizations) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:42:47.5219497Z ok (0.119s)
2023-01-11T20:42:47.5220236Z   test_example_inputs_runtime_use (__main__.TestOptimizations) ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:42:47.5220827Z ok (0.014s)
2023-01-11T20:42:47.5221228Z   test_has_mutation (__main__.TestOptimizations) ... ok (0.030s)
2023-01-11T20:42:47.5221885Z   test_has_mutation_factory (__main__.TestOptimizations) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:42:47.5222712Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T20:42:47.5223115Z ok (0.026s)
2023-01-11T20:42:47.5223705Z   test_inplacifier (__main__.TestOptimizations) ... optimizations [('out', 1), ('inplace', 1)]
2023-01-11T20:42:47.5224140Z ok (0.029s)
2023-01-11T20:42:47.5224554Z   test_ipex_bf16 (__main__.TestOptimizations) ... skip: requires ipex (0.001s)
2023-01-11T20:42:47.5243271Z   test_ipex_fp32 (__main__.TestOptimizations) ... skip: requires ipex (0.001s)
2023-01-11T20:42:47.5244115Z   test_log_conv_args (__main__.TestOptimizations) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:42:47.5244760Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:42:47.5245195Z ok (0.500s)
2023-01-11T20:42:47.5245403Z 
2023-01-11T20:42:47.5245638Z ----------------------------------------------------------------------
2023-01-11T20:42:47.5245878Z Ran 9 tests in 1.122s
2023-01-11T20:42:47.5245991Z 
2023-01-11T20:42:47.5246063Z OK (skipped=2)
2023-01-11T20:42:47.5246177Z 
2023-01-11T20:42:47.5246261Z Generating XML reports...
2023-01-11T20:42:47.5246689Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizations/TEST-NormalizeIRTests-20230111204245.xml
2023-01-11T20:42:47.5247243Z Generated XML report: test-reports/python-unittest/dynamo.test_optimizations/TEST-TestOptimizations-20230111204245.xml
2023-01-11T20:42:47.5247486Z 
2023-01-11T20:42:47.5247944Z ##[endgroup]
2023-01-11T20:42:47.5248362Z FINISHED PRINTING LOG FILE of dynamo/test_optimizations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_optimizations_mkjtp290)
2023-01-11T20:42:47.5248607Z 
2023-01-11T20:42:52.3655182Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:42:52.5264219Z Ignoring disabled issues:  ['91003']
2023-01-11T20:42:52.5590575Z Running profiler/test_profiler_tree ... [2023-01-11 20:42:52.558672]
2023-01-11T20:42:52.5591751Z Executing ['/opt/conda/bin/python', '-bb', 'profiler/test_profiler_tree.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:42:52.558961]
2023-01-11T20:42:57.5503889Z 
2023-01-11T20:42:57.5504496Z Expand the folded group to see the log file of profiler/test_profiler_tree
2023-01-11T20:42:57.5505745Z ##[group]PRINTING LOG FILE of profiler/test_profiler_tree (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_tree_bncdifeq)
2023-01-11T20:42:57.5506709Z Test results will be stored in test-reports/python-unittest/profiler.test_profiler_tree
2023-01-11T20:42:57.5507093Z 
2023-01-11T20:42:57.5507215Z Running tests...
2023-01-11T20:42:57.5507738Z ----------------------------------------------------------------------
2023-01-11T20:42:57.5509353Z   test_profiler_experimental_tree (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82499 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.320s)
2023-01-11T20:42:57.5510710Z   test_profiler_experimental_tree_cuda (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.002s)
2023-01-11T20:42:57.5511507Z   test_profiler_experimental_tree_cuda_detailed (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.003s)
2023-01-11T20:42:57.5512312Z   test_profiler_experimental_tree_cuda_with_stream (__main__.TestProfilerTree) ... skip: https://github.com/pytorch/pytorch/issues/83606 (0.001s)
2023-01-11T20:42:57.5514022Z   test_profiler_experimental_tree_with_memory (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82501 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T20:42:57.5515662Z   test_profiler_experimental_tree_with_memory_and_stack (__main__.TestProfilerTree) ... STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5516577Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5517363Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5518137Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5518861Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5519648Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5520429Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5521400Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5522143Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5522610Z ok (0.066s)
2023-01-11T20:42:57.5524132Z   test_profiler_experimental_tree_with_record_function (__main__.TestProfilerTree) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/83246 for platform(s) linux, rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.002s)
2023-01-11T20:42:57.5525847Z   test_profiler_experimental_tree_with_stack_and_modules (__main__.TestProfilerTree) ... STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5526754Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5527518Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5528292Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5529073Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5529872Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5530670Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5531436Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5532339Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5532798Z ok (0.063s)
2023-01-11T20:42:57.5533717Z   test_profiler_experimental_tree_with_stack_and_torch_dispatch (__main__.TestProfilerTree) ... STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5534659Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5535449Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5536212Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5536970Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5537744Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5538480Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5539318Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5540082Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5540604Z ok (0.020s)
2023-01-11T20:42:57.5541479Z   test_profiler_experimental_tree_with_stack_and_torch_function (__main__.TestProfilerTree) ... STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5542370Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5543139Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5543863Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5544595Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5545362Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5546112Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T20:42:57.5546854Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T20:42:57.5547622Z STAGE:2023-01-11 20:42:56 1415:1415 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T20:42:57.5548070Z ok (0.021s)
2023-01-11T20:42:57.5548248Z 
2023-01-11T20:42:57.5548621Z ----------------------------------------------------------------------
2023-01-11T20:42:57.5549039Z Ran 10 tests in 0.499s
2023-01-11T20:42:57.5549243Z 
2023-01-11T20:42:57.5549364Z OK (skipped=6)
2023-01-11T20:42:57.5549581Z 
2023-01-11T20:42:57.5549723Z Generating XML reports...
2023-01-11T20:42:57.5550446Z Generated XML report: test-reports/python-unittest/profiler.test_profiler_tree/TEST-TestProfilerTree-20230111204256.xml
2023-01-11T20:42:57.5550869Z 
2023-01-11T20:42:57.5551310Z ##[endgroup]
2023-01-11T20:42:57.5552042Z FINISHED PRINTING LOG FILE of profiler/test_profiler_tree (/var/lib/jenkins/workspace/test/test-reports/profiler-test_profiler_tree_bncdifeq)
2023-01-11T20:42:57.5552473Z 
2023-01-11T20:43:02.2802462Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:43:02.4482850Z Ignoring disabled issues:  ['91003']
2023-01-11T20:43:02.5350766Z Running dynamo/test_torchxla_integration ... [2023-01-11 20:43:02.534662]
2023-01-11T20:43:02.5352168Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_torchxla_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:43:02.534958]
2023-01-11T20:43:06.8720973Z 
2023-01-11T20:43:06.8721709Z Expand the folded group to see the log file of dynamo/test_torchxla_integration
2023-01-11T20:43:06.8722669Z ##[group]PRINTING LOG FILE of dynamo/test_torchxla_integration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_integration_rytlrycv)
2023-01-11T20:43:06.8723334Z Test results will be stored in test-reports/python-unittest/dynamo.test_torchxla_integration
2023-01-11T20:43:06.8723565Z 
2023-01-11T20:43:06.8723679Z Running tests...
2023-01-11T20:43:06.8724074Z ----------------------------------------------------------------------
2023-01-11T20:43:06.8724480Z   test_basic (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T20:43:06.8725004Z   test_inplace_update (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T20:43:06.8725533Z   test_linear (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T20:43:06.8726046Z   test_matmul (__main__.TorchXLAReuseGraphTest) ... skip: Skip the tests since torch_xla is not available or XLA devices are not specified (0.001s)
2023-01-11T20:43:06.8726329Z 
2023-01-11T20:43:06.8726577Z ----------------------------------------------------------------------
2023-01-11T20:43:06.8726877Z Ran 4 tests in 0.005s
2023-01-11T20:43:06.8727029Z 
2023-01-11T20:43:06.8727129Z OK (skipped=4)
2023-01-11T20:43:06.8727264Z 
2023-01-11T20:43:06.8727379Z Generating XML reports...
2023-01-11T20:43:06.8727871Z Generated XML report: test-reports/python-unittest/dynamo.test_torchxla_integration/TEST-TorchXLAReuseGraphTest-20230111204306.xml
2023-01-11T20:43:06.8728188Z 
2023-01-11T20:43:06.8728472Z ##[endgroup]
2023-01-11T20:43:06.8728976Z FINISHED PRINTING LOG FILE of dynamo/test_torchxla_integration (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_torchxla_integration_rytlrycv)
2023-01-11T20:43:06.8729216Z 
2023-01-11T20:43:11.5643842Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:43:11.7296267Z Ignoring disabled issues:  ['91003']
2023-01-11T20:43:11.7603515Z Running distributions/test_constraints ... [2023-01-11 20:43:11.759913]
2023-01-11T20:43:11.7604528Z Executing ['/opt/conda/bin/python', '-bb', '-m', 'pytest', 'distributions/test_constraints.py', '-v'] ... [2023-01-11 20:43:11.760181]
2023-01-11T20:43:17.8040314Z 
2023-01-11T20:43:17.8042194Z Expand the folded group to see the log file of distributions/test_constraints
2023-01-11T20:43:17.8043101Z ##[group]PRINTING LOG FILE of distributions/test_constraints (/var/lib/jenkins/workspace/test/test-reports/distributions-test_constraints_6a33t33x)
2023-01-11T20:43:17.8043702Z ============================= test session starts ==============================
2023-01-11T20:43:17.8044477Z platform linux -- Python 3.7.15, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T20:43:17.8044860Z cachedir: .pytest_cache
2023-01-11T20:43:17.8045302Z hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/var/lib/jenkins/workspace/test/.hypothesis/examples')
2023-01-11T20:43:17.8045695Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T20:43:17.8046132Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T20:43:17.8046418Z collecting ... collected 136 items
2023-01-11T20:43:17.8067144Z Running 136 items in this shard: test/distributions/test_constraints.py::test_constraint[False-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[False-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[False-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[False-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[False-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[False-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[False-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[False-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[False-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[False-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[False-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[False-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[False-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[False-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[False-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[False-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[False-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[False-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_constraint[True-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[True-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[True-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[True-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[True-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[True-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[True-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[True-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[True-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[True-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[True-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[True-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[True-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[True-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[True-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[True-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[True-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[True-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn24-args24]
2023-01-11T20:43:17.8080193Z 
2023-01-11T20:43:17.8080478Z distributions/test_constraints.py::test_constraint[False-constraint_fn0-False-value0] PASSED [  0%]
2023-01-11T20:43:17.8081085Z distributions/test_constraints.py::test_constraint[False-constraint_fn1-False-value1] PASSED [  1%]
2023-01-11T20:43:17.8081551Z distributions/test_constraints.py::test_constraint[False-constraint_fn2-False-value2] PASSED [  2%]
2023-01-11T20:43:17.8082009Z distributions/test_constraints.py::test_constraint[False-constraint_fn3-True-value3] PASSED [  2%]
2023-01-11T20:43:17.8082451Z distributions/test_constraints.py::test_constraint[False-constraint_fn4-False-value4] PASSED [  3%]
2023-01-11T20:43:17.8082903Z distributions/test_constraints.py::test_constraint[False-constraint_fn5-False-value5] PASSED [  4%]
2023-01-11T20:43:17.8083353Z distributions/test_constraints.py::test_constraint[False-constraint_fn6-True-value6] PASSED [  5%]
2023-01-11T20:43:17.8083804Z distributions/test_constraints.py::test_constraint[False-constraint_fn7-True-value7] PASSED [  5%]
2023-01-11T20:43:17.8084238Z distributions/test_constraints.py::test_constraint[False-constraint_fn8-False-value8] PASSED [  6%]
2023-01-11T20:43:17.8084779Z distributions/test_constraints.py::test_constraint[False-constraint_fn9-True-value9] PASSED [  7%]
2023-01-11T20:43:17.8085241Z distributions/test_constraints.py::test_constraint[False-constraint_fn10-False-value10] PASSED [  8%]
2023-01-11T20:43:17.8085702Z distributions/test_constraints.py::test_constraint[False-constraint_fn11-False-value11] PASSED [  8%]
2023-01-11T20:43:17.8086141Z distributions/test_constraints.py::test_constraint[False-constraint_fn12-True-value12] PASSED [  9%]
2023-01-11T20:43:17.8086593Z distributions/test_constraints.py::test_constraint[False-constraint_fn13-True-value13] PASSED [ 10%]
2023-01-11T20:43:17.8087045Z distributions/test_constraints.py::test_constraint[False-constraint_fn14-False-value14] PASSED [ 11%]
2023-01-11T20:43:17.8087486Z distributions/test_constraints.py::test_constraint[False-constraint_fn15-True-value15] PASSED [ 11%]
2023-01-11T20:43:17.8087935Z distributions/test_constraints.py::test_constraint[False-constraint_fn16-True-value16] PASSED [ 12%]
2023-01-11T20:43:17.8088389Z distributions/test_constraints.py::test_constraint[False-constraint_fn17-True-value17] PASSED [ 13%]
2023-01-11T20:43:17.8088887Z distributions/test_constraints.py::test_constraint[True-constraint_fn0-False-value0] SKIPPED [ 13%]
2023-01-11T20:43:17.8089333Z distributions/test_constraints.py::test_constraint[True-constraint_fn1-False-value1] SKIPPED [ 14%]
2023-01-11T20:43:17.8089783Z distributions/test_constraints.py::test_constraint[True-constraint_fn2-False-value2] SKIPPED [ 15%]
2023-01-11T20:43:17.8090231Z distributions/test_constraints.py::test_constraint[True-constraint_fn3-True-value3] SKIPPED [ 16%]
2023-01-11T20:43:17.8090681Z distributions/test_constraints.py::test_constraint[True-constraint_fn4-False-value4] SKIPPED [ 16%]
2023-01-11T20:43:17.8091112Z distributions/test_constraints.py::test_constraint[True-constraint_fn5-False-value5] SKIPPED [ 17%]
2023-01-11T20:43:17.8091557Z distributions/test_constraints.py::test_constraint[True-constraint_fn6-True-value6] SKIPPED [ 18%]
2023-01-11T20:43:17.8092008Z distributions/test_constraints.py::test_constraint[True-constraint_fn7-True-value7] SKIPPED [ 19%]
2023-01-11T20:43:17.8092446Z distributions/test_constraints.py::test_constraint[True-constraint_fn8-False-value8] SKIPPED [ 19%]
2023-01-11T20:43:17.8093047Z distributions/test_constraints.py::test_constraint[True-constraint_fn9-True-value9] SKIPPED [ 20%]
2023-01-11T20:43:17.8093610Z distributions/test_constraints.py::test_constraint[True-constraint_fn10-False-value10] SKIPPED [ 21%]
2023-01-11T20:43:17.8094068Z distributions/test_constraints.py::test_constraint[True-constraint_fn11-False-value11] SKIPPED [ 22%]
2023-01-11T20:43:17.8094508Z distributions/test_constraints.py::test_constraint[True-constraint_fn12-True-value12] SKIPPED [ 22%]
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2023-01-11T20:43:17.8095417Z distributions/test_constraints.py::test_constraint[True-constraint_fn14-False-value14] SKIPPED [ 24%]
2023-01-11T20:43:17.8095871Z distributions/test_constraints.py::test_constraint[True-constraint_fn15-True-value15] SKIPPED [ 25%]
2023-01-11T20:43:17.8096312Z distributions/test_constraints.py::test_constraint[True-constraint_fn16-True-value16] SKIPPED [ 25%]
2023-01-11T20:43:17.8096760Z distributions/test_constraints.py::test_constraint[True-constraint_fn17-True-value17] SKIPPED [ 26%]
2023-01-11T20:43:17.8097197Z distributions/test_constraints.py::test_biject_to[False-constraint_fn0-args0] PASSED [ 27%]
2023-01-11T20:43:17.8097611Z distributions/test_constraints.py::test_biject_to[False-constraint_fn1-args1] PASSED [ 27%]
2023-01-11T20:43:17.8098028Z distributions/test_constraints.py::test_biject_to[False-constraint_fn2-args2] PASSED [ 28%]
2023-01-11T20:43:17.8098449Z distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args3] PASSED [ 29%]
2023-01-11T20:43:17.8098865Z distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args4] PASSED [ 30%]
2023-01-11T20:43:17.8099312Z distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args5] PASSED [ 30%]
2023-01-11T20:43:17.8099724Z distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args6] PASSED [ 31%]
2023-01-11T20:43:17.8100148Z distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args7] PASSED [ 32%]
2023-01-11T20:43:17.8100644Z distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args8] PASSED [ 33%]
2023-01-11T20:43:17.8101067Z distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args9] PASSED [ 33%]
2023-01-11T20:43:17.8101487Z distributions/test_constraints.py::test_biject_to[False-_LessThan-args10] PASSED [ 34%]
2023-01-11T20:43:17.8101898Z distributions/test_constraints.py::test_biject_to[False-_LessThan-args11] PASSED [ 35%]
2023-01-11T20:43:17.8102291Z distributions/test_constraints.py::test_biject_to[False-_LessThan-args12] PASSED [ 36%]
2023-01-11T20:43:17.8102698Z distributions/test_constraints.py::test_biject_to[False-_LessThan-args13] PASSED [ 36%]
2023-01-11T20:43:17.8103119Z distributions/test_constraints.py::test_biject_to[False-constraint_fn14-args14] PASSED [ 37%]
2023-01-11T20:43:17.8103570Z distributions/test_constraints.py::test_biject_to[False-_Interval-args15] PASSED [ 38%]
2023-01-11T20:43:17.8103964Z distributions/test_constraints.py::test_biject_to[False-_Interval-args16] PASSED [ 38%]
2023-01-11T20:43:17.8104368Z distributions/test_constraints.py::test_biject_to[False-_Interval-args17] PASSED [ 39%]
2023-01-11T20:43:17.8104798Z distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args18] PASSED [ 40%]
2023-01-11T20:43:17.8105229Z distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args19] PASSED [ 41%]
2023-01-11T20:43:17.8105663Z distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args20] PASSED [ 41%]
2023-01-11T20:43:17.8106094Z distributions/test_constraints.py::test_biject_to[False-constraint_fn21-args21] PASSED [ 42%]
2023-01-11T20:43:17.8106522Z distributions/test_constraints.py::test_biject_to[False-constraint_fn22-args22] PASSED [ 43%]
2023-01-11T20:43:17.8106934Z distributions/test_constraints.py::test_biject_to[False-constraint_fn23-args23] SKIPPED [ 44%]
2023-01-11T20:43:17.8107359Z distributions/test_constraints.py::test_biject_to[False-constraint_fn24-args24] SKIPPED [ 44%]
2023-01-11T20:43:17.8107781Z distributions/test_constraints.py::test_biject_to[True-constraint_fn0-args0] SKIPPED [ 45%]
2023-01-11T20:43:17.8108191Z distributions/test_constraints.py::test_biject_to[True-constraint_fn1-args1] SKIPPED [ 46%]
2023-01-11T20:43:17.8108610Z distributions/test_constraints.py::test_biject_to[True-constraint_fn2-args2] SKIPPED [ 47%]
2023-01-11T20:43:17.8109029Z distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args3] SKIPPED [ 47%]
2023-01-11T20:43:17.8109448Z distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args4] SKIPPED [ 48%]
2023-01-11T20:43:17.8109854Z distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args5] SKIPPED [ 49%]
2023-01-11T20:43:17.8110268Z distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args6] SKIPPED [ 50%]
2023-01-11T20:43:17.8110692Z distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args7] SKIPPED [ 50%]
2023-01-11T20:43:17.8111123Z distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args8] SKIPPED [ 51%]
2023-01-11T20:43:17.8111532Z distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args9] SKIPPED [ 52%]
2023-01-11T20:43:17.8111946Z distributions/test_constraints.py::test_biject_to[True-_LessThan-args10] SKIPPED [ 52%]
2023-01-11T20:43:17.8112357Z distributions/test_constraints.py::test_biject_to[True-_LessThan-args11] SKIPPED [ 53%]
2023-01-11T20:43:17.8112750Z distributions/test_constraints.py::test_biject_to[True-_LessThan-args12] SKIPPED [ 54%]
2023-01-11T20:43:17.8113154Z distributions/test_constraints.py::test_biject_to[True-_LessThan-args13] SKIPPED [ 55%]
2023-01-11T20:43:17.8113607Z distributions/test_constraints.py::test_biject_to[True-constraint_fn14-args14] SKIPPED [ 55%]
2023-01-11T20:43:17.8114020Z distributions/test_constraints.py::test_biject_to[True-_Interval-args15] SKIPPED [ 56%]
2023-01-11T20:43:17.8114412Z distributions/test_constraints.py::test_biject_to[True-_Interval-args16] SKIPPED [ 57%]
2023-01-11T20:43:17.8114815Z distributions/test_constraints.py::test_biject_to[True-_Interval-args17] SKIPPED [ 58%]
2023-01-11T20:43:17.8115244Z distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args18] SKIPPED [ 58%]
2023-01-11T20:43:17.8115670Z distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args19] SKIPPED [ 59%]
2023-01-11T20:43:17.8116108Z distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args20] SKIPPED [ 60%]
2023-01-11T20:43:17.8116536Z distributions/test_constraints.py::test_biject_to[True-constraint_fn21-args21] SKIPPED [ 61%]
2023-01-11T20:43:17.8116960Z distributions/test_constraints.py::test_biject_to[True-constraint_fn22-args22] SKIPPED [ 61%]
2023-01-11T20:43:17.8117399Z distributions/test_constraints.py::test_biject_to[True-constraint_fn23-args23] SKIPPED [ 62%]
2023-01-11T20:43:17.8117818Z distributions/test_constraints.py::test_biject_to[True-constraint_fn24-args24] SKIPPED [ 63%]
2023-01-11T20:43:17.8118248Z distributions/test_constraints.py::test_transform_to[False-constraint_fn0-args0] PASSED [ 63%]
2023-01-11T20:43:17.8118672Z distributions/test_constraints.py::test_transform_to[False-constraint_fn1-args1] PASSED [ 64%]
2023-01-11T20:43:17.8119083Z distributions/test_constraints.py::test_transform_to[False-constraint_fn2-args2] PASSED [ 65%]
2023-01-11T20:43:17.8119506Z distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args3] PASSED [ 66%]
2023-01-11T20:43:17.8119933Z distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args4] PASSED [ 66%]
2023-01-11T20:43:17.8120348Z distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args5] PASSED [ 67%]
2023-01-11T20:43:17.8120896Z distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args6] PASSED [ 68%]
2023-01-11T20:43:17.8121330Z distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args7] PASSED [ 69%]
2023-01-11T20:43:17.8121758Z distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args8] PASSED [ 69%]
2023-01-11T20:43:17.8122174Z distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args9] PASSED [ 70%]
2023-01-11T20:43:17.8122596Z distributions/test_constraints.py::test_transform_to[False-_LessThan-args10] PASSED [ 71%]
2023-01-11T20:43:17.8123016Z distributions/test_constraints.py::test_transform_to[False-_LessThan-args11] PASSED [ 72%]
2023-01-11T20:43:17.8123417Z distributions/test_constraints.py::test_transform_to[False-_LessThan-args12] PASSED [ 72%]
2023-01-11T20:43:17.8123831Z distributions/test_constraints.py::test_transform_to[False-_LessThan-args13] PASSED [ 73%]
2023-01-11T20:43:17.8124259Z distributions/test_constraints.py::test_transform_to[False-constraint_fn14-args14] PASSED [ 74%]
2023-01-11T20:43:17.8124690Z distributions/test_constraints.py::test_transform_to[False-_Interval-args15] PASSED [ 75%]
2023-01-11T20:43:17.8125091Z distributions/test_constraints.py::test_transform_to[False-_Interval-args16] PASSED [ 75%]
2023-01-11T20:43:17.8125505Z distributions/test_constraints.py::test_transform_to[False-_Interval-args17] PASSED [ 76%]
2023-01-11T20:43:17.8125939Z distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args18] PASSED [ 77%]
2023-01-11T20:43:17.8126394Z distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args19] PASSED [ 77%]
2023-01-11T20:43:17.8126835Z distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args20] PASSED [ 78%]
2023-01-11T20:43:17.8127280Z distributions/test_constraints.py::test_transform_to[False-constraint_fn21-args21] PASSED [ 79%]
2023-01-11T20:43:17.8127778Z distributions/test_constraints.py::test_transform_to[False-constraint_fn22-args22] PASSED [ 80%]
2023-01-11T20:43:17.8128195Z distributions/test_constraints.py::test_transform_to[False-constraint_fn23-args23] PASSED [ 80%]
2023-01-11T20:43:17.8128621Z distributions/test_constraints.py::test_transform_to[False-constraint_fn24-args24] PASSED [ 81%]
2023-01-11T20:43:17.8129050Z distributions/test_constraints.py::test_transform_to[True-constraint_fn0-args0] SKIPPED [ 82%]
2023-01-11T20:43:17.8129475Z distributions/test_constraints.py::test_transform_to[True-constraint_fn1-args1] SKIPPED [ 83%]
2023-01-11T20:43:17.8129889Z distributions/test_constraints.py::test_transform_to[True-constraint_fn2-args2] SKIPPED [ 83%]
2023-01-11T20:43:17.8130311Z distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args3] SKIPPED [ 84%]
2023-01-11T20:43:17.8130736Z distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args4] SKIPPED [ 85%]
2023-01-11T20:43:17.8131160Z distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args5] SKIPPED [ 86%]
2023-01-11T20:43:17.8131568Z distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args6] SKIPPED [ 86%]
2023-01-11T20:43:17.8132054Z distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args7] SKIPPED [ 87%]
2023-01-11T20:43:17.8132488Z distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args8] SKIPPED [ 88%]
2023-01-11T20:43:17.8132906Z distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args9] SKIPPED [ 88%]
2023-01-11T20:43:17.8133327Z distributions/test_constraints.py::test_transform_to[True-_LessThan-args10] SKIPPED [ 89%]
2023-01-11T20:43:17.8133747Z distributions/test_constraints.py::test_transform_to[True-_LessThan-args11] SKIPPED [ 90%]
2023-01-11T20:43:17.8134159Z distributions/test_constraints.py::test_transform_to[True-_LessThan-args12] SKIPPED [ 91%]
2023-01-11T20:43:17.8134558Z distributions/test_constraints.py::test_transform_to[True-_LessThan-args13] SKIPPED [ 91%]
2023-01-11T20:43:17.8134990Z distributions/test_constraints.py::test_transform_to[True-constraint_fn14-args14] SKIPPED [ 92%]
2023-01-11T20:43:17.8135417Z distributions/test_constraints.py::test_transform_to[True-_Interval-args15] SKIPPED [ 93%]
2023-01-11T20:43:17.8135819Z distributions/test_constraints.py::test_transform_to[True-_Interval-args16] SKIPPED [ 94%]
2023-01-11T20:43:17.8136231Z distributions/test_constraints.py::test_transform_to[True-_Interval-args17] SKIPPED [ 94%]
2023-01-11T20:43:17.8136667Z distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args18] SKIPPED [ 95%]
2023-01-11T20:43:17.8137121Z distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args19] SKIPPED [ 96%]
2023-01-11T20:43:17.8137559Z distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args20] SKIPPED [ 97%]
2023-01-11T20:43:17.8138007Z distributions/test_constraints.py::test_transform_to[True-constraint_fn21-args21] SKIPPED [ 97%]
2023-01-11T20:43:17.8138443Z distributions/test_constraints.py::test_transform_to[True-constraint_fn22-args22] SKIPPED [ 98%]
2023-01-11T20:43:17.8138871Z distributions/test_constraints.py::test_transform_to[True-constraint_fn23-args23] SKIPPED [ 99%]
2023-01-11T20:43:17.8139298Z distributions/test_constraints.py::test_transform_to[True-constraint_fn24-args24] SKIPPED [100%]
2023-01-11T20:43:17.8139496Z 
2023-01-11T20:43:17.8139614Z ======================== 66 passed, 70 skipped in 4.29s ========================
2023-01-11T20:43:17.8139759Z 
2023-01-11T20:43:17.8140047Z ##[endgroup]
2023-01-11T20:43:17.8140563Z FINISHED PRINTING LOG FILE of distributions/test_constraints (/var/lib/jenkins/workspace/test/test-reports/distributions-test_constraints_6a33t33x)
2023-01-11T20:43:17.8140819Z 
2023-01-11T20:43:23.4965400Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:43:23.6863882Z Ignoring disabled issues:  ['91003']
2023-01-11T20:43:24.4058871Z Running inductor/test_perf ... [2023-01-11 20:43:24.405515]
2023-01-11T20:43:24.4060820Z Executing ['/opt/conda/bin/python', '-bb', 'inductor/test_perf.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:43:24.405837]
2023-01-11T20:43:46.7378717Z 
2023-01-11T20:43:46.7379228Z Expand the folded group to see the log file of inductor/test_perf
2023-01-11T20:43:46.7381929Z ##[group]PRINTING LOG FILE of inductor/test_perf (/var/lib/jenkins/workspace/test/test-reports/inductor-test_perf_aove2ntw)
2023-01-11T20:43:46.7382577Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:43:46.7382719Z 
2023-01-11T20:43:46.7382939Z ##[endgroup]
2023-01-11T20:43:46.7383339Z FINISHED PRINTING LOG FILE of inductor/test_perf (/var/lib/jenkins/workspace/test/test-reports/inductor-test_perf_aove2ntw)
2023-01-11T20:43:46.7383562Z 
2023-01-11T20:43:51.5845527Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:43:51.7530126Z Ignoring disabled issues:  ['91003']
2023-01-11T20:43:51.7870209Z Running nn/test_init ... [2023-01-11 20:43:51.786588]
2023-01-11T20:43:51.7871963Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_init.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:43:51.786963]
2023-01-11T20:43:57.0916440Z 
2023-01-11T20:43:57.0916907Z Expand the folded group to see the log file of nn/test_init
2023-01-11T20:43:57.0917738Z ##[group]PRINTING LOG FILE of nn/test_init (/var/lib/jenkins/workspace/test/test-reports/nn-test_init_jzeh7c2c)
2023-01-11T20:43:57.0918022Z 
2023-01-11T20:43:57.0918322Z ##[endgroup]
2023-01-11T20:43:57.0918953Z FINISHED PRINTING LOG FILE of nn/test_init (/var/lib/jenkins/workspace/test/test-reports/nn-test_init_jzeh7c2c)
2023-01-11T20:43:57.0919224Z 
2023-01-11T20:44:01.9569011Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:44:02.1170110Z Ignoring disabled issues:  ['91003']
2023-01-11T20:44:02.1365396Z Running test_cuda_sanitizer ... [2023-01-11 20:44:02.136232]
2023-01-11T20:44:02.1367792Z Executing ['/opt/conda/bin/python', '-bb', 'test_cuda_sanitizer.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:44:02.136527]
2023-01-11T20:44:06.4185294Z 
2023-01-11T20:44:06.4185850Z Expand the folded group to see the log file of test_cuda_sanitizer
2023-01-11T20:44:06.4186999Z ##[group]PRINTING LOG FILE of test_cuda_sanitizer (/var/lib/jenkins/workspace/test/test-reports/test_cuda_sanitizer_in_skg_v)
2023-01-11T20:44:06.4187323Z CUDA not available, skipping tests
2023-01-11T20:44:06.4187458Z 
2023-01-11T20:44:06.4187530Z Running tests...
2023-01-11T20:44:06.4187923Z ----------------------------------------------------------------------
2023-01-11T20:44:06.4188094Z 
2023-01-11T20:44:06.4190091Z ----------------------------------------------------------------------
2023-01-11T20:44:06.4190373Z Ran 0 tests in 0.000s
2023-01-11T20:44:06.4190520Z 
2023-01-11T20:44:06.4193075Z OK
2023-01-11T20:44:06.4193613Z 
2023-01-11T20:44:06.4193834Z Generating XML reports...
2023-01-11T20:44:06.4194395Z Test results will be stored in test-reports/python-unittest/test_cuda_sanitizer
2023-01-11T20:44:06.4194744Z 
2023-01-11T20:44:06.4196496Z ##[endgroup]
2023-01-11T20:44:06.4197661Z FINISHED PRINTING LOG FILE of test_cuda_sanitizer (/var/lib/jenkins/workspace/test/test-reports/test_cuda_sanitizer_in_skg_v)
2023-01-11T20:44:06.4198390Z 
2023-01-11T20:44:11.2226184Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:44:11.3937796Z Ignoring disabled issues:  ['91003']
2023-01-11T20:44:11.4134267Z Running test_matmul_cuda ... [2023-01-11 20:44:11.413076]
2023-01-11T20:44:11.4135912Z Executing ['/opt/conda/bin/python', '-bb', 'test_matmul_cuda.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:44:11.413374]
2023-01-11T20:44:16.0726265Z 
2023-01-11T20:44:16.0726839Z Expand the folded group to see the log file of test_matmul_cuda
2023-01-11T20:44:16.0727846Z ##[group]PRINTING LOG FILE of test_matmul_cuda (/var/lib/jenkins/workspace/test/test-reports/test_matmul_cuda_lcbvfibb)
2023-01-11T20:44:16.0728451Z 
2023-01-11T20:44:16.0736068Z Running tests...
2023-01-11T20:44:16.0736783Z ----------------------------------------------------------------------
2023-01-11T20:44:16.0737097Z 
2023-01-11T20:44:16.0737501Z ----------------------------------------------------------------------
2023-01-11T20:44:16.0737925Z Ran 0 tests in 0.000s
2023-01-11T20:44:16.0738135Z 
2023-01-11T20:44:16.0738237Z OK
2023-01-11T20:44:16.0738373Z 
2023-01-11T20:44:16.0738462Z Generating XML reports...
2023-01-11T20:44:16.0738785Z Test results will be stored in test-reports/python-unittest/test_matmul_cuda
2023-01-11T20:44:16.0738967Z 
2023-01-11T20:44:16.0742068Z ##[endgroup]
2023-01-11T20:44:16.0742900Z FINISHED PRINTING LOG FILE of test_matmul_cuda (/var/lib/jenkins/workspace/test/test-reports/test_matmul_cuda_lcbvfibb)
2023-01-11T20:44:16.0743283Z 
2023-01-11T20:44:20.9566411Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:44:21.1169496Z Ignoring disabled issues:  ['91003']
2023-01-11T20:44:21.1365893Z Running dynamo/test_minifier ... [2023-01-11 20:44:21.136328]
2023-01-11T20:44:21.1368670Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_minifier.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:44:21.136596]
2023-01-11T20:46:10.5546575Z 
2023-01-11T20:46:10.5547060Z Expand the folded group to see the log file of dynamo/test_minifier
2023-01-11T20:46:10.5548107Z ##[group]PRINTING LOG FILE of dynamo/test_minifier (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_minifier_kqxkcife)
2023-01-11T20:46:10.5550512Z Test results will be stored in test-reports/python-unittest/dynamo.test_minifier
2023-01-11T20:46:10.5550863Z 
2023-01-11T20:46:10.5550945Z Running tests...
2023-01-11T20:46:10.5551282Z ----------------------------------------------------------------------
2023-01-11T20:46:10.5551686Z   test_after_dynamo_cpu_accuracy_backend_passes (__main__.MinifierTests) ... ok (4.545s)
2023-01-11T20:46:10.5552199Z   test_after_dynamo_cpu_accuracy_error (__main__.MinifierTests) ... ok (13.287s)
2023-01-11T20:46:10.5552697Z   test_after_dynamo_cpu_compile_backend_passes (__main__.MinifierTests) ... ok (4.355s)
2023-01-11T20:46:10.5553240Z   test_after_dynamo_cpu_compile_error (__main__.MinifierTests) ... ok (12.964s)
2023-01-11T20:46:10.5553703Z   test_after_dynamo_cpu_runtime_backend_passes (__main__.MinifierTests) ... ok (4.358s)
2023-01-11T20:46:10.5554014Z   test_after_dynamo_cpu_runtime_error (__main__.MinifierTests) ... ok (12.817s)
2023-01-11T20:46:10.5554449Z   test_after_dynamo_cuda_accuracy_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.001s)
2023-01-11T20:46:10.5554959Z   test_after_dynamo_cuda_accuracy_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T20:46:10.5555447Z   test_after_dynamo_cuda_compile_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T20:46:10.5555800Z   test_after_dynamo_cuda_compile_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T20:46:10.5556186Z   test_after_dynamo_cuda_runtime_backend_passes (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T20:46:10.5556694Z   test_after_dynamo_cuda_runtime_error (__main__.MinifierTests) ... skip: requires cuda (0.000s)
2023-01-11T20:46:10.5557143Z   test_after_dynamo_custom_backend (__main__.MinifierTests) ... ok (8.717s)
2023-01-11T20:46:10.5557671Z   test_after_dynamo_with_modified_config_cpu_accuracy_error (__main__.MinifierTests) ... ok (12.972s)
2023-01-11T20:46:10.5558229Z   test_after_dynamo_with_modified_config_cpu_compile_error (__main__.MinifierTests) ... ok (12.981s)
2023-01-11T20:46:10.5558941Z   test_cpu_cuda_module_after_dynamo (__main__.MinifierTests) ... skip: requires cuda (0.002s)
2023-01-11T20:46:10.5559608Z   test_dynamo_config_serialization (__main__.MinifierTests) ... ok (4.286s)
2023-01-11T20:46:10.5560214Z   test_if_graph_minified (__main__.MinifierTests) ... ok (13.505s)
2023-01-11T20:46:10.5560533Z 
2023-01-11T20:46:10.5561418Z ----------------------------------------------------------------------
2023-01-11T20:46:10.5561932Z Ran 18 tests in 104.839s
2023-01-11T20:46:10.5562173Z 
2023-01-11T20:46:10.5562319Z OK (skipped=7)
2023-01-11T20:46:10.5562541Z 
2023-01-11T20:46:10.5562706Z Generating XML reports...
2023-01-11T20:46:10.5563607Z Generated XML report: test-reports/python-unittest/dynamo.test_minifier/TEST-MinifierTests-20230111204424.xml
2023-01-11T20:46:10.5564105Z 
2023-01-11T20:46:10.5564662Z ##[endgroup]
2023-01-11T20:46:10.5640239Z FINISHED PRINTING LOG FILE of dynamo/test_minifier (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_minifier_kqxkcife)
2023-01-11T20:46:10.5640480Z 
2023-01-11T20:46:15.4903405Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:46:15.6613603Z Ignoring disabled issues:  ['91003']
2023-01-11T20:46:15.7974482Z Running test_view_ops ... [2023-01-11 20:46:15.797058]
2023-01-11T20:46:15.7975997Z Executing ['/opt/conda/bin/python', '-bb', 'test_view_ops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:46:15.797369]
2023-01-11T20:47:10.2679227Z 
2023-01-11T20:47:10.2679748Z Expand the folded group to see the log file of test_view_ops
2023-01-11T20:47:10.2681382Z ##[group]PRINTING LOG FILE of test_view_ops (/var/lib/jenkins/workspace/test/test-reports/test_view_ops_gjmfh89f)
2023-01-11T20:47:10.2686125Z Test results will be stored in test-reports/python-unittest/test_view_ops
2023-01-11T20:47:10.2722610Z 
2023-01-11T20:47:10.2722847Z Running tests...
2023-01-11T20:47:10.2723471Z ----------------------------------------------------------------------
2023-01-11T20:47:10.2725096Z   test_T_cpu (__main__.TestOldViewOpsCPU) ... test_view_ops.py:1305: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorShape.cpp:3542.)
2023-01-11T20:47:10.2726049Z   t1 = a.T
2023-01-11T20:47:10.2726320Z ok (0.012s)
2023-01-11T20:47:10.2726718Z   test_atleast_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (2.304s)
2023-01-11T20:47:10.2727236Z   test_atleast_cpu_complex64 (__main__.TestOldViewOpsCPU) ... ok (2.171s)
2023-01-11T20:47:10.2727781Z   test_atleast_cpu_float16 (__main__.TestOldViewOpsCPU) ... ok (1.693s)
2023-01-11T20:47:10.2728314Z   test_atleast_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (1.704s)
2023-01-11T20:47:10.2728847Z   test_atleast_cpu_float64 (__main__.TestOldViewOpsCPU) ... ok (1.558s)
2023-01-11T20:47:10.2729168Z   test_atleast_cpu_int16 (__main__.TestOldViewOpsCPU) ... ok (0.389s)
2023-01-11T20:47:10.2729451Z   test_atleast_cpu_int32 (__main__.TestOldViewOpsCPU) ... ok (0.407s)
2023-01-11T20:47:10.2729730Z   test_atleast_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.337s)
2023-01-11T20:47:10.2729997Z   test_atleast_cpu_int8 (__main__.TestOldViewOpsCPU) ... ok (0.397s)
2023-01-11T20:47:10.2730287Z   test_atleast_cpu_uint8 (__main__.TestOldViewOpsCPU) ... ok (0.392s)
2023-01-11T20:47:10.2730582Z   test_atleast_gradient_cpu (__main__.TestOldViewOpsCPU) ... ok (0.477s)
2023-01-11T20:47:10.2730874Z   test_big_transpose_cpu (__main__.TestOldViewOpsCPU) ... ok (0.112s)
2023-01-11T20:47:10.2731148Z   test_broadcast_shapes_cpu (__main__.TestOldViewOpsCPU) ... ok (0.016s)
2023-01-11T20:47:10.2731452Z   test_broadcast_tensors_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2732612Z   test_broadcast_to_cpu_bool (__main__.TestOldViewOpsCPU) ... /opt/conda/lib/python3.7/site-packages/torch/testing/_comparison.py:679: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:210.)
2023-01-11T20:47:10.2733562Z   return torch.as_tensor(tensor_like)
2023-01-11T20:47:10.2733756Z ok (0.095s)
2023-01-11T20:47:10.2733985Z   test_broadcast_to_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (0.088s)
2023-01-11T20:47:10.2734296Z   test_broadcast_to_cpu_complex64 (__main__.TestOldViewOpsCPU) ... ok (0.088s)
2023-01-11T20:47:10.2734598Z   test_broadcast_to_cpu_float16 (__main__.TestOldViewOpsCPU) ... ok (0.084s)
2023-01-11T20:47:10.2734885Z   test_broadcast_to_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.085s)
2023-01-11T20:47:10.2735178Z   test_broadcast_to_cpu_float64 (__main__.TestOldViewOpsCPU) ... ok (0.083s)
2023-01-11T20:47:10.2735474Z   test_broadcast_to_cpu_int16 (__main__.TestOldViewOpsCPU) ... ok (0.077s)
2023-01-11T20:47:10.2735764Z   test_broadcast_to_cpu_int32 (__main__.TestOldViewOpsCPU) ... ok (0.077s)
2023-01-11T20:47:10.2736040Z   test_broadcast_to_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.075s)
2023-01-11T20:47:10.2736330Z   test_broadcast_to_cpu_int8 (__main__.TestOldViewOpsCPU) ... ok (0.077s)
2023-01-11T20:47:10.2736688Z   test_broadcast_to_cpu_uint8 (__main__.TestOldViewOpsCPU) ... ok (0.077s)
2023-01-11T20:47:10.2736956Z   test_chunk_cpu (__main__.TestOldViewOpsCPU) ... ok (0.013s)
2023-01-11T20:47:10.2737248Z   test_conj_neg_view_numpy_error_cpu (__main__.TestOldViewOpsCPU) ... ok (0.067s)
2023-01-11T20:47:10.2737851Z   test_contiguous_cpu (__main__.TestOldViewOpsCPU) ... test_view_ops.py:1683: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2738380Z   x.set_(x.storage(), 0, x.size(), stride)
2023-01-11T20:47:10.2738556Z ok (0.002s)
2023-01-11T20:47:10.2739123Z   test_crow_col_indices_cpu (__main__.TestOldViewOpsCPU) ... test_view_ops.py:1837: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T20:47:10.2739717Z   t = torch.sparse_csr_tensor(crow_indices, col_indices, values, size=(2, 2))
2023-01-11T20:47:10.2740219Z ok (0.007s)
2023-01-11T20:47:10.2740436Z   test_empty_reshape_cpu (__main__.TestOldViewOpsCPU) ... ok (0.008s)
2023-01-11T20:47:10.2740717Z   test_expand_cpu (__main__.TestOldViewOpsCPU) ... ok (0.018s)
2023-01-11T20:47:10.2740990Z   test_flatten_cpu (__main__.TestOldViewOpsCPU) ... ok (0.049s)
2023-01-11T20:47:10.2741279Z   test_memory_format_resize__cpu (__main__.TestOldViewOpsCPU) ... ok (0.007s)
2023-01-11T20:47:10.2741573Z   test_memory_format_resize_as_cpu (__main__.TestOldViewOpsCPU) ... ok (0.017s)
2023-01-11T20:47:10.2741863Z   test_narrow_cpu (__main__.TestOldViewOpsCPU) ... ok (0.006s)
2023-01-11T20:47:10.2742138Z   test_narrow_tensor_cpu (__main__.TestOldViewOpsCPU) ... ok (0.026s)
2023-01-11T20:47:10.2742415Z   test_python_types_cpu (__main__.TestOldViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2742691Z   test_ravel_cpu (__main__.TestOldViewOpsCPU) ... ok (0.018s)
2023-01-11T20:47:10.2742961Z   test_reshape_cpu (__main__.TestOldViewOpsCPU) ... ok (0.011s)
2023-01-11T20:47:10.2743567Z   test_reshape_view_semantics_cpu_bfloat16 (__main__.TestOldViewOpsCPU) ... test_view_ops.py:1669: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2744162Z   self.assertEqual(tensor.storage().data_ptr(), view_tensor.storage().data_ptr())
2023-01-11T20:47:10.2744712Z test_view_ops.py:1675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2745292Z   self.assertNotEqual(tensor.storage().data_ptr(), copy_tensor.storage().data_ptr())
2023-01-11T20:47:10.2745539Z ok (0.004s)
2023-01-11T20:47:10.2745769Z   test_reshape_view_semantics_cpu_bool (__main__.TestOldViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2746150Z   test_reshape_view_semantics_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2746481Z   test_reshape_view_semantics_cpu_complex64 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2746808Z   test_reshape_view_semantics_cpu_float16 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2747113Z   test_reshape_view_semantics_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2747433Z   test_reshape_view_semantics_cpu_float64 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2747751Z   test_reshape_view_semantics_cpu_int16 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2748084Z   test_reshape_view_semantics_cpu_int32 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2748395Z   test_reshape_view_semantics_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2748707Z   test_reshape_view_semantics_cpu_int8 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2749021Z   test_reshape_view_semantics_cpu_uint8 (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2749324Z   test_resize_all_dtypes_and_devices_cpu (__main__.TestOldViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2749642Z   test_resize_as_all_dtypes_and_devices_cpu (__main__.TestOldViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2749957Z   test_resize_as_preserves_strides_cpu (__main__.TestOldViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2750251Z   test_resize_overflow_cpu (__main__.TestOldViewOpsCPU) ... ok (0.029s)
2023-01-11T20:47:10.2750529Z   test_split_cpu (__main__.TestOldViewOpsCPU) ... ok (0.009s)
2023-01-11T20:47:10.2750793Z   test_t_cpu (__main__.TestOldViewOpsCPU) ... ok (0.040s)
2023-01-11T20:47:10.2751073Z   test_tensor_split_errors_cpu (__main__.TestOldViewOpsCPU) ... ok (0.082s)
2023-01-11T20:47:10.2751367Z   test_tensor_split_indices_cpu_bool (__main__.TestOldViewOpsCPU) ... ok (0.406s)
2023-01-11T20:47:10.2751679Z   test_tensor_split_indices_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (0.772s)
2023-01-11T20:47:10.2751999Z   test_tensor_split_indices_cpu_complex64 (__main__.TestOldViewOpsCPU) ... ok (0.823s)
2023-01-11T20:47:10.2752301Z   test_tensor_split_indices_cpu_float16 (__main__.TestOldViewOpsCPU) ... ok (0.753s)
2023-01-11T20:47:10.2752610Z   test_tensor_split_indices_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.691s)
2023-01-11T20:47:10.2752924Z   test_tensor_split_indices_cpu_float64 (__main__.TestOldViewOpsCPU) ... ok (0.641s)
2023-01-11T20:47:10.2753235Z   test_tensor_split_indices_cpu_int16 (__main__.TestOldViewOpsCPU) ... ok (0.422s)
2023-01-11T20:47:10.2753534Z   test_tensor_split_indices_cpu_int32 (__main__.TestOldViewOpsCPU) ... ok (0.398s)
2023-01-11T20:47:10.2753840Z   test_tensor_split_indices_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.332s)
2023-01-11T20:47:10.2754147Z   test_tensor_split_indices_cpu_int8 (__main__.TestOldViewOpsCPU) ... ok (0.446s)
2023-01-11T20:47:10.2754441Z   test_tensor_split_indices_cpu_uint8 (__main__.TestOldViewOpsCPU) ... ok (0.387s)
2023-01-11T20:47:10.2754749Z   test_tensor_split_sections_cpu_bool (__main__.TestOldViewOpsCPU) ... ok (1.297s)
2023-01-11T20:47:10.2755065Z   test_tensor_split_sections_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (2.726s)
2023-01-11T20:47:10.2755385Z   test_tensor_split_sections_cpu_complex64 (__main__.TestOldViewOpsCPU) ... ok (2.933s)
2023-01-11T20:47:10.2755689Z   test_tensor_split_sections_cpu_float16 (__main__.TestOldViewOpsCPU) ... ok (2.229s)
2023-01-11T20:47:10.2756037Z   test_tensor_split_sections_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (2.193s)
2023-01-11T20:47:10.2756348Z   test_tensor_split_sections_cpu_float64 (__main__.TestOldViewOpsCPU) ... ok (2.020s)
2023-01-11T20:47:10.2756642Z   test_tensor_split_sections_cpu_int16 (__main__.TestOldViewOpsCPU) ... ok (1.191s)
2023-01-11T20:47:10.2756952Z   test_tensor_split_sections_cpu_int32 (__main__.TestOldViewOpsCPU) ... ok (1.252s)
2023-01-11T20:47:10.2757259Z   test_tensor_split_sections_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (1.069s)
2023-01-11T20:47:10.2757567Z   test_tensor_split_sections_cpu_int8 (__main__.TestOldViewOpsCPU) ... ok (1.202s)
2023-01-11T20:47:10.2757864Z   test_tensor_split_sections_cpu_uint8 (__main__.TestOldViewOpsCPU) ... ok (1.181s)
2023-01-11T20:47:10.2758177Z   test_transpose_invalid_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (0.072s)
2023-01-11T20:47:10.2758495Z   test_transpose_invalid_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.070s)
2023-01-11T20:47:10.2758792Z   test_transpose_invalid_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.075s)
2023-01-11T20:47:10.2759141Z   test_transpose_vs_numpy_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (0.227s)
2023-01-11T20:47:10.2759455Z   test_transpose_vs_numpy_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.182s)
2023-01-11T20:47:10.2759763Z   test_transpose_vs_numpy_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.049s)
2023-01-11T20:47:10.2760052Z   test_transposes_cpu_bfloat16 (__main__.TestOldViewOpsCPU) ... ok (0.009s)
2023-01-11T20:47:10.2760342Z   test_transposes_cpu_bool (__main__.TestOldViewOpsCPU) ... ok (0.005s)
2023-01-11T20:47:10.2760856Z   test_transposes_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (0.014s)
2023-01-11T20:47:10.2761151Z   test_transposes_cpu_complex64 (__main__.TestOldViewOpsCPU) ... ok (0.016s)
2023-01-11T20:47:10.2761449Z   test_transposes_cpu_float16 (__main__.TestOldViewOpsCPU) ... ok (0.013s)
2023-01-11T20:47:10.2761745Z   test_transposes_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.013s)
2023-01-11T20:47:10.2762034Z   test_transposes_cpu_float64 (__main__.TestOldViewOpsCPU) ... ok (0.013s)
2023-01-11T20:47:10.2762314Z   test_transposes_cpu_int16 (__main__.TestOldViewOpsCPU) ... ok (0.008s)
2023-01-11T20:47:10.2762604Z   test_transposes_cpu_int32 (__main__.TestOldViewOpsCPU) ... ok (0.008s)
2023-01-11T20:47:10.2762892Z   test_transposes_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.007s)
2023-01-11T20:47:10.2763172Z   test_transposes_cpu_int8 (__main__.TestOldViewOpsCPU) ... ok (0.007s)
2023-01-11T20:47:10.2763460Z   test_transposes_cpu_uint8 (__main__.TestOldViewOpsCPU) ... ok (0.006s)
2023-01-11T20:47:10.2763760Z   test_transposes_errors_cpu_bfloat16 (__main__.TestOldViewOpsCPU) ... ok (0.026s)
2023-01-11T20:47:10.2764072Z   test_transposes_errors_cpu_bool (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2764369Z   test_transposes_errors_cpu_complex128 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2764689Z   test_transposes_errors_cpu_complex64 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2765002Z   test_transposes_errors_cpu_float16 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2765299Z   test_transposes_errors_cpu_float32 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2765600Z   test_transposes_errors_cpu_float64 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2765905Z   test_transposes_errors_cpu_int16 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2766206Z   test_transposes_errors_cpu_int32 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2766492Z   test_transposes_errors_cpu_int64 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2766793Z   test_transposes_errors_cpu_int8 (__main__.TestOldViewOpsCPU) ... ok (0.023s)
2023-01-11T20:47:10.2767091Z   test_transposes_errors_cpu_uint8 (__main__.TestOldViewOpsCPU) ... ok (0.029s)
2023-01-11T20:47:10.2767441Z   test_unsqueeze_cpu (__main__.TestOldViewOpsCPU) ... ok (0.007s)
2023-01-11T20:47:10.2767736Z   test_view_all_dtypes_and_devices_cpu (__main__.TestOldViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2768028Z   test_view_cpu (__main__.TestOldViewOpsCPU) ... ok (0.112s)
2023-01-11T20:47:10.2768301Z   test_view_empty_cpu (__main__.TestOldViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2768557Z   test_T_view_cpu (__main__.TestViewOpsCPU) ... ok (0.004s)
2023-01-11T20:47:10.2768845Z   test_advanced_indexing_assignment_cpu (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2769152Z   test_advanced_indexing_nonview_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2769750Z   test_as_strided_gradients_cpu (__main__.TestViewOpsCPU) ... test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2770329Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2770907Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2771416Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2771939Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2772489Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2773019Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2773522Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2774040Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2774540Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2775044Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2775539Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2776057Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2776557Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2777075Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2777594Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2778114Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2778615Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2779134Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2779628Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2779826Z ok (0.516s)
2023-01-11T20:47:10.2780118Z   test_as_strided_inplace_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2780411Z   test_as_strided_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2780724Z   test_basic_indexing_ellipses_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2781032Z   test_basic_indexing_newaxis_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2781333Z   test_basic_indexing_slice_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2781681Z   test_chunk_view_cpu (__main__.TestViewOpsCPU) ... skip: See https://github.com/pytorch/pytorch/pull/32720 (0.001s)
2023-01-11T20:47:10.2782022Z   test_conj_imag_view_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2782321Z   test_conj_imag_view_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2782611Z   test_conj_self_cpu_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2782884Z   test_conj_self_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2783165Z   test_conj_self_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2783444Z   test_conj_self_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2783707Z   test_conj_self_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2783981Z   test_conj_self_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2784249Z   test_conj_self_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2784518Z   test_conj_self_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2784775Z   test_conj_self_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2785062Z   test_conj_view_with_shared_memory_cpu (__main__.TestViewOpsCPU) ... ok (0.005s)
2023-01-11T20:47:10.2785358Z   test_contiguous_nonview_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2785626Z   test_contiguous_self_cpu (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2785903Z   test_diagonal_view_cpu (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2786178Z   test_expand_as_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2786450Z   test_expand_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2786711Z   test_flatten_nonview_cpu (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2786982Z   test_flatten_view_cpu (__main__.TestViewOpsCPU) ... ok (0.009s)
2023-01-11T20:47:10.2787265Z   test_imag_noncomplex_cpu_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.008s)
2023-01-11T20:47:10.2787551Z   test_imag_noncomplex_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.006s)
2023-01-11T20:47:10.2787844Z   test_imag_noncomplex_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.005s)
2023-01-11T20:47:10.2788135Z   test_imag_noncomplex_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.006s)
2023-01-11T20:47:10.2788407Z   test_imag_noncomplex_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.006s)
2023-01-11T20:47:10.2788728Z   test_imag_noncomplex_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.007s)
2023-01-11T20:47:10.2789015Z   test_imag_noncomplex_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.006s)
2023-01-11T20:47:10.2789303Z   test_imag_noncomplex_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.005s)
2023-01-11T20:47:10.2789579Z   test_imag_noncomplex_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.007s)
2023-01-11T20:47:10.2789857Z   test_movedim_view_cpu (__main__.TestViewOpsCPU) ... ok (0.020s)
2023-01-11T20:47:10.2790126Z   test_narrow_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2790382Z   test_permute_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2790664Z   test_real_imag_view_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.008s)
2023-01-11T20:47:10.2790963Z   test_real_imag_view_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.007s)
2023-01-11T20:47:10.2791249Z   test_reshape_as_view_cpu (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2791517Z   test_reshape_nonview_cpu (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2791789Z   test_reshape_view_cpu (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2792104Z   test_select_view_cpu (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2792388Z   test_set_real_imag_cpu_complex128_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2792699Z   test_set_real_imag_cpu_complex128_bool (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2793010Z   test_set_real_imag_cpu_complex128_complex128 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2793327Z   test_set_real_imag_cpu_complex128_complex64 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2793625Z   test_set_real_imag_cpu_complex128_float16 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2793938Z   test_set_real_imag_cpu_complex128_float32 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2794244Z   test_set_real_imag_cpu_complex128_float64 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2794530Z   test_set_real_imag_cpu_complex128_int16 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2794838Z   test_set_real_imag_cpu_complex128_int32 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2795141Z   test_set_real_imag_cpu_complex128_int64 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2795442Z   test_set_real_imag_cpu_complex128_int8 (__main__.TestViewOpsCPU) ... ok (0.005s)
2023-01-11T20:47:10.2795732Z   test_set_real_imag_cpu_complex128_uint8 (__main__.TestViewOpsCPU) ... ok (0.004s)
2023-01-11T20:47:10.2796039Z   test_set_real_imag_cpu_complex64_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2796339Z   test_set_real_imag_cpu_complex64_bool (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2796634Z   test_set_real_imag_cpu_complex64_complex128 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2796949Z   test_set_real_imag_cpu_complex64_complex64 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2797258Z   test_set_real_imag_cpu_complex64_float16 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2797567Z   test_set_real_imag_cpu_complex64_float32 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2797860Z   test_set_real_imag_cpu_complex64_float64 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2798162Z   test_set_real_imag_cpu_complex64_int16 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2798462Z   test_set_real_imag_cpu_complex64_int32 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2798750Z   test_set_real_imag_cpu_complex64_int64 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2799048Z   test_set_real_imag_cpu_complex64_int8 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2799347Z   test_set_real_imag_cpu_complex64_uint8 (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2799737Z   test_split_view_cpu (__main__.TestViewOpsCPU) ... skip: See https://github.com/pytorch/pytorch/pull/32720 (0.001s)
2023-01-11T20:47:10.2800063Z   test_squeeze_inplace_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2800343Z   test_squeeze_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2800808Z   test_t_inplace_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2801070Z   test_t_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2801348Z   test_transpose_inplace_view_cpu (__main__.TestViewOpsCPU) ... ok (0.004s)
2023-01-11T20:47:10.2801635Z   test_transpose_view_cpu (__main__.TestViewOpsCPU) ... ok (0.003s)
2023-01-11T20:47:10.2801904Z   test_unbind_cpu (__main__.TestViewOpsCPU) ... ok (0.027s)
2023-01-11T20:47:10.2802152Z   test_unbind_view_cpu (__main__.TestViewOpsCPU) ... ok (0.004s)
2023-01-11T20:47:10.2802417Z   test_unfold_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2802700Z   test_unsqueeze_inplace_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2802975Z   test_unsqueeze_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2803309Z   test_view_as_complex_cpu (__main__.TestViewOpsCPU) ... ok (0.061s)
2023-01-11T20:47:10.2803601Z   test_view_as_real_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.006s)
2023-01-11T20:47:10.2804333Z   test_view_as_real_cpu_complex32 (__main__.TestViewOpsCPU) ... test_view_ops.py:308: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T20:47:10.2804779Z   t = torch.randn(3, 4, dtype=dtype, device=device)
2023-01-11T20:47:10.2804980Z ok (0.005s)
2023-01-11T20:47:10.2805211Z   test_view_as_real_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.005s)
2023-01-11T20:47:10.2805476Z   test_view_as_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2805741Z   test_view_copy_cpu (__main__.TestViewOpsCPU) ... ok (0.004s)
2023-01-11T20:47:10.2806010Z   test_view_copy_out_cpu (__main__.TestViewOpsCPU) ... ok (0.013s)
2023-01-11T20:47:10.2806292Z   test_view_copy_output_contiguous_cpu (__main__.TestViewOpsCPU) ... ok (0.001s)
2023-01-11T20:47:10.2806588Z   test_view_dtype_new_cpu_bool (__main__.TestViewOpsCPU) ... ok (0.457s)
2023-01-11T20:47:10.2806876Z   test_view_dtype_new_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.454s)
2023-01-11T20:47:10.2807176Z   test_view_dtype_new_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.369s)
2023-01-11T20:47:10.2807459Z   test_view_dtype_new_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.392s)
2023-01-11T20:47:10.2807747Z   test_view_dtype_new_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.419s)
2023-01-11T20:47:10.2808030Z   test_view_dtype_new_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.362s)
2023-01-11T20:47:10.2808296Z   test_view_dtype_new_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.384s)
2023-01-11T20:47:10.2808576Z   test_view_dtype_new_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.388s)
2023-01-11T20:47:10.2808857Z   test_view_dtype_new_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.364s)
2023-01-11T20:47:10.2809141Z   test_view_dtype_new_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.379s)
2023-01-11T20:47:10.2809411Z   test_view_dtype_new_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.401s)
2023-01-11T20:47:10.2809708Z   test_view_dtype_upsize_errors_cpu_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.187s)
2023-01-11T20:47:10.2810054Z   test_view_dtype_upsize_errors_cpu_bool (__main__.TestViewOpsCPU) ... ok (0.277s)
2023-01-11T20:47:10.2810354Z   test_view_dtype_upsize_errors_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2810671Z   test_view_dtype_upsize_errors_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.032s)
2023-01-11T20:47:10.2810986Z   test_view_dtype_upsize_errors_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.184s)
2023-01-11T20:47:10.2811297Z   test_view_dtype_upsize_errors_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.124s)
2023-01-11T20:47:10.2811639Z   test_view_dtype_upsize_errors_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.033s)
2023-01-11T20:47:10.2811947Z   test_view_dtype_upsize_errors_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.194s)
2023-01-11T20:47:10.2812250Z   test_view_dtype_upsize_errors_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.137s)
2023-01-11T20:47:10.2812540Z   test_view_dtype_upsize_errors_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.033s)
2023-01-11T20:47:10.2812841Z   test_view_dtype_upsize_errors_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.278s)
2023-01-11T20:47:10.2813143Z   test_view_dtype_upsize_errors_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.339s)
2023-01-11T20:47:10.2813447Z   test_view_tensor_dsplit_cpu_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.004s)
2023-01-11T20:47:10.2813734Z   test_view_tensor_dsplit_cpu_bool (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2814035Z   test_view_tensor_dsplit_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2814346Z   test_view_tensor_dsplit_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2814633Z   test_view_tensor_dsplit_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2814957Z   test_view_tensor_dsplit_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2815253Z   test_view_tensor_dsplit_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2815547Z   test_view_tensor_dsplit_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2815828Z   test_view_tensor_dsplit_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2816118Z   test_view_tensor_dsplit_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2816408Z   test_view_tensor_dsplit_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2816690Z   test_view_tensor_dsplit_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2816988Z   test_view_tensor_hsplit_cpu_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2817284Z   test_view_tensor_hsplit_cpu_bool (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2817589Z   test_view_tensor_hsplit_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2817883Z   test_view_tensor_hsplit_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2818180Z   test_view_tensor_hsplit_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2818475Z   test_view_tensor_hsplit_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2818753Z   test_view_tensor_hsplit_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2819046Z   test_view_tensor_hsplit_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2819333Z   test_view_tensor_hsplit_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2819622Z   test_view_tensor_hsplit_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2819898Z   test_view_tensor_hsplit_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2820285Z   test_view_tensor_hsplit_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2820578Z   test_view_tensor_split_cpu_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2820854Z   test_view_tensor_split_cpu_bool (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2821156Z   test_view_tensor_split_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2821460Z   test_view_tensor_split_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2821760Z   test_view_tensor_split_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2822038Z   test_view_tensor_split_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2822323Z   test_view_tensor_split_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2822654Z   test_view_tensor_split_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2822931Z   test_view_tensor_split_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2823221Z   test_view_tensor_split_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2823510Z   test_view_tensor_split_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2823800Z   test_view_tensor_split_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2824142Z   test_view_tensor_vsplit_cpu_bfloat16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2824448Z   test_view_tensor_vsplit_cpu_bool (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2824744Z   test_view_tensor_vsplit_cpu_complex128 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2825093Z   test_view_tensor_vsplit_cpu_complex64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2825393Z   test_view_tensor_vsplit_cpu_float16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2825692Z   test_view_tensor_vsplit_cpu_float32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2826019Z   test_view_tensor_vsplit_cpu_float64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2826301Z   test_view_tensor_vsplit_cpu_int16 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2826589Z   test_view_tensor_vsplit_cpu_int32 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2826880Z   test_view_tensor_vsplit_cpu_int64 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2827156Z   test_view_tensor_vsplit_cpu_int8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2827446Z   test_view_tensor_vsplit_cpu_uint8 (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2827725Z   test_view_view_cpu (__main__.TestViewOpsCPU) ... ok (0.002s)
2023-01-11T20:47:10.2827989Z   test_T_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.545s)
2023-01-11T20:47:10.2828270Z   test_advanced_indexing_assignment_lazy (__main__.TestViewOpsLAZY) ... ok (0.009s)
2023-01-11T20:47:10.2828584Z   test_advanced_indexing_nonview_lazy (__main__.TestViewOpsLAZY) ... ok (0.029s)
2023-01-11T20:47:10.2829199Z   test_as_strided_gradients_lazy (__main__.TestViewOpsLAZY) ... test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2829783Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2830296Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2830804Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2831331Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2831833Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2832349Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2832829Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2833349Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2833912Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2834433Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2834931Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2835437Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2835938Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2836488Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2836986Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2837490Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2837981Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2838503Z test_view_ops.py:667: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:47:10.2839006Z   assert max_offset < len(y.storage()), "test case resizes storage"
2023-01-11T20:47:10.2839218Z ok (0.175s)
2023-01-11T20:47:10.2839442Z   test_as_strided_inplace_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.037s)
2023-01-11T20:47:10.2839731Z   test_as_strided_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.029s)
2023-01-11T20:47:10.2840029Z   test_basic_indexing_ellipses_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.056s)
2023-01-11T20:47:10.2840327Z   test_basic_indexing_newaxis_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.050s)
2023-01-11T20:47:10.2840766Z   test_basic_indexing_slice_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.047s)
2023-01-11T20:47:10.2841139Z   test_chunk_view_lazy (__main__.TestViewOpsLAZY) ... skip: See https://github.com/pytorch/pytorch/pull/32720 (0.001s)
2023-01-11T20:47:10.2841684Z   test_conj_imag_view_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2842175Z   test_conj_imag_view_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2842515Z   test_conj_self_lazy_bfloat16 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2842800Z   test_conj_self_lazy_float16 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2843067Z   test_conj_self_lazy_float32 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2843346Z   test_conj_self_lazy_float64 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2843625Z   test_conj_self_lazy_int16 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2843968Z   test_conj_self_lazy_int32 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2844227Z   test_conj_self_lazy_int64 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2844504Z   test_conj_self_lazy_int8 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2844780Z   test_conj_self_lazy_uint8 (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2845215Z   test_conj_view_with_shared_memory_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2845712Z   test_contiguous_nonview_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2846050Z   test_contiguous_self_lazy (__main__.TestViewOpsLAZY) ... ok (0.001s)
2023-01-11T20:47:10.2846328Z   test_diagonal_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.061s)
2023-01-11T20:47:10.2846597Z   test_expand_as_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.037s)
2023-01-11T20:47:10.2847017Z   test_expand_view_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2847499Z   test_flatten_nonview_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2848006Z   test_flatten_view_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2848499Z   test_imag_noncomplex_lazy_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2848996Z   test_imag_noncomplex_lazy_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2849493Z   test_imag_noncomplex_lazy_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2849968Z   test_imag_noncomplex_lazy_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2850455Z   test_imag_noncomplex_lazy_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2850949Z   test_imag_noncomplex_lazy_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2851436Z   test_imag_noncomplex_lazy_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2851907Z   test_imag_noncomplex_lazy_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2852394Z   test_imag_noncomplex_lazy_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2852723Z   test_movedim_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.428s)
2023-01-11T20:47:10.2853000Z   test_narrow_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.035s)
2023-01-11T20:47:10.2853264Z   test_permute_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.036s)
2023-01-11T20:47:10.2853704Z   test_real_imag_view_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2854207Z   test_real_imag_view_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2854549Z   test_reshape_as_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.025s)
2023-01-11T20:47:10.2854965Z   test_reshape_nonview_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2855298Z   test_reshape_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.034s)
2023-01-11T20:47:10.2855572Z   test_select_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.025s)
2023-01-11T20:47:10.2856140Z   test_set_real_imag_lazy_complex128_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2857058Z   test_set_real_imag_lazy_complex128_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2857626Z   test_set_real_imag_lazy_complex128_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2858149Z   test_set_real_imag_lazy_complex128_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2858651Z   test_set_real_imag_lazy_complex128_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2859164Z   test_set_real_imag_lazy_complex128_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2859680Z   test_set_real_imag_lazy_complex128_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2860251Z   test_set_real_imag_lazy_complex128_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2860754Z   test_set_real_imag_lazy_complex128_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2861297Z   test_set_real_imag_lazy_complex128_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2861808Z   test_set_real_imag_lazy_complex128_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2862313Z   test_set_real_imag_lazy_complex128_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2862810Z   test_set_real_imag_lazy_complex64_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2863312Z   test_set_real_imag_lazy_complex64_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2863826Z   test_set_real_imag_lazy_complex64_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2864350Z   test_set_real_imag_lazy_complex64_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2864854Z   test_set_real_imag_lazy_complex64_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2865365Z   test_set_real_imag_lazy_complex64_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2865878Z   test_set_real_imag_lazy_complex64_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2866388Z   test_set_real_imag_lazy_complex64_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2866879Z   test_set_real_imag_lazy_complex64_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2867388Z   test_set_real_imag_lazy_complex64_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2867890Z   test_set_real_imag_lazy_complex64_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2868394Z   test_set_real_imag_lazy_complex64_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2868793Z   test_split_view_lazy (__main__.TestViewOpsLAZY) ... skip: See https://github.com/pytorch/pytorch/pull/32720 (0.001s)
2023-01-11T20:47:10.2869137Z   test_squeeze_inplace_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.029s)
2023-01-11T20:47:10.2869422Z   test_squeeze_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.025s)
2023-01-11T20:47:10.2869704Z   test_t_inplace_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.040s)
2023-01-11T20:47:10.2869961Z   test_t_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.027s)
2023-01-11T20:47:10.2870278Z   test_transpose_inplace_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.076s)
2023-01-11T20:47:10.2870570Z   test_transpose_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.068s)
2023-01-11T20:47:10.2870827Z   test_unbind_lazy (__main__.TestViewOpsLAZY) ... ok (0.024s)
2023-01-11T20:47:10.2871242Z   test_unbind_view_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2871578Z   test_unfold_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.028s)
2023-01-11T20:47:10.2871869Z   test_unsqueeze_inplace_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.035s)
2023-01-11T20:47:10.2872147Z   test_unsqueeze_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.031s)
2023-01-11T20:47:10.2872580Z   test_view_as_complex_lazy (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.003s)
2023-01-11T20:47:10.2873084Z   test_view_as_real_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2873578Z   test_view_as_real_lazy_complex32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2874123Z   test_view_as_real_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2874473Z   test_view_as_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.007s)
2023-01-11T20:47:10.2874749Z   test_view_copy_lazy (__main__.TestViewOpsLAZY) ... ok (0.036s)
2023-01-11T20:47:10.2875015Z   test_view_copy_out_lazy (__main__.TestViewOpsLAZY) ... ok (0.019s)
2023-01-11T20:47:10.2875313Z   test_view_copy_output_contiguous_lazy (__main__.TestViewOpsLAZY) ... ok (0.002s)
2023-01-11T20:47:10.2875768Z   test_view_dtype_new_lazy_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2876271Z   test_view_dtype_new_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2876768Z   test_view_dtype_new_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2877271Z   test_view_dtype_new_lazy_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2877770Z   test_view_dtype_new_lazy_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.014s)
2023-01-11T20:47:10.2878265Z   test_view_dtype_new_lazy_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2878740Z   test_view_dtype_new_lazy_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2879229Z   test_view_dtype_new_lazy_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2879713Z   test_view_dtype_new_lazy_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2880211Z   test_view_dtype_new_lazy_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2880876Z   test_view_dtype_new_lazy_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.004s)
2023-01-11T20:47:10.2881614Z   test_view_dtype_upsize_errors_lazy_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2882397Z   test_view_dtype_upsize_errors_lazy_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2883162Z   test_view_dtype_upsize_errors_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2883937Z   test_view_dtype_upsize_errors_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2884797Z   test_view_dtype_upsize_errors_lazy_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2885565Z   test_view_dtype_upsize_errors_lazy_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2886322Z   test_view_dtype_upsize_errors_lazy_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2887056Z   test_view_dtype_upsize_errors_lazy_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2887803Z   test_view_dtype_upsize_errors_lazy_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2888556Z   test_view_dtype_upsize_errors_lazy_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2889113Z   test_view_dtype_upsize_errors_lazy_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2889669Z   test_view_dtype_upsize_errors_lazy_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.002s)
2023-01-11T20:47:10.2890181Z   test_view_tensor_dsplit_lazy_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2890672Z   test_view_tensor_dsplit_lazy_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2891183Z   test_view_tensor_dsplit_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2891681Z   test_view_tensor_dsplit_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2892186Z   test_view_tensor_dsplit_lazy_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2892686Z   test_view_tensor_dsplit_lazy_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2893180Z   test_view_tensor_dsplit_lazy_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2893663Z   test_view_tensor_dsplit_lazy_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2894154Z   test_view_tensor_dsplit_lazy_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2894646Z   test_view_tensor_dsplit_lazy_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2895136Z   test_view_tensor_dsplit_lazy_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2895617Z   test_view_tensor_dsplit_lazy_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2896116Z   test_view_tensor_hsplit_lazy_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2896611Z   test_view_tensor_hsplit_lazy_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2897102Z   test_view_tensor_hsplit_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2897608Z   test_view_tensor_hsplit_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2898113Z   test_view_tensor_hsplit_lazy_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2898615Z   test_view_tensor_hsplit_lazy_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2899139Z   test_view_tensor_hsplit_lazy_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2899646Z   test_view_tensor_hsplit_lazy_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2900249Z   test_view_tensor_hsplit_lazy_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2900747Z   test_view_tensor_hsplit_lazy_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2901229Z   test_view_tensor_hsplit_lazy_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2901727Z   test_view_tensor_hsplit_lazy_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2902229Z   test_view_tensor_split_lazy_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2902767Z   test_view_tensor_split_lazy_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2903267Z   test_view_tensor_split_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2903777Z   test_view_tensor_split_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2904285Z   test_view_tensor_split_lazy_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2904784Z   test_view_tensor_split_lazy_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2905270Z   test_view_tensor_split_lazy_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2905773Z   test_view_tensor_split_lazy_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2906268Z   test_view_tensor_split_lazy_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2906758Z   test_view_tensor_split_lazy_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2907234Z   test_view_tensor_split_lazy_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2907726Z   test_view_tensor_split_lazy_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2908234Z   test_view_tensor_vsplit_lazy_bfloat16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2908740Z   test_view_tensor_vsplit_lazy_bool (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2909238Z   test_view_tensor_vsplit_lazy_complex128 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2909753Z   test_view_tensor_vsplit_lazy_complex64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2910268Z   test_view_tensor_vsplit_lazy_float16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2910767Z   test_view_tensor_vsplit_lazy_float32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2911252Z   test_view_tensor_vsplit_lazy_float64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2911751Z   test_view_tensor_vsplit_lazy_int16 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2912289Z   test_view_tensor_vsplit_lazy_int32 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2912790Z   test_view_tensor_vsplit_lazy_int64 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2913271Z   test_view_tensor_vsplit_lazy_int8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2913767Z   test_view_tensor_vsplit_lazy_uint8 (__main__.TestViewOpsLAZY) ... skip: onlyNativeDeviceTypes: doesn't run on lazy (0.001s)
2023-01-11T20:47:10.2914107Z   test_view_view_lazy (__main__.TestViewOpsLAZY) ... ok (0.008s)
2023-01-11T20:47:10.2914267Z 
2023-01-11T20:47:10.2914471Z ----------------------------------------------------------------------
2023-01-11T20:47:10.2914704Z Ran 441 tests in 49.467s
2023-01-11T20:47:10.2914823Z 
2023-01-11T20:47:10.2914896Z OK (skipped=123)
2023-01-11T20:47:10.2915007Z 
2023-01-11T20:47:10.2915092Z Generating XML reports...
2023-01-11T20:47:10.2915492Z Generated XML report: test-reports/python-unittest/test_view_ops/TEST-TestOldViewOpsCPU-20230111204620.xml
2023-01-11T20:47:10.2916042Z Generated XML report: test-reports/python-unittest/test_view_ops/TEST-TestViewOpsCPU-20230111204620.xml
2023-01-11T20:47:10.2916551Z Generated XML report: test-reports/python-unittest/test_view_ops/TEST-TestViewOpsLAZY-20230111204620.xml
2023-01-11T20:47:10.2916779Z 
2023-01-11T20:47:10.2917115Z ##[endgroup]
2023-01-11T20:47:10.2917494Z FINISHED PRINTING LOG FILE of test_view_ops (/var/lib/jenkins/workspace/test/test-reports/test_view_ops_gjmfh89f)
2023-01-11T20:47:10.2917709Z 
2023-01-11T20:47:15.3993390Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:47:15.5811956Z Ignoring disabled issues:  ['91003']
2023-01-11T20:47:15.6077051Z Running test_tensorboard ... [2023-01-11 20:47:15.607189]
2023-01-11T20:47:15.6077690Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorboard.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:47:15.607477]
2023-01-11T20:47:22.9962814Z 
2023-01-11T20:47:22.9963372Z Expand the folded group to see the log file of test_tensorboard
2023-01-11T20:47:22.9964492Z ##[group]PRINTING LOG FILE of test_tensorboard (/var/lib/jenkins/workspace/test/test-reports/test_tensorboard_mmq19wan)
2023-01-11T20:47:22.9965441Z Test results will be stored in test-reports/python-unittest/test_tensorboard
2023-01-11T20:47:22.9965770Z 
2023-01-11T20:47:22.9965891Z Running tests...
2023-01-11T20:47:22.9966423Z ----------------------------------------------------------------------
2023-01-11T20:47:22.9967114Z   test_embedding (__main__.TestTensorBoardEmbedding) ... warning: Embedding dir exists, did you set global_step for add_embedding()?
2023-01-11T20:47:22.9967638Z ok (0.065s)
2023-01-11T20:47:22.9971674Z   test_embedding_64 (__main__.TestTensorBoardEmbedding) ... warning: Embedding dir exists, did you set global_step for add_embedding()?
2023-01-11T20:47:22.9972229Z ok (0.018s)
2023-01-11T20:47:22.9972682Z   test_figure (__main__.TestTensorBoardFigure) ... skip: no matplotlib (0.002s)
2023-01-11T20:47:22.9973268Z   test_figure_list (__main__.TestTensorBoardFigure) ... skip: no matplotlib (0.002s)
2023-01-11T20:47:22.9973827Z   test_caffe2_np (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.001s)
2023-01-11T20:47:22.9974411Z   test_caffe2_np_expect_fail (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.000s)
2023-01-11T20:47:22.9975019Z   test_caffe2_simple_cnnmodel (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.002s)
2023-01-11T20:47:23.0044685Z   test_caffe2_simple_model (__main__.TestTensorBoardNumpy) ... skip: no caffe2 (0.003s)
2023-01-11T20:47:23.0045298Z   test_pytorch_np_expect_fail (__main__.TestTensorBoardNumpy) ... ok (0.001s)
2023-01-11T20:47:23.0045820Z   test_scalar (__main__.TestTensorBoardNumpy) ... ok (0.002s)
2023-01-11T20:47:23.0046393Z   test_pytorch_autograd_np (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.001s)
2023-01-11T20:47:23.0047201Z   test_pytorch_histogram (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.017s)
2023-01-11T20:47:23.0047818Z   test_pytorch_histogram_raw (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.016s)
2023-01-11T20:47:23.0048426Z   test_pytorch_np (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.002s)
2023-01-11T20:47:23.0048991Z   test_pytorch_write (__main__.TestTensorBoardPyTorchNumpy) ... ok (0.009s)
2023-01-11T20:47:23.0049555Z   test_mlp_graph (__main__.TestTensorBoardPytorchGraph) ... ok (0.566s)
2023-01-11T20:47:23.0050141Z   test_nested_nn_squential (__main__.TestTensorBoardPytorchGraph) ... ok (0.234s)
2023-01-11T20:47:23.0050734Z   test_pytorch_graph (__main__.TestTensorBoardPytorchGraph) ... ok (0.051s)
2023-01-11T20:47:23.0052788Z   test_pytorch_graph_dict_input (__main__.TestTensorBoardPytorchGraph) ... Encountering a dict at the output of the tracer might cause the trace to be incorrect, this is only valid if the container structure does not change based on the module's inputs. Consider using a constant container instead (e.g. for `list`, use a `tuple` instead. for `dict`, use a `NamedTuple` instead). If you absolutely need this and know the side effects, pass strict=False to trace() to allow this behavior.
2023-01-11T20:47:23.0054200Z Error occurs, No graph saved
2023-01-11T20:47:23.0054525Z ok (0.091s)
2023-01-11T20:47:23.0055007Z   test_torchvision_smoke (__main__.TestTensorBoardPytorchGraph) ... skip: no torchvision (0.001s)
2023-01-11T20:47:23.0055732Z   test_wrong_input_size (__main__.TestTensorBoardPytorchGraph) ... mat1 and mat2 shapes cannot be multiplied (1x9 and 3x5)
2023-01-11T20:47:23.0056270Z Error occurs, No graph saved
2023-01-11T20:47:23.0056574Z ok (0.032s)
2023-01-11T20:47:23.0057062Z   test_audio (__main__.TestTensorBoardSummary) ... warning: audio amplitude out of range, auto clipped.
2023-01-11T20:47:23.0057529Z ok (0.012s)
2023-01-11T20:47:23.0057935Z   test_custom_scalars (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T20:47:23.0058459Z   test_empty_input (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T20:47:23.0058957Z   test_float32_image (__main__.TestTensorBoardSummary)
2023-01-11T20:47:23.0059485Z Tests that float32 image (pixel values in [0, 1]) are scaled correctly ... ok (0.001s)
2023-01-11T20:47:23.0060112Z   test_histogram_auto (__main__.TestTensorBoardSummary) ... ok (0.003s)
2023-01-11T20:47:23.0060655Z   test_histogram_doane (__main__.TestTensorBoardSummary) ... ok (0.003s)
2023-01-11T20:47:23.0061189Z   test_histogram_fd (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0061709Z   test_hparams_bool (__main__.TestTensorBoardSummary) ... ok (0.014s)
2023-01-11T20:47:23.0062254Z   test_hparams_domain_discrete (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0114041Z   test_hparams_number (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0114677Z   test_hparams_smoke (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0115279Z   test_hparams_string (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0116006Z   test_hparams_wrong_parameter (__main__.TestTensorBoardSummary) ... parameter: hparam_dict should be a dictionary, nothing logged.
2023-01-11T20:47:23.0116682Z parameter: metric_dict should be a dictionary, nothing logged.
2023-01-11T20:47:23.0117110Z ok (0.010s)
2023-01-11T20:47:23.0117577Z   test_image_with_3_channel_batched (__main__.TestTensorBoardSummary) ... ok (0.003s)
2023-01-11T20:47:23.0118171Z   test_image_with_boxes (__main__.TestTensorBoardSummary) ... ok (0.084s)
2023-01-11T20:47:23.0118730Z   test_image_with_one_channel (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0119344Z   test_image_with_one_channel_batched (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0164962Z   test_image_without_channel (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0165559Z   test_list_input (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T20:47:23.0166317Z   test_mesh (__main__.TestTensorBoardSummary) ... ok (0.009s)
2023-01-11T20:47:23.0167812Z   test_scalar_new_style (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0168328Z   test_text (__main__.TestTensorBoardSummary) ... ok (0.002s)
2023-01-11T20:47:23.0168809Z   test_uint8_image (__main__.TestTensorBoardSummary)
2023-01-11T20:47:23.0170237Z Tests that uint8 image (pixel values in [0, 255]) is not changed ... ok (0.001s)
2023-01-11T20:47:23.0170778Z   test_video (__main__.TestTensorBoardSummary) ... ok (0.001s)
2023-01-11T20:47:23.0171551Z   test_pathlib (__main__.TestTensorBoardSummaryWriter) ... ok (0.018s)
2023-01-11T20:47:23.0173084Z   test_summary_writer_close (__main__.TestTensorBoardSummaryWriter) ... ok (0.002s)
2023-01-11T20:47:23.0173706Z   test_summary_writer_ctx (__main__.TestTensorBoardSummaryWriter) ... ok (0.021s)
2023-01-11T20:47:23.0174291Z   test_convert_to_HWC_dtype_remains_same (__main__.TestTensorBoardUtils) ... ok (0.001s)
2023-01-11T20:47:23.0174860Z   test_numpy_vid_uint8 (__main__.TestTensorBoardUtils) ... ok (0.048s)
2023-01-11T20:47:23.0175396Z   test_prepare_video (__main__.TestTensorBoardUtils) ... ok (0.375s)
2023-01-11T20:47:23.0176026Z   test_to_HWC (__main__.TestTensorBoardUtils) ... ok (0.002s)
2023-01-11T20:47:23.0176608Z   test_writer (__main__.TestTensorBoardWriter) ... add_video needs package moviepy
2023-01-11T20:47:23.0177043Z ok (0.087s)
2023-01-11T20:47:23.0177223Z 
2023-01-11T20:47:23.0177667Z ----------------------------------------------------------------------
2023-01-11T20:47:23.0178083Z Ran 53 tests in 1.836s
2023-01-11T20:47:23.0178274Z 
2023-01-11T20:47:23.0178396Z OK (skipped=7)
2023-01-11T20:47:23.0178589Z 
2023-01-11T20:47:23.0178741Z Generating XML reports...
2023-01-11T20:47:23.0179571Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardEmbedding-20230111204720.xml
2023-01-11T20:47:23.0180721Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardNumpy-20230111204720.xml
2023-01-11T20:47:23.0181776Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPyTorchNumpy-20230111204720.xml
2023-01-11T20:47:23.0182890Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPytorchGraph-20230111204720.xml
2023-01-11T20:47:23.0183927Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummary-20230111204720.xml
2023-01-11T20:47:23.0184994Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummaryWriter-20230111204720.xml
2023-01-11T20:47:23.0186024Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardUtils-20230111204720.xml
2023-01-11T20:47:23.0187034Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardWriter-20230111204720.xml
2023-01-11T20:47:23.0188032Z Generated XML report: test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardFigure-20230111204720.xml
2023-01-11T20:47:23.0188487Z 
2023-01-11T20:47:23.0189084Z ##[endgroup]
2023-01-11T20:47:23.0189812Z FINISHED PRINTING LOG FILE of test_tensorboard (/var/lib/jenkins/workspace/test/test-reports/test_tensorboard_mmq19wan)
2023-01-11T20:47:23.0190219Z 
2023-01-11T20:47:28.0249007Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:47:28.1992642Z Ignoring disabled issues:  ['91003']
2023-01-11T20:47:28.2378839Z Running nn/test_parametrization ... [2023-01-11 20:47:28.237471]
2023-01-11T20:47:28.2380225Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_parametrization.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:47:28.237782]
2023-01-11T20:47:34.7246100Z 
2023-01-11T20:47:34.7246826Z Expand the folded group to see the log file of nn/test_parametrization
2023-01-11T20:47:34.7247846Z ##[group]PRINTING LOG FILE of nn/test_parametrization (/var/lib/jenkins/workspace/test/test-reports/nn-test_parametrization_uoiu6z50)
2023-01-11T20:47:34.7249141Z Test results will be stored in test-reports/python-unittest/nn.test_parametrization
2023-01-11T20:47:34.7249504Z 
2023-01-11T20:47:34.7249632Z Running tests...
2023-01-11T20:47:34.7250180Z ----------------------------------------------------------------------
2023-01-11T20:47:34.7250714Z   test_caching_parametrization (__main__.TestNNParametrization)
2023-01-11T20:47:34.7251201Z Test the caching system of a parametrization ... ok (0.030s)
2023-01-11T20:47:34.7251532Z   test_caching_parametrization_with_transfer_parametrizations_and_params (__main__.TestNNParametrization)
2023-01-11T20:47:34.7251977Z Test that transferring parametrizations doesn't cause issues with caching ... ok (0.009s)
2023-01-11T20:47:34.7252306Z   test_deepcopy_after_parametrization (__main__.TestNNParametrization)
2023-01-11T20:47:34.7252715Z Test that we are able to create a deepcopy of the module when it's parametrized. ... ok (0.027s)
2023-01-11T20:47:34.7253061Z   test_errors_parametrized_tensor_parametrization (__main__.TestNNParametrization) ... ok (0.010s)
2023-01-11T20:47:34.7253430Z   test_errors_unparametrized_tensor_parametrization (__main__.TestNNParametrization) ... ok (0.012s)
2023-01-11T20:47:34.7253844Z   test_initialization_parametrization (__main__.TestNNParametrization)
2023-01-11T20:47:34.7254151Z Test that it is possible to initialize a parametrization when it ... ok (0.025s)
2023-01-11T20:47:34.7254476Z   test_multiple_inputs_parametrization (__main__.TestNNParametrization) ... ok (0.043s)
2023-01-11T20:47:34.7254797Z   test_new_spectral_norm (__main__.TestNNParametrization) ... ok (0.239s)
2023-01-11T20:47:34.7255099Z   test_new_spectral_norm_dim (__main__.TestNNParametrization) ... ok (0.014s)
2023-01-11T20:47:34.7255396Z   test_new_spectral_norm_forward (__main__.TestNNParametrization) ... ok (0.010s)
2023-01-11T20:47:34.7255722Z   test_new_spectral_norm_load_state_dict (__main__.TestNNParametrization) ... ok (0.056s)
2023-01-11T20:47:34.7256040Z   test_orthogonal_errors (__main__.TestNNParametrization) ... ok (0.007s)
2023-01-11T20:47:34.7256345Z   test_orthogonal_parametrization (__main__.TestNNParametrization) ... ok (0.840s)
2023-01-11T20:47:34.7256671Z   test_parametrization_same_training_mode (__main__.TestNNParametrization)
2023-01-11T20:47:34.7256991Z Test training mode updated on parametrization registration ... ok (0.002s)
2023-01-11T20:47:34.7257316Z   test_register_and_remove_buffer_parametrization (__main__.TestNNParametrization)
2023-01-11T20:47:34.7257635Z Test that it is possible to add and remove parametrizations on buffers ... ok (0.005s)
2023-01-11T20:47:34.7257969Z   test_register_and_remove_nested_parametrization (__main__.TestNNParametrization)
2023-01-11T20:47:34.7258283Z Test that it is possible to nest the parametrizations ... ok (0.006s)
2023-01-11T20:47:34.7258574Z   test_register_and_remove_parametrization (__main__.TestNNParametrization)
2023-01-11T20:47:34.7258878Z Test that it is possible to add a few parametrizations ... ok (0.057s)
2023-01-11T20:47:34.7259180Z   test_serialization_parametrization (__main__.TestNNParametrization)
2023-01-11T20:47:34.7259503Z Test that it is possible to serialize a parametrized model via state_dict ... ok (0.033s)
2023-01-11T20:47:34.7259817Z   test_transfer_parametrizations_and_params (__main__.TestNNParametrization)
2023-01-11T20:47:34.7260258Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.012s)
2023-01-11T20:47:34.7260624Z   test_transfer_parametrizations_and_params_many_to_one (__main__.TestNNParametrization) ... ok (0.015s)
2023-01-11T20:47:34.7260977Z   test_transfer_parametrizations_and_params_right_inverse (__main__.TestNNParametrization)
2023-01-11T20:47:34.7261332Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.005s)
2023-01-11T20:47:34.7261684Z   test_transfer_parametrizations_and_params_single_param (__main__.TestNNParametrization)
2023-01-11T20:47:34.7262036Z Test that all parametrizations and their associated parameters are transferred. ... ok (0.007s)
2023-01-11T20:47:34.7262401Z   test_type_before_parametrizations (__main__.TestNNParametrization)
2023-01-11T20:47:34.7262719Z Test that type_before_parametrizations always retrieves original type ... ok (0.002s)
2023-01-11T20:47:34.7262899Z 
2023-01-11T20:47:34.7263108Z ----------------------------------------------------------------------
2023-01-11T20:47:34.7263332Z Ran 23 tests in 1.465s
2023-01-11T20:47:34.7263447Z 
2023-01-11T20:47:34.7263505Z OK
2023-01-11T20:47:34.7263594Z 
2023-01-11T20:47:34.7263677Z Generating XML reports...
2023-01-11T20:47:34.7264124Z Generated XML report: test-reports/python-unittest/nn.test_parametrization/TEST-TestNNParametrization-20230111204732.xml
2023-01-11T20:47:34.7264367Z 
2023-01-11T20:47:34.7264642Z ##[endgroup]
2023-01-11T20:47:34.7265062Z FINISHED PRINTING LOG FILE of nn/test_parametrization (/var/lib/jenkins/workspace/test/test-reports/nn-test_parametrization_uoiu6z50)
2023-01-11T20:47:34.7265301Z 
2023-01-11T20:47:39.8265170Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:47:40.0445013Z Ignoring disabled issues:  ['91003']
2023-01-11T20:47:40.0776136Z Running dynamo/test_verify_correctness ... [2023-01-11 20:47:40.076967]
2023-01-11T20:47:40.0777571Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:47:40.077351]
2023-01-11T20:47:46.3056050Z 
2023-01-11T20:47:46.3056777Z Expand the folded group to see the log file of dynamo/test_verify_correctness
2023-01-11T20:47:46.3058321Z ##[group]PRINTING LOG FILE of dynamo/test_verify_correctness (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_verify_correctness_75ta2zgw)
2023-01-11T20:47:46.3059491Z Test results will be stored in test-reports/python-unittest/dynamo.test_verify_correctness
2023-01-11T20:47:46.3059857Z 
2023-01-11T20:47:46.3060041Z Running tests...
2023-01-11T20:47:46.3060608Z ----------------------------------------------------------------------
2023-01-11T20:47:46.3061150Z   test_example_inputs (__main__.TestVerifyCorrectness) ... ok (0.516s)
2023-01-11T20:47:46.3061712Z   test_incorrect_verify_false (__main__.TestVerifyCorrectness)
2023-01-11T20:47:46.3062543Z The bad optimization return a graph that ... stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:47:46.3063137Z frames [('total', 2), ('ok', 2)]
2023-01-11T20:47:46.3063700Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 2)]
2023-01-11T20:47:46.3064118Z ok (0.037s)
2023-01-11T20:47:46.3064535Z   test_incorrect_verify_true (__main__.TestVerifyCorrectness)
2023-01-11T20:47:46.3065367Z If a bad optimization return a graph that ... [2023-01-11 20:47:44,980] torch._dynamo.output_graph: [ERROR] error in verify_correctness
2023-01-11T20:47:46.3065917Z Traceback (most recent call last):
2023-01-11T20:47:46.3066606Z   File "/opt/conda/lib/python3.7/site-packages/torch/_dynamo/output_graph.py", line 173, in __call__
2023-01-11T20:47:46.3067178Z     raise RuntimeError(f"incorrect results of backend {self}")
2023-01-11T20:47:46.3067822Z RuntimeError: incorrect results of backend <torch._dynamo.output_graph.WrapperBackend object at 0x7f538a813a10>
2023-01-11T20:47:46.3068429Z frames [('total', 2), ('ok', 1)]
2023-01-11T20:47:46.3069005Z stats [('calls_captured', 7), ('fusions_possible', 5), ('unique_graphs', 1)]
2023-01-11T20:47:46.3069403Z ok (0.033s)
2023-01-11T20:47:46.3069856Z   test_ipex_fp32 (__main__.TestVerifyCorrectness) ... skip: requires ipex (0.001s)
2023-01-11T20:47:46.3070560Z   test_nnc (__main__.TestVerifyCorrectness) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:47:46.3071211Z stats [('calls_captured', 4), ('fusions_possible', 3), ('unique_graphs', 1)]
2023-01-11T20:47:46.3071622Z ok (0.636s)
2023-01-11T20:47:46.3071806Z 
2023-01-11T20:47:46.3072169Z ----------------------------------------------------------------------
2023-01-11T20:47:46.3072588Z Ran 5 tests in 1.224s
2023-01-11T20:47:46.3072795Z 
2023-01-11T20:47:46.3072924Z OK (skipped=1)
2023-01-11T20:47:46.3073380Z 
2023-01-11T20:47:46.3073533Z Generating XML reports...
2023-01-11T20:47:46.3074392Z Generated XML report: test-reports/python-unittest/dynamo.test_verify_correctness/TEST-TestVerifyCorrectness-20230111204744.xml
2023-01-11T20:47:46.3074876Z 
2023-01-11T20:47:46.3075315Z ##[endgroup]
2023-01-11T20:47:46.3076129Z FINISHED PRINTING LOG FILE of dynamo/test_verify_correctness (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_verify_correctness_75ta2zgw)
2023-01-11T20:47:46.3076594Z 
2023-01-11T20:47:51.6323062Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:47:51.8085688Z Ignoring disabled issues:  ['91003']
2023-01-11T20:47:51.8281026Z Running test_native_mha ... [2023-01-11 20:47:51.827770]
2023-01-11T20:47:51.8282595Z Executing ['/opt/conda/bin/python', '-bb', 'test_native_mha.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:47:51.828036]
2023-01-11T20:47:58.1459158Z 
2023-01-11T20:47:58.1459643Z Expand the folded group to see the log file of test_native_mha
2023-01-11T20:47:58.1460714Z ##[group]PRINTING LOG FILE of test_native_mha (/var/lib/jenkins/workspace/test/test-reports/test_native_mha_pcih3xzr)
2023-01-11T20:47:58.1461726Z Test results will be stored in test-reports/python-unittest/test_native_mha
2023-01-11T20:47:58.1461924Z 
2023-01-11T20:47:58.1461998Z Running tests...
2023-01-11T20:47:58.1462309Z ----------------------------------------------------------------------
2023-01-11T20:47:58.1462693Z   test_native_multihead_attention_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.048s)
2023-01-11T20:47:58.1463049Z   test_native_multihead_encoder_decoder_attention_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.022s)
2023-01-11T20:47:58.1463571Z   test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.037s)
2023-01-11T20:47:58.1464175Z   test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.056s)
2023-01-11T20:47:58.1464766Z   test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.034s)
2023-01-11T20:47:58.1465305Z   test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.027s)
2023-01-11T20:47:58.1465906Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.031s)
2023-01-11T20:47:58.1466493Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.030s)
2023-01-11T20:47:58.1467083Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.029s)
2023-01-11T20:47:58.1467622Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.028s)
2023-01-11T20:47:58.1468201Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.062s)
2023-01-11T20:47:58.1468781Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.032s)
2023-01-11T20:47:58.1469367Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.041s)
2023-01-11T20:47:58.1469995Z   test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.039s)
2023-01-11T20:47:58.1470819Z   test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... test_native_mha.py:207: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T20:47:58.1471454Z   q = torch.nested.nested_tensor(qs, device=device, dtype=dtype)
2023-01-11T20:47:58.1471717Z ok (0.064s)
2023-01-11T20:47:58.1472081Z   test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.058s)
2023-01-11T20:47:58.1472711Z   test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.074s)
2023-01-11T20:47:58.1473297Z   test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.035s)
2023-01-11T20:47:58.1473860Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.039s)
2023-01-11T20:47:58.1474403Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.039s)
2023-01-11T20:47:58.1474985Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.038s)
2023-01-11T20:47:58.1475565Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.039s)
2023-01-11T20:47:58.1476134Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.045s)
2023-01-11T20:47:58.1476669Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.044s)
2023-01-11T20:47:58.1477255Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.044s)
2023-01-11T20:47:58.1477842Z   test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.044s)
2023-01-11T20:47:58.1478272Z   test_transform_bias_rescale_qkv_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... ok (0.069s)
2023-01-11T20:47:58.1478689Z   test_transform_bias_rescale_qkv_nested_cpu_float32 (__main__.TestMHADeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:47:58.1478900Z 
2023-01-11T20:47:58.1479115Z ----------------------------------------------------------------------
2023-01-11T20:47:58.1493386Z Ran 28 tests in 1.152s
2023-01-11T20:47:58.1493527Z 
2023-01-11T20:47:58.1493619Z OK (skipped=1)
2023-01-11T20:47:58.1493746Z 
2023-01-11T20:47:58.1493835Z Generating XML reports...
2023-01-11T20:47:58.1494531Z Generated XML report: test-reports/python-unittest/test_native_mha/TEST-TestMHADeviceTypeCPU-20230111204756.xml
2023-01-11T20:47:58.1494781Z 
2023-01-11T20:47:58.1495060Z ##[endgroup]
2023-01-11T20:47:58.1495483Z FINISHED PRINTING LOG FILE of test_native_mha (/var/lib/jenkins/workspace/test/test-reports/test_native_mha_pcih3xzr)
2023-01-11T20:47:58.1495693Z 
2023-01-11T20:48:03.7993422Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:48:03.9809041Z Ignoring disabled issues:  ['91003']
2023-01-11T20:48:04.0010046Z Running dynamo/test_python_autograd ... [2023-01-11 20:48:04.000612]
2023-01-11T20:48:04.0012317Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:48:04.000962]
2023-01-11T20:48:09.7106074Z 
2023-01-11T20:48:09.7106914Z Expand the folded group to see the log file of dynamo/test_python_autograd
2023-01-11T20:48:09.7108012Z ##[group]PRINTING LOG FILE of dynamo/test_python_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_python_autograd_ynrf8r71)
2023-01-11T20:48:09.7108955Z Test results will be stored in test-reports/python-unittest/dynamo.test_python_autograd
2023-01-11T20:48:09.7109539Z 
2023-01-11T20:48:09.7109661Z Running tests...
2023-01-11T20:48:09.7110158Z ----------------------------------------------------------------------
2023-01-11T20:48:09.7110652Z   test_backwards1 (__main__.TestPythonAutograd) ... ok (0.602s)
2023-01-11T20:48:09.7111107Z   test_backwards2 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T20:48:09.7111782Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T20:48:09.7112223Z inline_call []
2023-01-11T20:48:09.7112769Z stats [('calls_captured', 8), ('fusions_possible', 7), ('unique_graphs', 1)]
2023-01-11T20:48:09.7113222Z ok (0.216s)
2023-01-11T20:48:09.7113655Z   test_forwards1 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T20:48:09.7114316Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:48:09.7114734Z ok (0.081s)
2023-01-11T20:48:09.7115162Z   test_forwards2 (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T20:48:09.7115834Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:48:09.7116241Z ok (0.054s)
2023-01-11T20:48:09.7116660Z   test_split (__main__.TestPythonAutograd) ... inline_call []
2023-01-11T20:48:09.7117312Z stats [('calls_captured', 8), ('fusions_possible', 6), ('unique_graphs', 2)]
2023-01-11T20:48:09.7117723Z ok (0.180s)
2023-01-11T20:48:09.7117914Z 
2023-01-11T20:48:09.7118285Z ----------------------------------------------------------------------
2023-01-11T20:48:09.7118737Z Ran 5 tests in 1.135s
2023-01-11T20:48:09.7118955Z 
2023-01-11T20:48:09.7119069Z OK
2023-01-11T20:48:09.7119230Z 
2023-01-11T20:48:09.7119387Z Generating XML reports...
2023-01-11T20:48:09.7120220Z Generated XML report: test-reports/python-unittest/dynamo.test_python_autograd/TEST-TestPythonAutograd-20230111204807.xml
2023-01-11T20:48:09.7120926Z 
2023-01-11T20:48:09.7121406Z ##[endgroup]
2023-01-11T20:48:09.7122222Z FINISHED PRINTING LOG FILE of dynamo/test_python_autograd (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_python_autograd_ynrf8r71)
2023-01-11T20:48:09.7122690Z 
2023-01-11T20:48:14.9491499Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:48:15.1609883Z Ignoring disabled issues:  ['91003']
2023-01-11T20:48:15.1931147Z Running test_futures ... [2023-01-11 20:48:15.192684]
2023-01-11T20:48:15.1933004Z Executing ['/opt/conda/bin/python', '-bb', 'test_futures.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:48:15.193029]
2023-01-11T20:48:20.8402870Z 
2023-01-11T20:48:20.8403741Z Expand the folded group to see the log file of test_futures
2023-01-11T20:48:20.8405107Z ##[group]PRINTING LOG FILE of test_futures (/var/lib/jenkins/workspace/test/test-reports/test_futures_367qbelt)
2023-01-11T20:48:20.8406029Z Test results will be stored in test-reports/python-unittest/test_futures
2023-01-11T20:48:20.8406214Z 
2023-01-11T20:48:20.8441467Z Running tests...
2023-01-11T20:48:20.8441997Z ----------------------------------------------------------------------
2023-01-11T20:48:20.8442713Z   test_add_done_callback_error_is_ignored (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: ValueError: Expected error
2023-01-11T20:48:20.8443176Z 
2023-01-11T20:48:20.8443294Z At:
2023-01-11T20:48:20.8443614Z   test_futures.py(236): raise_value_error
2023-01-11T20:48:20.8444269Z   /opt/conda/lib/python3.7/site-packages/torch/futures/__init__.py(244): set_result
2023-01-11T20:48:20.8444647Z   test_futures.py(229): _test_add_done_callback_error_ignored
2023-01-11T20:48:20.8444918Z   test_futures.py(238): test_add_done_callback_error_is_ignored
2023-01-11T20:48:20.8445174Z   /opt/conda/lib/python3.7/unittest/case.py(628): run
2023-01-11T20:48:20.8445569Z   /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py(2154): _run_with_retry
2023-01-11T20:48:20.8481515Z   /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py(2230): run
2023-01-11T20:48:20.8482248Z   /opt/conda/lib/python3.7/unittest/case.py(676): __call__
2023-01-11T20:48:20.8482734Z   /opt/conda/lib/python3.7/unittest/suite.py(122): run
2023-01-11T20:48:20.8483228Z   /opt/conda/lib/python3.7/unittest/suite.py(84): __call__
2023-01-11T20:48:20.8483714Z   /opt/conda/lib/python3.7/unittest/suite.py(122): run
2023-01-11T20:48:20.8484133Z   /opt/conda/lib/python3.7/unittest/suite.py(84): __call__
2023-01-11T20:48:20.8528400Z   /opt/conda/lib/python3.7/site-packages/xmlrunner/runner.py(67): run
2023-01-11T20:48:20.8528955Z   /opt/conda/lib/python3.7/unittest/main.py(271): runTests
2023-01-11T20:48:20.8529440Z   /opt/conda/lib/python3.7/unittest/main.py(101): __init__
2023-01-11T20:48:20.8530164Z   /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py(790): run_tests
2023-01-11T20:48:20.8530598Z   test_futures.py(340): <module>
2023-01-11T20:48:20.8530799Z 
2023-01-11T20:48:20.8530925Z ok (0.390s)
2023-01-11T20:48:20.8531387Z   test_add_done_callback_maintains_callback_order (__main__.TestFuture) ... ok (0.006s)
2023-01-11T20:48:20.8532207Z   test_add_done_callback_no_arg_error_is_ignored (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: TypeError: no_arg() takes 0 positional arguments but 1 was given
2023-01-11T20:48:20.8532809Z ok (0.001s)
2023-01-11T20:48:20.8533509Z   test_add_done_callback_simple (__main__.TestFuture) ... ok (0.003s)
2023-01-11T20:48:20.8534020Z   test_chained_then (__main__.TestFuture) ... ok (0.024s)
2023-01-11T20:48:20.8534482Z   test_collect_all (__main__.TestFuture) ... ok (0.103s)
2023-01-11T20:48:20.8534921Z   test_done (__main__.TestFuture) ... ok (0.002s)
2023-01-11T20:48:20.8535358Z   test_done_exception (__main__.TestFuture) ... ok (0.003s)
2023-01-11T20:48:20.8535953Z   test_interleaving_then_and_add_done_callback_maintains_callback_order (__main__.TestFuture) ... ok (0.005s)
2023-01-11T20:48:20.8536844Z   test_interleaving_then_and_add_done_callback_propagates_error (__main__.TestFuture) ... [E pybind_utils.h:212] Got the following error when running the callback: ValueError: Expected error
2023-01-11T20:48:20.8537363Z 
2023-01-11T20:48:20.8537473Z At:
2023-01-11T20:48:20.8537788Z   test_futures.py(280): raise_value_error
2023-01-11T20:48:20.8538496Z   /opt/conda/lib/python3.7/site-packages/torch/futures/__init__.py(244): set_result
2023-01-11T20:48:20.8539105Z   test_futures.py(285): test_interleaving_then_and_add_done_callback_propagates_error
2023-01-11T20:48:20.8539631Z   /opt/conda/lib/python3.7/unittest/case.py(628): run
2023-01-11T20:48:20.8540473Z   /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py(2154): _run_with_retry
2023-01-11T20:48:20.8541272Z   /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py(2230): run
2023-01-11T20:48:20.8542022Z   /opt/conda/lib/python3.7/unittest/case.py(676): __call__
2023-01-11T20:48:20.8542481Z   /opt/conda/lib/python3.7/unittest/suite.py(122): run
2023-01-11T20:48:20.8542956Z   /opt/conda/lib/python3.7/unittest/suite.py(84): __call__
2023-01-11T20:48:20.8543434Z   /opt/conda/lib/python3.7/unittest/suite.py(122): run
2023-01-11T20:48:20.8543895Z   /opt/conda/lib/python3.7/unittest/suite.py(84): __call__
2023-01-11T20:48:20.8544532Z   /opt/conda/lib/python3.7/site-packages/xmlrunner/runner.py(67): run
2023-01-11T20:48:20.8545050Z   /opt/conda/lib/python3.7/unittest/main.py(271): runTests
2023-01-11T20:48:20.8545506Z   /opt/conda/lib/python3.7/unittest/main.py(101): __init__
2023-01-11T20:48:20.8546216Z   /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py(790): run_tests
2023-01-11T20:48:20.8546715Z   test_futures.py(340): <module>
2023-01-11T20:48:20.8546939Z 
2023-01-11T20:48:20.8547054Z ok (0.004s)
2023-01-11T20:48:20.8547429Z   test_mark_future_twice (__main__.TestFuture) ... ok (0.005s)
2023-01-11T20:48:20.8547903Z   test_pickle_future (__main__.TestFuture) ... ok (0.006s)
2023-01-11T20:48:20.8548368Z   test_set_exception (__main__.TestFuture) ... ok (0.003s)
2023-01-11T20:48:20.8548964Z   test_set_exception_multithreading (__main__.TestFuture) ... ok (0.004s)
2023-01-11T20:48:20.8549455Z   test_then (__main__.TestFuture) ... ok (0.004s)
2023-01-11T20:48:20.8549895Z   test_then_no_arg (__main__.TestFuture) ... ok (0.002s)
2023-01-11T20:48:20.8550327Z   test_then_raise (__main__.TestFuture) ... ok (0.002s)
2023-01-11T20:48:20.8550808Z   test_then_wrong_arg (__main__.TestFuture) ... ok (0.002s)
2023-01-11T20:48:20.8551245Z   test_wait (__main__.TestFuture) ... ok (0.002s)
2023-01-11T20:48:20.8551666Z   test_wait_all (__main__.TestFuture) ... [1, 2]
2023-01-11T20:48:20.8552011Z ok (0.003s)
2023-01-11T20:48:20.8552401Z   test_wait_multi_thread (__main__.TestFuture) ... ok (0.504s)
2023-01-11T20:48:20.8552862Z   test_wait_none (__main__.TestFuture) ... ok (0.010s)
2023-01-11T20:48:20.8553120Z 
2023-01-11T20:48:20.8553487Z ----------------------------------------------------------------------
2023-01-11T20:48:20.8553931Z Ran 22 tests in 1.089s
2023-01-11T20:48:20.8554135Z 
2023-01-11T20:48:20.8554242Z OK
2023-01-11T20:48:20.8554409Z 
2023-01-11T20:48:20.8554563Z Generating XML reports...
2023-01-11T20:48:20.8555269Z Generated XML report: test-reports/python-unittest/test_futures/TEST-TestFuture-20230111204819.xml
2023-01-11T20:48:20.8555669Z 
2023-01-11T20:48:20.8556234Z ##[endgroup]
2023-01-11T20:48:20.8556927Z FINISHED PRINTING LOG FILE of test_futures (/var/lib/jenkins/workspace/test/test-reports/test_futures_367qbelt)
2023-01-11T20:48:20.8557315Z 
2023-01-11T20:48:26.2968244Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:48:26.4741053Z Ignoring disabled issues:  ['91003']
2023-01-11T20:48:26.5016858Z Running test_fx_reinplace_pass ... [2023-01-11 20:48:26.501167]
2023-01-11T20:48:26.5017817Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_reinplace_pass.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:48:26.501481]
2023-01-11T20:48:33.3651144Z 
2023-01-11T20:48:33.3651701Z Expand the folded group to see the log file of test_fx_reinplace_pass
2023-01-11T20:48:33.3652925Z ##[group]PRINTING LOG FILE of test_fx_reinplace_pass (/var/lib/jenkins/workspace/test/test-reports/test_fx_reinplace_pass_le6q_nub)
2023-01-11T20:48:33.3653872Z Test results will be stored in test-reports/python-unittest/test_fx_reinplace_pass
2023-01-11T20:48:33.3654213Z 
2023-01-11T20:48:33.3654342Z Running tests...
2023-01-11T20:48:33.3654897Z ----------------------------------------------------------------------
2023-01-11T20:48:33.3655441Z   test_out_node_updated (__main__.TestReinplacePass) ... ok (0.505s)
2023-01-11T20:48:33.3723751Z   test_reinplace_basic (__main__.TestReinplacePass) ... ok (0.198s)
2023-01-11T20:48:33.3724388Z   test_reinplace_different_metadata (__main__.TestReinplacePass) ... ok (0.059s)
2023-01-11T20:48:33.3724942Z   test_reinplace_index_mutation (__main__.TestReinplacePass) ... ok (0.226s)
2023-01-11T20:48:33.3725803Z   test_reinplace_overlapping_memory (__main__.TestReinplacePass) ... ok (0.054s)
2023-01-11T20:48:33.3726360Z   test_reinplace_scatter_op (__main__.TestReinplacePass) ... ok (0.371s)
2023-01-11T20:48:33.3726885Z   test_reinplace_scatter_twice (__main__.TestReinplacePass) ... ok (0.344s)
2023-01-11T20:48:33.3727508Z   test_reinplace_scatter_twice_with_different_view_op_invalid (__main__.TestReinplacePass) ... ok (0.119s)
2023-01-11T20:48:33.3728172Z   test_reinplace_scatter_twice_with_different_view_op_invalid2 (__main__.TestReinplacePass) ... ok (0.118s)
2023-01-11T20:48:33.3728838Z   test_reinplace_scatter_twice_with_different_view_op_valid (__main__.TestReinplacePass) ... ok (0.125s)
2023-01-11T20:48:33.3729433Z   test_reinplace_with_view (__main__.TestReinplacePass) ... ok (0.073s)
2023-01-11T20:48:33.3729736Z 
2023-01-11T20:48:33.3730173Z ----------------------------------------------------------------------
2023-01-11T20:48:33.3730609Z Ran 11 tests in 2.192s
2023-01-11T20:48:33.3730795Z 
2023-01-11T20:48:33.3730903Z OK
2023-01-11T20:48:33.3731072Z 
2023-01-11T20:48:33.3731226Z Generating XML reports...
2023-01-11T20:48:33.3732145Z Generated XML report: test-reports/python-unittest/test_fx_reinplace_pass/TEST-TestReinplacePass-20230111204830.xml
2023-01-11T20:48:33.3732535Z 
2023-01-11T20:48:33.3732883Z ##[endgroup]
2023-01-11T20:48:33.3733299Z FINISHED PRINTING LOG FILE of test_fx_reinplace_pass (/var/lib/jenkins/workspace/test/test-reports/test_fx_reinplace_pass_le6q_nub)
2023-01-11T20:48:33.3733530Z 
2023-01-11T20:48:38.4860113Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:48:38.6666082Z Ignoring disabled issues:  ['91003']
2023-01-11T20:48:38.6874374Z Running test_model_dump ... [2023-01-11 20:48:38.686797]
2023-01-11T20:48:38.6875438Z Executing ['/opt/conda/bin/python', '-bb', 'test_model_dump.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:48:38.687150]
2023-01-11T20:48:44.4358523Z 
2023-01-11T20:48:44.4359383Z Expand the folded group to see the log file of test_model_dump
2023-01-11T20:48:44.4360808Z ##[group]PRINTING LOG FILE of test_model_dump (/var/lib/jenkins/workspace/test/test-reports/test_model_dump_2qhp7hco)
2023-01-11T20:48:44.4361822Z Test results will be stored in test-reports/python-unittest/test_model_dump
2023-01-11T20:48:44.4362367Z 
2023-01-11T20:48:44.4362676Z Running tests...
2023-01-11T20:48:44.4363249Z ----------------------------------------------------------------------
2023-01-11T20:48:44.4363730Z   test_inline_skeleton (__main__.TestModelDump) ... ok (0.376s)
2023-01-11T20:48:44.4366072Z   test_invalid_json (__main__.TestModelDump) ... ok (0.071s)
2023-01-11T20:48:44.4366685Z   test_main (__main__.TestModelDump) ... ok (0.045s)
2023-01-11T20:48:44.4367322Z   test_memory_computation (__main__.TestModelDump) ... skip: Webdriver not requested (0.002s)
2023-01-11T20:48:44.4367951Z   test_model_with_lists (__main__.TestModelDump) ... ok (0.011s)
2023-01-11T20:48:44.4368649Z   test_optimized_quantized_model (__main__.TestModelDump) ... skip: QNNPACK not available (0.001s)
2023-01-11T20:48:44.4369363Z   test_quantized_model (__main__.TestModelDump) ... skip: QNNPACK not available (0.000s)
2023-01-11T20:48:44.4369850Z   test_scripted_model (__main__.TestModelDump) ... ok (0.023s)
2023-01-11T20:48:44.4370277Z   test_traced_model (__main__.TestModelDump) ... ok (0.482s)
2023-01-11T20:48:44.4370513Z 
2023-01-11T20:48:44.4370828Z ----------------------------------------------------------------------
2023-01-11T20:48:44.4376059Z Ran 9 tests in 1.012s
2023-01-11T20:48:44.4376256Z 
2023-01-11T20:48:44.4376356Z OK (skipped=3)
2023-01-11T20:48:44.4376527Z 
2023-01-11T20:48:44.4376661Z Generating XML reports...
2023-01-11T20:48:44.4378548Z Generated XML report: test-reports/python-unittest/test_model_dump/TEST-TestModelDump-20230111204842.xml
2023-01-11T20:48:44.4378895Z 
2023-01-11T20:48:44.4379532Z ##[endgroup]
2023-01-11T20:48:44.4380277Z FINISHED PRINTING LOG FILE of test_model_dump (/var/lib/jenkins/workspace/test/test-reports/test_model_dump_2qhp7hco)
2023-01-11T20:48:44.4381023Z 
2023-01-11T20:48:49.6114316Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:48:49.7809732Z Ignoring disabled issues:  ['91003']
2023-01-11T20:48:49.8013815Z Running test_datapipe ... [2023-01-11 20:48:49.800855]
2023-01-11T20:48:49.8014421Z Executing ['/opt/conda/bin/python', '-bb', 'test_datapipe.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:48:49.801143]
2023-01-11T20:48:55.6114821Z 
2023-01-11T20:48:55.6115716Z Expand the folded group to see the log file of test_datapipe
2023-01-11T20:48:55.6116989Z ##[group]PRINTING LOG FILE of test_datapipe (/var/lib/jenkins/workspace/test/test-reports/test_datapipe_nvv47c7m)
2023-01-11T20:48:55.6117652Z 
2023-01-11T20:48:55.6118004Z Running tests...
2023-01-11T20:48:55.6118609Z ----------------------------------------------------------------------
2023-01-11T20:48:55.6119443Z   test_basic_capture (__main__.TestCaptureDataFrame) ... Test results will be stored in test-reports/python-unittest/test_datapipe
2023-01-11T20:48:55.6120109Z skip: no dataframes (pandas) (0.001s)
2023-01-11T20:48:55.6121130Z   test_circular_serialization_with_dill (__main__.TestCircularSerialization) ... skip: no dill (0.004s)
2023-01-11T20:48:55.6122695Z   test_circular_serialization_with_pickle (__main__.TestCircularSerialization) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/iter/combining.py:298: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T20:48:55.6123788Z   "the buffer and each child DataPipe will read from the start again.", UserWarning)
2023-01-11T20:48:55.6124209Z ok (0.385s)
2023-01-11T20:48:55.6124595Z   test_as_string (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T20:48:55.6125029Z   test_getitem (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T20:48:55.6125272Z   test_iter (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T20:48:55.6125513Z   test_len (__main__.TestDataChunk) ... ok (0.000s)
2023-01-11T20:48:55.6125767Z   test_random_shuffle (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T20:48:55.6126010Z   test_reverse (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T20:48:55.6126249Z   test_sort (__main__.TestDataChunk) ... ok (0.001s)
2023-01-11T20:48:55.6126542Z   test_batch (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.001s)
2023-01-11T20:48:55.6126876Z   test_capture (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.001s)
2023-01-11T20:48:55.6127193Z   test_collate (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.001s)
2023-01-11T20:48:55.6127520Z   test_filter (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.001s)
2023-01-11T20:48:55.6127844Z   test_shuffle (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.001s)
2023-01-11T20:48:55.6128159Z   test_unbatch (__main__.TestDataFramesPipes) ... skip: no dataframes (pandas) (0.000s)
2023-01-11T20:48:55.6128489Z   test_batch_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.004s)
2023-01-11T20:48:55.6129222Z   test_collate_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6129724Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6129924Z ok (0.012s)
2023-01-11T20:48:55.6130174Z   test_concat_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.004s)
2023-01-11T20:48:55.6130895Z   test_demux_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6131470Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6132047Z /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6132470Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6133034Z /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6133454Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6134001Z /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6134416Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6134627Z ok (0.055s)
2023-01-11T20:48:55.6134862Z   test_filter_datapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.014s)
2023-01-11T20:48:55.6135227Z   test_fork_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.028s)
2023-01-11T20:48:55.6135567Z   test_iterable_wrapper_datapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.005s)
2023-01-11T20:48:55.6135918Z   test_map_dict_with_col_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.043s)
2023-01-11T20:48:55.6136628Z   test_map_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6137116Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6137327Z ok (0.021s)
2023-01-11T20:48:55.6137586Z   test_map_tuple_list_with_col_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.045s)
2023-01-11T20:48:55.6137931Z   test_mux_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.003s)
2023-01-11T20:48:55.6138263Z   test_sampler_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T20:48:55.6139030Z   test_serializable (__main__.TestFunctionalIterDataPipe) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/iter/combining.py:298: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T20:48:55.6139567Z   "the buffer and each child DataPipe will read from the start again.", UserWarning)
2023-01-11T20:48:55.6140292Z /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/iter/combining.py:298: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T20:48:55.6140781Z   "the buffer and each child DataPipe will read from the start again.", UserWarning)
2023-01-11T20:48:55.6141022Z ok (0.060s)
2023-01-11T20:48:55.6141252Z   test_serializable_with_dill (__main__.TestFunctionalIterDataPipe)
2023-01-11T20:48:55.6141558Z Only for DataPipes that take in a function as argument ... ok (0.021s)
2023-01-11T20:48:55.6141880Z   test_shuffler_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.009s)
2023-01-11T20:48:55.6142225Z   test_stream_reader_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.002s)
2023-01-11T20:48:55.6142558Z   test_unbatch_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.006s)
2023-01-11T20:48:55.6142887Z   test_zip_iterdatapipe (__main__.TestFunctionalIterDataPipe) ... ok (0.003s)
2023-01-11T20:48:55.6143216Z   test_batch_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.004s)
2023-01-11T20:48:55.6143531Z   test_concat_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.003s)
2023-01-11T20:48:55.6144295Z   test_map_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6144778Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6144988Z ok (0.011s)
2023-01-11T20:48:55.6145273Z   test_sequence_wrapper_datapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.003s)
2023-01-11T20:48:55.6145739Z   test_serializable (__main__.TestFunctionalMapDataPipe) ... ok (0.013s)
2023-01-11T20:48:55.6146051Z   test_serializable_with_dill (__main__.TestFunctionalMapDataPipe)
2023-01-11T20:48:55.6146337Z Only for DataPipes that take in a function as argument ... ok (0.006s)
2023-01-11T20:48:55.6146649Z   test_shuffler_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.006s)
2023-01-11T20:48:55.6146975Z   test_zip_mapdatapipe (__main__.TestFunctionalMapDataPipe) ... ok (0.003s)
2023-01-11T20:48:55.6147684Z   test_simple_traverse (__main__.TestGraph) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6148130Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6148342Z ok (0.003s)
2023-01-11T20:48:55.6148566Z   test_traverse_circular_datapipe (__main__.TestGraph) ... ok (0.002s)
2023-01-11T20:48:55.6148839Z   test_traverse_forked (__main__.TestGraph) ... ok (0.004s)
2023-01-11T20:48:55.6149089Z   test_traverse_mapdatapipe (__main__.TestGraph) ... ok (0.016s)
2023-01-11T20:48:55.6149366Z   test_traverse_mixdatapipe (__main__.TestGraph) ... ok (0.001s)
2023-01-11T20:48:55.6149644Z   test_traverse_unhashable_datapipe (__main__.TestGraph) ... ok (0.002s)
2023-01-11T20:48:55.6149997Z   test_iterdatapipe_sample_yielded_generator_function (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T20:48:55.6150448Z   test_iterdatapipe_sample_yielded_generator_function_exception (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.002s)
2023-01-11T20:48:55.6150877Z   test_iterdatapipe_sample_yielded_next (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T20:48:55.6151290Z   test_iterdatapipe_sample_yielded_next_exception (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.002s)
2023-01-11T20:48:55.6151699Z   test_iterdatapipe_sample_yielded_return_self (__main__.TestIterDataPipeCountSampleYielded) ... ok (0.001s)
2023-01-11T20:48:55.6152103Z   test_simple_snapshot_custom_non_generator (__main__.TestIterDataPipeGraphFastForward) ... ok (0.001s)
2023-01-11T20:48:55.6152491Z   test_simple_snapshot_custom_self_next (__main__.TestIterDataPipeGraphFastForward) ... ok (0.002s)
2023-01-11T20:48:55.6152849Z   test_simple_snapshot_graph (__main__.TestIterDataPipeGraphFastForward) ... ok (0.020s)
2023-01-11T20:48:55.6153685Z   test_simple_snapshot_graph_repeated (__main__.TestIterDataPipeGraphFastForward) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/iter/combining.py:298: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.
2023-01-11T20:48:55.6154267Z   "the buffer and each child DataPipe will read from the start again.", UserWarning)
2023-01-11T20:48:55.6154507Z ok (0.007s)
2023-01-11T20:48:55.6154784Z   test_simple_snapshot_graph_with_serialization (__main__.TestIterDataPipeGraphFastForward) ... ok (0.016s)
2023-01-11T20:48:55.6155174Z   test_iterdatapipe_singleton_buggy (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T20:48:55.6155622Z Buggy test case case where IterDataPipe's `__iter__` returns a new object, but also has ... ok (0.004s)
2023-01-11T20:48:55.6156008Z   test_iterdatapipe_singleton_constraint_multiple_outputs (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T20:48:55.6156420Z Testing for the case where IterDataPipe has multiple child DataPipes as outputs. ... ok (0.009s)
2023-01-11T20:48:55.6156787Z   test_iterdatapipe_singleton_generator (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T20:48:55.6157221Z Testing for the case where IterDataPipe's `__iter__` is a generator function. ... ok (0.004s)
2023-01-11T20:48:55.6157574Z   test_iterdatapipe_singleton_new_object (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T20:48:55.6158011Z Testing for the case where IterDataPipe's `__iter__` isn't a generator nor returns `self`, ... ok (0.003s)
2023-01-11T20:48:55.6158379Z   test_iterdatapipe_singleton_self_next (__main__.TestIterDataPipeSingletonConstraint)
2023-01-11T20:48:55.6158842Z Testing for the case where IterDataPipe's `__iter__` returns `self` and there is a `__next__` method ... ok (0.005s)
2023-01-11T20:48:55.6159577Z   test_demux_mux_datapipe (__main__.TestIterableDataPipeBasic) ... /opt/conda/lib/python3.7/site-packages/torch/utils/data/datapipes/utils/common.py:138: UserWarning: Local function is not supported by pickle, please use regular python function or functools.partial instead.
2023-01-11T20:48:55.6160056Z   "Local function is not supported by pickle, please use "
2023-01-11T20:48:55.6160299Z ok (0.004s)
2023-01-11T20:48:55.6160558Z   test_groupby_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.009s)
2023-01-11T20:48:55.6161006Z   test_listdirfiles_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.003s)
2023-01-11T20:48:55.6161387Z   test_listdirfilesdeterministic_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.004s)
2023-01-11T20:48:55.6161766Z   test_map_with_col_file_handle_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.006s)
2023-01-11T20:48:55.6162129Z   test_openfilesfromdisk_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.009s)
2023-01-11T20:48:55.6162479Z   test_routeddecoder_iterable_datapipe (__main__.TestIterableDataPipeBasic) ... ok (0.006s)
2023-01-11T20:48:55.6162817Z   test_spawn_lambdas_iter (__main__.TestSerialization) ... skip: no dill (0.001s)
2023-01-11T20:48:55.6163130Z   test_spawn_lambdas_map (__main__.TestSerialization) ... skip: no dill (0.001s)
2023-01-11T20:48:55.6163404Z   test_multi_sharding (__main__.TestSharding) ... ok (0.006s)
2023-01-11T20:48:55.6163670Z   test_old_dataloader (__main__.TestSharding) ... ok (0.073s)
2023-01-11T20:48:55.6163936Z   test_sharding_groups (__main__.TestSharding) ... ok (0.003s)
2023-01-11T20:48:55.6164201Z   test_sharding_length (__main__.TestSharding) ... ok (0.002s)
2023-01-11T20:48:55.6164449Z   test_simple_sharding (__main__.TestSharding) ... ok (0.004s)
2023-01-11T20:48:55.6164706Z   test_api (__main__.TestStreamWrapper) ... ok (0.001s)
2023-01-11T20:48:55.6164959Z   test_dir (__main__.TestStreamWrapper) ... ok (0.001s)
2023-01-11T20:48:55.6165205Z   test_pickle (__main__.TestStreamWrapper) ... ok (0.002s)
2023-01-11T20:48:55.6165462Z   test_repr (__main__.TestStreamWrapper) ... ok (0.001s)
2023-01-11T20:48:55.6165751Z   test_compile_time (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.005s)
2023-01-11T20:48:55.6166046Z   test_construct_time (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T20:48:55.6166419Z   test_isinstance (__main__.TestTyping) ... ok (0.001s)
2023-01-11T20:48:55.6166695Z   test_issubinstance (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.002s)
2023-01-11T20:48:55.6166968Z   test_protocol (__main__.TestTyping) ... ok (0.001s)
2023-01-11T20:48:55.6167223Z   test_reinforce (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T20:48:55.6167512Z   test_runtime (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.001s)
2023-01-11T20:48:55.6167805Z   test_subtype (__main__.TestTyping) ... skip: TODO: Fix typing bug (0.002s)
2023-01-11T20:48:55.6167967Z 
2023-01-11T20:48:55.6168161Z ----------------------------------------------------------------------
2023-01-11T20:48:55.6168401Z Ran 89 tests in 1.046s
2023-01-11T20:48:55.6168590Z 
2023-01-11T20:48:55.6168662Z OK (skipped=16)
2023-01-11T20:48:55.6168772Z 
2023-01-11T20:48:55.6168855Z Generating XML reports...
2023-01-11T20:48:55.6169285Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestCircularSerialization-20230111204853.xml
2023-01-11T20:48:55.6169815Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestDataChunk-20230111204853.xml
2023-01-11T20:48:55.6170356Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestFunctionalIterDataPipe-20230111204853.xml
2023-01-11T20:48:55.6170909Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestFunctionalMapDataPipe-20230111204853.xml
2023-01-11T20:48:55.6171423Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestGraph-20230111204853.xml
2023-01-11T20:48:55.6171973Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeCountSampleYielded-20230111204853.xml
2023-01-11T20:48:55.6172591Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeGraphFastForward-20230111204853.xml
2023-01-11T20:48:55.6173245Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeSingletonConstraint-20230111204853.xml
2023-01-11T20:48:55.6173832Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestIterableDataPipeBasic-20230111204853.xml
2023-01-11T20:48:55.6174346Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestSharding-20230111204853.xml
2023-01-11T20:48:55.6174841Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestStreamWrapper-20230111204853.xml
2023-01-11T20:48:55.6175317Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestTyping-20230111204853.xml
2023-01-11T20:48:55.6175815Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestCaptureDataFrame-20230111204853.xml
2023-01-11T20:48:55.6176338Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestDataFramesPipes-20230111204853.xml
2023-01-11T20:48:55.6176936Z Generated XML report: test-reports/python-unittest/test_datapipe/TEST-TestSerialization-20230111204853.xml
2023-01-11T20:48:55.6177234Z 
2023-01-11T20:48:55.6177541Z ##[endgroup]
2023-01-11T20:48:55.6177926Z FINISHED PRINTING LOG FILE of test_datapipe (/var/lib/jenkins/workspace/test/test-reports/test_datapipe_nvv47c7m)
2023-01-11T20:48:55.6178144Z 
2023-01-11T20:49:00.6778674Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:49:00.8408032Z Ignoring disabled issues:  ['91003']
2023-01-11T20:49:00.8608238Z Running test_autocast ... [2023-01-11 20:49:00.860564]
2023-01-11T20:49:00.8611224Z Executing ['/opt/conda/bin/python', '-bb', 'test_autocast.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:49:00.860896]
2023-01-11T20:49:06.3065537Z 
2023-01-11T20:49:06.3066034Z Expand the folded group to see the log file of test_autocast
2023-01-11T20:49:06.3066787Z ##[group]PRINTING LOG FILE of test_autocast (/var/lib/jenkins/workspace/test/test-reports/test_autocast_y9xc83rh)
2023-01-11T20:49:06.3067420Z Test results will be stored in test-reports/python-unittest/test_autocast
2023-01-11T20:49:06.3067600Z 
2023-01-11T20:49:06.3067680Z Running tests...
2023-01-11T20:49:06.3067989Z ----------------------------------------------------------------------
2023-01-11T20:49:06.3068366Z   test_autocast_methods_expect_builtin_promote (__main__.TestAutocastCPU) ... ok (0.360s)
2023-01-11T20:49:06.3068675Z   test_autocast_nn_bf16 (__main__.TestAutocastCPU) ... ok (0.013s)
2023-01-11T20:49:06.3069005Z   test_autocast_nn_fp32 (__main__.TestAutocastCPU) ... ok (0.039s)
2023-01-11T20:49:06.3069282Z   test_autocast_torch_bf16 (__main__.TestAutocastCPU) ... ok (0.036s)
2023-01-11T20:49:06.3069574Z   test_autocast_torch_expect_builtin_promote (__main__.TestAutocastCPU) ... ok (0.014s)
2023-01-11T20:49:06.3069932Z   test_autocast_torch_fp32 (__main__.TestAutocastCPU) ... ok (0.447s)
2023-01-11T20:49:06.3070476Z   test_autocast_torch_need_autocast_promote (__main__.TestAutocastCPU) ... ok (0.012s)
2023-01-11T20:49:06.3070766Z   test_cast_cache_is_global (__main__.TestAutocastGPU)
2023-01-11T20:49:06.3071069Z Verifies that the autocast cache is global. This is done by ... skip: requires cuda (0.002s)
2023-01-11T20:49:06.3071443Z   test_autocast_fast_dtype (__main__.TestTorchAutocast) ... ok (0.001s)
2023-01-11T20:49:06.3071607Z 
2023-01-11T20:49:06.3071812Z ----------------------------------------------------------------------
2023-01-11T20:49:06.3072090Z Ran 9 tests in 0.926s
2023-01-11T20:49:06.3072205Z 
2023-01-11T20:49:06.3072277Z OK (skipped=1)
2023-01-11T20:49:06.3072381Z 
2023-01-11T20:49:06.3072464Z Generating XML reports...
2023-01-11T20:49:06.3072928Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestAutocastCPU-20230111204904.xml
2023-01-11T20:49:06.3073438Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestTorchAutocast-20230111204904.xml
2023-01-11T20:49:06.3074008Z Generated XML report: test-reports/python-unittest/test_autocast/TEST-TestAutocastGPU-20230111204904.xml
2023-01-11T20:49:06.3074231Z 
2023-01-11T20:49:06.3074512Z ##[endgroup]
2023-01-11T20:49:06.3074961Z FINISHED PRINTING LOG FILE of test_autocast (/var/lib/jenkins/workspace/test/test-reports/test_autocast_y9xc83rh)
2023-01-11T20:49:06.3075240Z 
2023-01-11T20:49:11.4260068Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:49:11.5966364Z Ignoring disabled issues:  ['91003']
2023-01-11T20:49:11.6166772Z Running test_native_functions ... [2023-01-11 20:49:11.616300]
2023-01-11T20:49:11.6167910Z Executing ['/opt/conda/bin/python', '-bb', 'test_native_functions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:49:11.616571]
2023-01-11T20:49:17.2291005Z 
2023-01-11T20:49:17.2291643Z Expand the folded group to see the log file of test_native_functions
2023-01-11T20:49:17.2292755Z ##[group]PRINTING LOG FILE of test_native_functions (/var/lib/jenkins/workspace/test/test-reports/test_native_functions_9wge8wsv)
2023-01-11T20:49:17.2293518Z Test results will be stored in test-reports/python-unittest/test_native_functions
2023-01-11T20:49:17.2293701Z 
2023-01-11T20:49:17.2293781Z Running tests...
2023-01-11T20:49:17.2294084Z ----------------------------------------------------------------------
2023-01-11T20:49:17.2294398Z   test_intlist_error_with_overload (__main__.TestNativeFunctions) ... ok (0.359s)
2023-01-11T20:49:17.2294902Z   test_optional_filled_intlist (__main__.TestNativeFunctions) ... pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2295355Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2295758Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2296153Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2296544Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2296944Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2297302Z pad(): argument 'pad' (position 2) must be tuple of ints, not str
2023-01-11T20:49:17.2297505Z ok (0.383s)
2023-01-11T20:49:17.2297741Z   test_optional_floatlist (__main__.TestNativeFunctions) ... ok (0.038s)
2023-01-11T20:49:17.2298047Z   test_optional_floatlist_invalid (__main__.TestNativeFunctions) ... ok (0.021s)
2023-01-11T20:49:17.2298336Z   test_optional_intlist (__main__.TestNativeFunctions) ... ok (0.037s)
2023-01-11T20:49:17.2298630Z   test_optional_intlist_invalid (__main__.TestNativeFunctions) ... ok (0.018s)
2023-01-11T20:49:17.2298926Z   test_string_defaults (__main__.TestNativeFunctions) ... ok (0.015s)
2023-01-11T20:49:17.2299408Z   test_symintlist_error (__main__.TestNativeFunctions) ... pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2300111Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2300514Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2300921Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2301298Z pad(): argument 'pad' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2301692Z pad(): argument 'pad' (position 2) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2302050Z pad(): argument 'pad' (position 2) must be tuple of ints, not str
2023-01-11T20:49:17.2302266Z ok (0.032s)
2023-01-11T20:49:17.2302696Z   test_symintlist_error_with_overload (__main__.TestNativeFunctions) ... view() received an invalid combination of arguments - got (tuple), but expected one of:
2023-01-11T20:49:17.2303017Z  * (torch.dtype dtype)
2023-01-11T20:49:17.2303342Z       didn't match because some of the arguments have invalid types: (!tuple of (str,)!)
2023-01-11T20:49:17.2303571Z  * (tuple of ints size)
2023-01-11T20:49:17.2303951Z       didn't match because some of the arguments have invalid types: (!tuple of (str,)!)
2023-01-11T20:49:17.2304130Z 
2023-01-11T20:49:17.2304344Z view(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2304730Z view() received an invalid combination of arguments - got (tuple), but expected one of:
2023-01-11T20:49:17.2304969Z  * (torch.dtype dtype)
2023-01-11T20:49:17.2305293Z       didn't match because some of the arguments have invalid types: (!tuple of (str, int)!)
2023-01-11T20:49:17.2305541Z  * (tuple of ints size)
2023-01-11T20:49:17.2305855Z       didn't match because some of the arguments have invalid types: (!tuple of (str, int)!)
2023-01-11T20:49:17.2306032Z 
2023-01-11T20:49:17.2306249Z view() received an invalid combination of arguments - got (list), but expected one of:
2023-01-11T20:49:17.2306500Z  * (torch.dtype dtype)
2023-01-11T20:49:17.2306819Z       didn't match because some of the arguments have invalid types: (!list of [str]!)
2023-01-11T20:49:17.2307052Z  * (tuple of ints size)
2023-01-11T20:49:17.2307359Z       didn't match because some of the arguments have invalid types: (!list of [str]!)
2023-01-11T20:49:17.2307528Z 
2023-01-11T20:49:17.2307738Z view(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2308106Z view() received an invalid combination of arguments - got (list), but expected one of:
2023-01-11T20:49:17.2308357Z  * (torch.dtype dtype)
2023-01-11T20:49:17.2308678Z       didn't match because some of the arguments have invalid types: (!list of [str, int]!)
2023-01-11T20:49:17.2308912Z  * (tuple of ints size)
2023-01-11T20:49:17.2309235Z       didn't match because some of the arguments have invalid types: (!list of [str, int]!)
2023-01-11T20:49:17.2309411Z 
2023-01-11T20:49:17.2309628Z view() received an invalid combination of arguments - got (str), but expected one of:
2023-01-11T20:49:17.2309873Z  * (torch.dtype dtype)
2023-01-11T20:49:17.2310163Z       didn't match because some of the arguments have invalid types: (!str!)
2023-01-11T20:49:17.2310398Z  * (tuple of ints size)
2023-01-11T20:49:17.2310696Z       didn't match because some of the arguments have invalid types: (!str!)
2023-01-11T20:49:17.2310860Z 
2023-01-11T20:49:17.2310913Z ok (0.031s)
2023-01-11T20:49:17.2311393Z   test_symintlist_error_with_overload_but_is_unique (__main__.TestNativeFunctions) ... set_() received an invalid combination of arguments - got (Tensor, int, tuple), but expected one of:
2023-01-11T20:49:17.2311718Z  * ()
2023-01-11T20:49:17.2311882Z  * (torch.Storage source)
2023-01-11T20:49:17.2312130Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2312370Z  * (Tensor source)
2023-01-11T20:49:17.2312609Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2312820Z 
2023-01-11T20:49:17.2313020Z set_(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2313440Z set_() received an invalid combination of arguments - got (Tensor, int, tuple), but expected one of:
2023-01-11T20:49:17.2313692Z  * ()
2023-01-11T20:49:17.2313846Z  * (torch.Storage source)
2023-01-11T20:49:17.2314107Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2314344Z  * (Tensor source)
2023-01-11T20:49:17.2314581Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2314735Z 
2023-01-11T20:49:17.2314973Z set_() received an invalid combination of arguments - got (Tensor, int, list), but expected one of:
2023-01-11T20:49:17.2315217Z  * ()
2023-01-11T20:49:17.2315380Z  * (torch.Storage source)
2023-01-11T20:49:17.2315624Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2315863Z  * (Tensor source)
2023-01-11T20:49:17.2316103Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2316264Z 
2023-01-11T20:49:17.2316505Z set_(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2316901Z set_() received an invalid combination of arguments - got (Tensor, int, list), but expected one of:
2023-01-11T20:49:17.2317142Z  * ()
2023-01-11T20:49:17.2317305Z  * (torch.Storage source)
2023-01-11T20:49:17.2317550Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2317789Z  * (Tensor source)
2023-01-11T20:49:17.2318026Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2318191Z 
2023-01-11T20:49:17.2318416Z set_() received an invalid combination of arguments - got (Tensor, int, str), but expected one of:
2023-01-11T20:49:17.2318664Z  * ()
2023-01-11T20:49:17.2318839Z  * (torch.Storage source)
2023-01-11T20:49:17.2319098Z  * (torch.Storage source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2319322Z  * (Tensor source)
2023-01-11T20:49:17.2319564Z  * (Tensor source, int storage_offset, tuple of ints size, tuple of ints stride)
2023-01-11T20:49:17.2319727Z 
2023-01-11T20:49:17.2319791Z ok (0.032s)
2023-01-11T20:49:17.2320217Z   test_vararg_symintlist_error (__main__.TestNativeFunctions) ... rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2320853Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2321256Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2321666Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2322042Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2322443Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2322941Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2323337Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2323720Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2324112Z rand() received an invalid combination of arguments - got (str, int), but expected one of:
2023-01-11T20:49:17.2324539Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2325018Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2325544Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2325980Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2326212Z 
2023-01-11T20:49:17.2326445Z rand(): argument 'size' (position 1) must be tuple of ints, but found element of type str at pos 0
2023-01-11T20:49:17.2326821Z rand(): argument 'size' must be tuple of ints, but found element of type str at pos 2
2023-01-11T20:49:17.2327207Z rand() received an invalid combination of arguments - got (str, int), but expected one of:
2023-01-11T20:49:17.2327633Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2328121Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2328597Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2329025Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2329256Z 
2023-01-11T20:49:17.2329491Z rand() received an invalid combination of arguments - got (str, str, str), but expected one of:
2023-01-11T20:49:17.2329913Z  * (tuple of ints size, *, torch.Generator generator, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2330399Z  * (tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2330829Z  * (tuple of ints size, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2331252Z  * (tuple of ints size, *, tuple of names names, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
2023-01-11T20:49:17.2331483Z 
2023-01-11T20:49:17.2331547Z ok (0.075s)
2023-01-11T20:49:17.2331648Z 
2023-01-11T20:49:17.2331848Z ----------------------------------------------------------------------
2023-01-11T20:49:17.2332076Z Ran 11 tests in 1.042s
2023-01-11T20:49:17.2332186Z 
2023-01-11T20:49:17.2332244Z OK
2023-01-11T20:49:17.2332332Z 
2023-01-11T20:49:17.2332414Z Generating XML reports...
2023-01-11T20:49:17.2332832Z Generated XML report: test-reports/python-unittest/test_native_functions/TEST-TestNativeFunctions-20230111204915.xml
2023-01-11T20:49:17.2333077Z 
2023-01-11T20:49:17.2333358Z ##[endgroup]
2023-01-11T20:49:17.2333754Z FINISHED PRINTING LOG FILE of test_native_functions (/var/lib/jenkins/workspace/test/test-reports/test_native_functions_9wge8wsv)
2023-01-11T20:49:17.2333981Z 
2023-01-11T20:49:22.0765412Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:49:22.2371746Z Ignoring disabled issues:  ['91003']
2023-01-11T20:49:22.2573441Z Running benchmark_utils/test_benchmark_utils ... [2023-01-11 20:49:22.257050]
2023-01-11T20:49:22.2575521Z Executing ['/opt/conda/bin/python', '-bb', 'benchmark_utils/test_benchmark_utils.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:49:22.257323]
2023-01-11T20:49:28.0589651Z 
2023-01-11T20:49:28.0590230Z Expand the folded group to see the log file of benchmark_utils/test_benchmark_utils
2023-01-11T20:49:28.0591182Z ##[group]PRINTING LOG FILE of benchmark_utils/test_benchmark_utils (/var/lib/jenkins/workspace/test/test-reports/benchmark_utils-test_benchmark_utils_d89f9_3e)
2023-01-11T20:49:28.0591978Z Test results will be stored in test-reports/python-unittest/benchmark_utils.test_benchmark_utils
2023-01-11T20:49:28.0592872Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:49:28.0593023Z 
2023-01-11T20:49:28.0593083Z Running tests...
2023-01-11T20:49:28.0593387Z ----------------------------------------------------------------------
2023-01-11T20:49:28.0593887Z   test_adaptive_timer (__main__.TestBenchmarkUtils) ... [W ParallelNative.cpp:230] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
2023-01-11T20:49:28.0594493Z [W ParallelNative.cpp:230] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
2023-01-11T20:49:28.0595033Z [W ParallelNative.cpp:230] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
2023-01-11T20:49:28.0595365Z ok (0.311s)
2023-01-11T20:49:28.0595661Z   test_collect_callgrind (__main__.TestBenchmarkUtils) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T20:49:28.0596120Z   test_collect_cpp_callgrind (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.001s)
2023-01-11T20:49:28.0596428Z   test_compare (__main__.TestBenchmarkUtils) ... ok (0.347s)
2023-01-11T20:49:28.0596716Z   test_cpp_timer (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.001s)
2023-01-11T20:49:28.0597007Z   test_fuzzer (__main__.TestBenchmarkUtils) ... ok (0.007s)
2023-01-11T20:49:28.0597296Z   test_manipulate_callgrind_stats (__main__.TestBenchmarkUtils) ... ok (0.085s)
2023-01-11T20:49:28.0597569Z   test_timer (__main__.TestBenchmarkUtils) ... ok (0.082s)
2023-01-11T20:49:28.0597876Z   test_timer_tiny_fast_snippet (__main__.TestBenchmarkUtils) ... skip: Failing on clang, see 74398 (0.001s)
2023-01-11T20:49:28.0598070Z 
2023-01-11T20:49:28.0598271Z ----------------------------------------------------------------------
2023-01-11T20:49:28.0598507Z Ran 9 tests in 0.838s
2023-01-11T20:49:28.0598606Z 
2023-01-11T20:49:28.0598674Z OK (skipped=4)
2023-01-11T20:49:28.0598780Z 
2023-01-11T20:49:28.0598862Z Generating XML reports...
2023-01-11T20:49:28.0599328Z Generated XML report: test-reports/python-unittest/benchmark_utils.test_benchmark_utils/TEST-TestBenchmarkUtils-20230111204926.xml
2023-01-11T20:49:28.0599586Z 
2023-01-11T20:49:28.0599804Z ##[endgroup]
2023-01-11T20:49:28.0600249Z FINISHED PRINTING LOG FILE of benchmark_utils/test_benchmark_utils (/var/lib/jenkins/workspace/test/test-reports/benchmark_utils-test_benchmark_utils_d89f9_3e)
2023-01-11T20:49:28.0600496Z 
2023-01-11T20:49:32.8524809Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:49:33.0140204Z Ignoring disabled issues:  ['91003']
2023-01-11T20:49:33.0354825Z Running test_fx_passes ... [2023-01-11 20:49:33.035153]
2023-01-11T20:49:33.0357241Z Executing ['/opt/conda/bin/python', '-bb', 'test_fx_passes.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:49:33.035456]
2023-01-11T20:49:38.2917589Z 
2023-01-11T20:49:38.2918172Z Expand the folded group to see the log file of test_fx_passes
2023-01-11T20:49:38.2919175Z ##[group]PRINTING LOG FILE of test_fx_passes (/var/lib/jenkins/workspace/test/test-reports/test_fx_passes_vado6vee)
2023-01-11T20:49:38.2919741Z Test results will be stored in test-reports/python-unittest/test_fx_passes
2023-01-11T20:49:38.2919909Z 
2023-01-11T20:49:38.2919983Z Running tests...
2023-01-11T20:49:38.2920288Z ----------------------------------------------------------------------
2023-01-11T20:49:38.2920594Z   test_fuser_pass_deep_model (__main__.TestFXGraphPasses) ... ok (0.406s)
2023-01-11T20:49:38.2921098Z   test_fuser_util_partition_[['add', 'add_1', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.024s)
2023-01-11T20:49:38.2921526Z   test_fuser_util_partition_[['add', 'add_1'], ['add_5', 'add_6']] (__main__.TestFXGraphPasses) ... ok (0.011s)
2023-01-11T20:49:38.2922271Z   test_fuser_util_partition_[['add', 'linear', 'add_1', 'param', 'add_2', 'add_3', 'add_4', 'linear2', 'add_5', 'add_6', 'relu']] (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2922726Z   test_fuser_util_partition_[['add_2', 'add_3']] (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2923107Z   test_fuser_util_partition_[['add_3', 'add_4']] (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2923522Z   test_fuser_util_partition_[['add_4', 'add_1', 'add_3', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2923974Z   test_fuser_util_partition_[['add_5', 'add_6'], ['add_1', 'add_2', 'add_3', 'add_4']] (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2924392Z   test_fuser_util_partition_[['add_5', 'linear2']] (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2924771Z   test_fuser_util_partition_[['add_6', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2925158Z   test_fuser_util_partition_[['add_6', 'relu']] (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2925621Z   test_fuser_util_partition_[['param', 'add_1', 'linear']] (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2926015Z   test_fuser_util_partition_[['param', 'add_2']] (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2926433Z   test_fuser_util_xfail_partition_[['add', 'add_1', 'add_3']] (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T20:49:38.2926878Z   test_fuser_util_xfail_partition_[['add', 'add_1'], ['add_1', 'add_5', 'add_6']] (__main__.TestFXGraphPasses) ... ok (0.005s)
2023-01-11T20:49:38.2927297Z   test_fuser_util_xfail_partition_[['add_4', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T20:49:38.2927692Z   test_fuser_util_xfail_partition_[['relu', 'add_5']] (__main__.TestFXGraphPasses) ... ok (0.004s)
2023-01-11T20:49:38.2928338Z   test_partitioner_fn_<function TestPartitionFunctions_forward1 at 0x7f23e255d290>_expected_partition_[['add_7', 'add_6'], ['add_5', 'add_4', 'add_3'], ['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2929070Z   test_partitioner_fn_<function TestPartitionFunctions_forward10 at 0x7f23e251cc20>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.019s)
2023-01-11T20:49:38.2929752Z   test_partitioner_fn_<function TestPartitionFunctions_forward11 at 0x7f23e251ccb0>_expected_partition_[['add_1'], ['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2930463Z   test_partitioner_fn_<function TestPartitionFunctions_forward12 at 0x7f23e251cd40>_expected_partition_[['add_2'], ['add_3', 'add_4', 'add_1'], ['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2931161Z   test_partitioner_fn_<function TestPartitionFunctions_forward13 at 0x7f23e251cdd0>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.008s)
2023-01-11T20:49:38.2931863Z   test_partitioner_fn_<function TestPartitionFunctions_forward14 at 0x7f23e251ce60>_expected_partition_[['add', 'std_mean', 'getitem', 'getitem_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.008s)
2023-01-11T20:49:38.2932639Z   test_partitioner_fn_<function TestPartitionFunctions_forward15 at 0x7f23e251cef0>_expected_partition_[['add_1', 'add', 'permute_1', 'view', 'permute_2', 'permute_3', 'permute']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.020s)
2023-01-11T20:49:38.2933369Z   test_partitioner_fn_<function TestPartitionFunctions_forward15 at 0x7f23e251cef0>_expected_partition_[['permute_1', 'add_1', 'add']]_bookend_non_compute_pass_True (__main__.TestFXGraphPasses) ... ok (0.009s)
2023-01-11T20:49:38.2934120Z   test_partitioner_fn_<function TestPartitionFunctions_forward16 at 0x7f23e251cf80>_expected_partition_[['add_1', 'add', 'permute_1', 'view', 'permute_2', 'permute_3', 'permute']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2934885Z   test_partitioner_fn_<function TestPartitionFunctions_forward16 at 0x7f23e251cf80>_expected_partition_[['permute_1', 'add_1', 'add']]_bookend_non_compute_pass_True (__main__.TestFXGraphPasses) ... ok (0.012s)
2023-01-11T20:49:38.2935570Z   test_partitioner_fn_<function TestPartitionFunctions_forward2 at 0x7f23e25604d0>_expected_partition_[['add_3', 'add_2'], ['add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.012s)
2023-01-11T20:49:38.2936251Z   test_partitioner_fn_<function TestPartitionFunctions_forward3 at 0x7f23e25600e0>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.011s)
2023-01-11T20:49:38.2936934Z   test_partitioner_fn_<function TestPartitionFunctions_forward4 at 0x7f23e251c8c0>_expected_partition_[['add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.011s)
2023-01-11T20:49:38.2937631Z   test_partitioner_fn_<function TestPartitionFunctions_forward5 at 0x7f23e251c950>_expected_partition_[['add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.011s)
2023-01-11T20:49:38.2938291Z   test_partitioner_fn_<function TestPartitionFunctions_forward6 at 0x7f23e251c9e0>_expected_partition_[['add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2938973Z   test_partitioner_fn_<function TestPartitionFunctions_forward7 at 0x7f23e251ca70>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.011s)
2023-01-11T20:49:38.2939648Z   test_partitioner_fn_<function TestPartitionFunctions_forward8 at 0x7f23e251cb00>_expected_partition_[['add_3', 'add_2', 'add', 'add_1']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.010s)
2023-01-11T20:49:38.2940421Z   test_partitioner_fn_<function TestPartitionFunctions_forward9 at 0x7f23e251cb90>_expected_partition_[['add_3', 'add_2', 'add_1', 'add']]_bookend_non_compute_pass_False (__main__.TestFXGraphPasses) ... ok (0.011s)
2023-01-11T20:49:38.2940987Z   test_subgraph_matcher_test_model_<class '__main___ChainRepeatedPattern'> (__main__.TestFXMatcherUtils) ... ok (0.007s)
2023-01-11T20:49:38.2941499Z   test_subgraph_matcher_test_model_<class '__main___DiamondShapePatternTestCase'> (__main__.TestFXMatcherUtils) ... ok (0.007s)
2023-01-11T20:49:38.2942021Z   test_subgraph_matcher_test_model_<class '__main___MultiOutputWithWithInvalidMatches'> (__main__.TestFXMatcherUtils) ... ok (0.008s)
2023-01-11T20:49:38.2942558Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsHorizontalPattern'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T20:49:38.2943095Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsIdenticalAnchor'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T20:49:38.2943672Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsMultipleNonOverlappingMatches'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T20:49:38.2944255Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsMultipleOverlappingMatches'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T20:49:38.2944809Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsWithDependency'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T20:49:38.2945338Z   test_subgraph_matcher_test_model_<class '__main___MultipleOutputsWithoutDependency'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T20:49:38.2945829Z   test_subgraph_matcher_test_model_<class '__main___NoAnchorFound'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T20:49:38.2946298Z   test_subgraph_matcher_test_model_<class '__main___NonFullyContainedMatches'> (__main__.TestFXMatcherUtils) ... ok (0.006s)
2023-01-11T20:49:38.2946833Z   test_subgraph_matcher_test_model_<class '__main___QuantizationFp8Pattern'> (__main__.TestFXMatcherUtils) ... ok (0.007s)
2023-01-11T20:49:38.2947316Z   test_subgraph_matcher_test_model_<class '__main___QuantizationModel'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T20:49:38.2947801Z   test_subgraph_matcher_test_model_<class '__main___SimpleFullGraphMatching'> (__main__.TestFXMatcherUtils) ... ok (0.005s)
2023-01-11T20:49:38.2948259Z   test_subgraph_matcher_test_model_<class '__main___SimplePattern'> (__main__.TestFXMatcherUtils) ... ok (0.006s)
2023-01-11T20:49:38.2948720Z   test_subgraph_matcher_test_model_<class '__main___SingleNodePattern'> (__main__.TestFXMatcherUtils) ... ok (0.004s)
2023-01-11T20:49:38.2948926Z 
2023-01-11T20:49:38.2949123Z ----------------------------------------------------------------------
2023-01-11T20:49:38.2949360Z Ran 51 tests in 0.846s
2023-01-11T20:49:38.2949460Z 
2023-01-11T20:49:38.2949520Z OK
2023-01-11T20:49:38.2949612Z 
2023-01-11T20:49:38.2949697Z Generating XML reports...
2023-01-11T20:49:38.2950106Z Generated XML report: test-reports/python-unittest/test_fx_passes/TEST-TestFXGraphPasses-20230111204936.xml
2023-01-11T20:49:38.2950646Z Generated XML report: test-reports/python-unittest/test_fx_passes/TEST-TestFXMatcherUtils-20230111204936.xml
2023-01-11T20:49:38.2950875Z 
2023-01-11T20:49:38.2951134Z ##[endgroup]
2023-01-11T20:49:38.2951510Z FINISHED PRINTING LOG FILE of test_fx_passes (/var/lib/jenkins/workspace/test/test-reports/test_fx_passes_vado6vee)
2023-01-11T20:49:38.2951719Z 
2023-01-11T20:49:43.1242426Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:49:43.2915435Z Ignoring disabled issues:  ['91003']
2023-01-11T20:49:43.3119427Z Running test_monitor ... [2023-01-11 20:49:43.311569]
2023-01-11T20:49:43.3120499Z Executing ['/opt/conda/bin/python', '-bb', 'test_monitor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:49:43.311842]
2023-01-11T20:49:48.2999523Z 
2023-01-11T20:49:48.3000017Z Expand the folded group to see the log file of test_monitor
2023-01-11T20:49:48.3001171Z ##[group]PRINTING LOG FILE of test_monitor (/var/lib/jenkins/workspace/test/test-reports/test_monitor_eynq69bh)
2023-01-11T20:49:48.3002036Z Test results will be stored in test-reports/python-unittest/test_monitor
2023-01-11T20:49:48.3002341Z 
2023-01-11T20:49:48.3002441Z Running tests...
2023-01-11T20:49:48.3002948Z ----------------------------------------------------------------------
2023-01-11T20:49:48.3003395Z   test_event_handler (__main__.TestMonitor) ... ok (0.332s)
2023-01-11T20:49:48.3004218Z   test_fixed_count_stat (__main__.TestMonitor) ... ok (0.002s)
2023-01-11T20:49:48.3004636Z   test_interval_stat (__main__.TestMonitor) ... ok (0.002s)
2023-01-11T20:49:48.3005094Z   test_log_event (__main__.TestMonitor) ... ok (0.001s)
2023-01-11T20:49:48.3005608Z   test_event_handler (__main__.TestMonitorTensorboard) ... ok (0.304s)
2023-01-11T20:49:48.3005931Z 
2023-01-11T20:49:48.3006293Z ----------------------------------------------------------------------
2023-01-11T20:49:48.3006762Z Ran 5 tests in 0.642s
2023-01-11T20:49:48.3006981Z 
2023-01-11T20:49:48.3009256Z OK
2023-01-11T20:49:48.3009500Z 
2023-01-11T20:49:48.3009681Z Generating XML reports...
2023-01-11T20:49:48.3010490Z Generated XML report: test-reports/python-unittest/test_monitor/TEST-TestMonitor-20230111204947.xml
2023-01-11T20:49:48.3011465Z Generated XML report: test-reports/python-unittest/test_monitor/TEST-TestMonitorTensorboard-20230111204947.xml
2023-01-11T20:49:48.3011933Z 
2023-01-11T20:49:48.3012470Z ##[endgroup]
2023-01-11T20:49:48.3013184Z FINISHED PRINTING LOG FILE of test_monitor (/var/lib/jenkins/workspace/test/test-reports/test_monitor_eynq69bh)
2023-01-11T20:49:48.3013588Z 
2023-01-11T20:49:53.0469771Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:49:53.2069784Z Ignoring disabled issues:  ['91003']
2023-01-11T20:49:53.2285473Z Running dynamo/test_global ... [2023-01-11 20:49:53.226887]
2023-01-11T20:49:53.2286224Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_global.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:49:53.227147]
2023-01-11T20:49:58.3530358Z 
2023-01-11T20:49:58.3530916Z Expand the folded group to see the log file of dynamo/test_global
2023-01-11T20:49:58.3532332Z ##[group]PRINTING LOG FILE of dynamo/test_global (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_njfk0qqm)
2023-01-11T20:49:58.3532879Z Test results will be stored in test-reports/python-unittest/dynamo.test_global
2023-01-11T20:49:58.3533126Z 
2023-01-11T20:49:58.3533201Z Running tests...
2023-01-11T20:49:58.3533512Z ----------------------------------------------------------------------
2023-01-11T20:49:58.3533858Z   test_store_global_1 (__main__.TestGlobals) ... ok (0.484s)
2023-01-11T20:49:58.3534195Z   test_store_global_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3534602Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3534903Z frames [('total', 2), ('ok', 2)]
2023-01-11T20:49:58.3535200Z stats [('calls_captured', 2), ('unique_graphs', 2), ('fusions_possible', 0)]
2023-01-11T20:49:58.3535487Z ok (0.015s)
2023-01-11T20:49:58.3535998Z   test_store_global_cross_file (__main__.TestGlobals) ... frames [('total', 2), ('ok', 2)]
2023-01-11T20:49:58.3536301Z unimplemented []
2023-01-11T20:49:58.3536715Z graph_break [('call_function BuiltinVariable(setattr) [PythonModuleVariable(), ConstantVariable(str), TensorVariable()] {}', 1)]
2023-01-11T20:49:58.3537200Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T20:49:58.3537424Z ok (0.013s)
2023-01-11T20:49:58.3537766Z   test_store_global_dict (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3542002Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3542270Z ok (0.009s)
2023-01-11T20:49:58.3542755Z   test_store_global_dict_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3543133Z inline_call []
2023-01-11T20:49:58.3543531Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3543754Z ok (0.010s)
2023-01-11T20:49:58.3544060Z   test_store_global_inline_1 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3544278Z inline_call []
2023-01-11T20:49:58.3544573Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3544793Z ok (0.014s)
2023-01-11T20:49:58.3545081Z   test_store_global_inline_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3545317Z inline_call []
2023-01-11T20:49:58.3545605Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3545823Z ok (0.015s)
2023-01-11T20:49:58.3546106Z   test_store_global_list (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3546456Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3546676Z ok (0.009s)
2023-01-11T20:49:58.3547081Z   test_store_global_list_2 (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3547315Z inline_call []
2023-01-11T20:49:58.3547646Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3547854Z ok (0.011s)
2023-01-11T20:49:58.3548154Z   test_store_global_new (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3548505Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T20:49:58.3548722Z ok (0.008s)
2023-01-11T20:49:58.3549008Z   test_store_global_object (__main__.TestGlobals) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:49:58.3562177Z stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:49:58.3562784Z ok (0.008s)
2023-01-11T20:49:58.3562972Z 
2023-01-11T20:49:58.3563372Z ----------------------------------------------------------------------
2023-01-11T20:49:58.3563847Z Ran 11 tests in 0.598s
2023-01-11T20:49:58.3563962Z 
2023-01-11T20:49:58.3564009Z OK
2023-01-11T20:49:58.3564098Z 
2023-01-11T20:49:58.3564183Z Generating XML reports...
2023-01-11T20:49:58.3564602Z Generated XML report: test-reports/python-unittest/dynamo.test_global/TEST-TestGlobals-20230111204957.xml
2023-01-11T20:49:58.3564828Z 
2023-01-11T20:49:58.3565166Z ##[endgroup]
2023-01-11T20:49:58.3565547Z FINISHED PRINTING LOG FILE of dynamo/test_global (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_global_njfk0qqm)
2023-01-11T20:49:58.3565771Z 
2023-01-11T20:50:03.1543560Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:50:03.3596379Z Ignoring disabled issues:  ['91003']
2023-01-11T20:50:03.3893141Z Running dynamo/test_comptime ... [2023-01-11 20:50:03.388863]
2023-01-11T20:50:03.3894101Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_comptime.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:50:03.389191]
2023-01-11T20:50:08.4665892Z 
2023-01-11T20:50:08.4666527Z Expand the folded group to see the log file of dynamo/test_comptime
2023-01-11T20:50:08.4669026Z ##[group]PRINTING LOG FILE of dynamo/test_comptime (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_comptime_1g1g732f)
2023-01-11T20:50:08.4671676Z Test results will be stored in test-reports/python-unittest/dynamo.test_comptime
2023-01-11T20:50:08.4672026Z 
2023-01-11T20:50:08.4672162Z Running tests...
2023-01-11T20:50:08.4672688Z ----------------------------------------------------------------------
2023-01-11T20:50:08.4673175Z   test_get_local (__main__.ComptimeTests) ... ok (0.355s)
2023-01-11T20:50:08.4673545Z   test_graph_break (__main__.ComptimeTests) ... frames [('total', 1), ('ok', 1)]
2023-01-11T20:50:08.4673888Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:50:08.4674260Z frames [('total', 6), ('ok', 6)]
2023-01-11T20:50:08.4674767Z stats [('calls_captured', 5), ('unique_graphs', 4), ('fusions_possible', 1)]
2023-01-11T20:50:08.4675035Z unimplemented []
2023-01-11T20:50:08.4675432Z graph_break [('ComptimeContext.graph_break', 2)]
2023-01-11T20:50:08.4675992Z inline_call [('ComptimeContext.graph_break', 1)]
2023-01-11T20:50:08.4676336Z ok (0.027s)
2023-01-11T20:50:08.4676576Z   test_print_bt (__main__.ComptimeTests) ...   File "dynamo/test_comptime.py", line 152, in f
2023-01-11T20:50:08.4676822Z     y = g(y)
2023-01-11T20:50:08.4677023Z   File "dynamo/test_comptime.py", line 145, in g
2023-01-11T20:50:08.4677222Z     comptime.print_bt()
2023-01-11T20:50:08.4677338Z 
2023-01-11T20:50:08.4677455Z frames [('total', 1), ('ok', 1)]
2023-01-11T20:50:08.4677637Z inline_call []
2023-01-11T20:50:08.4677922Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:50:08.4678143Z ok (0.110s)
2023-01-11T20:50:08.4678379Z   test_print_disas (__main__.ComptimeTests) ...  54           0 LOAD_FAST                0 (x)
2023-01-11T20:50:08.4678619Z               2 LOAD_CONST               1 (2)
2023-01-11T20:50:08.4678818Z               4 BINARY_MULTIPLY
2023-01-11T20:50:08.4679015Z               6 STORE_FAST               1 (y)
2023-01-11T20:50:08.4679136Z 
2023-01-11T20:50:08.4679235Z  56           8 LOAD_GLOBAL              0 (comptime)
2023-01-11T20:50:08.4679508Z              10 LOAD_CONST               2 (<code object _ at 0x7f06bcf198a0, file "dynamo/test_comptime.py", line 56>)
2023-01-11T20:50:08.4679913Z              12 LOAD_CONST               3 ('ComptimeTests.test_print_disas.<locals>.f.<locals>._')
2023-01-11T20:50:08.4680169Z              14 MAKE_FUNCTION            0
2023-01-11T20:50:08.4680360Z              16 CALL_FUNCTION            1
2023-01-11T20:50:08.4680583Z              18 STORE_FAST               2 (_)
2023-01-11T20:50:08.4680922Z 
2023-01-11T20:50:08.4681022Z  60          20 LOAD_GLOBAL              0 (comptime)
2023-01-11T20:50:08.4681265Z              22 LOAD_METHOD              1 (print_disas)
2023-01-11T20:50:08.4681493Z              24 CALL_METHOD              0
2023-01-11T20:50:08.4681881Z     -->      26 POP_TOP
2023-01-11T20:50:08.4682039Z 
2023-01-11T20:50:08.4682175Z  62          28 LOAD_FAST                1 (y)
2023-01-11T20:50:08.4682422Z              30 LOAD_CONST               4 (3)
2023-01-11T20:50:08.4682653Z              32 BINARY_ADD
2023-01-11T20:50:08.4682928Z              34 RETURN_VALUE
2023-01-11T20:50:08.4683101Z 
2023-01-11T20:50:08.4683307Z frames [('total', 1), ('ok', 1)]
2023-01-11T20:50:08.4683633Z inline_call []
2023-01-11T20:50:08.4684183Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T20:50:08.4684581Z ok (0.011s)
2023-01-11T20:50:08.4684938Z   test_print_graph (__main__.ComptimeTests) ... 
2023-01-11T20:50:08.4685196Z 
2023-01-11T20:50:08.4685203Z 
2023-01-11T20:50:08.4685360Z def forward(self, x : torch.Tensor):
2023-01-11T20:50:08.4685666Z     # File: dynamo/test_comptime.py:26, code: y = x * 2
2023-01-11T20:50:08.4685990Z     mul = x * 2;  x = None
2023-01-11T20:50:08.4686277Z     
2023-01-11T20:50:08.4686699Z frames [('total', 1), ('ok', 1)]
2023-01-11T20:50:08.4687024Z inline_call []
2023-01-11T20:50:08.4687551Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T20:50:08.4688078Z ok (0.010s)
2023-01-11T20:50:08.4688321Z   test_print_guards (__main__.ComptimeTests) ... -
2023-01-11T20:50:08.4688569Z             local 'x' TENSOR_MATCH
2023-01-11T20:50:08.4688748Z             {
2023-01-11T20:50:08.4688960Z                 'guard_types': None,
2023-01-11T20:50:08.4689166Z                 'code': None,
2023-01-11T20:50:08.4689388Z                 'obj_weakref': None
2023-01-11T20:50:08.4689621Z                 'guarded_class': None
2023-01-11T20:50:08.4689785Z             }
2023-01-11T20:50:08.4689940Z             
2023-01-11T20:50:08.4690145Z frames [('total', 1), ('ok', 1)]
2023-01-11T20:50:08.4690315Z inline_call []
2023-01-11T20:50:08.4690607Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T20:50:08.4690836Z ok (0.010s)
2023-01-11T20:50:08.4691054Z   test_print_locals (__main__.ComptimeTests) ... x = TensorVariable()
2023-01-11T20:50:08.4691286Z y = TensorVariable()
2023-01-11T20:50:08.4691484Z _ = ConstantVariable(NoneType)
2023-01-11T20:50:08.4691712Z frames [('total', 1), ('ok', 1)]
2023-01-11T20:50:08.4691891Z inline_call []
2023-01-11T20:50:08.4692186Z stats [('calls_captured', 2), ('fusions_possible', 1), ('unique_graphs', 1)]
2023-01-11T20:50:08.4692395Z ok (0.009s)
2023-01-11T20:50:08.4692685Z   test_print_value_stack (__main__.ComptimeTests) ... - TensorVariable()
2023-01-11T20:50:08.4692963Z frames [('total', 1), ('ok', 1)]
2023-01-11T20:50:08.4693142Z inline_call []
2023-01-11T20:50:08.4693420Z stats [('calls_captured', 3), ('fusions_possible', 2), ('unique_graphs', 1)]
2023-01-11T20:50:08.4693643Z ok (0.012s)
2023-01-11T20:50:08.4693742Z 
2023-01-11T20:50:08.4693936Z ----------------------------------------------------------------------
2023-01-11T20:50:08.4694160Z Ran 8 tests in 0.546s
2023-01-11T20:50:08.4694273Z 
2023-01-11T20:50:08.4694333Z OK
2023-01-11T20:50:08.4694425Z 
2023-01-11T20:50:08.4694506Z Generating XML reports...
2023-01-11T20:50:08.4694910Z Generated XML report: test-reports/python-unittest/dynamo.test_comptime/TEST-ComptimeTests-20230111205007.xml
2023-01-11T20:50:08.4695144Z 
2023-01-11T20:50:08.4695424Z ##[endgroup]
2023-01-11T20:50:08.4695832Z FINISHED PRINTING LOG FILE of dynamo/test_comptime (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_comptime_1g1g732f)
2023-01-11T20:50:08.4696061Z 
2023-01-11T20:50:13.3747192Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:50:13.5433766Z Ignoring disabled issues:  ['91003']
2023-01-11T20:50:13.5638865Z Running dynamo/test_skip_non_tensor ... [2023-01-11 20:50:13.563465]
2023-01-11T20:50:13.5640400Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:50:13.563817]
2023-01-11T20:50:18.6135885Z 
2023-01-11T20:50:18.6136416Z Expand the folded group to see the log file of dynamo/test_skip_non_tensor
2023-01-11T20:50:18.6137385Z ##[group]PRINTING LOG FILE of dynamo/test_skip_non_tensor (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_skip_non_tensor_zyl84iag)
2023-01-11T20:50:18.6138010Z Test results will be stored in test-reports/python-unittest/dynamo.test_skip_non_tensor
2023-01-11T20:50:18.6138284Z 
2023-01-11T20:50:18.6138360Z Running tests...
2023-01-11T20:50:18.6138658Z ----------------------------------------------------------------------
2023-01-11T20:50:18.6138955Z   test_add_skip (__main__.SkipNonTensorTests) ... ok (0.343s)
2023-01-11T20:50:18.6139400Z   test_add_tensor1 (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:50:18.6139661Z ok (0.111s)
2023-01-11T20:50:18.6140103Z   test_add_tensor2 (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:50:18.6140382Z ok (0.007s)
2023-01-11T20:50:18.6140770Z   test_add_tensor_dict (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:50:18.6141033Z ok (0.007s)
2023-01-11T20:50:18.6141620Z   test_add_tensor_list (__main__.SkipNonTensorTests) ... stats [('calls_captured', 1), ('unique_graphs', 1), ('fusions_possible', 0)]
2023-01-11T20:50:18.6141898Z ok (0.007s)
2023-01-11T20:50:18.6142108Z   test_custom_list (__main__.SkipNonTensorTests) ... ok (0.002s)
2023-01-11T20:50:18.6142393Z   test_recursive_list (__main__.SkipNonTensorTests) ... ok (0.001s)
2023-01-11T20:50:18.6142553Z 
2023-01-11T20:50:18.6142752Z ----------------------------------------------------------------------
2023-01-11T20:50:18.6142998Z Ran 7 tests in 0.479s
2023-01-11T20:50:18.6143099Z 
2023-01-11T20:50:18.6143161Z OK
2023-01-11T20:50:18.6143253Z 
2023-01-11T20:50:18.6143339Z Generating XML reports...
2023-01-11T20:50:18.6143783Z Generated XML report: test-reports/python-unittest/dynamo.test_skip_non_tensor/TEST-SkipNonTensorTests-20230111205017.xml
2023-01-11T20:50:18.6144031Z 
2023-01-11T20:50:18.6144261Z ##[endgroup]
2023-01-11T20:50:18.6144687Z FINISHED PRINTING LOG FILE of dynamo/test_skip_non_tensor (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_skip_non_tensor_zyl84iag)
2023-01-11T20:50:18.6144927Z 
2023-01-11T20:50:23.4517860Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:50:23.6181950Z Ignoring disabled issues:  ['91003']
2023-01-11T20:50:23.6387619Z Running test_tensorexpr_pybind ... [2023-01-11 20:50:23.638400]
2023-01-11T20:50:23.6389823Z Executing ['/opt/conda/bin/python', '-bb', 'test_tensorexpr_pybind.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:50:23.638699]
2023-01-11T20:50:28.6024519Z 
2023-01-11T20:50:28.6025020Z Expand the folded group to see the log file of test_tensorexpr_pybind
2023-01-11T20:50:28.6026239Z ##[group]PRINTING LOG FILE of test_tensorexpr_pybind (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_pybind_0zoxvqfq)
2023-01-11T20:50:28.6027119Z Test results will be stored in test-reports/python-unittest/test_tensorexpr_pybind
2023-01-11T20:50:28.6027309Z 
2023-01-11T20:50:28.6027391Z Running tests...
2023-01-11T20:50:28.6027696Z ----------------------------------------------------------------------
2023-01-11T20:50:28.6027983Z   test_unary_ops (__main__.TestExprHandlePyBind) ... ok (0.430s)
2023-01-11T20:50:28.6028303Z   test_alloc_in_loop (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6028618Z   test_call_raw (__main__.TestTensorExprPyBind) ... ok (0.005s)
2023-01-11T20:50:28.6028890Z   test_dtype_error (__main__.TestTensorExprPyBind) ... ok (0.004s)
2023-01-11T20:50:28.6029174Z   test_dynamic_shape (__main__.TestTensorExprPyBind) ... ok (0.020s)
2023-01-11T20:50:28.6029467Z   test_dynamic_shape_2d (__main__.TestTensorExprPyBind) ... ok (0.017s)
2023-01-11T20:50:28.6029759Z   test_external_calls (__main__.TestTensorExprPyBind) ... ok (0.004s)
2023-01-11T20:50:28.6030270Z   test_kernel_shape_prop (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6030640Z   test_kernel_shape_prop_module (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.002s)
2023-01-11T20:50:28.6031014Z   test_kernel_with_custom_lowering (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6031364Z   test_kernel_with_expand (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6031722Z   test_kernel_with_permute (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6032088Z   test_kernel_with_scalar_inputs (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6032442Z   test_kernel_with_t (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6032785Z   test_kernel_with_tensor_inputs (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6033147Z   test_kernel_with_transpose (__main__.TestTensorExprPyBind) ... skip: LLVM backend not enabled (0.001s)
2023-01-11T20:50:28.6033533Z   test_simple_sum (__main__.TestTensorExprPyBind) ... ok (0.004s)
2023-01-11T20:50:28.6033696Z 
2023-01-11T20:50:28.6033898Z ----------------------------------------------------------------------
2023-01-11T20:50:28.6034124Z Ran 17 tests in 0.496s
2023-01-11T20:50:28.6034236Z 
2023-01-11T20:50:28.6034308Z OK (skipped=10)
2023-01-11T20:50:28.6034415Z 
2023-01-11T20:50:28.6034496Z Generating XML reports...
2023-01-11T20:50:28.6034928Z Generated XML report: test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestExprHandlePyBind-20230111205027.xml
2023-01-11T20:50:28.6035482Z Generated XML report: test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestTensorExprPyBind-20230111205027.xml
2023-01-11T20:50:28.6035727Z 
2023-01-11T20:50:28.6035975Z ##[endgroup]
2023-01-11T20:50:28.6036369Z FINISHED PRINTING LOG FILE of test_tensorexpr_pybind (/var/lib/jenkins/workspace/test/test-reports/test_tensorexpr_pybind_0zoxvqfq)
2023-01-11T20:50:28.6036599Z 
2023-01-11T20:50:33.4674293Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:50:33.6319174Z Ignoring disabled issues:  ['91003']
2023-01-11T20:50:33.6523918Z Running dynamo/test_export_mutations ... [2023-01-11 20:50:33.651952]
2023-01-11T20:50:33.6525012Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_export_mutations.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:50:33.652242]
2023-01-11T20:50:38.5636726Z 
2023-01-11T20:50:38.5637299Z Expand the folded group to see the log file of dynamo/test_export_mutations
2023-01-11T20:50:38.5638272Z ##[group]PRINTING LOG FILE of dynamo/test_export_mutations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_mutations_by0_w6fq)
2023-01-11T20:50:38.5638870Z Test results will be stored in test-reports/python-unittest/dynamo.test_export_mutations
2023-01-11T20:50:38.5639080Z 
2023-01-11T20:50:38.5639152Z Running tests...
2023-01-11T20:50:38.5639441Z ----------------------------------------------------------------------
2023-01-11T20:50:38.5640368Z   test_module_attribute_mutation_violation_negative_1 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88468 for platform(s) linux, macos, mac. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.323s)
2023-01-11T20:50:38.5641712Z   test_module_attribute_mutation_violation_negative_2 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88475 for platform(s) linux, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T20:50:38.5643102Z   test_module_attribute_mutation_violation_negative_3 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88466 for platform(s) linux, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T20:50:38.5644289Z   test_module_attribute_mutation_violation_negative_4 (__main__.MutationExportTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/88467 for platform(s) linux, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T20:50:38.5644916Z   test_module_attribute_mutation_violation_positive_1 (__main__.MutationExportTests) ... ok (0.042s)
2023-01-11T20:50:38.5645280Z   test_module_attribute_mutation_violation_positive_2 (__main__.MutationExportTests) ... ok (0.004s)
2023-01-11T20:50:38.5645702Z   test_module_attribute_mutation_violation_positive_3 (__main__.MutationExportTests) ... ok (0.003s)
2023-01-11T20:50:38.5646138Z   test_module_attribute_mutation_violation_positive_4 (__main__.MutationExportTests) ... inline_call []
2023-01-11T20:50:38.5646395Z ok (0.005s)
2023-01-11T20:50:38.5646497Z 
2023-01-11T20:50:38.5646702Z ----------------------------------------------------------------------
2023-01-11T20:50:38.5646928Z Ran 8 tests in 0.381s
2023-01-11T20:50:38.5647041Z 
2023-01-11T20:50:38.5647113Z OK (skipped=4)
2023-01-11T20:50:38.5647218Z 
2023-01-11T20:50:38.5647301Z Generating XML reports...
2023-01-11T20:50:38.5647740Z Generated XML report: test-reports/python-unittest/dynamo.test_export_mutations/TEST-MutationExportTests-20230111205037.xml
2023-01-11T20:50:38.5647980Z 
2023-01-11T20:50:38.5648225Z ##[endgroup]
2023-01-11T20:50:38.5648645Z FINISHED PRINTING LOG FILE of dynamo/test_export_mutations (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_export_mutations_by0_w6fq)
2023-01-11T20:50:38.5648887Z 
2023-01-11T20:50:43.4451659Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:50:43.6084612Z Ignoring disabled issues:  ['91003']
2023-01-11T20:50:43.6293444Z Running dynamo/test_nops ... [2023-01-11 20:50:43.628968]
2023-01-11T20:50:43.6294512Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_nops.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:50:43.629239]
2023-01-11T20:50:48.4970886Z 
2023-01-11T20:50:48.4971417Z Expand the folded group to see the log file of dynamo/test_nops
2023-01-11T20:50:48.4972460Z ##[group]PRINTING LOG FILE of dynamo/test_nops (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_nops_rf797dxh)
2023-01-11T20:50:48.4973368Z Test results will be stored in test-reports/python-unittest/dynamo.test_nops
2023-01-11T20:50:48.4973729Z 
2023-01-11T20:50:48.4973862Z Running tests...
2023-01-11T20:50:48.4974405Z ----------------------------------------------------------------------
2023-01-11T20:50:48.4974891Z   test1 (__main__.NopTests) ... ok (0.319s)
2023-01-11T20:50:48.5116875Z   test2 (__main__.NopTests) ... ok (0.003s)
2023-01-11T20:50:48.5117264Z   test3 (__main__.NopTests) ... ok (0.002s)
2023-01-11T20:50:48.5117651Z   test_extended_args (__main__.NopTests) ... ok (0.050s)
2023-01-11T20:50:48.5117910Z 
2023-01-11T20:50:48.5118335Z ----------------------------------------------------------------------
2023-01-11T20:50:48.5118711Z Ran 4 tests in 0.374s
2023-01-11T20:50:48.5118892Z 
2023-01-11T20:50:48.5118990Z OK
2023-01-11T20:50:48.5119137Z 
2023-01-11T20:50:48.5119274Z Generating XML reports...
2023-01-11T20:50:48.5119930Z Generated XML report: test-reports/python-unittest/dynamo.test_nops/TEST-NopTests-20230111205047.xml
2023-01-11T20:50:48.5120290Z 
2023-01-11T20:50:48.5120934Z ##[endgroup]
2023-01-11T20:50:48.5121801Z FINISHED PRINTING LOG FILE of dynamo/test_nops (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_nops_rf797dxh)
2023-01-11T20:50:48.5122161Z 
2023-01-11T20:50:53.2142481Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:50:53.3784488Z Ignoring disabled issues:  ['91003']
2023-01-11T20:50:53.3993217Z Running test_pytree ... [2023-01-11 20:50:53.398897]
2023-01-11T20:50:53.3994846Z Executing ['/opt/conda/bin/python', '-bb', 'test_pytree.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:50:53.399183]
2023-01-11T20:50:58.2068072Z 
2023-01-11T20:50:58.2068541Z Expand the folded group to see the log file of test_pytree
2023-01-11T20:50:58.2069571Z ##[group]PRINTING LOG FILE of test_pytree (/var/lib/jenkins/workspace/test/test-reports/test_pytree_cxv0ci3o)
2023-01-11T20:50:58.2070251Z Test results will be stored in test-reports/python-unittest/test_pytree
2023-01-11T20:50:58.2070440Z 
2023-01-11T20:50:58.2070516Z Running tests...
2023-01-11T20:50:58.2070829Z ----------------------------------------------------------------------
2023-01-11T20:50:58.2071281Z   test_broadcast_to_and_flatten (__main__.TestPytree) ... ok (0.326s)
2023-01-11T20:50:58.2072036Z   test_flatten_unflatten_dict (__main__.TestPytree) ... ok (0.008s)
2023-01-11T20:50:58.2072428Z   test_flatten_unflatten_leaf (__main__.TestPytree) ... ok (0.003s)
2023-01-11T20:50:58.2072707Z   test_flatten_unflatten_list (__main__.TestPytree) ... ok (0.003s)
2023-01-11T20:50:58.2073053Z   test_flatten_unflatten_namedtuple (__main__.TestPytree) ... ok (0.003s)
2023-01-11T20:50:58.2073349Z   test_flatten_unflatten_nested (__main__.TestPytree) ... ok (0.001s)
2023-01-11T20:50:58.2073617Z   test_flatten_unflatten_odict (__main__.TestPytree) ... ok (0.003s)
2023-01-11T20:50:58.2073906Z   test_flatten_unflatten_return_type_max (__main__.TestPytree) ... ok (0.003s)
2023-01-11T20:50:58.2074214Z   test_flatten_unflatten_return_type_min (__main__.TestPytree) ... ok (0.002s)
2023-01-11T20:50:58.2074509Z   test_flatten_unflatten_tuple (__main__.TestPytree) ... ok (0.003s)
2023-01-11T20:50:58.2074760Z   test_tree_all_any (__main__.TestPytree) ... ok (0.001s)
2023-01-11T20:50:58.2075008Z   test_tree_only (__main__.TestPytree) ... ok (0.001s)
2023-01-11T20:50:58.2075252Z   test_treemap (__main__.TestPytree) ... ok (0.001s)
2023-01-11T20:50:58.2075491Z   test_treespec_equality (__main__.TestPytree) ... ok (0.001s)
2023-01-11T20:50:58.2077582Z   test_treespec_repr (__main__.TestPytree) ... ok (0.001s)
2023-01-11T20:50:58.2077975Z 
2023-01-11T20:50:58.2078423Z ----------------------------------------------------------------------
2023-01-11T20:50:58.2078906Z Ran 15 tests in 0.362s
2023-01-11T20:50:58.2079042Z 
2023-01-11T20:50:58.2079104Z OK
2023-01-11T20:50:58.2079196Z 
2023-01-11T20:50:58.2079283Z Generating XML reports...
2023-01-11T20:50:58.2079688Z Generated XML report: test-reports/python-unittest/test_pytree/TEST-TestPytree-20230111205057.xml
2023-01-11T20:50:58.2079906Z 
2023-01-11T20:50:58.2080202Z ##[endgroup]
2023-01-11T20:50:58.2080573Z FINISHED PRINTING LOG FILE of test_pytree (/var/lib/jenkins/workspace/test/test-reports/test_pytree_cxv0ci3o)
2023-01-11T20:50:58.2081011Z 
2023-01-11T20:51:03.0357012Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:51:03.1982718Z Ignoring disabled issues:  ['91003']
2023-01-11T20:51:03.2189193Z Running nn/test_module_hooks ... [2023-01-11 20:51:03.218607]
2023-01-11T20:51:03.2191731Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_module_hooks.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:51:03.218949]
2023-01-11T20:51:08.3361631Z 
2023-01-11T20:51:08.3362201Z Expand the folded group to see the log file of nn/test_module_hooks
2023-01-11T20:51:08.3363331Z ##[group]PRINTING LOG FILE of nn/test_module_hooks (/var/lib/jenkins/workspace/test/test-reports/nn-test_module_hooks_q8y8f08h)
2023-01-11T20:51:08.3364258Z Test results will be stored in test-reports/python-unittest/nn.test_module_hooks
2023-01-11T20:51:08.3364829Z 
2023-01-11T20:51:08.3364936Z Running tests...
2023-01-11T20:51:08.3365485Z ----------------------------------------------------------------------
2023-01-11T20:51:08.3367180Z   test_global_and_local_hooks_order (__main__.TestModuleGlobalHooks) ... /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1331: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T20:51:08.3368516Z   warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes "
2023-01-11T20:51:08.3368988Z ok (0.017s)
2023-01-11T20:51:08.3369442Z   test_module_backward_global_hook_writeable (__main__.TestModuleGlobalHooks) ... ok (0.004s)
2023-01-11T20:51:08.3370063Z   test_module_forward_forward_hook_removable (__main__.TestModuleGlobalHooks)
2023-01-11T20:51:08.3370663Z This test is to test when multiple forward hook functions can be registered ... ok (0.003s)
2023-01-11T20:51:08.3371235Z   test_module_forward_preforward_hook_removable (__main__.TestModuleGlobalHooks)
2023-01-11T20:51:08.3372061Z This test is to test when multiple pre-forward hook functions can be ... ok (0.002s)
2023-01-11T20:51:08.3372647Z   test_module_global_forward_preforward_hook_writeable (__main__.TestModuleGlobalHooks) ... ok (0.004s)
2023-01-11T20:51:08.3373293Z   test_module_global_hook_invalid_outputs (__main__.TestModuleGlobalHooks) ... ok (0.007s)
2023-01-11T20:51:08.3373850Z   test_module_global_hooks (__main__.TestModuleGlobalHooks) ... ok (0.033s)
2023-01-11T20:51:08.3374316Z   test_backward_hooks_interaction (__main__.TestModuleHookNN) ... ok (0.003s)
2023-01-11T20:51:08.3374838Z   test_hook_backward_size (__main__.TestModuleHookNN) ... ok (0.004s)
2023-01-11T20:51:08.3375385Z   test_hook_backward_writeable (__main__.TestModuleHookNN) ... ok (0.003s)
2023-01-11T20:51:08.3375915Z   test_hook_buffer_registration (__main__.TestModuleHookNN) ... ok (0.003s)
2023-01-11T20:51:08.3376363Z   test_hook_cpp (__main__.TestModuleHookNN) ... ok (0.006s)
2023-01-11T20:51:08.3376840Z   test_hook_extra_input (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T20:51:08.3377342Z   test_hook_forward_preforward_writable (__main__.TestModuleHookNN) ... ok (0.004s)
2023-01-11T20:51:08.3377837Z   test_hook_inplace (__main__.TestModuleHookNN) ... ok (0.055s)
2023-01-11T20:51:08.3378323Z   test_hook_invalid_outputs (__main__.TestModuleHookNN) ... ok (0.005s)
2023-01-11T20:51:08.3378825Z   test_hook_last_arg_requires_grad (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T20:51:08.3379307Z   test_hook_no_requires_grad (__main__.TestModuleHookNN) ... ok (0.006s)
2023-01-11T20:51:08.3379913Z   test_hook_non_full_warning (__main__.TestModuleHookNN) ... ok (0.011s)
2023-01-11T20:51:08.3380422Z   test_hook_parameter_registration (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T20:51:08.3380913Z   test_hook_requires_grad (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T20:51:08.3381430Z   test_hook_submodule_registration (__main__.TestModuleHookNN) ... ok (0.002s)
2023-01-11T20:51:08.3382964Z   test_hooks (__main__.TestModuleHookNN) ... /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1331: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.
2023-01-11T20:51:08.3384155Z   warnings.warn("Using a non-full backward hook when the forward contains multiple autograd Nodes "
2023-01-11T20:51:08.3384606Z ok (0.075s)
2023-01-11T20:51:08.3384978Z   test_forward_hooks (__main__.TestModuleHooks) ... ok (0.012s)
2023-01-11T20:51:08.3385460Z   test_forward_pre_hooks (__main__.TestModuleHooks) ... ok (0.011s)
2023-01-11T20:51:08.3385952Z   test_full_backward_hooks (__main__.TestModuleHooks) ... ok (0.012s)
2023-01-11T20:51:08.3386586Z   test_full_backward_pre_hooks (__main__.TestModuleHooks) ... ok (0.012s)
2023-01-11T20:51:08.3387087Z   test_kwarg_hooks (__main__.TestModuleHooks) ... ok (0.009s)
2023-01-11T20:51:08.3387561Z   test_mixed_hooks (__main__.TestModuleHooks) ... ok (0.011s)
2023-01-11T20:51:08.3388002Z   test_remove_kwarg_hooks (__main__.TestModuleHooks) ... ok (0.007s)
2023-01-11T20:51:08.3388523Z   test_load_state_dict_module_pre_hook (__main__.TestStateDictHooks) ... ok (0.003s)
2023-01-11T20:51:08.3389065Z   test_load_state_dict_post_hook (__main__.TestStateDictHooks) ... ok (0.003s)
2023-01-11T20:51:08.3389633Z   test_load_state_dict_post_hook_backward_compatibility (__main__.TestStateDictHooks) ... ok (0.004s)
2023-01-11T20:51:08.3390190Z   test_load_state_dict_pre_hook (__main__.TestStateDictHooks) ... ok (0.005s)
2023-01-11T20:51:08.3390699Z   test_no_extra_ref_to_module (__main__.TestStateDictHooks) ... ok (0.001s)
2023-01-11T20:51:08.3391189Z   test_pickled_hook (__main__.TestStateDictHooks) ... ok (0.002s)
2023-01-11T20:51:08.3391473Z 
2023-01-11T20:51:08.3391809Z ----------------------------------------------------------------------
2023-01-11T20:51:08.3392222Z Ran 36 tests in 0.349s
2023-01-11T20:51:08.3392415Z 
2023-01-11T20:51:08.3392612Z OK
2023-01-11T20:51:08.3392775Z 
2023-01-11T20:51:08.3392930Z Generating XML reports...
2023-01-11T20:51:08.3393673Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleGlobalHooks-20230111205107.xml
2023-01-11T20:51:08.3394598Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHookNN-20230111205107.xml
2023-01-11T20:51:08.3395689Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHooks-20230111205107.xml
2023-01-11T20:51:08.3396587Z Generated XML report: test-reports/python-unittest/nn.test_module_hooks/TEST-TestStateDictHooks-20230111205107.xml
2023-01-11T20:51:08.3396987Z 
2023-01-11T20:51:08.3397449Z ##[endgroup]
2023-01-11T20:51:08.3398151Z FINISHED PRINTING LOG FILE of nn/test_module_hooks (/var/lib/jenkins/workspace/test/test-reports/nn-test_module_hooks_q8y8f08h)
2023-01-11T20:51:08.3398532Z 
2023-01-11T20:51:13.1689586Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:51:13.3305797Z Ignoring disabled issues:  ['91003']
2023-01-11T20:51:13.3512995Z Running test_per_overload_api ... [2023-01-11 20:51:13.351018]
2023-01-11T20:51:13.3515890Z Executing ['/opt/conda/bin/python', '-bb', 'test_per_overload_api.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:51:13.351358]
2023-01-11T20:51:18.0553222Z 
2023-01-11T20:51:18.0553963Z Expand the folded group to see the log file of test_per_overload_api
2023-01-11T20:51:18.0554829Z ##[group]PRINTING LOG FILE of test_per_overload_api (/var/lib/jenkins/workspace/test/test-reports/test_per_overload_api_fwrrvdqj)
2023-01-11T20:51:18.0555400Z Test results will be stored in test-reports/python-unittest/test_per_overload_api
2023-01-11T20:51:18.0555602Z 
2023-01-11T20:51:18.0555677Z Running tests...
2023-01-11T20:51:18.0555965Z ----------------------------------------------------------------------
2023-01-11T20:51:18.0556277Z   test_basics_opoverload (__main__.TestPerOverloadAPI) ... ok (0.321s)
2023-01-11T20:51:18.0556583Z   test_basics_opoverloadpacket (__main__.TestPerOverloadAPI) ... ok (0.002s)
2023-01-11T20:51:18.0556863Z   test_decompose (__main__.TestPerOverloadAPI) ... ok (0.033s)
2023-01-11T20:51:18.0557016Z 
2023-01-11T20:51:18.0557208Z ----------------------------------------------------------------------
2023-01-11T20:51:18.0557441Z Ran 3 tests in 0.356s
2023-01-11T20:51:18.0557552Z 
2023-01-11T20:51:18.0557601Z OK
2023-01-11T20:51:18.0557689Z 
2023-01-11T20:51:18.0557774Z Generating XML reports...
2023-01-11T20:51:18.0558206Z Generated XML report: test-reports/python-unittest/test_per_overload_api/TEST-TestPerOverloadAPI-20230111205117.xml
2023-01-11T20:51:18.0558451Z 
2023-01-11T20:51:18.0558655Z ##[endgroup]
2023-01-11T20:51:18.0559265Z FINISHED PRINTING LOG FILE of test_per_overload_api (/var/lib/jenkins/workspace/test/test-reports/test_per_overload_api_fwrrvdqj)
2023-01-11T20:51:18.0559492Z 
2023-01-11T20:51:22.8757261Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:51:23.0422186Z Ignoring disabled issues:  ['91003']
2023-01-11T20:51:23.0630162Z Running test_license ... [2023-01-11 20:51:23.062700]
2023-01-11T20:51:23.0632499Z Executing ['/opt/conda/bin/python', '-bb', 'test_license.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:51:23.063049]
2023-01-11T20:51:27.8023058Z 
2023-01-11T20:51:27.8023551Z Expand the folded group to see the log file of test_license
2023-01-11T20:51:27.8024430Z ##[group]PRINTING LOG FILE of test_license (/var/lib/jenkins/workspace/test/test-reports/test_license_rtyq9ulp)
2023-01-11T20:51:27.8025179Z Test results will be stored in test-reports/python-unittest/test_license
2023-01-11T20:51:27.8025436Z 
2023-01-11T20:51:27.8025541Z Running tests...
2023-01-11T20:51:27.8026020Z ----------------------------------------------------------------------
2023-01-11T20:51:27.8026398Z   test_distinfo_license (__main__.TestLicense)
2023-01-11T20:51:27.8027092Z If run when pytorch is installed via a wheel, the license will be in ... ok (0.336s)
2023-01-11T20:51:27.8027607Z   test_license_for_wheel (__main__.TestLicense) ... skip: can only be run in a source tree (0.001s)
2023-01-11T20:51:27.8027875Z 
2023-01-11T20:51:27.8028161Z ----------------------------------------------------------------------
2023-01-11T20:51:27.8028517Z Ran 2 tests in 0.337s
2023-01-11T20:51:27.8028679Z 
2023-01-11T20:51:27.8028783Z OK (skipped=1)
2023-01-11T20:51:27.8028934Z 
2023-01-11T20:51:27.8029052Z Generating XML reports...
2023-01-11T20:51:27.8029619Z Generated XML report: test-reports/python-unittest/test_license/TEST-TestLicense-20230111205126.xml
2023-01-11T20:51:27.8029937Z 
2023-01-11T20:51:27.8030308Z ##[endgroup]
2023-01-11T20:51:27.8030867Z FINISHED PRINTING LOG FILE of test_license (/var/lib/jenkins/workspace/test/test-reports/test_license_rtyq9ulp)
2023-01-11T20:51:27.8031176Z 
2023-01-11T20:51:32.5321343Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:51:32.7091484Z Ignoring disabled issues:  ['91003']
2023-01-11T20:51:32.7296311Z Running test_set_default_mobile_cpu_allocator ... [2023-01-11 20:51:32.729336]
2023-01-11T20:51:32.7298727Z Executing ['/opt/conda/bin/python', '-bb', 'test_set_default_mobile_cpu_allocator.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:51:32.729605]
2023-01-11T20:51:37.4838311Z 
2023-01-11T20:51:37.4839282Z Expand the folded group to see the log file of test_set_default_mobile_cpu_allocator
2023-01-11T20:51:37.4840521Z ##[group]PRINTING LOG FILE of test_set_default_mobile_cpu_allocator (/var/lib/jenkins/workspace/test/test-reports/test_set_default_mobile_cpu_allocator_tyq4pmsh)
2023-01-11T20:51:37.4841700Z Test results will be stored in test-reports/python-unittest/test_set_default_mobile_cpu_allocator
2023-01-11T20:51:37.4842058Z 
2023-01-11T20:51:37.4842177Z Running tests...
2023-01-11T20:51:37.4842644Z ----------------------------------------------------------------------
2023-01-11T20:51:37.4843182Z   test_exception (__main__.TestSetDefaultMobileCPUAllocator) ... ok (0.326s)
2023-01-11T20:51:37.4843752Z   test_no_exception (__main__.TestSetDefaultMobileCPUAllocator) ... ok (0.001s)
2023-01-11T20:51:37.4844109Z 
2023-01-11T20:51:37.4844472Z ----------------------------------------------------------------------
2023-01-11T20:51:37.4844917Z Ran 2 tests in 0.327s
2023-01-11T20:51:37.4845119Z 
2023-01-11T20:51:37.4845237Z OK
2023-01-11T20:51:37.4845409Z 
2023-01-11T20:51:37.4845571Z Generating XML reports...
2023-01-11T20:51:37.4846524Z Generated XML report: test-reports/python-unittest/test_set_default_mobile_cpu_allocator/TEST-TestSetDefaultMobileCPUAllocator-20230111205136.xml
2023-01-11T20:51:37.4847088Z 
2023-01-11T20:51:37.4847542Z ##[endgroup]
2023-01-11T20:51:37.4848401Z FINISHED PRINTING LOG FILE of test_set_default_mobile_cpu_allocator (/var/lib/jenkins/workspace/test/test-reports/test_set_default_mobile_cpu_allocator_tyq4pmsh)
2023-01-11T20:51:37.4849194Z 
2023-01-11T20:51:42.2687993Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:51:42.4291519Z Ignoring disabled issues:  ['91003']
2023-01-11T20:51:42.4498396Z Running test_type_info ... [2023-01-11 20:51:42.449569]
2023-01-11T20:51:42.4500704Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_info.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:51:42.449838]
2023-01-11T20:51:47.1279928Z 
2023-01-11T20:51:47.1280412Z Expand the folded group to see the log file of test_type_info
2023-01-11T20:51:47.1281613Z ##[group]PRINTING LOG FILE of test_type_info (/var/lib/jenkins/workspace/test/test-reports/test_type_info_nkfg_lz3)
2023-01-11T20:51:47.1282400Z Test results will be stored in test-reports/python-unittest/test_type_info
2023-01-11T20:51:47.1282691Z 
2023-01-11T20:51:47.1282809Z Running tests...
2023-01-11T20:51:47.1283296Z ----------------------------------------------------------------------
2023-01-11T20:51:47.1283714Z   test_finfo (__main__.TestDTypeInfo) ... ok (0.331s)
2023-01-11T20:51:47.1284323Z   test_iinfo (__main__.TestDTypeInfo) ... ok (0.003s)
2023-01-11T20:51:47.1284675Z   test_invalid_input (__main__.TestDTypeInfo) ... ok (0.001s)
2023-01-11T20:51:47.1284897Z 
2023-01-11T20:51:47.1285214Z ----------------------------------------------------------------------
2023-01-11T20:51:47.1285600Z Ran 3 tests in 0.335s
2023-01-11T20:51:47.1285774Z 
2023-01-11T20:51:47.1285857Z OK
2023-01-11T20:51:47.1285992Z 
2023-01-11T20:51:47.1286128Z Generating XML reports...
2023-01-11T20:51:47.1286761Z Generated XML report: test-reports/python-unittest/test_type_info/TEST-TestDTypeInfo-20230111205146.xml
2023-01-11T20:51:47.1287097Z 
2023-01-11T20:51:47.1287428Z ##[endgroup]
2023-01-11T20:51:47.1287988Z FINISHED PRINTING LOG FILE of test_type_info (/var/lib/jenkins/workspace/test/test-reports/test_type_info_nkfg_lz3)
2023-01-11T20:51:47.1288314Z 
2023-01-11T20:51:51.9316915Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:51:52.0923593Z Ignoring disabled issues:  ['91003']
2023-01-11T20:51:52.1135832Z Running test_itt ... [2023-01-11 20:51:52.113278]
2023-01-11T20:51:52.1138013Z Executing ['/opt/conda/bin/python', '-bb', 'test_itt.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:51:52.113580]
2023-01-11T20:51:56.7772776Z 
2023-01-11T20:51:56.7773220Z Expand the folded group to see the log file of test_itt
2023-01-11T20:51:56.7773889Z ##[group]PRINTING LOG FILE of test_itt (/var/lib/jenkins/workspace/test/test-reports/test_itt_kkqn1xno)
2023-01-11T20:51:56.7774420Z Test results will be stored in test-reports/python-unittest/test_itt
2023-01-11T20:51:56.7774656Z 
2023-01-11T20:51:56.7774730Z Running tests...
2023-01-11T20:51:56.7775025Z ----------------------------------------------------------------------
2023-01-11T20:51:56.7775346Z   test_itt (__main__.TestItt) ... ok (0.335s)
2023-01-11T20:51:56.7775501Z 
2023-01-11T20:51:56.7775699Z ----------------------------------------------------------------------
2023-01-11T20:51:56.7775938Z Ran 1 test in 0.335s
2023-01-11T20:51:56.7776077Z 
2023-01-11T20:51:56.7776160Z OK
2023-01-11T20:51:56.7776251Z 
2023-01-11T20:51:56.7776335Z Generating XML reports...
2023-01-11T20:51:56.7776717Z Generated XML report: test-reports/python-unittest/test_itt/TEST-TestItt-20230111205155.xml
2023-01-11T20:51:56.7776983Z 
2023-01-11T20:51:56.7777188Z ##[endgroup]
2023-01-11T20:51:56.7777545Z FINISHED PRINTING LOG FILE of test_itt (/var/lib/jenkins/workspace/test/test-reports/test_itt_kkqn1xno)
2023-01-11T20:51:56.7777801Z 
2023-01-11T20:52:01.6013034Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:52:01.7600915Z Ignoring disabled issues:  ['91003']
2023-01-11T20:52:01.7809888Z Running test_numpy_interop ... [2023-01-11 20:52:01.780737]
2023-01-11T20:52:01.7812554Z Executing ['/opt/conda/bin/python', '-bb', 'test_numpy_interop.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:52:01.781036]
2023-01-11T20:52:06.9925379Z 
2023-01-11T20:52:06.9925913Z Expand the folded group to see the log file of test_numpy_interop
2023-01-11T20:52:06.9927098Z ##[group]PRINTING LOG FILE of test_numpy_interop (/var/lib/jenkins/workspace/test/test-reports/test_numpy_interop_bgoxu0aw)
2023-01-11T20:52:06.9928031Z Test results will be stored in test-reports/python-unittest/test_numpy_interop
2023-01-11T20:52:06.9928412Z 
2023-01-11T20:52:06.9928541Z Running tests...
2023-01-11T20:52:06.9929101Z ----------------------------------------------------------------------
2023-01-11T20:52:06.9930292Z   test_ctor_with_invalid_numpy_array_sequence_cpu (__main__.TestNumPyInteropCPU) ... test_numpy_interop.py:265: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_new.cpp:259.)
2023-01-11T20:52:06.9931490Z   torch.tensor([np.random.random(size=(3, 3)), np.random.random(size=(3, 0))], device=device)
2023-01-11T20:52:06.9953808Z ok (0.015s)
2023-01-11T20:52:06.9954385Z   test_ctor_with_numpy_scalar_ctor_cpu (__main__.TestNumPyInteropCPU) ... ok (0.002s)
2023-01-11T20:52:06.9954990Z   test_from_list_of_ndarray_warning_cpu (__main__.TestNumPyInteropCPU) ... ok (0.001s)
2023-01-11T20:52:06.9955555Z   test_from_numpy_cpu (__main__.TestNumPyInteropCPU) ... ok (0.018s)
2023-01-11T20:52:06.9956706Z   test_has_storage_numpy_cpu (__main__.TestNumPyInteropCPU) ... test_numpy_interop.py:441: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:52:06.9957869Z   self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.float32).storage())
2023-01-11T20:52:06.9958956Z test_numpy_interop.py:442: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:52:06.9959970Z   self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.double).storage())
2023-01-11T20:52:06.9961224Z test_numpy_interop.py:443: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:52:06.9962247Z   self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.int).storage())
2023-01-11T20:52:06.9963294Z test_numpy_interop.py:444: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:52:06.9964315Z   self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.long).storage())
2023-01-11T20:52:06.9965365Z test_numpy_interop.py:445: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:52:06.9966404Z   self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.uint8).storage())
2023-01-11T20:52:06.9966863Z ok (0.004s)
2023-01-11T20:52:06.9967319Z   test_multiplication_numpy_scalar_cpu (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9968053Z   test_numpy_array_interface_cpu (__main__.TestNumPyInteropCPU) ... ok (0.019s)
2023-01-11T20:52:06.9968613Z   test_numpy_index_cpu (__main__.TestNumPyInteropCPU) ... ok (0.003s)
2023-01-11T20:52:06.9969176Z   test_numpy_non_writeable_cpu (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9969736Z   test_numpy_scalar_cmp_cpu_bfloat16 (__main__.TestNumPyInteropCPU) ... ok (0.009s)
2023-01-11T20:52:06.9970321Z   test_numpy_scalar_cmp_cpu_bool (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9970914Z   test_numpy_scalar_cmp_cpu_complex128 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9971713Z   test_numpy_scalar_cmp_cpu_complex64 (__main__.TestNumPyInteropCPU) ... test_numpy_interop.py:471: ComplexWarning: Casting complex values to real discards the imaginary part
2023-01-11T20:52:06.9972346Z   self.assertFalse(t == a)
2023-01-11T20:52:06.9972676Z ok (0.004s)
2023-01-11T20:52:06.9973122Z   test_numpy_scalar_cmp_cpu_float16 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9973703Z   test_numpy_scalar_cmp_cpu_float32 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9974388Z   test_numpy_scalar_cmp_cpu_float64 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9974982Z   test_numpy_scalar_cmp_cpu_int16 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9975580Z   test_numpy_scalar_cmp_cpu_int32 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9976139Z   test_numpy_scalar_cmp_cpu_int64 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9976711Z   test_numpy_scalar_cmp_cpu_int8 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9977289Z   test_numpy_scalar_cmp_cpu_uint8 (__main__.TestNumPyInteropCPU) ... ok (0.004s)
2023-01-11T20:52:06.9977848Z   test_numpy_unresizable_cpu (__main__.TestNumPyInteropCPU) ... ok (0.026s)
2023-01-11T20:52:06.9979028Z   test_parse_numpy_int_cpu (__main__.TestNumPyInteropCPU) ... test_numpy_interop.py:424: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T20:52:06.9980496Z   self.assertEqual(torch.Storage(np_val).size(), scalar)  # type: ignore[attr-defined]
2023-01-11T20:52:06.9980941Z ok (0.014s)
2023-01-11T20:52:06.9981361Z   test_to_numpy_bool_cpu (__main__.TestNumPyInteropCPU) ... ok (0.002s)
2023-01-11T20:52:06.9981878Z   test_to_numpy_cpu (__main__.TestNumPyInteropCPU) ... ok (0.076s)
2023-01-11T20:52:06.9982437Z   test_to_numpy_force_argument_cpu (__main__.TestNumPyInteropCPU) ... ok (0.044s)
2023-01-11T20:52:06.9982770Z 
2023-01-11T20:52:06.9983150Z ----------------------------------------------------------------------
2023-01-11T20:52:06.9983575Z Ran 26 tests in 0.284s
2023-01-11T20:52:06.9983773Z 
2023-01-11T20:52:06.9983881Z OK
2023-01-11T20:52:06.9984053Z 
2023-01-11T20:52:06.9984203Z Generating XML reports...
2023-01-11T20:52:06.9985004Z Generated XML report: test-reports/python-unittest/test_numpy_interop/TEST-TestNumPyInteropCPU-20230111205205.xml
2023-01-11T20:52:06.9985465Z 
2023-01-11T20:52:06.9985922Z ##[endgroup]
2023-01-11T20:52:06.9986650Z FINISHED PRINTING LOG FILE of test_numpy_interop (/var/lib/jenkins/workspace/test/test-reports/test_numpy_interop_bgoxu0aw)
2023-01-11T20:52:06.9987060Z 
2023-01-11T20:52:11.9115192Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:52:12.0756004Z Ignoring disabled issues:  ['91003']
2023-01-11T20:52:12.0967178Z Running nn/test_pruning ... [2023-01-11 20:52:12.096348]
2023-01-11T20:52:12.0968167Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_pruning.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:52:12.096621]
2023-01-11T20:52:17.1241206Z 
2023-01-11T20:52:17.1241692Z Expand the folded group to see the log file of nn/test_pruning
2023-01-11T20:52:17.1243037Z ##[group]PRINTING LOG FILE of nn/test_pruning (/var/lib/jenkins/workspace/test/test-reports/nn-test_pruning_z706f_a3)
2023-01-11T20:52:17.1244008Z Test results will be stored in test-reports/python-unittest/nn.test_pruning
2023-01-11T20:52:17.1244345Z 
2023-01-11T20:52:17.1244479Z Running tests...
2023-01-11T20:52:17.1245048Z ----------------------------------------------------------------------
2023-01-11T20:52:17.1245561Z   test_compute_nparams_to_prune (__main__.TestPruningNN)
2023-01-11T20:52:17.1246105Z Test that requested pruning `amount` gets translated into the ... ok (0.013s)
2023-01-11T20:52:17.1246636Z   test_custom_from_mask_pruning (__main__.TestPruningNN)
2023-01-11T20:52:17.1247151Z Test that the CustomFromMask is capable of receiving ... ok (0.003s)
2023-01-11T20:52:17.1247631Z   test_global_pruning (__main__.TestPruningNN)
2023-01-11T20:52:17.1248165Z Test that global l1 unstructured pruning over 2 parameters removes ... ok (0.009s)
2023-01-11T20:52:17.1248742Z   test_global_pruning_importance_scores (__main__.TestPruningNN)
2023-01-11T20:52:17.1249296Z Test that global l1 unstructured pruning over 2 parameters removes ... ok (0.006s)
2023-01-11T20:52:17.1249958Z   test_identity_pruning (__main__.TestPruningNN)
2023-01-11T20:52:17.1250474Z Test that a mask of 1s does not change forward or backward. ... ok (0.008s)
2023-01-11T20:52:17.1250991Z   test_l1_unstructured_pruning (__main__.TestPruningNN)
2023-01-11T20:52:17.1251508Z Test that l1 unstructured pruning actually removes the lowest ... ok (0.004s)
2023-01-11T20:52:17.1252088Z   test_l1_unstructured_pruning_with_importance_scores (__main__.TestPruningNN)
2023-01-11T20:52:17.1252673Z Test that l1 unstructured pruning actually removes the lowest ... ok (0.004s)
2023-01-11T20:52:17.1253180Z   test_ln_structured_pruning (__main__.TestPruningNN)
2023-01-11T20:52:17.1253652Z Check Ln structured pruning by hand. ... ok (0.005s)
2023-01-11T20:52:17.1254164Z   test_ln_structured_pruning_importance_scores (__main__.TestPruningNN)
2023-01-11T20:52:17.1254667Z Check Ln structured pruning by hand. ... ok (0.005s)
2023-01-11T20:52:17.1255171Z   test_multiple_pruning_calls (__main__.TestPruningNN) ... ok (0.005s)
2023-01-11T20:52:17.1255667Z   test_prune (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T20:52:17.1256161Z   test_prune_importance_scores (__main__.TestPruningNN) ... ok (0.002s)
2023-01-11T20:52:17.1256715Z   test_prune_importance_scores_mimic_default (__main__.TestPruningNN) ... ok (0.003s)
2023-01-11T20:52:17.1257272Z   test_pruning_container (__main__.TestPruningNN) ... ok (0.001s)
2023-01-11T20:52:17.1257792Z   test_pruning_container_compute_mask (__main__.TestPruningNN)
2023-01-11T20:52:17.1258313Z Test `compute_mask` of pruning container with a known `t` and ... ok (0.006s)
2023-01-11T20:52:17.1258828Z   test_pruning_id_consistency (__main__.TestPruningNN)
2023-01-11T20:52:17.1259612Z Test that pruning doesn't change the id of the parameters, which ... ok (0.002s)
2023-01-11T20:52:17.1260122Z   test_pruning_rollback (__main__.TestPruningNN)
2023-01-11T20:52:17.1260638Z Test that if something fails when the we try to compute the mask, ... ok (0.003s)
2023-01-11T20:52:17.1261202Z   test_pruning_serialization_model (__main__.TestPruningNN) ... ok (0.008s)
2023-01-11T20:52:17.1261776Z   test_pruning_serialization_state_dict (__main__.TestPruningNN) ... ok (0.007s)
2023-01-11T20:52:17.1262299Z   test_random_pruning (__main__.TestPruningNN) ... ok (0.006s)
2023-01-11T20:52:17.1262781Z   test_random_pruning_0perc (__main__.TestPruningNN)
2023-01-11T20:52:17.1263289Z Test that a mask of 1s does not change forward or backward. ... ok (0.008s)
2023-01-11T20:52:17.1263783Z   test_random_pruning_forward (__main__.TestPruningNN)
2023-01-11T20:52:17.1264245Z check forward with mask (by hand). ... ok (0.004s)
2023-01-11T20:52:17.1264714Z   test_random_pruning_new_weight (__main__.TestPruningNN)
2023-01-11T20:52:17.1265224Z Test that module.name now contains a pruned version of ... ok (0.006s)
2023-01-11T20:52:17.1265707Z   test_random_pruning_orig (__main__.TestPruningNN)
2023-01-11T20:52:17.1266479Z Test that original tensor is correctly stored in 'orig' ... ok (0.005s)
2023-01-11T20:52:17.1267012Z   test_random_pruning_pickle (__main__.TestPruningNN) ... ok (0.010s)
2023-01-11T20:52:17.1267498Z   test_random_pruning_sizes (__main__.TestPruningNN)
2023-01-11T20:52:17.1268030Z Test that the new parameters and buffers created by the pruning ... ok (0.019s)
2023-01-11T20:52:17.1268613Z   test_random_structured_pruning_amount (__main__.TestPruningNN) ... ok (0.004s)
2023-01-11T20:52:17.1269105Z   test_remove_pruning (__main__.TestPruningNN)
2023-01-11T20:52:17.1269619Z `prune.remove` removes the hook and the reparametrization ... ok (0.007s)
2023-01-11T20:52:17.1270150Z   test_remove_pruning_exception (__main__.TestPruningNN)
2023-01-11T20:52:17.1270679Z Removing from an unpruned tensor throws an assertion error ... ok (0.002s)
2023-01-11T20:52:17.1271181Z   test_remove_pruning_forward (__main__.TestPruningNN)
2023-01-11T20:52:17.1271715Z Remove pruning and check forward is unchanged from previous ... ok (0.003s)
2023-01-11T20:52:17.1272247Z   test_rnn_pruning (__main__.TestPruningNN) ... ok (0.004s)
2023-01-11T20:52:17.1272813Z   test_unstructured_pruning_same_magnitude (__main__.TestPruningNN)
2023-01-11T20:52:17.1273386Z Since it may happen that the tensor to prune has entries with the ... ok (0.004s)
2023-01-11T20:52:17.1273917Z   test_validate_pruning_amount (__main__.TestPruningNN)
2023-01-11T20:52:17.1274424Z Tests the second util function that validates the pruning ... ok (0.001s)
2023-01-11T20:52:17.1274956Z   test_validate_pruning_amount_init (__main__.TestPruningNN)
2023-01-11T20:52:17.1275477Z Test the first util function that validates the pruning ... ok (0.001s)
2023-01-11T20:52:17.1275783Z 
2023-01-11T20:52:17.1276161Z ----------------------------------------------------------------------
2023-01-11T20:52:17.1276580Z Ran 34 tests in 0.185s
2023-01-11T20:52:17.1276785Z 
2023-01-11T20:52:17.1276895Z OK
2023-01-11T20:52:17.1277065Z 
2023-01-11T20:52:17.1277219Z Generating XML reports...
2023-01-11T20:52:17.1277967Z Generated XML report: test-reports/python-unittest/nn.test_pruning/TEST-TestPruningNN-20230111205216.xml
2023-01-11T20:52:17.1278390Z 
2023-01-11T20:52:17.1278822Z ##[endgroup]
2023-01-11T20:52:17.1279539Z FINISHED PRINTING LOG FILE of nn/test_pruning (/var/lib/jenkins/workspace/test/test-reports/nn-test_pruning_z706f_a3)
2023-01-11T20:52:17.1279943Z 
2023-01-11T20:52:21.9030026Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:52:22.0677283Z Ignoring disabled issues:  ['91003']
2023-01-11T20:52:22.0888574Z Running test_decomp ... [2023-01-11 20:52:22.088484]
2023-01-11T20:52:22.0889631Z Executing ['/opt/conda/bin/python', '-bb', 'test_decomp.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:52:22.088755]
2023-01-11T20:52:32.4230200Z 
2023-01-11T20:52:32.4230682Z Expand the folded group to see the log file of test_decomp
2023-01-11T20:52:32.4231394Z ##[group]PRINTING LOG FILE of test_decomp (/var/lib/jenkins/workspace/test/test-reports/test_decomp_0t79n_gg)
2023-01-11T20:52:32.4251889Z Test results will be stored in test-reports/python-unittest/test_decomp
2023-01-11T20:52:32.4252197Z 
2023-01-11T20:52:32.4252331Z Running tests...
2023-01-11T20:52:32.4252829Z ----------------------------------------------------------------------
2023-01-11T20:52:32.4253339Z   test_amp_batch_norm_backward_cpu (__main__.DecompAmpTestsCPU) ... skip: Skipped under ASAN (0.002s)
2023-01-11T20:52:32.4253932Z   test_contiguous_log_softmax_cpu (__main__.DecompContiguousTestsCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4255943Z   test_contiguous_softmax_cpu (__main__.DecompContiguousTestsCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4332196Z   test_comprehensive_H_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4332768Z   test_comprehensive_H_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4333506Z   test_comprehensive_H_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4334037Z   test_comprehensive_H_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4334550Z   test_comprehensive_H_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4335068Z   test_comprehensive_H_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4335521Z   test_comprehensive_H_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4336023Z   test_comprehensive_H_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4337237Z   test_comprehensive_H_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4338725Z   test_comprehensive_H_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4339686Z   test_comprehensive_H_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4340201Z   test_comprehensive_H_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4341889Z   test_comprehensive_H_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4342608Z   test_comprehensive_T_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4343293Z   test_comprehensive_T_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4343820Z   test_comprehensive_T_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4344299Z   test_comprehensive_T_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4345213Z   test_comprehensive_T_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4349748Z   test_comprehensive_T_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4350956Z   test_comprehensive_T_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4351436Z   test_comprehensive_T_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4351960Z   test_comprehensive_T_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4352463Z   test_comprehensive_T_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4352938Z   test_comprehensive_T_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4353434Z   test_comprehensive_T_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4353832Z   test_comprehensive_T_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4354289Z   test_comprehensive___getitem___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4354862Z   test_comprehensive___getitem___cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4355360Z   test_comprehensive___getitem___cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4355885Z   test_comprehensive___getitem___cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4356379Z   test_comprehensive___getitem___cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4356899Z   test_comprehensive___getitem___cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4357438Z   test_comprehensive___getitem___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4357978Z   test_comprehensive___getitem___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4358634Z   test_comprehensive___getitem___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4359160Z   test_comprehensive___getitem___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4359685Z   test_comprehensive___getitem___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4360240Z   test_comprehensive___getitem___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4360864Z   test_comprehensive___getitem___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4361300Z   test_comprehensive___radd___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4364242Z   test_comprehensive___radd___cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4364933Z   test_comprehensive___radd___cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4365542Z   test_comprehensive___radd___cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4366470Z   test_comprehensive___radd___cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4367241Z   test_comprehensive___radd___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4368010Z   test_comprehensive___radd___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4368787Z   test_comprehensive___radd___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4369558Z   test_comprehensive___radd___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4402467Z   test_comprehensive___radd___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4403369Z   test_comprehensive___radd___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4404070Z   test_comprehensive___radd___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4404740Z   test_comprehensive___rand___cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4405389Z   test_comprehensive___rand___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4406052Z   test_comprehensive___rand___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4406706Z   test_comprehensive___rand___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4407368Z   test_comprehensive___rand___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4408021Z   test_comprehensive___rand___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4408694Z   test_comprehensive___rdiv___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4444540Z   test_comprehensive___rdiv___cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4445380Z   test_comprehensive___rdiv___cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4445971Z   test_comprehensive___rdiv___cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4446331Z   test_comprehensive___rdiv___cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4446685Z   test_comprehensive___rdiv___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4447023Z   test_comprehensive___rdiv___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4447376Z   test_comprehensive___rdiv___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4448091Z   test_comprehensive___rdiv___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4448521Z   test_comprehensive___rdiv___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4448869Z   test_comprehensive___rdiv___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4449216Z   test_comprehensive___rdiv___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4449609Z   test_comprehensive___rmatmul___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4450281Z   test_comprehensive___rmatmul___cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4450824Z   test_comprehensive___rmatmul___cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4451187Z   test_comprehensive___rmatmul___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4451620Z   test_comprehensive___rmatmul___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4451958Z   test_comprehensive___rmatmul___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4452311Z   test_comprehensive___rmatmul___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4452656Z   test_comprehensive___rmatmul___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4453012Z   test_comprehensive___rmatmul___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4453347Z   test_comprehensive___rmatmul___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4453709Z   test_comprehensive___rmod___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4454057Z   test_comprehensive___rmod___cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4454409Z   test_comprehensive___rmod___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4454744Z   test_comprehensive___rmod___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4455089Z   test_comprehensive___rmul___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4455437Z   test_comprehensive___rmul___cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4455772Z   test_comprehensive___rmul___cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4456130Z   test_comprehensive___rmul___cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4456481Z   test_comprehensive___rmul___cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4456828Z   test_comprehensive___rmul___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4457160Z   test_comprehensive___rmul___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4457505Z   test_comprehensive___rmul___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4457849Z   test_comprehensive___rmul___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4458185Z   test_comprehensive___rmul___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4458516Z   test_comprehensive___rmul___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4458858Z   test_comprehensive___rmul___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4459346Z   test_comprehensive___ror___cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4459692Z   test_comprehensive___ror___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4460018Z   test_comprehensive___ror___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4460357Z   test_comprehensive___ror___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4460695Z   test_comprehensive___ror___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4461024Z   test_comprehensive___ror___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4461371Z   test_comprehensive___rpow___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4461728Z   test_comprehensive___rpow___cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4462089Z   test_comprehensive___rpow___cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4462462Z   test_comprehensive___rpow___cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4462811Z   test_comprehensive___rpow___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4463155Z   test_comprehensive___rpow___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4464925Z   test_comprehensive___rpow___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4465562Z   test_comprehensive___rpow___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4466183Z   test_comprehensive___rpow___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4466538Z   test_comprehensive___rpow___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4466874Z   test_comprehensive___rpow___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4467230Z   test_comprehensive___rsub___cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4467584Z   test_comprehensive___rsub___cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4468060Z   test_comprehensive___rsub___cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4468663Z   test_comprehensive___rsub___cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4469310Z   test_comprehensive___rsub___cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4469889Z   test_comprehensive___rsub___cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4470482Z   test_comprehensive___rsub___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4471116Z   test_comprehensive___rsub___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4471782Z   test_comprehensive___rsub___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4472289Z   test_comprehensive___rsub___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4472634Z   test_comprehensive___rsub___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4472964Z   test_comprehensive___rxor___cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4473305Z   test_comprehensive___rxor___cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4473645Z   test_comprehensive___rxor___cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4474029Z   test_comprehensive___rxor___cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4474374Z   test_comprehensive___rxor___cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4474719Z   test_comprehensive___rxor___cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4475089Z   test_comprehensive__native_batch_norm_legit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4475466Z   test_comprehensive__native_batch_norm_legit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4475860Z   test_comprehensive__native_batch_norm_legit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4476308Z   test_comprehensive__softmax_backward_data_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4476985Z   test_comprehensive__softmax_backward_data_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4477627Z   test_comprehensive__softmax_backward_data_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4478301Z   test_comprehensive_abs_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4478742Z   test_comprehensive_abs_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.011s)
2023-01-11T20:52:32.4479095Z   test_comprehensive_abs_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4479435Z   test_comprehensive_abs_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4479787Z   test_comprehensive_abs_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4480136Z   test_comprehensive_abs_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4480470Z   test_comprehensive_abs_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4480971Z   test_comprehensive_abs_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4481544Z   test_comprehensive_abs_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4482099Z   test_comprehensive_abs_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4482707Z   test_comprehensive_abs_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4483326Z   test_comprehensive_abs_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4483964Z   test_comprehensive_acos_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4484386Z   test_comprehensive_acos_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4484723Z   test_comprehensive_acos_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4485079Z   test_comprehensive_acos_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4485430Z   test_comprehensive_acos_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4485764Z   test_comprehensive_acos_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4486106Z   test_comprehensive_acos_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4486463Z   test_comprehensive_acos_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4486997Z   test_comprehensive_acos_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4487560Z   test_comprehensive_acos_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4488298Z   test_comprehensive_acos_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4488949Z   test_comprehensive_acosh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4489594Z   test_comprehensive_acosh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4490204Z   test_comprehensive_acosh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4490866Z   test_comprehensive_acosh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4491530Z   test_comprehensive_acosh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4492129Z   test_comprehensive_acosh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4492464Z   test_comprehensive_acosh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4492812Z   test_comprehensive_acosh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4493208Z   test_comprehensive_acosh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4493537Z   test_comprehensive_acosh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4493883Z   test_comprehensive_acosh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4494259Z   test_comprehensive_add_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4494813Z   test_comprehensive_add_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4495369Z   test_comprehensive_add_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4496113Z   test_comprehensive_add_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4496675Z   test_comprehensive_add_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4497240Z   test_comprehensive_add_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4497805Z   test_comprehensive_add_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4498367Z   test_comprehensive_add_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4498919Z   test_comprehensive_add_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4499629Z   test_comprehensive_add_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4500195Z   test_comprehensive_add_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4500755Z   test_comprehensive_add_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4501289Z   test_comprehensive_add_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4501870Z   test_comprehensive_addbmm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4502471Z   test_comprehensive_addbmm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4503076Z   test_comprehensive_addbmm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4503644Z   test_comprehensive_addbmm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4504231Z   test_comprehensive_addbmm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4504829Z   test_comprehensive_addbmm_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4505472Z   test_comprehensive_addbmm_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4506029Z   test_comprehensive_addbmm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4506603Z   test_comprehensive_addbmm_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4507186Z   test_comprehensive_addbmm_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4507746Z   test_comprehensive_addcdiv_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4508347Z   test_comprehensive_addcdiv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4508949Z   test_comprehensive_addcdiv_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4509844Z   test_comprehensive_addcdiv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4510620Z   test_comprehensive_addcdiv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4511410Z   test_comprehensive_addcmul_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4512162Z   test_comprehensive_addcmul_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4512899Z   test_comprehensive_addcmul_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4513478Z   test_comprehensive_addcmul_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4514194Z   test_comprehensive_addcmul_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4514952Z   test_comprehensive_addcmul_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4515676Z   test_comprehensive_addcmul_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4516374Z   test_comprehensive_addcmul_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4517104Z   test_comprehensive_addcmul_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4517818Z   test_comprehensive_addcmul_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4518543Z   test_comprehensive_addmm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4519285Z   test_comprehensive_addmm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4520007Z   test_comprehensive_addmm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4520797Z   test_comprehensive_addmm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4521454Z   test_comprehensive_addmm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4522072Z   test_comprehensive_addmm_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4522595Z   test_comprehensive_addmm_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4523224Z   test_comprehensive_addmm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4523852Z   test_comprehensive_addmm_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4524475Z   test_comprehensive_addmm_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4525139Z   test_comprehensive_addmm_decomposed_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4525813Z   test_comprehensive_addmm_decomposed_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4526699Z   test_comprehensive_addmm_decomposed_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4527440Z   test_comprehensive_addmm_decomposed_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4528203Z   test_comprehensive_addmm_decomposed_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4528945Z   test_comprehensive_addmm_decomposed_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4529809Z   test_comprehensive_addmm_decomposed_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4530536Z   test_comprehensive_addmm_decomposed_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4531283Z   test_comprehensive_addmm_decomposed_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4532020Z   test_comprehensive_addmm_decomposed_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4532818Z   test_comprehensive_addmv_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4533608Z   test_comprehensive_addmv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4534200Z   test_comprehensive_addmv_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4534912Z   test_comprehensive_addmv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4535598Z   test_comprehensive_addmv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4536322Z   test_comprehensive_addmv_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4537051Z   test_comprehensive_addmv_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4537763Z   test_comprehensive_addmv_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4538444Z   test_comprehensive_addmv_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4539140Z   test_comprehensive_addmv_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4539918Z   test_comprehensive_addr_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4540620Z   test_comprehensive_addr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4541312Z   test_comprehensive_addr_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4542030Z   test_comprehensive_addr_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4542614Z   test_comprehensive_addr_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4543324Z   test_comprehensive_addr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4544008Z   test_comprehensive_addr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4544705Z   test_comprehensive_addr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4545405Z   test_comprehensive_addr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4546095Z   test_comprehensive_addr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4546764Z   test_comprehensive_addr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4547457Z   test_comprehensive_addr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4548165Z   test_comprehensive_all_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4548934Z   test_comprehensive_all_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4549500Z   test_comprehensive_all_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4550233Z   test_comprehensive_all_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4550804Z   test_comprehensive_all_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4551488Z   test_comprehensive_all_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4552045Z   test_comprehensive_all_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4552602Z   test_comprehensive_all_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4553175Z   test_comprehensive_all_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4553853Z   test_comprehensive_all_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4554512Z   test_comprehensive_all_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4555082Z   test_comprehensive_all_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4555656Z   test_comprehensive_allclose_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4556237Z   test_comprehensive_allclose_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4556863Z   test_comprehensive_allclose_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4557532Z   test_comprehensive_allclose_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4558196Z   test_comprehensive_allclose_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4558839Z   test_comprehensive_allclose_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4559495Z   test_comprehensive_amax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4559945Z   test_comprehensive_amax_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4560293Z   test_comprehensive_amax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4560725Z   test_comprehensive_amax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4561138Z   test_comprehensive_amax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4561487Z   test_comprehensive_amax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4561819Z   test_comprehensive_amax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4562225Z   test_comprehensive_amax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4562859Z   test_comprehensive_amax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4563420Z   test_comprehensive_amax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4563755Z   test_comprehensive_amin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4564103Z   test_comprehensive_amin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4564449Z   test_comprehensive_amin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4564794Z   test_comprehensive_amin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4565130Z   test_comprehensive_amin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4565557Z   test_comprehensive_amin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4565907Z   test_comprehensive_amin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4566239Z   test_comprehensive_amin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4566581Z   test_comprehensive_amin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4566932Z   test_comprehensive_amin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4567280Z   test_comprehensive_aminmax_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4567624Z   test_comprehensive_aminmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4567981Z   test_comprehensive_aminmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4568332Z   test_comprehensive_aminmax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4568717Z   test_comprehensive_aminmax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4569050Z   test_comprehensive_aminmax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4569399Z   test_comprehensive_aminmax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4569745Z   test_comprehensive_aminmax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4570083Z   test_comprehensive_angle_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4570431Z   test_comprehensive_angle_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4570785Z   test_comprehensive_angle_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4571146Z   test_comprehensive_angle_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4571490Z   test_comprehensive_angle_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.001s)
2023-01-11T20:52:32.4571845Z   test_comprehensive_angle_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4572191Z   test_comprehensive_angle_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4572532Z   test_comprehensive_angle_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4572862Z   test_comprehensive_angle_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4573202Z   test_comprehensive_angle_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4573547Z   test_comprehensive_angle_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4573880Z   test_comprehensive_angle_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4574227Z   test_comprehensive_any_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4574572Z   test_comprehensive_any_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4574920Z   test_comprehensive_any_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4575260Z   test_comprehensive_any_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4575606Z   test_comprehensive_any_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4575945Z   test_comprehensive_any_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4576315Z   test_comprehensive_any_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4576640Z   test_comprehensive_any_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4576987Z   test_comprehensive_any_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4577324Z   test_comprehensive_any_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4577652Z   test_comprehensive_any_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4577989Z   test_comprehensive_any_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4578338Z   test_comprehensive_arange_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4578694Z   test_comprehensive_arange_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4579036Z   test_comprehensive_arange_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4579494Z   test_comprehensive_arange_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4579853Z   test_comprehensive_arange_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4580206Z   test_comprehensive_arange_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4580542Z   test_comprehensive_arange_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4580889Z   test_comprehensive_arange_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4581245Z   test_comprehensive_arange_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4581584Z   test_comprehensive_argmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4581945Z   test_comprehensive_argmax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4582304Z   test_comprehensive_argmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4582660Z   test_comprehensive_argmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4582998Z   test_comprehensive_argmax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4583342Z   test_comprehensive_argmax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4583690Z   test_comprehensive_argmax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4584036Z   test_comprehensive_argmax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4584369Z   test_comprehensive_argmax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4584726Z   test_comprehensive_argmin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4585080Z   test_comprehensive_argmin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4585431Z   test_comprehensive_argmin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4585766Z   test_comprehensive_argmin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4586114Z   test_comprehensive_argmin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4586462Z   test_comprehensive_argmin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4586796Z   test_comprehensive_argmin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4587139Z   test_comprehensive_argmin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4587516Z   test_comprehensive_argmin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4587869Z   test_comprehensive_argsort_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4588207Z   test_comprehensive_argsort_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4588554Z   test_comprehensive_argsort_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4588905Z   test_comprehensive_argsort_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4589259Z   test_comprehensive_argsort_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4589597Z   test_comprehensive_argsort_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4589945Z   test_comprehensive_argsort_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4590315Z   test_comprehensive_argsort_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4590650Z   test_comprehensive_argsort_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4591001Z   test_comprehensive_argsort_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4591367Z   test_comprehensive_argwhere_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4591723Z   test_comprehensive_argwhere_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4592071Z   test_comprehensive_argwhere_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4592441Z   test_comprehensive_argwhere_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4592802Z   test_comprehensive_argwhere_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4593166Z   test_comprehensive_argwhere_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4593507Z   test_comprehensive_argwhere_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4593867Z   test_comprehensive_argwhere_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4594221Z   test_comprehensive_argwhere_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4594554Z   test_comprehensive_argwhere_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4594905Z   test_comprehensive_argwhere_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4595259Z   test_comprehensive_argwhere_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4595620Z   test_comprehensive_as_strided_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4595968Z   test_comprehensive_as_strided_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4596328Z   test_comprehensive_as_strided_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4596699Z   test_comprehensive_as_strided_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4597062Z   test_comprehensive_as_strided_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4597408Z   test_comprehensive_as_strided_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4597772Z   test_comprehensive_as_strided_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4598174Z   test_comprehensive_as_strided_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4598527Z   test_comprehensive_as_strided_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4598870Z   test_comprehensive_as_strided_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4599222Z   test_comprehensive_as_strided_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4599576Z   test_comprehensive_as_strided_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4599914Z   test_comprehensive_as_strided_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4600289Z   test_comprehensive_as_strided_partial_views_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4600812Z   test_comprehensive_as_strided_partial_views_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4601216Z   test_comprehensive_as_strided_partial_views_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4601653Z   test_comprehensive_as_strided_partial_views_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4602050Z   test_comprehensive_as_strided_partial_views_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4602443Z   test_comprehensive_as_strided_partial_views_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4602832Z   test_comprehensive_as_strided_partial_views_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4603209Z   test_comprehensive_as_strided_partial_views_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4603594Z   test_comprehensive_as_strided_partial_views_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4603984Z   test_comprehensive_as_strided_partial_views_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4604370Z   test_comprehensive_as_strided_partial_views_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4604739Z   test_comprehensive_as_strided_partial_views_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4605122Z   test_comprehensive_as_strided_partial_views_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4605529Z   test_comprehensive_as_strided_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4605962Z   test_comprehensive_as_strided_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4606383Z   test_comprehensive_as_strided_scatter_cpu_complex128 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4606821Z   test_comprehensive_as_strided_scatter_cpu_complex32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4607249Z   test_comprehensive_as_strided_scatter_cpu_complex64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4607679Z   test_comprehensive_as_strided_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4608094Z   test_comprehensive_as_strided_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4608520Z   test_comprehensive_as_strided_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4608989Z   test_comprehensive_as_strided_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4609412Z   test_comprehensive_as_strided_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4609817Z   test_comprehensive_as_strided_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4610235Z   test_comprehensive_as_strided_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4610656Z   test_comprehensive_as_strided_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.4615297Z   test_comprehensive_asin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4615657Z   test_comprehensive_asin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4616012Z   test_comprehensive_asin_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4616437Z   test_comprehensive_asin_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4616796Z   test_comprehensive_asin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4617131Z   test_comprehensive_asin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4617482Z   test_comprehensive_asin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4617829Z   test_comprehensive_asin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4618173Z   test_comprehensive_asin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4618506Z   test_comprehensive_asin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4618853Z   test_comprehensive_asin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4619200Z   test_comprehensive_asinh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4619643Z   test_comprehensive_asinh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4619983Z   test_comprehensive_asinh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4620341Z   test_comprehensive_asinh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4620697Z   test_comprehensive_asinh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4621035Z   test_comprehensive_asinh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4621382Z   test_comprehensive_asinh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4621729Z   test_comprehensive_asinh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4622072Z   test_comprehensive_asinh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4622398Z   test_comprehensive_asinh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4622740Z   test_comprehensive_asinh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4623089Z   test_comprehensive_atan2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4623430Z   test_comprehensive_atan2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4623763Z   test_comprehensive_atan2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4624148Z   test_comprehensive_atan2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4624487Z   test_comprehensive_atan2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4624816Z   test_comprehensive_atan2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4625155Z   test_comprehensive_atan2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4625492Z   test_comprehensive_atan2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4625835Z   test_comprehensive_atan2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4626166Z   test_comprehensive_atan_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4626514Z   test_comprehensive_atan_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4626868Z   test_comprehensive_atan_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4627253Z   test_comprehensive_atan_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4627591Z   test_comprehensive_atan_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4627937Z   test_comprehensive_atan_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4628280Z   test_comprehensive_atan_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4628606Z   test_comprehensive_atan_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4628942Z   test_comprehensive_atan_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4629278Z   test_comprehensive_atan_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4629616Z   test_comprehensive_atan_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4629951Z   test_comprehensive_atanh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4630411Z   test_comprehensive_atanh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4630761Z   test_comprehensive_atanh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4631122Z   test_comprehensive_atanh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4631461Z   test_comprehensive_atanh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4631812Z   test_comprehensive_atanh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4632155Z   test_comprehensive_atanh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4632486Z   test_comprehensive_atanh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4632829Z   test_comprehensive_atanh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4633174Z   test_comprehensive_atanh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4633521Z   test_comprehensive_atanh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4633862Z   test_comprehensive_atleast_1d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4634218Z   test_comprehensive_atleast_1d_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4634581Z   test_comprehensive_atleast_1d_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4634951Z   test_comprehensive_atleast_1d_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4635339Z   test_comprehensive_atleast_1d_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4635702Z   test_comprehensive_atleast_1d_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4636058Z   test_comprehensive_atleast_1d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4636411Z   test_comprehensive_atleast_1d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4636746Z   test_comprehensive_atleast_1d_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4637097Z   test_comprehensive_atleast_1d_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4637449Z   test_comprehensive_atleast_1d_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4637793Z   test_comprehensive_atleast_1d_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4638169Z   test_comprehensive_atleast_1d_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4638526Z   test_comprehensive_atleast_2d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4638884Z   test_comprehensive_atleast_2d_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4639234Z   test_comprehensive_atleast_2d_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4639597Z   test_comprehensive_atleast_2d_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4639957Z   test_comprehensive_atleast_2d_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4640316Z   test_comprehensive_atleast_2d_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4640845Z   test_comprehensive_atleast_2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4641211Z   test_comprehensive_atleast_2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4641563Z   test_comprehensive_atleast_2d_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4641902Z   test_comprehensive_atleast_2d_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4642253Z   test_comprehensive_atleast_2d_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4642606Z   test_comprehensive_atleast_2d_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4642958Z   test_comprehensive_atleast_2d_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4643309Z   test_comprehensive_atleast_3d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4643669Z   test_comprehensive_atleast_3d_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4644030Z   test_comprehensive_atleast_3d_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4644396Z   test_comprehensive_atleast_3d_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4644746Z   test_comprehensive_atleast_3d_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4645104Z   test_comprehensive_atleast_3d_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4645462Z   test_comprehensive_atleast_3d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4645813Z   test_comprehensive_atleast_3d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4646210Z   test_comprehensive_atleast_3d_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4646568Z   test_comprehensive_atleast_3d_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4646921Z   test_comprehensive_atleast_3d_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4647261Z   test_comprehensive_atleast_3d_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4647616Z   test_comprehensive_atleast_3d_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4647971Z   test_comprehensive_baddbmm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4648331Z   test_comprehensive_baddbmm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4648681Z   test_comprehensive_baddbmm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4649078Z   test_comprehensive_baddbmm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4649429Z   test_comprehensive_baddbmm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4649777Z   test_comprehensive_baddbmm_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4650166Z   test_comprehensive_baddbmm_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4650517Z   test_comprehensive_baddbmm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4650861Z   test_comprehensive_baddbmm_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4651211Z   test_comprehensive_baddbmm_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4651554Z   test_comprehensive_bernoulli_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4651913Z   test_comprehensive_bernoulli_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4652272Z   test_comprehensive_bernoulli_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4652616Z   test_comprehensive_bfloat16_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4652969Z   test_comprehensive_bfloat16_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4653328Z   test_comprehensive_bfloat16_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4653687Z   test_comprehensive_bfloat16_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4654030Z   test_comprehensive_bfloat16_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4654388Z   test_comprehensive_bfloat16_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4654744Z   test_comprehensive_bfloat16_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4655098Z   test_comprehensive_bfloat16_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4655438Z   test_comprehensive_bfloat16_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4655784Z   test_comprehensive_bfloat16_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4656125Z   test_comprehensive_bfloat16_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4656470Z   test_comprehensive_bfloat16_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4656842Z   test_comprehensive_bfloat16_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4657190Z   test_comprehensive_bincount_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4657537Z   test_comprehensive_bincount_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4657870Z   test_comprehensive_bincount_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4658219Z   test_comprehensive_bincount_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4658566Z   test_comprehensive_bincount_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4658915Z   test_comprehensive_bitwise_and_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4659330Z   test_comprehensive_bitwise_and_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4659693Z   test_comprehensive_bitwise_and_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4660078Z   test_comprehensive_bitwise_and_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4660437Z   test_comprehensive_bitwise_and_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4660776Z   test_comprehensive_bitwise_and_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4661137Z   test_comprehensive_bitwise_left_shift_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4661507Z   test_comprehensive_bitwise_left_shift_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4661856Z   test_comprehensive_bitwise_left_shift_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4662228Z   test_comprehensive_bitwise_left_shift_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4662595Z   test_comprehensive_bitwise_left_shift_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4662957Z   test_comprehensive_bitwise_not_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4663297Z   test_comprehensive_bitwise_not_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4663646Z   test_comprehensive_bitwise_not_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4663997Z   test_comprehensive_bitwise_not_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4664350Z   test_comprehensive_bitwise_not_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4664687Z   test_comprehensive_bitwise_not_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4665036Z   test_comprehensive_bitwise_or_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4665389Z   test_comprehensive_bitwise_or_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4665738Z   test_comprehensive_bitwise_or_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4666076Z   test_comprehensive_bitwise_or_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4666425Z   test_comprehensive_bitwise_or_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4666774Z   test_comprehensive_bitwise_or_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4667126Z   test_comprehensive_bitwise_right_shift_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4667495Z   test_comprehensive_bitwise_right_shift_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4667910Z   test_comprehensive_bitwise_right_shift_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4668282Z   test_comprehensive_bitwise_right_shift_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4668632Z   test_comprehensive_bitwise_right_shift_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4668990Z   test_comprehensive_bitwise_xor_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4669348Z   test_comprehensive_bitwise_xor_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4669701Z   test_comprehensive_bitwise_xor_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4670042Z   test_comprehensive_bitwise_xor_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4670396Z   test_comprehensive_bitwise_xor_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4670778Z   test_comprehensive_bitwise_xor_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4671139Z   test_comprehensive_block_diag_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4671485Z   test_comprehensive_block_diag_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4671849Z   test_comprehensive_block_diag_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4672217Z   test_comprehensive_block_diag_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4672566Z   test_comprehensive_block_diag_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4672930Z   test_comprehensive_block_diag_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4673291Z   test_comprehensive_block_diag_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4673650Z   test_comprehensive_block_diag_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.002s)
2023-01-11T20:52:32.4673991Z   test_comprehensive_block_diag_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4674344Z   test_comprehensive_block_diag_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4674695Z   test_comprehensive_block_diag_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4675049Z   test_comprehensive_block_diag_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4675387Z   test_comprehensive_block_diag_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4675737Z   test_comprehensive_bmm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4676092Z   test_comprehensive_bmm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4676445Z   test_comprehensive_bmm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4676779Z   test_comprehensive_bmm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4677124Z   test_comprehensive_bmm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4677466Z   test_comprehensive_bmm_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4677791Z   test_comprehensive_bmm_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4678126Z   test_comprehensive_bmm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4678519Z   test_comprehensive_bmm_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4678862Z   test_comprehensive_bmm_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4679192Z   test_comprehensive_bool_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4679541Z   test_comprehensive_bool_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4679888Z   test_comprehensive_bool_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4680242Z   test_comprehensive_bool_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4680583Z   test_comprehensive_bool_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4681069Z   test_comprehensive_bool_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4681420Z   test_comprehensive_bool_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4681815Z   test_comprehensive_bool_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4682162Z   test_comprehensive_bool_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4682511Z   test_comprehensive_bool_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4682853Z   test_comprehensive_bool_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4683183Z   test_comprehensive_bool_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4683524Z   test_comprehensive_bool_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4683884Z   test_comprehensive_broadcast_shapes_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4684272Z   test_comprehensive_broadcast_tensors_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4684640Z   test_comprehensive_broadcast_tensors_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4685021Z   test_comprehensive_broadcast_tensors_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4685403Z   test_comprehensive_broadcast_tensors_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4685781Z   test_comprehensive_broadcast_tensors_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4686138Z   test_comprehensive_broadcast_tensors_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4686504Z   test_comprehensive_broadcast_tensors_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4686882Z   test_comprehensive_broadcast_tensors_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4687235Z   test_comprehensive_broadcast_tensors_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4687599Z   test_comprehensive_broadcast_tensors_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4687964Z   test_comprehensive_broadcast_tensors_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4688328Z   test_comprehensive_broadcast_tensors_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4688681Z   test_comprehensive_broadcast_to_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4689041Z   test_comprehensive_broadcast_to_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4689448Z   test_comprehensive_broadcast_to_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4689818Z   test_comprehensive_broadcast_to_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4690172Z   test_comprehensive_broadcast_to_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4690534Z   test_comprehensive_broadcast_to_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4690893Z   test_comprehensive_broadcast_to_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4691252Z   test_comprehensive_broadcast_to_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4691599Z   test_comprehensive_broadcast_to_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4691957Z   test_comprehensive_broadcast_to_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4692313Z   test_comprehensive_broadcast_to_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4692684Z   test_comprehensive_broadcast_to_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4693044Z   test_comprehensive_bucketize_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4693399Z   test_comprehensive_bucketize_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4693754Z   test_comprehensive_bucketize_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4694094Z   test_comprehensive_bucketize_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4694449Z   test_comprehensive_bucketize_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4694801Z   test_comprehensive_bucketize_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4695155Z   test_comprehensive_bucketize_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4695494Z   test_comprehensive_bucketize_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4695843Z   test_comprehensive_bucketize_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4696191Z   test_comprehensive_byte_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4696533Z   test_comprehensive_byte_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4696865Z   test_comprehensive_byte_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4697214Z   test_comprehensive_byte_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4697566Z   test_comprehensive_byte_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4697900Z   test_comprehensive_byte_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4698242Z   test_comprehensive_byte_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4698582Z   test_comprehensive_byte_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4698920Z   test_comprehensive_byte_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4699318Z   test_comprehensive_byte_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4699663Z   test_comprehensive_byte_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4700003Z   test_comprehensive_byte_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4700407Z   test_comprehensive_cartesian_prod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4700766Z   test_comprehensive_cartesian_prod_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4701141Z   test_comprehensive_cartesian_prod_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4701521Z   test_comprehensive_cartesian_prod_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4701880Z   test_comprehensive_cartesian_prod_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4702251Z   test_comprehensive_cartesian_prod_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4702615Z   test_comprehensive_cartesian_prod_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4702992Z   test_comprehensive_cartesian_prod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4703376Z   test_comprehensive_cartesian_prod_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4703739Z   test_comprehensive_cartesian_prod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4704103Z   test_comprehensive_cartesian_prod_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4704466Z   test_comprehensive_cartesian_prod_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4704804Z   test_comprehensive_cat_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4705150Z   test_comprehensive_cat_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4705496Z   test_comprehensive_cat_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4705849Z   test_comprehensive_cat_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4706187Z   test_comprehensive_cat_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4706535Z   test_comprehensive_cat_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4706880Z   test_comprehensive_cat_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4707212Z   test_comprehensive_cat_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4707549Z   test_comprehensive_cat_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4707884Z   test_comprehensive_cat_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4708213Z   test_comprehensive_cat_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4708540Z   test_comprehensive_cat_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4708876Z   test_comprehensive_cat_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4709215Z   test_comprehensive_cdist_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4709563Z   test_comprehensive_cdist_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4709903Z   test_comprehensive_cdouble_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4710254Z   test_comprehensive_cdouble_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4710611Z   test_comprehensive_cdouble_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4710961Z   test_comprehensive_cdouble_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4711351Z   test_comprehensive_cdouble_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4711708Z   test_comprehensive_cdouble_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4712059Z   test_comprehensive_cdouble_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4712398Z   test_comprehensive_cdouble_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4712746Z   test_comprehensive_cdouble_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4713097Z   test_comprehensive_cdouble_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4713440Z   test_comprehensive_cdouble_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4713773Z   test_comprehensive_cdouble_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4714119Z   test_comprehensive_cdouble_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4714494Z   test_comprehensive_ceil_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4714839Z   test_comprehensive_ceil_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4715174Z   test_comprehensive_ceil_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4715517Z   test_comprehensive_ceil_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4715860Z   test_comprehensive_ceil_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4716182Z   test_comprehensive_ceil_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4716522Z   test_comprehensive_ceil_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4716863Z   test_comprehensive_ceil_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4717209Z   test_comprehensive_cfloat_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4717545Z   test_comprehensive_cfloat_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4717896Z   test_comprehensive_cfloat_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4718254Z   test_comprehensive_cfloat_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4718612Z   test_comprehensive_cfloat_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4718955Z   test_comprehensive_cfloat_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4719310Z   test_comprehensive_cfloat_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4719658Z   test_comprehensive_cfloat_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4719994Z   test_comprehensive_cfloat_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4720338Z   test_comprehensive_cfloat_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4720802Z   test_comprehensive_cfloat_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4721155Z   test_comprehensive_cfloat_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4721489Z   test_comprehensive_cfloat_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4721843Z   test_comprehensive_chalf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4722289Z   test_comprehensive_chalf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4722645Z   test_comprehensive_chalf_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4722990Z   test_comprehensive_chalf_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4723345Z   test_comprehensive_chalf_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4723703Z   test_comprehensive_chalf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4724038Z   test_comprehensive_chalf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4724385Z   test_comprehensive_chalf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4724731Z   test_comprehensive_chalf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4725079Z   test_comprehensive_chalf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4725446Z   test_comprehensive_chalf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4725792Z   test_comprehensive_chalf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4726135Z   test_comprehensive_chalf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4726481Z   test_comprehensive_char_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4726813Z   test_comprehensive_char_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4727167Z   test_comprehensive_char_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4727521Z   test_comprehensive_char_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4727864Z   test_comprehensive_char_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4728222Z   test_comprehensive_char_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4728570Z   test_comprehensive_char_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4728916Z   test_comprehensive_char_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4729245Z   test_comprehensive_char_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4729589Z   test_comprehensive_char_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4729932Z   test_comprehensive_char_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4730274Z   test_comprehensive_char_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4730605Z   test_comprehensive_char_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4730962Z   test_comprehensive_cholesky_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4731327Z   test_comprehensive_cholesky_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4731687Z   test_comprehensive_cholesky_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4732032Z   test_comprehensive_cholesky_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4732403Z   test_comprehensive_cholesky_inverse_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4732787Z   test_comprehensive_cholesky_inverse_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4733185Z   test_comprehensive_cholesky_inverse_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4733558Z   test_comprehensive_cholesky_inverse_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4733924Z   test_comprehensive_cholesky_solve_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4734299Z   test_comprehensive_cholesky_solve_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4734652Z   test_comprehensive_cholesky_solve_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4735012Z   test_comprehensive_cholesky_solve_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4735371Z   test_comprehensive_chunk_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4735718Z   test_comprehensive_chunk_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4736060Z   test_comprehensive_chunk_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4736443Z   test_comprehensive_chunk_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4736799Z   test_comprehensive_chunk_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4737149Z   test_comprehensive_chunk_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4737487Z   test_comprehensive_chunk_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4737833Z   test_comprehensive_chunk_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4738173Z   test_comprehensive_chunk_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4738502Z   test_comprehensive_chunk_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4738842Z   test_comprehensive_chunk_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4739185Z   test_comprehensive_chunk_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4739613Z   test_comprehensive_chunk_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4739948Z   test_comprehensive_clamp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4740298Z   test_comprehensive_clamp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4740646Z   test_comprehensive_clamp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4740989Z   test_comprehensive_clamp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4741323Z   test_comprehensive_clamp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4741672Z   test_comprehensive_clamp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4742020Z   test_comprehensive_clamp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4742353Z   test_comprehensive_clamp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4742703Z   test_comprehensive_clamp_max_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4743060Z   test_comprehensive_clamp_max_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4743416Z   test_comprehensive_clamp_max_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4743757Z   test_comprehensive_clamp_max_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4744155Z   test_comprehensive_clamp_max_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4744511Z   test_comprehensive_clamp_max_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4744860Z   test_comprehensive_clamp_max_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4745197Z   test_comprehensive_clamp_max_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4745548Z   test_comprehensive_clamp_max_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4745898Z   test_comprehensive_clamp_max_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4746246Z   test_comprehensive_clamp_min_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4746599Z   test_comprehensive_clamp_min_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4746951Z   test_comprehensive_clamp_min_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4747334Z   test_comprehensive_clamp_min_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4747675Z   test_comprehensive_clamp_min_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4748026Z   test_comprehensive_clamp_min_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4748372Z   test_comprehensive_clamp_min_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4748718Z   test_comprehensive_clamp_min_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4749056Z   test_comprehensive_clamp_min_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4749404Z   test_comprehensive_clamp_min_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4749750Z   test_comprehensive_clone_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4750093Z   test_comprehensive_clone_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4750431Z   test_comprehensive_clone_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4750784Z   test_comprehensive_clone_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4751134Z   test_comprehensive_clone_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4751468Z   test_comprehensive_clone_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4751816Z   test_comprehensive_clone_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4752162Z   test_comprehensive_clone_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4752503Z   test_comprehensive_clone_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4752832Z   test_comprehensive_clone_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4753172Z   test_comprehensive_clone_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4753513Z   test_comprehensive_clone_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4753852Z   test_comprehensive_clone_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4754193Z   test_comprehensive_column_stack_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4754554Z   test_comprehensive_column_stack_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4754949Z   test_comprehensive_column_stack_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4755305Z   test_comprehensive_column_stack_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4755675Z   test_comprehensive_column_stack_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4756036Z   test_comprehensive_column_stack_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4756393Z   test_comprehensive_column_stack_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4756740Z   test_comprehensive_column_stack_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4757098Z   test_comprehensive_column_stack_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4757455Z   test_comprehensive_column_stack_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4757836Z   test_comprehensive_column_stack_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4758177Z   test_comprehensive_column_stack_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4758531Z   test_comprehensive_column_stack_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4758885Z   test_comprehensive_combinations_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4759247Z   test_comprehensive_combinations_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4759602Z   test_comprehensive_combinations_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4759972Z   test_comprehensive_combinations_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4760342Z   test_comprehensive_combinations_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4760828Z   test_comprehensive_combinations_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4761181Z   test_comprehensive_combinations_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4761547Z   test_comprehensive_combinations_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4761909Z   test_comprehensive_combinations_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4762252Z   test_comprehensive_combinations_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4762607Z   test_comprehensive_combinations_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4762968Z   test_comprehensive_combinations_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4763321Z   test_comprehensive_complex_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4763665Z   test_comprehensive_complex_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4764016Z   test_comprehensive_complex_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4764367Z   test_comprehensive_conj_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4764712Z   test_comprehensive_conj_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4765047Z   test_comprehensive_conj_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4765398Z   test_comprehensive_conj_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4765805Z   test_comprehensive_conj_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4766145Z   test_comprehensive_conj_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4766498Z   test_comprehensive_conj_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4766845Z   test_comprehensive_conj_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4767194Z   test_comprehensive_conj_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4767526Z   test_comprehensive_conj_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4767876Z   test_comprehensive_conj_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4768217Z   test_comprehensive_conj_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4768558Z   test_comprehensive_conj_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4768939Z   test_comprehensive_conj_physical_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4769307Z   test_comprehensive_conj_physical_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4769675Z   test_comprehensive_conj_physical_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4770049Z   test_comprehensive_conj_physical_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4770403Z   test_comprehensive_conj_physical_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4770764Z   test_comprehensive_conj_physical_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4771136Z   test_comprehensive_conj_physical_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4771492Z   test_comprehensive_conj_physical_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4771854Z   test_comprehensive_conj_physical_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4772208Z   test_comprehensive_conj_physical_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4772559Z   test_comprehensive_conj_physical_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4772900Z   test_comprehensive_conj_physical_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4773258Z   test_comprehensive_conj_physical_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4773625Z   test_comprehensive_constant_pad_nd_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4773999Z   test_comprehensive_constant_pad_nd_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4774357Z   test_comprehensive_constant_pad_nd_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4774732Z   test_comprehensive_constant_pad_nd_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4775104Z   test_comprehensive_constant_pad_nd_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4775469Z   test_comprehensive_constant_pad_nd_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4775818Z   test_comprehensive_constant_pad_nd_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4776182Z   test_comprehensive_constant_pad_nd_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4776575Z   test_comprehensive_constant_pad_nd_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4776926Z   test_comprehensive_constant_pad_nd_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4777290Z   test_comprehensive_constant_pad_nd_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4777654Z   test_comprehensive_constant_pad_nd_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4778017Z   test_comprehensive_contiguous_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4778366Z   test_comprehensive_contiguous_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4778727Z   test_comprehensive_contiguous_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4779096Z   test_comprehensive_contiguous_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4779594Z   test_comprehensive_contiguous_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4779951Z   test_comprehensive_contiguous_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4780314Z   test_comprehensive_contiguous_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4780672Z   test_comprehensive_contiguous_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4781028Z   test_comprehensive_contiguous_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4781373Z   test_comprehensive_contiguous_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4781727Z   test_comprehensive_contiguous_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4782089Z   test_comprehensive_contiguous_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4782431Z   test_comprehensive_contiguous_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4782791Z   test_comprehensive_copysign_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4783148Z   test_comprehensive_copysign_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4783502Z   test_comprehensive_copysign_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4783845Z   test_comprehensive_copysign_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4784197Z   test_comprehensive_copysign_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4784551Z   test_comprehensive_copysign_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4784906Z   test_comprehensive_copysign_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4785246Z   test_comprehensive_copysign_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4785602Z   test_comprehensive_copysign_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4785952Z   test_comprehensive_copysign_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4786305Z   test_comprehensive_corrcoef_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4786654Z   test_comprehensive_corrcoef_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4787018Z   test_comprehensive_corrcoef_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4787408Z   test_comprehensive_corrcoef_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4787756Z   test_comprehensive_corrcoef_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4788108Z   test_comprehensive_corrcoef_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4788453Z   test_comprehensive_corrcoef_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4788801Z   test_comprehensive_corrcoef_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4789138Z   test_comprehensive_corrcoef_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4789490Z   test_comprehensive_corrcoef_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4789837Z   test_comprehensive_cos_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4790184Z   test_comprehensive_cos_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4790543Z   test_comprehensive_cos_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4790893Z   test_comprehensive_cos_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4791238Z   test_comprehensive_cos_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4791569Z   test_comprehensive_cos_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4791909Z   test_comprehensive_cos_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4792246Z   test_comprehensive_cos_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4792578Z   test_comprehensive_cos_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4792907Z   test_comprehensive_cos_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4793244Z   test_comprehensive_cos_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4793586Z   test_comprehensive_cosh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4793928Z   test_comprehensive_cosh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4794263Z   test_comprehensive_cosh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4794613Z   test_comprehensive_cosh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4794960Z   test_comprehensive_cosh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4795293Z   test_comprehensive_cosh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4795641Z   test_comprehensive_cosh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4795984Z   test_comprehensive_cosh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4796325Z   test_comprehensive_cosh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4796648Z   test_comprehensive_cosh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4796989Z   test_comprehensive_cosh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4797340Z   test_comprehensive_count_nonzero_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4797702Z   test_comprehensive_count_nonzero_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4798062Z   test_comprehensive_count_nonzero_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4798467Z   test_comprehensive_count_nonzero_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4798839Z   test_comprehensive_count_nonzero_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4799203Z   test_comprehensive_count_nonzero_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4799548Z   test_comprehensive_count_nonzero_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4799910Z   test_comprehensive_count_nonzero_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4800266Z   test_comprehensive_count_nonzero_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4800749Z   test_comprehensive_count_nonzero_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4801122Z   test_comprehensive_count_nonzero_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4801539Z   test_comprehensive_count_nonzero_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4801895Z   test_comprehensive_cov_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4802239Z   test_comprehensive_cov_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4802593Z   test_comprehensive_cov_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4802938Z   test_comprehensive_cov_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4803282Z   test_comprehensive_cov_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4803609Z   test_comprehensive_cov_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4803951Z   test_comprehensive_cov_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4804289Z   test_comprehensive_cov_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4804614Z   test_comprehensive_cov_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4804945Z   test_comprehensive_cov_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4805290Z   test_comprehensive_cross_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4805646Z   test_comprehensive_cross_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4805987Z   test_comprehensive_cross_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4806337Z   test_comprehensive_cross_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4806687Z   test_comprehensive_cross_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4807030Z   test_comprehensive_cross_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4807362Z   test_comprehensive_cross_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4807702Z   test_comprehensive_cross_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4808041Z   test_comprehensive_cross_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4808367Z   test_comprehensive_cross_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4808714Z   test_comprehensive_cummax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4809068Z   test_comprehensive_cummax_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4809454Z   test_comprehensive_cummax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4809794Z   test_comprehensive_cummax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4810141Z   test_comprehensive_cummax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4810487Z   test_comprehensive_cummax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4810836Z   test_comprehensive_cummax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4811164Z   test_comprehensive_cummax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4841095Z   test_comprehensive_cummax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4841464Z   test_comprehensive_cummin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4841839Z   test_comprehensive_cummin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4842332Z   test_comprehensive_cummin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4842692Z   test_comprehensive_cummin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4843036Z   test_comprehensive_cummin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4843384Z   test_comprehensive_cummin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4843735Z   test_comprehensive_cummin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4844069Z   test_comprehensive_cummin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4844418Z   test_comprehensive_cummin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4844779Z   test_comprehensive_cumprod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4845143Z   test_comprehensive_cumprod_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4845496Z   test_comprehensive_cumprod_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4845854Z   test_comprehensive_cumprod_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4846203Z   test_comprehensive_cumprod_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4846556Z   test_comprehensive_cumprod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4846888Z   test_comprehensive_cumprod_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4847237Z   test_comprehensive_cumprod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4847583Z   test_comprehensive_cumprod_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4847921Z   test_comprehensive_cumprod_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4848276Z   test_comprehensive_cumsum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4848634Z   test_comprehensive_cumsum_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4848991Z   test_comprehensive_cumsum_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4849329Z   test_comprehensive_cumsum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4849675Z   test_comprehensive_cumsum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4850078Z   test_comprehensive_cumsum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4850423Z   test_comprehensive_cumsum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4850752Z   test_comprehensive_cumsum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4851096Z   test_comprehensive_cumsum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4851447Z   test_comprehensive_cumsum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4851806Z   test_comprehensive_cumulative_trapezoid_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4852204Z   test_comprehensive_cumulative_trapezoid_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4852603Z   test_comprehensive_cumulative_trapezoid_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4853026Z   test_comprehensive_cumulative_trapezoid_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4853394Z   test_comprehensive_cumulative_trapezoid_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4853773Z   test_comprehensive_cumulative_trapezoid_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4854145Z   test_comprehensive_cumulative_trapezoid_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4854521Z   test_comprehensive_cumulative_trapezoid_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4854885Z   test_comprehensive_cumulative_trapezoid_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4855266Z   test_comprehensive_cumulative_trapezoid_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4855636Z   test_comprehensive_deg2rad_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4855988Z   test_comprehensive_deg2rad_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4856328Z   test_comprehensive_deg2rad_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4856682Z   test_comprehensive_deg2rad_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4857034Z   test_comprehensive_deg2rad_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4857391Z   test_comprehensive_deg2rad_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4857727Z   test_comprehensive_deg2rad_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4858074Z   test_comprehensive_deg2rad_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4858420Z   test_comprehensive_deg2rad_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4858754Z   test_comprehensive_deg2rad_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4859099Z   test_comprehensive_diag_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4859531Z   test_comprehensive_diag_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4859885Z   test_comprehensive_diag_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4860228Z   test_comprehensive_diag_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4860588Z   test_comprehensive_diag_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4860975Z   test_comprehensive_diag_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4861321Z   test_comprehensive_diag_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4861471Z   test_comprehensive_diag_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4861627Z   test_comprehensive_diag_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4861781Z   test_comprehensive_diag_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4861940Z   test_comprehensive_diag_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4862100Z   test_comprehensive_diag_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4862274Z   test_comprehensive_diag_embed_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4862444Z   test_comprehensive_diag_embed_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.4862654Z   test_comprehensive_diag_embed_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4862834Z   test_comprehensive_diag_embed_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4862995Z   test_comprehensive_diag_embed_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4863169Z   test_comprehensive_diag_embed_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4863339Z   test_comprehensive_diag_embed_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4863506Z   test_comprehensive_diag_embed_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4863677Z   test_comprehensive_diag_embed_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4863844Z   test_comprehensive_diag_embed_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4864015Z   test_comprehensive_diag_embed_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4864183Z   test_comprehensive_diag_embed_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4864353Z   test_comprehensive_diag_embed_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4864512Z   test_comprehensive_diagflat_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4864676Z   test_comprehensive_diagflat_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4864847Z   test_comprehensive_diagflat_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4865021Z   test_comprehensive_diagflat_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4865190Z   test_comprehensive_diagflat_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4865360Z   test_comprehensive_diagflat_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4865526Z   test_comprehensive_diagflat_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4865694Z   test_comprehensive_diagflat_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4865860Z   test_comprehensive_diagflat_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4866012Z   test_comprehensive_diagflat_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4866179Z   test_comprehensive_diagflat_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4866384Z   test_comprehensive_diagflat_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4866565Z   test_comprehensive_diagonal_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4866737Z   test_comprehensive_diagonal_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4866917Z   test_comprehensive_diagonal_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4867098Z   test_comprehensive_diagonal_copy_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4867278Z   test_comprehensive_diagonal_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4867450Z   test_comprehensive_diagonal_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4867613Z   test_comprehensive_diagonal_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4867810Z   test_comprehensive_diagonal_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4867983Z   test_comprehensive_diagonal_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4868156Z   test_comprehensive_diagonal_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4868324Z   test_comprehensive_diagonal_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4868495Z   test_comprehensive_diagonal_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4868661Z   test_comprehensive_diagonal_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4868831Z   test_comprehensive_diagonal_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4869000Z   test_comprehensive_diagonal_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4869162Z   test_comprehensive_diagonal_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4869331Z   test_comprehensive_diagonal_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4869500Z   test_comprehensive_diagonal_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4869671Z   test_comprehensive_diagonal_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4869835Z   test_comprehensive_diagonal_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4870004Z   test_comprehensive_diagonal_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4870174Z   test_comprehensive_diagonal_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4870340Z   test_comprehensive_diagonal_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4870508Z   test_comprehensive_diagonal_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4870663Z   test_comprehensive_diagonal_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4870831Z   test_comprehensive_diagonal_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4871014Z   test_comprehensive_diagonal_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4871187Z   test_comprehensive_diagonal_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4871362Z   test_comprehensive_diagonal_scatter_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4871575Z   test_comprehensive_diagonal_scatter_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4871757Z   test_comprehensive_diagonal_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4871936Z   test_comprehensive_diagonal_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4872114Z   test_comprehensive_diagonal_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4872276Z   test_comprehensive_diagonal_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4872451Z   test_comprehensive_diagonal_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4872626Z   test_comprehensive_diagonal_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4872800Z   test_comprehensive_diagonal_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4873003Z   test_comprehensive_diagonal_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4873169Z   test_comprehensive_diff_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4873328Z   test_comprehensive_diff_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4873498Z   test_comprehensive_diff_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4873668Z   test_comprehensive_diff_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4873831Z   test_comprehensive_diff_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4873981Z   test_comprehensive_diff_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4874140Z   test_comprehensive_diff_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4874303Z   test_comprehensive_diff_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4874460Z   test_comprehensive_diff_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4874622Z   test_comprehensive_diff_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4874781Z   test_comprehensive_diff_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4874943Z   test_comprehensive_diff_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4875114Z   test_comprehensive_digamma_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4875270Z   test_comprehensive_digamma_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4875441Z   test_comprehensive_digamma_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4875610Z   test_comprehensive_digamma_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4875776Z   test_comprehensive_digamma_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4875935Z   test_comprehensive_digamma_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4876096Z   test_comprehensive_digamma_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4876260Z   test_comprehensive_digamma_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4876424Z   test_comprehensive_digamma_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4876614Z   test_comprehensive_dist_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4876768Z   test_comprehensive_dist_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4876938Z   test_comprehensive_dist_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4877100Z   test_comprehensive_dist_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4877258Z   test_comprehensive_dist_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4877414Z   test_comprehensive_dist_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4877597Z   test_comprehensive_div_floor_rounding_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4877776Z   test_comprehensive_div_floor_rounding_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4877959Z   test_comprehensive_div_floor_rounding_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4878160Z   test_comprehensive_div_floor_rounding_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4878325Z   test_comprehensive_div_floor_rounding_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4878503Z   test_comprehensive_div_floor_rounding_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4878679Z   test_comprehensive_div_floor_rounding_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4878854Z   test_comprehensive_div_floor_rounding_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4879030Z   test_comprehensive_div_floor_rounding_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4879217Z   test_comprehensive_div_no_rounding_mode_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4879401Z   test_comprehensive_div_no_rounding_mode_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4879590Z   test_comprehensive_div_no_rounding_mode_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4879773Z   test_comprehensive_div_no_rounding_mode_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4879956Z   test_comprehensive_div_no_rounding_mode_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4880122Z   test_comprehensive_div_no_rounding_mode_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4880295Z   test_comprehensive_div_no_rounding_mode_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4880473Z   test_comprehensive_div_no_rounding_mode_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4880769Z   test_comprehensive_div_no_rounding_mode_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4880951Z   test_comprehensive_div_no_rounding_mode_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4881130Z   test_comprehensive_div_no_rounding_mode_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4881312Z   test_comprehensive_div_no_rounding_mode_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4881497Z   test_comprehensive_div_trunc_rounding_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4881677Z   test_comprehensive_div_trunc_rounding_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4881890Z   test_comprehensive_div_trunc_rounding_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4882066Z   test_comprehensive_div_trunc_rounding_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4882242Z   test_comprehensive_div_trunc_rounding_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4882422Z   test_comprehensive_div_trunc_rounding_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4882598Z   test_comprehensive_div_trunc_rounding_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4882775Z   test_comprehensive_div_trunc_rounding_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4882953Z   test_comprehensive_div_trunc_rounding_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4883120Z   test_comprehensive_dot_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4883297Z   test_comprehensive_dot_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4883518Z   test_comprehensive_dot_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4883689Z   test_comprehensive_dot_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4883850Z   test_comprehensive_dot_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4884012Z   test_comprehensive_dot_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4884174Z   test_comprehensive_dot_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4884334Z   test_comprehensive_dot_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4884499Z   test_comprehensive_dot_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4884660Z   test_comprehensive_dot_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4884834Z   test_comprehensive_double_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4884985Z   test_comprehensive_double_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4885159Z   test_comprehensive_double_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4885330Z   test_comprehensive_double_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4885502Z   test_comprehensive_double_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4885671Z   test_comprehensive_double_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4885842Z   test_comprehensive_double_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4886013Z   test_comprehensive_double_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4886176Z   test_comprehensive_double_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4886337Z   test_comprehensive_double_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4886482Z   test_comprehensive_double_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4886691Z   test_comprehensive_double_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4886878Z   test_comprehensive_double_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4887052Z   test_comprehensive_dsplit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4887261Z   test_comprehensive_dsplit_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4887435Z   test_comprehensive_dsplit_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4887607Z   test_comprehensive_dsplit_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4887777Z   test_comprehensive_dsplit_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4887944Z   test_comprehensive_dsplit_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4888095Z   test_comprehensive_dsplit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4888263Z   test_comprehensive_dsplit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4888427Z   test_comprehensive_dsplit_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4888590Z   test_comprehensive_dsplit_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4888769Z   test_comprehensive_dsplit_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4888936Z   test_comprehensive_dsplit_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4889099Z   test_comprehensive_dsplit_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4889270Z   test_comprehensive_dstack_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4889435Z   test_comprehensive_dstack_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4889595Z   test_comprehensive_dstack_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4889761Z   test_comprehensive_dstack_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4889932Z   test_comprehensive_dstack_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4890100Z   test_comprehensive_dstack_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4890265Z   test_comprehensive_dstack_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4890431Z   test_comprehensive_dstack_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4890593Z   test_comprehensive_dstack_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4890753Z   test_comprehensive_dstack_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4890909Z   test_comprehensive_dstack_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4891060Z   test_comprehensive_dstack_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4891219Z   test_comprehensive_dstack_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4891387Z   test_comprehensive_einsum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4891559Z   test_comprehensive_einsum_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4891728Z   test_comprehensive_einsum_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4891892Z   test_comprehensive_einsum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4892057Z   test_comprehensive_einsum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4892218Z   test_comprehensive_einsum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4892406Z   test_comprehensive_einsum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4892553Z   test_comprehensive_einsum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4892713Z   test_comprehensive_einsum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4892869Z   test_comprehensive_einsum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4893034Z   test_comprehensive_empty_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4893197Z   test_comprehensive_empty_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4893367Z   test_comprehensive_empty_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4893536Z   test_comprehensive_empty_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4893708Z   test_comprehensive_empty_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4893886Z   test_comprehensive_empty_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4894051Z   test_comprehensive_empty_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4894210Z   test_comprehensive_empty_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4894372Z   test_comprehensive_empty_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4894528Z   test_comprehensive_empty_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4894689Z   test_comprehensive_empty_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4894851Z   test_comprehensive_empty_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4895015Z   test_comprehensive_empty_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4895190Z   test_comprehensive_empty_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4895345Z   test_comprehensive_empty_like_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4895520Z   test_comprehensive_empty_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4895688Z   test_comprehensive_empty_like_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4895862Z   test_comprehensive_empty_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4896026Z   test_comprehensive_empty_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4896198Z   test_comprehensive_empty_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4896368Z   test_comprehensive_empty_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4896538Z   test_comprehensive_empty_like_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4896708Z   test_comprehensive_empty_like_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4896863Z   test_comprehensive_empty_like_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4897028Z   test_comprehensive_empty_like_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4897195Z   test_comprehensive_empty_like_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4897359Z   test_comprehensive_eq_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4897545Z   test_comprehensive_eq_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4897715Z   test_comprehensive_eq_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4897882Z   test_comprehensive_eq_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4898047Z   test_comprehensive_eq_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4898207Z   test_comprehensive_eq_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4898350Z   test_comprehensive_eq_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4898504Z   test_comprehensive_eq_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4898663Z   test_comprehensive_eq_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4898823Z   test_comprehensive_eq_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4899015Z   test_comprehensive_eq_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4899168Z   test_comprehensive_eq_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4899410Z   test_comprehensive_eq_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4899586Z   test_comprehensive_equal_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4899750Z   test_comprehensive_equal_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4899907Z   test_comprehensive_equal_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4900079Z   test_comprehensive_equal_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4900250Z   test_comprehensive_equal_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4900415Z   test_comprehensive_equal_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4900581Z   test_comprehensive_equal_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4900744Z   test_comprehensive_equal_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4900904Z   test_comprehensive_equal_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4901064Z   test_comprehensive_equal_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4901227Z   test_comprehensive_equal_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4901378Z   test_comprehensive_equal_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4901548Z   test_comprehensive_erf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4901711Z   test_comprehensive_erf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4901876Z   test_comprehensive_erf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4902037Z   test_comprehensive_erf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4902198Z   test_comprehensive_erf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4902358Z   test_comprehensive_erf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4902519Z   test_comprehensive_erf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4902680Z   test_comprehensive_erf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4902859Z   test_comprehensive_erf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4903027Z   test_comprehensive_erfc_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4903187Z   test_comprehensive_erfc_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4903352Z   test_comprehensive_erfc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4903506Z   test_comprehensive_erfc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4903667Z   test_comprehensive_erfc_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4903822Z   test_comprehensive_erfc_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4903977Z   test_comprehensive_erfc_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4904125Z   test_comprehensive_erfc_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4904316Z   test_comprehensive_erfc_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4904486Z   test_comprehensive_erfinv_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4904647Z   test_comprehensive_erfinv_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4904814Z   test_comprehensive_erfinv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4904982Z   test_comprehensive_erfinv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4905146Z   test_comprehensive_erfinv_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4905306Z   test_comprehensive_erfinv_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4905475Z   test_comprehensive_erfinv_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4905626Z   test_comprehensive_erfinv_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4905790Z   test_comprehensive_erfinv_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4905957Z   test_comprehensive_exp2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4906117Z   test_comprehensive_exp2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4906282Z   test_comprehensive_exp2_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4906445Z   test_comprehensive_exp2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4906604Z   test_comprehensive_exp2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4906770Z   test_comprehensive_exp2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4906931Z   test_comprehensive_exp2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4907080Z   test_comprehensive_exp2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4907239Z   test_comprehensive_exp2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4907400Z   test_comprehensive_exp2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4907564Z   test_comprehensive_exp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4907725Z   test_comprehensive_exp_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4907894Z   test_comprehensive_exp_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4908091Z   test_comprehensive_exp_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4908257Z   test_comprehensive_exp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4908416Z   test_comprehensive_exp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4908564Z   test_comprehensive_exp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4908722Z   test_comprehensive_exp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4908880Z   test_comprehensive_exp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4909033Z   test_comprehensive_exp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4909192Z   test_comprehensive_exp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4909365Z   test_comprehensive_expand_as_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4909563Z   test_comprehensive_expand_as_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4909734Z   test_comprehensive_expand_as_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4909900Z   test_comprehensive_expand_as_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4910059Z   test_comprehensive_expand_as_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4910229Z   test_comprehensive_expand_as_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4910390Z   test_comprehensive_expand_as_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4910563Z   test_comprehensive_expand_as_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4910723Z   test_comprehensive_expand_as_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4910883Z   test_comprehensive_expand_as_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4911051Z   test_comprehensive_expand_as_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4911213Z   test_comprehensive_expand_as_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4911371Z   test_comprehensive_expand_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4911523Z   test_comprehensive_expand_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4911695Z   test_comprehensive_expand_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4911861Z   test_comprehensive_expand_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4912022Z   test_comprehensive_expand_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4912184Z   test_comprehensive_expand_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4912344Z   test_comprehensive_expand_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4912512Z   test_comprehensive_expand_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4912670Z   test_comprehensive_expand_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4912823Z   test_comprehensive_expand_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4912974Z   test_comprehensive_expand_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4913165Z   test_comprehensive_expand_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4913325Z   test_comprehensive_expm1_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4913487Z   test_comprehensive_expm1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4913644Z   test_comprehensive_expm1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4913799Z   test_comprehensive_expm1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4913960Z   test_comprehensive_expm1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4914107Z   test_comprehensive_expm1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4914269Z   test_comprehensive_expm1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4914420Z   test_comprehensive_expm1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4914606Z   test_comprehensive_expm1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4914768Z   test_comprehensive_eye_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4914929Z   test_comprehensive_eye_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4915096Z   test_comprehensive_eye_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4915258Z   test_comprehensive_eye_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4915408Z   test_comprehensive_eye_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4915557Z   test_comprehensive_eye_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4915707Z   test_comprehensive_eye_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4915867Z   test_comprehensive_eye_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4916016Z   test_comprehensive_eye_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4916167Z   test_comprehensive_eye_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4916325Z   test_comprehensive_eye_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4916482Z   test_comprehensive_fft_fft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4916646Z   test_comprehensive_fft_fft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4916812Z   test_comprehensive_fft_fft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4916981Z   test_comprehensive_fft_fft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4917139Z   test_comprehensive_fft_fft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4917296Z   test_comprehensive_fft_fft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4917448Z   test_comprehensive_fft_fft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4917610Z   test_comprehensive_fft_fft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4917766Z   test_comprehensive_fft_fft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4917922Z   test_comprehensive_fft_fft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4918117Z   test_comprehensive_fft_fft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4918283Z   test_comprehensive_fft_fft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4918446Z   test_comprehensive_fft_fft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4918600Z   test_comprehensive_fft_fft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4918766Z   test_comprehensive_fft_fft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4918919Z   test_comprehensive_fft_fft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4919080Z   test_comprehensive_fft_fft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4919231Z   test_comprehensive_fft_fft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4919389Z   test_comprehensive_fft_fft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4919575Z   test_comprehensive_fft_fft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4919727Z   test_comprehensive_fft_fftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4919895Z   test_comprehensive_fft_fftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4920053Z   test_comprehensive_fft_fftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4920222Z   test_comprehensive_fft_fftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4920380Z   test_comprehensive_fft_fftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4920537Z   test_comprehensive_fft_fftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4920842Z   test_comprehensive_fft_fftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4921002Z   test_comprehensive_fft_fftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4921161Z   test_comprehensive_fft_fftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4921326Z   test_comprehensive_fft_fftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4921496Z   test_comprehensive_fft_fftshift_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4921655Z   test_comprehensive_fft_fftshift_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4921827Z   test_comprehensive_fft_fftshift_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4922007Z   test_comprehensive_fft_fftshift_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4922176Z   test_comprehensive_fft_fftshift_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4922351Z   test_comprehensive_fft_fftshift_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4922524Z   test_comprehensive_fft_fftshift_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4922693Z   test_comprehensive_fft_fftshift_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4922855Z   test_comprehensive_fft_fftshift_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4923027Z   test_comprehensive_fft_fftshift_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4923186Z   test_comprehensive_fft_fftshift_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4923415Z   test_comprehensive_fft_fftshift_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4923577Z   test_comprehensive_fft_fftshift_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4923743Z   test_comprehensive_fft_hfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4923908Z   test_comprehensive_fft_hfft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4924073Z   test_comprehensive_fft_hfft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4924237Z   test_comprehensive_fft_hfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4924405Z   test_comprehensive_fft_hfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4924565Z   test_comprehensive_fft_hfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4924768Z   test_comprehensive_fft_hfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4924918Z   test_comprehensive_fft_hfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4925081Z   test_comprehensive_fft_hfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4925233Z   test_comprehensive_fft_hfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4925399Z   test_comprehensive_fft_hfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4925564Z   test_comprehensive_fft_hfft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4925729Z   test_comprehensive_fft_hfft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4925898Z   test_comprehensive_fft_hfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4926059Z   test_comprehensive_fft_hfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4926218Z   test_comprehensive_fft_hfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4926366Z   test_comprehensive_fft_hfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4926525Z   test_comprehensive_fft_hfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4926683Z   test_comprehensive_fft_hfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4926841Z   test_comprehensive_fft_hfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4927005Z   test_comprehensive_fft_hfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4927176Z   test_comprehensive_fft_hfftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4927343Z   test_comprehensive_fft_hfftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4927512Z   test_comprehensive_fft_hfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4927671Z   test_comprehensive_fft_hfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4927824Z   test_comprehensive_fft_hfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4927977Z   test_comprehensive_fft_hfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4928141Z   test_comprehensive_fft_hfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4928335Z   test_comprehensive_fft_hfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4928496Z   test_comprehensive_fft_hfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4928659Z   test_comprehensive_fft_ifft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4928825Z   test_comprehensive_fft_ifft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4928987Z   test_comprehensive_fft_ifft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4929146Z   test_comprehensive_fft_ifft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4929302Z   test_comprehensive_fft_ifft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4929456Z   test_comprehensive_fft_ifft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4929613Z   test_comprehensive_fft_ifft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4929806Z   test_comprehensive_fft_ifft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4929966Z   test_comprehensive_fft_ifft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4930122Z   test_comprehensive_fft_ifft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4930284Z   test_comprehensive_fft_ifft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4930449Z   test_comprehensive_fft_ifft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4930619Z   test_comprehensive_fft_ifft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4930774Z   test_comprehensive_fft_ifft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4930936Z   test_comprehensive_fft_ifft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4931096Z   test_comprehensive_fft_ifft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4931260Z   test_comprehensive_fft_ifft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4931414Z   test_comprehensive_fft_ifft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4931571Z   test_comprehensive_fft_ifft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4931727Z   test_comprehensive_fft_ifft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4931887Z   test_comprehensive_fft_ifftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4932057Z   test_comprehensive_fft_ifftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4932219Z   test_comprehensive_fft_ifftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4932380Z   test_comprehensive_fft_ifftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4932541Z   test_comprehensive_fft_ifftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4932704Z   test_comprehensive_fft_ifftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4932864Z   test_comprehensive_fft_ifftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4933021Z   test_comprehensive_fft_ifftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4933186Z   test_comprehensive_fft_ifftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4933370Z   test_comprehensive_fft_ifftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4933542Z   test_comprehensive_fft_ifftshift_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4933702Z   test_comprehensive_fft_ifftshift_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4933875Z   test_comprehensive_fft_ifftshift_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4934049Z   test_comprehensive_fft_ifftshift_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4934220Z   test_comprehensive_fft_ifftshift_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4934393Z   test_comprehensive_fft_ifftshift_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4934560Z   test_comprehensive_fft_ifftshift_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4934749Z   test_comprehensive_fft_ifftshift_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4934915Z   test_comprehensive_fft_ifftshift_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4935083Z   test_comprehensive_fft_ifftshift_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4935240Z   test_comprehensive_fft_ifftshift_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4935403Z   test_comprehensive_fft_ifftshift_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4935562Z   test_comprehensive_fft_ifftshift_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4935728Z   test_comprehensive_fft_ihfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4935890Z   test_comprehensive_fft_ihfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4936052Z   test_comprehensive_fft_ihfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4936211Z   test_comprehensive_fft_ihfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4936378Z   test_comprehensive_fft_ihfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4936536Z   test_comprehensive_fft_ihfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4936688Z   test_comprehensive_fft_ihfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4936856Z   test_comprehensive_fft_ihfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4937017Z   test_comprehensive_fft_ihfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4937188Z   test_comprehensive_fft_ihfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4937350Z   test_comprehensive_fft_ihfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4937508Z   test_comprehensive_fft_ihfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4937667Z   test_comprehensive_fft_ihfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4937829Z   test_comprehensive_fft_ihfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4937987Z   test_comprehensive_fft_ihfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.002s)
2023-01-11T20:52:32.4938139Z   test_comprehensive_fft_ihfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4938328Z   test_comprehensive_fft_ihfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4938501Z   test_comprehensive_fft_ihfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4938665Z   test_comprehensive_fft_ihfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4938826Z   test_comprehensive_fft_ihfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4938991Z   test_comprehensive_fft_ihfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4939148Z   test_comprehensive_fft_ihfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4939374Z   test_comprehensive_fft_ihfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4939543Z   test_comprehensive_fft_ihfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4939700Z   test_comprehensive_fft_irfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4939900Z   test_comprehensive_fft_irfft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4940069Z   test_comprehensive_fft_irfft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4940230Z   test_comprehensive_fft_irfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4940390Z   test_comprehensive_fft_irfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4940558Z   test_comprehensive_fft_irfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4940716Z   test_comprehensive_fft_irfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4940871Z   test_comprehensive_fft_irfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4941038Z   test_comprehensive_fft_irfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4941191Z   test_comprehensive_fft_irfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4941351Z   test_comprehensive_fft_irfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4941517Z   test_comprehensive_fft_irfft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4941692Z   test_comprehensive_fft_irfft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4941854Z   test_comprehensive_fft_irfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4942018Z   test_comprehensive_fft_irfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4942185Z   test_comprehensive_fft_irfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4942345Z   test_comprehensive_fft_irfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4942501Z   test_comprehensive_fft_irfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4942655Z   test_comprehensive_fft_irfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4942820Z   test_comprehensive_fft_irfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4942977Z   test_comprehensive_fft_irfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4943149Z   test_comprehensive_fft_irfftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4943323Z   test_comprehensive_fft_irfftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4943519Z   test_comprehensive_fft_irfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4943691Z   test_comprehensive_fft_irfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4943855Z   test_comprehensive_fft_irfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4944015Z   test_comprehensive_fft_irfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4944164Z   test_comprehensive_fft_irfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4944331Z   test_comprehensive_fft_irfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4944489Z   test_comprehensive_fft_irfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4944658Z   test_comprehensive_fft_rfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4944847Z   test_comprehensive_fft_rfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4945006Z   test_comprehensive_fft_rfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4945168Z   test_comprehensive_fft_rfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4945324Z   test_comprehensive_fft_rfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4945486Z   test_comprehensive_fft_rfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4945638Z   test_comprehensive_fft_rfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4945796Z   test_comprehensive_fft_rfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4945957Z   test_comprehensive_fft_rfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4946126Z   test_comprehensive_fft_rfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4946286Z   test_comprehensive_fft_rfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4946445Z   test_comprehensive_fft_rfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4946605Z   test_comprehensive_fft_rfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4946760Z   test_comprehensive_fft_rfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4946917Z   test_comprehensive_fft_rfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4947071Z   test_comprehensive_fft_rfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4947238Z   test_comprehensive_fft_rfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4947403Z   test_comprehensive_fft_rfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4947568Z   test_comprehensive_fft_rfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4947732Z   test_comprehensive_fft_rfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4947893Z   test_comprehensive_fft_rfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4948052Z   test_comprehensive_fft_rfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4948216Z   test_comprehensive_fft_rfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4948414Z   test_comprehensive_fft_rfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4948570Z   test_comprehensive_fill_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4948734Z   test_comprehensive_fill_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4948892Z   test_comprehensive_fill_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4949054Z   test_comprehensive_fill_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4949219Z   test_comprehensive_fill_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4949378Z   test_comprehensive_fill_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4949539Z   test_comprehensive_fill_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4949694Z   test_comprehensive_fill_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4949885Z   test_comprehensive_fill_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4950030Z   test_comprehensive_fill_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4950185Z   test_comprehensive_fill_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4950334Z   test_comprehensive_fill_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4950490Z   test_comprehensive_fill_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4950661Z   test_comprehensive_flatten_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4950819Z   test_comprehensive_flatten_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4950989Z   test_comprehensive_flatten_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4951162Z   test_comprehensive_flatten_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4951325Z   test_comprehensive_flatten_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4951480Z   test_comprehensive_flatten_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4951642Z   test_comprehensive_flatten_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4951806Z   test_comprehensive_flatten_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4951964Z   test_comprehensive_flatten_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4952124Z   test_comprehensive_flatten_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4952286Z   test_comprehensive_flatten_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4952449Z   test_comprehensive_flatten_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4952610Z   test_comprehensive_flatten_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4952769Z   test_comprehensive_flip_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4952920Z   test_comprehensive_flip_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4953081Z   test_comprehensive_flip_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4953240Z   test_comprehensive_flip_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4953428Z   test_comprehensive_flip_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4953582Z   test_comprehensive_flip_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4953735Z   test_comprehensive_flip_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4953890Z   test_comprehensive_flip_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4954041Z   test_comprehensive_flip_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4954183Z   test_comprehensive_flip_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4954334Z   test_comprehensive_flip_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4954490Z   test_comprehensive_flip_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4954656Z   test_comprehensive_fliplr_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4954822Z   test_comprehensive_fliplr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4955017Z   test_comprehensive_fliplr_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4955190Z   test_comprehensive_fliplr_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4955351Z   test_comprehensive_fliplr_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4955513Z   test_comprehensive_fliplr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4955668Z   test_comprehensive_fliplr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4955831Z   test_comprehensive_fliplr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4955995Z   test_comprehensive_fliplr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4956150Z   test_comprehensive_fliplr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4956306Z   test_comprehensive_fliplr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4956472Z   test_comprehensive_fliplr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4956633Z   test_comprehensive_flipud_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4956793Z   test_comprehensive_flipud_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4956964Z   test_comprehensive_flipud_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4957121Z   test_comprehensive_flipud_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4957287Z   test_comprehensive_flipud_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4957449Z   test_comprehensive_flipud_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4957614Z   test_comprehensive_flipud_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4957773Z   test_comprehensive_flipud_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4957936Z   test_comprehensive_flipud_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4958091Z   test_comprehensive_flipud_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4958247Z   test_comprehensive_flipud_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4958402Z   test_comprehensive_flipud_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4958594Z   test_comprehensive_float_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4958760Z   test_comprehensive_float_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4958927Z   test_comprehensive_float_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4959090Z   test_comprehensive_float_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4959258Z   test_comprehensive_float_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4959419Z   test_comprehensive_float_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4959582Z   test_comprehensive_float_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4959742Z   test_comprehensive_float_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4959904Z   test_comprehensive_float_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4960075Z   test_comprehensive_float_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4960232Z   test_comprehensive_float_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4960390Z   test_comprehensive_float_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4960544Z   test_comprehensive_float_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4960878Z   test_comprehensive_float_power_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4961052Z   test_comprehensive_float_power_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4961230Z   test_comprehensive_float_power_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4961410Z   test_comprehensive_float_power_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4961577Z   test_comprehensive_float_power_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4961736Z   test_comprehensive_float_power_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4961897Z   test_comprehensive_float_power_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4962064Z   test_comprehensive_float_power_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4962223Z   test_comprehensive_float_power_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4962390Z   test_comprehensive_float_power_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4962556Z   test_comprehensive_float_power_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4962720Z   test_comprehensive_float_power_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4962881Z   test_comprehensive_floor_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4963047Z   test_comprehensive_floor_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4963194Z   test_comprehensive_floor_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4963350Z   test_comprehensive_floor_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4963507Z   test_comprehensive_floor_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4963660Z   test_comprehensive_floor_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4963916Z   test_comprehensive_floor_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4964075Z   test_comprehensive_floor_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4964252Z   test_comprehensive_floor_divide_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4964422Z   test_comprehensive_floor_divide_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4964591Z   test_comprehensive_floor_divide_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4964752Z   test_comprehensive_floor_divide_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4964924Z   test_comprehensive_floor_divide_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4965092Z   test_comprehensive_floor_divide_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4965296Z   test_comprehensive_floor_divide_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4965462Z   test_comprehensive_floor_divide_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4965629Z   test_comprehensive_floor_divide_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4965794Z   test_comprehensive_fmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4965951Z   test_comprehensive_fmax_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4966118Z   test_comprehensive_fmax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4966268Z   test_comprehensive_fmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4966430Z   test_comprehensive_fmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4966586Z   test_comprehensive_fmax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4966741Z   test_comprehensive_fmax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4966896Z   test_comprehensive_fmax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4967050Z   test_comprehensive_fmax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4967206Z   test_comprehensive_fmax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4967371Z   test_comprehensive_fmin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4967525Z   test_comprehensive_fmin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4967676Z   test_comprehensive_fmin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4967832Z   test_comprehensive_fmin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4967996Z   test_comprehensive_fmin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4968149Z   test_comprehensive_fmin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4968297Z   test_comprehensive_fmin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4968452Z   test_comprehensive_fmin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4968605Z   test_comprehensive_fmin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4968764Z   test_comprehensive_fmin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4968959Z   test_comprehensive_fmod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4969115Z   test_comprehensive_fmod_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4969272Z   test_comprehensive_fmod_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4969429Z   test_comprehensive_fmod_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4969586Z   test_comprehensive_fmod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4969738Z   test_comprehensive_fmod_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4969892Z   test_comprehensive_fmod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4970044Z   test_comprehensive_fmod_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4970200Z   test_comprehensive_fmod_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4970383Z   test_comprehensive_frac_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4970550Z   test_comprehensive_frac_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4970702Z   test_comprehensive_frac_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4970857Z   test_comprehensive_frac_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4971019Z   test_comprehensive_frexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4971177Z   test_comprehensive_frexp_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4971336Z   test_comprehensive_frexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4971490Z   test_comprehensive_frexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4971648Z   test_comprehensive_full_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4971797Z   test_comprehensive_full_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4971960Z   test_comprehensive_full_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4972121Z   test_comprehensive_full_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4972285Z   test_comprehensive_full_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4972444Z   test_comprehensive_full_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4972598Z   test_comprehensive_full_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4972758Z   test_comprehensive_full_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4972916Z   test_comprehensive_full_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4973069Z   test_comprehensive_full_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4973210Z   test_comprehensive_full_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4973366Z   test_comprehensive_full_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4973519Z   test_comprehensive_full_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4973685Z   test_comprehensive_full_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4973876Z   test_comprehensive_full_like_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4974049Z   test_comprehensive_full_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4974224Z   test_comprehensive_full_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4974388Z   test_comprehensive_full_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4974553Z   test_comprehensive_full_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4974709Z   test_comprehensive_full_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4974876Z   test_comprehensive_full_like_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4975034Z   test_comprehensive_full_like_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4975201Z   test_comprehensive_full_like_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4975395Z   test_comprehensive_full_like_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4975562Z   test_comprehensive_full_like_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4975727Z   test_comprehensive_gather_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4975889Z   test_comprehensive_gather_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4976062Z   test_comprehensive_gather_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4976220Z   test_comprehensive_gather_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4976382Z   test_comprehensive_gather_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4976546Z   test_comprehensive_gather_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4976714Z   test_comprehensive_gather_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4976874Z   test_comprehensive_gather_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4977029Z   test_comprehensive_gather_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4977187Z   test_comprehensive_gather_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4977344Z   test_comprehensive_gather_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4977497Z   test_comprehensive_gather_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4977645Z   test_comprehensive_gcd_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4977807Z   test_comprehensive_gcd_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4977964Z   test_comprehensive_gcd_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4978126Z   test_comprehensive_gcd_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4978277Z   test_comprehensive_gcd_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4978439Z   test_comprehensive_ge_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4978592Z   test_comprehensive_ge_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4978753Z   test_comprehensive_ge_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4978911Z   test_comprehensive_ge_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4979081Z   test_comprehensive_ge_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4979318Z   test_comprehensive_ge_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4979477Z   test_comprehensive_ge_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4979635Z   test_comprehensive_ge_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4979790Z   test_comprehensive_ge_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4979942Z   test_comprehensive_ge_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4980112Z   test_comprehensive_geqrf_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4980277Z   test_comprehensive_geqrf_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4980442Z   test_comprehensive_geqrf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4980623Z   test_comprehensive_geqrf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4980800Z   test_comprehensive_gradient_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4980973Z   test_comprehensive_gradient_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4981146Z   test_comprehensive_gradient_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4981317Z   test_comprehensive_gradient_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4981485Z   test_comprehensive_gradient_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4981661Z   test_comprehensive_gradient_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4981826Z   test_comprehensive_gradient_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4981993Z   test_comprehensive_gradient_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4982151Z   test_comprehensive_gradient_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4982317Z   test_comprehensive_gradient_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4982490Z   test_comprehensive_grid_sampler_2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4982663Z   test_comprehensive_grid_sampler_2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4982829Z   test_comprehensive_gt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4982988Z   test_comprehensive_gt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4983148Z   test_comprehensive_gt_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4983308Z   test_comprehensive_gt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4983463Z   test_comprehensive_gt_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4983609Z   test_comprehensive_gt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4983761Z   test_comprehensive_gt_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4983920Z   test_comprehensive_gt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4984072Z   test_comprehensive_gt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4984262Z   test_comprehensive_gt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4984426Z   test_comprehensive_half_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4984582Z   test_comprehensive_half_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4984752Z   test_comprehensive_half_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4984907Z   test_comprehensive_half_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4985066Z   test_comprehensive_half_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4985224Z   test_comprehensive_half_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4985377Z   test_comprehensive_half_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4985535Z   test_comprehensive_half_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4985720Z   test_comprehensive_half_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4985877Z   test_comprehensive_half_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4986037Z   test_comprehensive_half_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4986192Z   test_comprehensive_half_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4986355Z   test_comprehensive_heaviside_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4986518Z   test_comprehensive_heaviside_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4986689Z   test_comprehensive_heaviside_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4986856Z   test_comprehensive_heaviside_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4987029Z   test_comprehensive_heaviside_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4987194Z   test_comprehensive_heaviside_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4987357Z   test_comprehensive_heaviside_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4987519Z   test_comprehensive_heaviside_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4987684Z   test_comprehensive_heaviside_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4987841Z   test_comprehensive_heaviside_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4988008Z   test_comprehensive_histc_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4988170Z   test_comprehensive_histc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4988333Z   test_comprehensive_histc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4988505Z   test_comprehensive_histogram_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4988670Z   test_comprehensive_histogram_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4988840Z   test_comprehensive_histogramdd_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4989015Z   test_comprehensive_histogramdd_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4989176Z   test_comprehensive_hsplit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4989357Z   test_comprehensive_hsplit_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4989524Z   test_comprehensive_hsplit_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4989693Z   test_comprehensive_hsplit_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4989855Z   test_comprehensive_hsplit_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4990018Z   test_comprehensive_hsplit_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4990185Z   test_comprehensive_hsplit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4990350Z   test_comprehensive_hsplit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4990511Z   test_comprehensive_hsplit_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4990677Z   test_comprehensive_hsplit_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4990858Z   test_comprehensive_hsplit_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4991017Z   test_comprehensive_hsplit_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4991173Z   test_comprehensive_hsplit_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4991341Z   test_comprehensive_hstack_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4991497Z   test_comprehensive_hstack_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4991661Z   test_comprehensive_hstack_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4991826Z   test_comprehensive_hstack_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4991993Z   test_comprehensive_hstack_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4992155Z   test_comprehensive_hstack_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4992304Z   test_comprehensive_hstack_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4992470Z   test_comprehensive_hstack_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4992632Z   test_comprehensive_hstack_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4992791Z   test_comprehensive_hstack_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4992946Z   test_comprehensive_hstack_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4993103Z   test_comprehensive_hstack_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4993264Z   test_comprehensive_hstack_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4993430Z   test_comprehensive_hypot_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4993595Z   test_comprehensive_hypot_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4993744Z   test_comprehensive_hypot_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4993904Z   test_comprehensive_i0_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4994063Z   test_comprehensive_i0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4994226Z   test_comprehensive_i0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4994431Z   test_comprehensive_i0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4994592Z   test_comprehensive_i0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4994754Z   test_comprehensive_i0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4994912Z   test_comprehensive_i0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4995070Z   test_comprehensive_i0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4995216Z   test_comprehensive_i0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4995387Z   test_comprehensive_igamma_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4995553Z   test_comprehensive_igamma_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4995724Z   test_comprehensive_igamma_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4995889Z   test_comprehensive_igamma_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4996087Z   test_comprehensive_igammac_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4996257Z   test_comprehensive_igammac_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4996419Z   test_comprehensive_igammac_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4996575Z   test_comprehensive_igammac_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4996744Z   test_comprehensive_imag_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4996913Z   test_comprehensive_imag_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4997080Z   test_comprehensive_imag_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4997254Z   test_comprehensive_index_add_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4997421Z   test_comprehensive_index_add_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4997598Z   test_comprehensive_index_add_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4997774Z   test_comprehensive_index_add_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4997949Z   test_comprehensive_index_add_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4998117Z   test_comprehensive_index_add_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4998276Z   test_comprehensive_index_add_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4998448Z   test_comprehensive_index_add_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4998619Z   test_comprehensive_index_add_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4998785Z   test_comprehensive_index_add_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4998947Z   test_comprehensive_index_add_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4999115Z   test_comprehensive_index_add_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4999283Z   test_comprehensive_index_add_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4999458Z   test_comprehensive_index_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4999664Z   test_comprehensive_index_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.4999830Z   test_comprehensive_index_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5000005Z   test_comprehensive_index_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5000178Z   test_comprehensive_index_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5000348Z   test_comprehensive_index_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5000517Z   test_comprehensive_index_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5000862Z   test_comprehensive_index_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5001051Z   test_comprehensive_index_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5001223Z   test_comprehensive_index_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5001442Z   test_comprehensive_index_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5001597Z   test_comprehensive_index_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5001771Z   test_comprehensive_index_fill_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5001939Z   test_comprehensive_index_fill_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5002115Z   test_comprehensive_index_fill_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5002291Z   test_comprehensive_index_fill_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5002463Z   test_comprehensive_index_fill_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5002633Z   test_comprehensive_index_fill_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5002804Z   test_comprehensive_index_fill_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5002972Z   test_comprehensive_index_fill_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5003126Z   test_comprehensive_index_fill_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5003294Z   test_comprehensive_index_fill_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5003460Z   test_comprehensive_index_fill_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5003631Z   test_comprehensive_index_fill_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5003804Z   test_comprehensive_index_put_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5003978Z   test_comprehensive_index_put_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5004155Z   test_comprehensive_index_put_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5004330Z   test_comprehensive_index_put_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5004503Z   test_comprehensive_index_put_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5004661Z   test_comprehensive_index_put_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5004831Z   test_comprehensive_index_put_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5005000Z   test_comprehensive_index_put_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5005210Z   test_comprehensive_index_put_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5005378Z   test_comprehensive_index_put_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5005545Z   test_comprehensive_index_put_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5005712Z   test_comprehensive_index_put_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5005879Z   test_comprehensive_index_put_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5006055Z   test_comprehensive_index_reduce_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5006218Z   test_comprehensive_index_reduce_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5006397Z   test_comprehensive_index_reduce_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5006614Z   test_comprehensive_index_reduce_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5006786Z   test_comprehensive_index_reduce_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5006954Z   test_comprehensive_index_reduce_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5007124Z   test_comprehensive_index_reduce_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5007292Z   test_comprehensive_index_reduce_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5007464Z   test_comprehensive_index_reduce_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5007639Z   test_comprehensive_index_select_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5007797Z   test_comprehensive_index_select_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5007984Z   test_comprehensive_index_select_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5008163Z   test_comprehensive_index_select_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5008341Z   test_comprehensive_index_select_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5008513Z   test_comprehensive_index_select_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5008685Z   test_comprehensive_index_select_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5008855Z   test_comprehensive_index_select_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5009027Z   test_comprehensive_index_select_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5009198Z   test_comprehensive_index_select_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5009355Z   test_comprehensive_index_select_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5009523Z   test_comprehensive_index_select_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5009691Z   test_comprehensive_index_select_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5009862Z   test_comprehensive_inner_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5010030Z   test_comprehensive_inner_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5010197Z   test_comprehensive_inner_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5010396Z   test_comprehensive_inner_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5010560Z   test_comprehensive_inner_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5010724Z   test_comprehensive_inner_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5010870Z   test_comprehensive_inner_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5011031Z   test_comprehensive_inner_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5011192Z   test_comprehensive_inner_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5011351Z   test_comprehensive_inner_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5011515Z   test_comprehensive_int_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5011681Z   test_comprehensive_int_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5011882Z   test_comprehensive_int_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5012050Z   test_comprehensive_int_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5012212Z   test_comprehensive_int_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5012357Z   test_comprehensive_int_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5012513Z   test_comprehensive_int_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5012672Z   test_comprehensive_int_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5012832Z   test_comprehensive_int_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5012994Z   test_comprehensive_int_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.012s)
2023-01-11T20:52:32.5013156Z   test_comprehensive_int_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5013314Z   test_comprehensive_int_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5013487Z   test_comprehensive_isclose_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5013654Z   test_comprehensive_isclose_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5013815Z   test_comprehensive_isclose_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5013987Z   test_comprehensive_isclose_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5014155Z   test_comprehensive_isclose_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5014319Z   test_comprehensive_isclose_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5014488Z   test_comprehensive_isclose_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5014653Z   test_comprehensive_isclose_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5014814Z   test_comprehensive_isclose_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5014971Z   test_comprehensive_isclose_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5015137Z   test_comprehensive_isclose_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5015288Z   test_comprehensive_isclose_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5015490Z   test_comprehensive_isfinite_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5015653Z   test_comprehensive_isfinite_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5015829Z   test_comprehensive_isfinite_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5016004Z   test_comprehensive_isfinite_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5016176Z   test_comprehensive_isfinite_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5016347Z   test_comprehensive_isfinite_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5016516Z   test_comprehensive_isfinite_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5016683Z   test_comprehensive_isfinite_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5016838Z   test_comprehensive_isfinite_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5017035Z   test_comprehensive_isfinite_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5017201Z   test_comprehensive_isfinite_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5017369Z   test_comprehensive_isfinite_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5017534Z   test_comprehensive_isfinite_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5017699Z   test_comprehensive_isin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5017861Z   test_comprehensive_isin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5018025Z   test_comprehensive_isin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5018185Z   test_comprehensive_isin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5018328Z   test_comprehensive_isin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5018488Z   test_comprehensive_isin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5018652Z   test_comprehensive_isin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5018819Z   test_comprehensive_isinf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5018980Z   test_comprehensive_isinf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5019149Z   test_comprehensive_isinf_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5019389Z   test_comprehensive_isinf_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5019563Z   test_comprehensive_isinf_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5019718Z   test_comprehensive_isinf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5019883Z   test_comprehensive_isinf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5020045Z   test_comprehensive_isinf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5020209Z   test_comprehensive_isinf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5020368Z   test_comprehensive_isinf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5020524Z   test_comprehensive_isinf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5020723Z   test_comprehensive_isinf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5020881Z   test_comprehensive_isinf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5021053Z   test_comprehensive_isnan_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5021200Z   test_comprehensive_isnan_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5021371Z   test_comprehensive_isnan_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5021538Z   test_comprehensive_isnan_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5021702Z   test_comprehensive_isnan_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5021865Z   test_comprehensive_isnan_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5022025Z   test_comprehensive_isnan_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5022216Z   test_comprehensive_isnan_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5022375Z   test_comprehensive_isnan_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5022532Z   test_comprehensive_isnan_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5022680Z   test_comprehensive_isnan_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5022841Z   test_comprehensive_isnan_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5023014Z   test_comprehensive_isneginf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5023179Z   test_comprehensive_isneginf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5023351Z   test_comprehensive_isneginf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5023520Z   test_comprehensive_isneginf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5023687Z   test_comprehensive_isneginf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5023853Z   test_comprehensive_isneginf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5024020Z   test_comprehensive_isneginf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5024173Z   test_comprehensive_isneginf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5024336Z   test_comprehensive_isneginf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5024500Z   test_comprehensive_isneginf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5024674Z   test_comprehensive_isposinf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5024843Z   test_comprehensive_isposinf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5025014Z   test_comprehensive_isposinf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5025181Z   test_comprehensive_isposinf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5025349Z   test_comprehensive_isposinf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5025513Z   test_comprehensive_isposinf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5025666Z   test_comprehensive_isposinf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5025862Z   test_comprehensive_isposinf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5026027Z   test_comprehensive_isposinf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5026195Z   test_comprehensive_isposinf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5026364Z   test_comprehensive_isreal_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5026530Z   test_comprehensive_isreal_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5026702Z   test_comprehensive_isreal_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5026872Z   test_comprehensive_isreal_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5027041Z   test_comprehensive_isreal_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5027196Z   test_comprehensive_isreal_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5027390Z   test_comprehensive_isreal_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5027555Z   test_comprehensive_isreal_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5027719Z   test_comprehensive_isreal_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5027880Z   test_comprehensive_isreal_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5028037Z   test_comprehensive_isreal_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5028198Z   test_comprehensive_isreal_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5028356Z   test_comprehensive_isreal_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5028526Z   test_comprehensive_istft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5028683Z   test_comprehensive_istft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5028873Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5029060Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5029254Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5029444Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5029629Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5029818Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5030009Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5030196Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5030382Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5030555Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5030738Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5030922Z   test_comprehensive_jiterator_2inputs_2outputs_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5031165Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5031360Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5031559Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5031760Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5031953Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5032146Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5032343Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5032553Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5032747Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5032938Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5033128Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5033322Z   test_comprehensive_jiterator_4inputs_with_extra_args_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5033504Z   test_comprehensive_jiterator_binary_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5033686Z   test_comprehensive_jiterator_binary_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5033873Z   test_comprehensive_jiterator_binary_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5034054Z   test_comprehensive_jiterator_binary_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5034219Z   test_comprehensive_jiterator_binary_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5034400Z   test_comprehensive_jiterator_binary_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5034578Z   test_comprehensive_jiterator_binary_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5034752Z   test_comprehensive_jiterator_binary_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5034928Z   test_comprehensive_jiterator_binary_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5035103Z   test_comprehensive_jiterator_binary_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5035277Z   test_comprehensive_jiterator_binary_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5035447Z   test_comprehensive_jiterator_binary_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5035639Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5035827Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5036009Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5036244Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5036442Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5036633Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5036823Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5037011Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5037199Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5037383Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5037572Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5037786Z   test_comprehensive_jiterator_binary_return_by_ref_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5037953Z   test_comprehensive_jiterator_unary_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5038129Z   test_comprehensive_jiterator_unary_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5038312Z   test_comprehensive_jiterator_unary_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5038489Z   test_comprehensive_jiterator_unary_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5038666Z   test_comprehensive_jiterator_unary_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5038843Z   test_comprehensive_jiterator_unary_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5039022Z   test_comprehensive_jiterator_unary_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5039195Z   test_comprehensive_jiterator_unary_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5039367Z   test_comprehensive_jiterator_unary_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5039522Z   test_comprehensive_jiterator_unary_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5039697Z   test_comprehensive_jiterator_unary_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5039870Z   test_comprehensive_jiterator_unary_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5040038Z   test_comprehensive_kron_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5040202Z   test_comprehensive_kron_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5040375Z   test_comprehensive_kron_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5040542Z   test_comprehensive_kron_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5040879Z   test_comprehensive_kron_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5041049Z   test_comprehensive_kron_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5041192Z   test_comprehensive_kron_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5041355Z   test_comprehensive_kron_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5041568Z   test_comprehensive_kron_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5041733Z   test_comprehensive_kron_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5041894Z   test_comprehensive_kron_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5042059Z   test_comprehensive_kron_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5042232Z   test_comprehensive_kthvalue_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5042405Z   test_comprehensive_kthvalue_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5042575Z   test_comprehensive_kthvalue_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5042729Z   test_comprehensive_kthvalue_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5042899Z   test_comprehensive_kthvalue_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5043134Z   test_comprehensive_kthvalue_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5043303Z   test_comprehensive_kthvalue_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5043469Z   test_comprehensive_kthvalue_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5043631Z   test_comprehensive_lcm_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5043792Z   test_comprehensive_lcm_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5043952Z   test_comprehensive_lcm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5044108Z   test_comprehensive_lcm_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5044255Z   test_comprehensive_lcm_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5044420Z   test_comprehensive_ldexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5044583Z   test_comprehensive_ldexp_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5044753Z   test_comprehensive_ldexp_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5044921Z   test_comprehensive_ldexp_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5045085Z   test_comprehensive_ldexp_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5045244Z   test_comprehensive_ldexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5045407Z   test_comprehensive_ldexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5045573Z   test_comprehensive_ldexp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5045722Z   test_comprehensive_ldexp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5045886Z   test_comprehensive_ldexp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5046047Z   test_comprehensive_ldexp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5046209Z   test_comprehensive_ldexp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5046368Z   test_comprehensive_le_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5046529Z   test_comprehensive_le_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5046723Z   test_comprehensive_le_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5046881Z   test_comprehensive_le_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5047027Z   test_comprehensive_le_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5047187Z   test_comprehensive_le_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5047345Z   test_comprehensive_le_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5047504Z   test_comprehensive_le_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5047663Z   test_comprehensive_le_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5047818Z   test_comprehensive_le_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5047987Z   test_comprehensive_lerp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5048156Z   test_comprehensive_lerp_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5048352Z   test_comprehensive_lerp_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5048507Z   test_comprehensive_lerp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5048670Z   test_comprehensive_lerp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5048838Z   test_comprehensive_lgamma_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5049004Z   test_comprehensive_lgamma_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5049179Z   test_comprehensive_lgamma_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5049350Z   test_comprehensive_lgamma_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5049516Z   test_comprehensive_lgamma_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5049678Z   test_comprehensive_lgamma_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5049835Z   test_comprehensive_lgamma_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5049985Z   test_comprehensive_lgamma_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5050150Z   test_comprehensive_lgamma_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5050331Z   test_comprehensive_linalg_cholesky_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5050512Z   test_comprehensive_linalg_cholesky_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5050694Z   test_comprehensive_linalg_cholesky_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5050877Z   test_comprehensive_linalg_cholesky_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5051064Z   test_comprehensive_linalg_cholesky_ex_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5051248Z   test_comprehensive_linalg_cholesky_ex_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5051426Z   test_comprehensive_linalg_cholesky_ex_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5051592Z   test_comprehensive_linalg_cholesky_ex_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5051770Z   test_comprehensive_linalg_cond_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5051976Z   test_comprehensive_linalg_cond_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5052152Z   test_comprehensive_linalg_cond_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5052325Z   test_comprehensive_linalg_cond_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5052498Z   test_comprehensive_linalg_cross_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5052676Z   test_comprehensive_linalg_cross_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5052855Z   test_comprehensive_linalg_cross_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5053031Z   test_comprehensive_linalg_cross_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5053205Z   test_comprehensive_linalg_cross_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5053363Z   test_comprehensive_linalg_cross_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5053568Z   test_comprehensive_linalg_cross_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5053735Z   test_comprehensive_linalg_cross_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5053905Z   test_comprehensive_linalg_cross_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5054074Z   test_comprehensive_linalg_cross_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5054251Z   test_comprehensive_linalg_det_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5054427Z   test_comprehensive_linalg_det_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5054599Z   test_comprehensive_linalg_det_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5054772Z   test_comprehensive_linalg_det_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5054947Z   test_comprehensive_linalg_det_singular_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5055132Z   test_comprehensive_linalg_det_singular_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5055315Z   test_comprehensive_linalg_det_singular_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5055497Z   test_comprehensive_linalg_det_singular_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5055670Z   test_comprehensive_linalg_eig_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5055847Z   test_comprehensive_linalg_eig_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5056020Z   test_comprehensive_linalg_eig_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5056190Z   test_comprehensive_linalg_eig_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5056370Z   test_comprehensive_linalg_eigh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5056532Z   test_comprehensive_linalg_eigh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5056707Z   test_comprehensive_linalg_eigh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5056880Z   test_comprehensive_linalg_eigh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5057064Z   test_comprehensive_linalg_eigvals_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5057276Z   test_comprehensive_linalg_eigvals_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5057457Z   test_comprehensive_linalg_eigvals_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5057634Z   test_comprehensive_linalg_eigvals_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5057815Z   test_comprehensive_linalg_eigvalsh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5057994Z   test_comprehensive_linalg_eigvalsh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5058160Z   test_comprehensive_linalg_eigvalsh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5058338Z   test_comprehensive_linalg_eigvalsh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5058533Z   test_comprehensive_linalg_householder_product_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5058759Z   test_comprehensive_linalg_householder_product_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5058949Z   test_comprehensive_linalg_householder_product_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5059137Z   test_comprehensive_linalg_householder_product_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5059384Z   test_comprehensive_linalg_inv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5059562Z   test_comprehensive_linalg_inv_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5059736Z   test_comprehensive_linalg_inv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5059912Z   test_comprehensive_linalg_inv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5060080Z   test_comprehensive_linalg_inv_ex_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5060258Z   test_comprehensive_linalg_inv_ex_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5060435Z   test_comprehensive_linalg_inv_ex_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5060609Z   test_comprehensive_linalg_inv_ex_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5060795Z   test_comprehensive_linalg_ldl_factor_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5060980Z   test_comprehensive_linalg_ldl_factor_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5061162Z   test_comprehensive_linalg_ldl_factor_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5061345Z   test_comprehensive_linalg_ldl_factor_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5061534Z   test_comprehensive_linalg_ldl_factor_ex_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5061706Z   test_comprehensive_linalg_ldl_factor_ex_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5061887Z   test_comprehensive_linalg_ldl_factor_ex_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5062066Z   test_comprehensive_linalg_ldl_factor_ex_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5062247Z   test_comprehensive_linalg_ldl_solve_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5062465Z   test_comprehensive_linalg_ldl_solve_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5062646Z   test_comprehensive_linalg_ldl_solve_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5062826Z   test_comprehensive_linalg_ldl_solve_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5063005Z   test_comprehensive_linalg_lstsq_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5063185Z   test_comprehensive_linalg_lstsq_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5063358Z   test_comprehensive_linalg_lstsq_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5063518Z   test_comprehensive_linalg_lstsq_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5063711Z   test_comprehensive_linalg_lstsq_grad_oriented_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5063934Z   test_comprehensive_linalg_lstsq_grad_oriented_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5064125Z   test_comprehensive_linalg_lstsq_grad_oriented_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5064314Z   test_comprehensive_linalg_lstsq_grad_oriented_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5064490Z   test_comprehensive_linalg_lu_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5064665Z   test_comprehensive_linalg_lu_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5064836Z   test_comprehensive_linalg_lu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5065010Z   test_comprehensive_linalg_lu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5065180Z   test_comprehensive_linalg_lu_factor_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5065363Z   test_comprehensive_linalg_lu_factor_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5065540Z   test_comprehensive_linalg_lu_factor_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5065718Z   test_comprehensive_linalg_lu_factor_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5065902Z   test_comprehensive_linalg_lu_factor_ex_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5070653Z   test_comprehensive_linalg_multi_dot_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5073478Z   test_comprehensive_linalg_norm_subgradients_at_zero_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5073665Z   test_comprehensive_linalg_norm_subgradients_at_zero_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5073860Z   test_comprehensive_linalg_norm_subgradients_at_zero_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5074056Z   test_comprehensive_linalg_norm_subgradients_at_zero_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5074279Z   test_comprehensive_linalg_norm_subgradients_at_zero_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5074473Z   test_comprehensive_linalg_norm_subgradients_at_zero_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5074650Z   test_comprehensive_linalg_pinv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5076090Z   test_comprehensive_linalg_pinv_singular_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5076813Z   test_comprehensive_linalg_qr_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5077495Z   test_comprehensive_linalg_slogdet_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5078236Z   test_comprehensive_linalg_solve_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5079670Z   test_comprehensive_linalg_solve_triangular_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5080406Z   test_comprehensive_linalg_svd_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5081928Z   test_comprehensive_linalg_tensorinv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5082644Z   test_comprehensive_linalg_tensorsolve_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5083415Z   test_comprehensive_linalg_vander_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5086964Z   test_comprehensive_linspace_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5090010Z   test_comprehensive_log10_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5090171Z   test_comprehensive_log10_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5090320Z   test_comprehensive_log10_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5090478Z   test_comprehensive_log10_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5090645Z   test_comprehensive_log1p_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5090806Z   test_comprehensive_log1p_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5090979Z   test_comprehensive_log1p_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5091148Z   test_comprehensive_log1p_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5091311Z   test_comprehensive_log1p_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5091473Z   test_comprehensive_log1p_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5091636Z   test_comprehensive_log1p_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5091785Z   test_comprehensive_log1p_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5091941Z   test_comprehensive_log1p_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5092101Z   test_comprehensive_log1p_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5092259Z   test_comprehensive_log1p_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5092426Z   test_comprehensive_log2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5092587Z   test_comprehensive_log2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.002s)
2023-01-11T20:52:32.5092755Z   test_comprehensive_log2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5092923Z   test_comprehensive_log2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5093087Z   test_comprehensive_log2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5093234Z   test_comprehensive_log2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5093398Z   test_comprehensive_log2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5093583Z   test_comprehensive_log2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5093739Z   test_comprehensive_log2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5093899Z   test_comprehensive_log2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5094060Z   test_comprehensive_log2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5094227Z   test_comprehensive_log_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5094389Z   test_comprehensive_log_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5094556Z   test_comprehensive_log_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5094710Z   test_comprehensive_log_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5094875Z   test_comprehensive_log_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5095061Z   test_comprehensive_log_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5095222Z   test_comprehensive_log_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5095380Z   test_comprehensive_log_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5095538Z   test_comprehensive_log_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5095699Z   test_comprehensive_log_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5095858Z   test_comprehensive_log_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5096034Z   test_comprehensive_log_softmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5096195Z   test_comprehensive_log_softmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5096369Z   test_comprehensive_log_softmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5096554Z   test_comprehensive_log_softmax_with_dtype_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5096735Z   test_comprehensive_log_softmax_with_dtype_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5096920Z   test_comprehensive_log_softmax_with_dtype_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5097110Z   test_comprehensive_log_softmax_with_dtype_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5097297Z   test_comprehensive_log_softmax_with_dtype_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5097481Z   test_comprehensive_log_softmax_with_dtype_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5097667Z   test_comprehensive_log_softmax_with_dtype_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5097836Z   test_comprehensive_log_softmax_with_dtype_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5098021Z   test_comprehensive_log_softmax_with_dtype_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5098199Z   test_comprehensive_log_softmax_with_dtype_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5098373Z   test_comprehensive_log_softmax_with_dtype_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5098551Z   test_comprehensive_log_softmax_with_dtype_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5098758Z   test_comprehensive_log_softmax_with_dtype_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5098936Z   test_comprehensive_logaddexp2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5099111Z   test_comprehensive_logaddexp2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5099364Z   test_comprehensive_logaddexp2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5099529Z   test_comprehensive_logaddexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5099703Z   test_comprehensive_logaddexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5099874Z   test_comprehensive_logaddexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5100056Z   test_comprehensive_logcumsumexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5100263Z   test_comprehensive_logcumsumexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5100449Z   test_comprehensive_logcumsumexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5100623Z   test_comprehensive_logdet_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5100797Z   test_comprehensive_logdet_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5100967Z   test_comprehensive_logdet_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5101121Z   test_comprehensive_logdet_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5101297Z   test_comprehensive_logical_and_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5101471Z   test_comprehensive_logical_and_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5101652Z   test_comprehensive_logical_and_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5101831Z   test_comprehensive_logical_and_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5102002Z   test_comprehensive_logical_and_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5102174Z   test_comprehensive_logical_and_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5102343Z   test_comprehensive_logical_and_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5102515Z   test_comprehensive_logical_and_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5102685Z   test_comprehensive_logical_and_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5102843Z   test_comprehensive_logical_and_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5103015Z   test_comprehensive_logical_and_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5103182Z   test_comprehensive_logical_and_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5103356Z   test_comprehensive_logical_not_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5103526Z   test_comprehensive_logical_not_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5103705Z   test_comprehensive_logical_not_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5103881Z   test_comprehensive_logical_not_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5104100Z   test_comprehensive_logical_not_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5104273Z   test_comprehensive_logical_not_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5104432Z   test_comprehensive_logical_not_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5104599Z   test_comprehensive_logical_not_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5104769Z   test_comprehensive_logical_not_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5104937Z   test_comprehensive_logical_not_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5105108Z   test_comprehensive_logical_not_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5105278Z   test_comprehensive_logical_not_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5105479Z   test_comprehensive_logical_or_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5105650Z   test_comprehensive_logical_or_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5105827Z   test_comprehensive_logical_or_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5105989Z   test_comprehensive_logical_or_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5106162Z   test_comprehensive_logical_or_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5106335Z   test_comprehensive_logical_or_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5106501Z   test_comprehensive_logical_or_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5106674Z   test_comprehensive_logical_or_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5106841Z   test_comprehensive_logical_or_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5107011Z   test_comprehensive_logical_or_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5107179Z   test_comprehensive_logical_or_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5107349Z   test_comprehensive_logical_or_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5107509Z   test_comprehensive_logical_xor_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5107677Z   test_comprehensive_logical_xor_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5107859Z   test_comprehensive_logical_xor_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5108037Z   test_comprehensive_logical_xor_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5108206Z   test_comprehensive_logical_xor_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5108382Z   test_comprehensive_logical_xor_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5108554Z   test_comprehensive_logical_xor_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5108724Z   test_comprehensive_logical_xor_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5108891Z   test_comprehensive_logical_xor_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5109046Z   test_comprehensive_logical_xor_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5109243Z   test_comprehensive_logical_xor_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5109412Z   test_comprehensive_logical_xor_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5109584Z   test_comprehensive_logit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5109747Z   test_comprehensive_logit_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5109912Z   test_comprehensive_logit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5110079Z   test_comprehensive_logit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5110242Z   test_comprehensive_logit_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5110399Z   test_comprehensive_logit_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5110546Z   test_comprehensive_logit_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5110735Z   test_comprehensive_logit_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5110898Z   test_comprehensive_logit_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5111068Z   test_comprehensive_logspace_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5111242Z   test_comprehensive_logspace_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5111418Z   test_comprehensive_logspace_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5111589Z   test_comprehensive_logspace_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5111757Z   test_comprehensive_logspace_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5111928Z   test_comprehensive_logspace_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5112086Z   test_comprehensive_logspace_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5112255Z   test_comprehensive_logspace_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5112422Z   test_comprehensive_logspace_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5112590Z   test_comprehensive_logspace_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5112765Z   test_comprehensive_logsumexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5112933Z   test_comprehensive_logsumexp_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5113105Z   test_comprehensive_logsumexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5113276Z   test_comprehensive_logsumexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5113445Z   test_comprehensive_logsumexp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5113599Z   test_comprehensive_logsumexp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5113770Z   test_comprehensive_logsumexp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5113935Z   test_comprehensive_logsumexp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5114101Z   test_comprehensive_logsumexp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5114264Z   test_comprehensive_long_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5114459Z   test_comprehensive_long_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5114627Z   test_comprehensive_long_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5114797Z   test_comprehensive_long_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5114963Z   test_comprehensive_long_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5115115Z   test_comprehensive_long_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5115275Z   test_comprehensive_long_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5115433Z   test_comprehensive_long_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5115594Z   test_comprehensive_long_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5115753Z   test_comprehensive_long_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5115945Z   test_comprehensive_long_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5116104Z   test_comprehensive_long_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5116266Z   test_comprehensive_long_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5116428Z   test_comprehensive_lt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5116577Z   test_comprehensive_lt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5116739Z   test_comprehensive_lt_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5116895Z   test_comprehensive_lt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5117051Z   test_comprehensive_lt_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5117212Z   test_comprehensive_lt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5117372Z   test_comprehensive_lt_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5117534Z   test_comprehensive_lt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5117692Z   test_comprehensive_lt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5117835Z   test_comprehensive_lt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5118003Z   test_comprehensive_lu_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5118167Z   test_comprehensive_lu_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5118331Z   test_comprehensive_lu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5118490Z   test_comprehensive_lu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5118665Z   test_comprehensive_lu_solve_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5118836Z   test_comprehensive_lu_solve_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5119002Z   test_comprehensive_lu_solve_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5119167Z   test_comprehensive_lu_solve_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5119331Z   test_comprehensive_lu_unpack_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5119543Z   test_comprehensive_lu_unpack_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5119716Z   test_comprehensive_lu_unpack_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5119886Z   test_comprehensive_lu_unpack_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5120047Z   test_comprehensive_mH_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5120208Z   test_comprehensive_mH_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5120374Z   test_comprehensive_mH_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5120537Z   test_comprehensive_mH_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5120813Z   test_comprehensive_mH_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5120967Z   test_comprehensive_mH_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5121172Z   test_comprehensive_mH_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5121328Z   test_comprehensive_mH_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5121487Z   test_comprehensive_mH_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5121645Z   test_comprehensive_mH_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5121801Z   test_comprehensive_mH_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5121957Z   test_comprehensive_mH_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5122116Z   test_comprehensive_mH_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5122282Z   test_comprehensive_mT_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5122428Z   test_comprehensive_mT_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5122596Z   test_comprehensive_mT_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5122761Z   test_comprehensive_mT_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5122923Z   test_comprehensive_mT_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5123087Z   test_comprehensive_mT_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5123244Z   test_comprehensive_mT_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5123407Z   test_comprehensive_mT_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5123568Z   test_comprehensive_mT_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5123729Z   test_comprehensive_mT_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5123874Z   test_comprehensive_mT_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5124033Z   test_comprehensive_mT_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5124189Z   test_comprehensive_mT_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5124364Z   test_comprehensive_masked_amax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5124537Z   test_comprehensive_masked_amax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5124713Z   test_comprehensive_masked_amax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5124959Z   test_comprehensive_masked_amax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5125133Z   test_comprehensive_masked_amax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5125301Z   test_comprehensive_masked_amax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5125458Z   test_comprehensive_masked_amax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5125628Z   test_comprehensive_masked_amax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5125797Z   test_comprehensive_masked_amax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5125973Z   test_comprehensive_masked_amin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5126145Z   test_comprehensive_masked_amin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5126319Z   test_comprehensive_masked_amin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5126519Z   test_comprehensive_masked_amin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5126689Z   test_comprehensive_masked_amin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5126851Z   test_comprehensive_masked_amin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5127007Z   test_comprehensive_masked_amin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5127180Z   test_comprehensive_masked_amin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5127348Z   test_comprehensive_masked_amin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5127529Z   test_comprehensive_masked_argmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5127707Z   test_comprehensive_masked_argmax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5127881Z   test_comprehensive_masked_argmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5128050Z   test_comprehensive_masked_argmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5128223Z   test_comprehensive_masked_argmax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5128394Z   test_comprehensive_masked_argmax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5128553Z   test_comprehensive_masked_argmax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5128728Z   test_comprehensive_masked_argmax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5128898Z   test_comprehensive_masked_argmax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5129080Z   test_comprehensive_masked_argmin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5129253Z   test_comprehensive_masked_argmin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5129428Z   test_comprehensive_masked_argmin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5129602Z   test_comprehensive_masked_argmin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5129774Z   test_comprehensive_masked_argmin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5129943Z   test_comprehensive_masked_argmin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5130129Z   test_comprehensive_masked_argmin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5130304Z   test_comprehensive_masked_argmin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5130473Z   test_comprehensive_masked_argmin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5130653Z   test_comprehensive_masked_cumprod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5130835Z   test_comprehensive_masked_cumprod_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5131016Z   test_comprehensive_masked_cumprod_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5131194Z   test_comprehensive_masked_cumprod_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5131372Z   test_comprehensive_masked_cumprod_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5131582Z   test_comprehensive_masked_cumprod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5131745Z   test_comprehensive_masked_cumprod_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5131920Z   test_comprehensive_masked_cumprod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5132093Z   test_comprehensive_masked_cumprod_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5132264Z   test_comprehensive_masked_cumprod_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5132438Z   test_comprehensive_masked_cumsum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5132616Z   test_comprehensive_masked_cumsum_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5132798Z   test_comprehensive_masked_cumsum_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5132973Z   test_comprehensive_masked_cumsum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5133147Z   test_comprehensive_masked_cumsum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5133304Z   test_comprehensive_masked_cumsum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5133473Z   test_comprehensive_masked_cumsum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.020s)
2023-01-11T20:52:32.5133640Z   test_comprehensive_masked_cumsum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5133811Z   test_comprehensive_masked_cumsum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5133984Z   test_comprehensive_masked_cumsum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5134158Z   test_comprehensive_masked_fill_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5134327Z   test_comprehensive_masked_fill_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5134504Z   test_comprehensive_masked_fill_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5134678Z   test_comprehensive_masked_fill_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5134850Z   test_comprehensive_masked_fill_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5135009Z   test_comprehensive_masked_fill_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5135180Z   test_comprehensive_masked_fill_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5135378Z   test_comprehensive_masked_fill_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5135551Z   test_comprehensive_masked_fill_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5135716Z   test_comprehensive_masked_fill_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5135884Z   test_comprehensive_masked_fill_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5136053Z   test_comprehensive_masked_fill_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5136223Z   test_comprehensive_masked_fill_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5136409Z   test_comprehensive_masked_log_softmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5136580Z   test_comprehensive_masked_log_softmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5136781Z   test_comprehensive_masked_log_softmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5136963Z   test_comprehensive_masked_logaddexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5137145Z   test_comprehensive_masked_logaddexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5137324Z   test_comprehensive_masked_logaddexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5137496Z   test_comprehensive_masked_logsumexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5137667Z   test_comprehensive_masked_logsumexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5137838Z   test_comprehensive_masked_logsumexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5138006Z   test_comprehensive_masked_logsumexp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5138163Z   test_comprehensive_masked_logsumexp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5138323Z   test_comprehensive_masked_logsumexp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5138488Z   test_comprehensive_masked_logsumexp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5138648Z   test_comprehensive_masked_logsumexp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped! (0.000s)
2023-01-11T20:52:32.5138822Z   test_comprehensive_masked_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5138992Z   test_comprehensive_masked_mean_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5139171Z   test_comprehensive_masked_mean_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5139429Z   test_comprehensive_masked_mean_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5139608Z   test_comprehensive_masked_mean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5139767Z   test_comprehensive_masked_mean_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5139938Z   test_comprehensive_masked_mean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5140107Z   test_comprehensive_masked_mean_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5140276Z   test_comprehensive_masked_mean_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5140444Z   test_comprehensive_masked_mean_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5140663Z   test_comprehensive_masked_mean_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5140838Z   test_comprehensive_masked_mean_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5141016Z   test_comprehensive_masked_median_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5141190Z   test_comprehensive_masked_median_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5141352Z   test_comprehensive_masked_median_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5141526Z   test_comprehensive_masked_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5141696Z   test_comprehensive_masked_norm_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5141867Z   test_comprehensive_masked_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5142061Z   test_comprehensive_masked_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5142244Z   test_comprehensive_masked_normalize_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5142428Z   test_comprehensive_masked_normalize_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5142610Z   test_comprehensive_masked_normalize_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5142789Z   test_comprehensive_masked_normalize_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5142955Z   test_comprehensive_masked_normalize_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5143137Z   test_comprehensive_masked_normalize_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5143311Z   test_comprehensive_masked_prod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5143482Z   test_comprehensive_masked_prod_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5143662Z   test_comprehensive_masked_prod_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5143838Z   test_comprehensive_masked_prod_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5144011Z   test_comprehensive_masked_prod_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5144184Z   test_comprehensive_masked_prod_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5144359Z   test_comprehensive_masked_prod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5144517Z   test_comprehensive_masked_prod_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5144695Z   test_comprehensive_masked_prod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5144866Z   test_comprehensive_masked_prod_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5145033Z   test_comprehensive_masked_prod_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5145212Z   test_comprehensive_masked_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5145383Z   test_comprehensive_masked_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5145562Z   test_comprehensive_masked_scatter_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5145776Z   test_comprehensive_masked_scatter_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5145955Z   test_comprehensive_masked_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5146130Z   test_comprehensive_masked_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5146292Z   test_comprehensive_masked_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5146466Z   test_comprehensive_masked_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5146640Z   test_comprehensive_masked_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5146811Z   test_comprehensive_masked_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5146983Z   test_comprehensive_masked_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5147155Z   test_comprehensive_masked_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5147365Z   test_comprehensive_masked_select_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5147539Z   test_comprehensive_masked_select_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5147718Z   test_comprehensive_masked_select_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5147885Z   test_comprehensive_masked_select_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5148060Z   test_comprehensive_masked_select_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5148233Z   test_comprehensive_masked_select_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5148411Z   test_comprehensive_masked_select_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5148584Z   test_comprehensive_masked_select_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5148755Z   test_comprehensive_masked_select_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5148924Z   test_comprehensive_masked_select_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5149096Z   test_comprehensive_masked_select_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5149268Z   test_comprehensive_masked_select_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5149434Z   test_comprehensive_masked_softmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5149611Z   test_comprehensive_masked_softmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5149787Z   test_comprehensive_masked_softmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5149961Z   test_comprehensive_masked_softmin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5150133Z   test_comprehensive_masked_softmin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5150309Z   test_comprehensive_masked_softmin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5150484Z   test_comprehensive_masked_std_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5150659Z   test_comprehensive_masked_std_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5150833Z   test_comprehensive_masked_std_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5151020Z   test_comprehensive_masked_std_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5151195Z   test_comprehensive_masked_std_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5151366Z   test_comprehensive_masked_std_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5151532Z   test_comprehensive_masked_std_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5151701Z   test_comprehensive_masked_std_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5151871Z   test_comprehensive_masked_std_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5152039Z   test_comprehensive_masked_std_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5152212Z   test_comprehensive_masked_sum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5152411Z   test_comprehensive_masked_sum_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5152573Z   test_comprehensive_masked_sum_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5152749Z   test_comprehensive_masked_sum_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5152921Z   test_comprehensive_masked_sum_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5153092Z   test_comprehensive_masked_sum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5153257Z   test_comprehensive_masked_sum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5153425Z   test_comprehensive_masked_sum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5153598Z   test_comprehensive_masked_sum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5153765Z   test_comprehensive_masked_sum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5153935Z   test_comprehensive_masked_sum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5154089Z   test_comprehensive_masked_sum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5154262Z   test_comprehensive_masked_var_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5154437Z   test_comprehensive_masked_var_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5154613Z   test_comprehensive_masked_var_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5154789Z   test_comprehensive_masked_var_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5154962Z   test_comprehensive_masked_var_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5155131Z   test_comprehensive_masked_var_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5155298Z   test_comprehensive_masked_var_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5155462Z   test_comprehensive_masked_var_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5155618Z   test_comprehensive_masked_var_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5155785Z   test_comprehensive_masked_var_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5155954Z   test_comprehensive_masked_var_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5156149Z   test_comprehensive_matmul_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5156323Z   test_comprehensive_matmul_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5156491Z   test_comprehensive_matmul_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5156660Z   test_comprehensive_matmul_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5156829Z   test_comprehensive_matmul_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5156996Z   test_comprehensive_matmul_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5157149Z   test_comprehensive_matmul_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5157313Z   test_comprehensive_matmul_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5157477Z   test_comprehensive_matmul_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5157671Z   test_comprehensive_matmul_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5157845Z   test_comprehensive_matrix_exp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5158021Z   test_comprehensive_matrix_exp_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5158199Z   test_comprehensive_matrix_exp_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5158371Z   test_comprehensive_matrix_exp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5158541Z   test_comprehensive_matrix_exp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5158703Z   test_comprehensive_max_binary_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5158873Z   test_comprehensive_max_binary_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5159045Z   test_comprehensive_max_binary_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5159213Z   test_comprehensive_max_binary_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5159378Z   test_comprehensive_max_binary_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5159546Z   test_comprehensive_max_binary_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5159711Z   test_comprehensive_max_binary_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5159881Z   test_comprehensive_max_binary_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5160050Z   test_comprehensive_max_binary_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5160208Z   test_comprehensive_max_binary_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5160405Z   test_comprehensive_max_pool2d_with_indices_backward_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5160707Z   test_comprehensive_max_pool2d_with_indices_backward_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5160907Z   test_comprehensive_max_pool2d_with_indices_backward_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5161092Z   test_comprehensive_max_reduction_no_dim_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5161273Z   test_comprehensive_max_reduction_no_dim_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5161507Z   test_comprehensive_max_reduction_no_dim_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5161692Z   test_comprehensive_max_reduction_no_dim_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5161867Z   test_comprehensive_max_reduction_no_dim_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5162045Z   test_comprehensive_max_reduction_no_dim_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5162214Z   test_comprehensive_max_reduction_no_dim_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5162389Z   test_comprehensive_max_reduction_no_dim_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5162572Z   test_comprehensive_max_reduction_no_dim_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5162754Z   test_comprehensive_max_reduction_no_dim_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5162978Z   test_comprehensive_max_reduction_with_dim_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5163162Z   test_comprehensive_max_reduction_with_dim_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5163347Z   test_comprehensive_max_reduction_with_dim_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5187683Z   test_comprehensive_mul_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5188012Z   test_comprehensive_mul_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5188175Z   test_comprehensive_mul_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5188336Z   test_comprehensive_mul_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5188497Z   test_comprehensive_mul_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5188656Z   test_comprehensive_mul_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5188847Z   test_comprehensive_mul_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5188994Z   test_comprehensive_mul_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5189155Z   test_comprehensive_mul_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5189313Z   test_comprehensive_mul_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5189489Z   test_comprehensive_multinomial_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5189664Z   test_comprehensive_multinomial_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5189839Z   test_comprehensive_multinomial_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5190003Z   test_comprehensive_mv_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5190167Z   test_comprehensive_mv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5190331Z   test_comprehensive_mv_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5190480Z   test_comprehensive_mv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5190637Z   test_comprehensive_mv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5190796Z   test_comprehensive_mv_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5190956Z   test_comprehensive_mv_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5191115Z   test_comprehensive_mv_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5191276Z   test_comprehensive_mv_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5191435Z   test_comprehensive_mv_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5191623Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5191805Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5191970Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5192150Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5192330Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5192537Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5192719Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5192893Z   test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5193076Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5193256Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5193431Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5193608Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5193777Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5193979Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5194158Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5194331Z   test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5194515Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5194696Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5194873Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5195049Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5195231Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5195392Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5195569Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5195742Z   test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5195914Z   test_comprehensive_nan_to_num_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5196080Z   test_comprehensive_nan_to_num_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5196253Z   test_comprehensive_nan_to_num_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5196424Z   test_comprehensive_nan_to_num_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5196590Z   test_comprehensive_nan_to_num_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5196758Z   test_comprehensive_nan_to_num_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5196913Z   test_comprehensive_nan_to_num_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5197080Z   test_comprehensive_nan_to_num_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5197244Z   test_comprehensive_nan_to_num_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5197446Z   test_comprehensive_nan_to_num_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5197611Z   test_comprehensive_nanmean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5197783Z   test_comprehensive_nanmean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5197949Z   test_comprehensive_nanmean_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5198119Z   test_comprehensive_nanmean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5198293Z   test_comprehensive_nanmedian_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5198454Z   test_comprehensive_nanmedian_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5198625Z   test_comprehensive_nanmedian_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5198796Z   test_comprehensive_nanmedian_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5198994Z   test_comprehensive_nanmedian_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5199162Z   test_comprehensive_nanmedian_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5199335Z   test_comprehensive_nanmedian_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5199501Z   test_comprehensive_nanmedian_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5199676Z   test_comprehensive_nanquantile_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5199851Z   test_comprehensive_nanquantile_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5200003Z   test_comprehensive_nansum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5200170Z   test_comprehensive_nansum_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5200338Z   test_comprehensive_nansum_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5200505Z   test_comprehensive_nansum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5200776Z   test_comprehensive_nansum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5200944Z   test_comprehensive_nansum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5201110Z   test_comprehensive_nansum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5201274Z   test_comprehensive_nansum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5201437Z   test_comprehensive_nansum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5201587Z   test_comprehensive_nansum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5201770Z   test_comprehensive_narrow_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5201941Z   test_comprehensive_narrow_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5202124Z   test_comprehensive_narrow_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5202300Z   test_comprehensive_narrow_copy_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5202475Z   test_comprehensive_narrow_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5202650Z   test_comprehensive_narrow_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5202878Z   test_comprehensive_narrow_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5203054Z   test_comprehensive_narrow_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5203213Z   test_comprehensive_narrow_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5203382Z   test_comprehensive_narrow_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5203552Z   test_comprehensive_narrow_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5203723Z   test_comprehensive_narrow_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5203892Z   test_comprehensive_narrow_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5204065Z   test_comprehensive_narrow_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5204233Z   test_comprehensive_narrow_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5204472Z   test_comprehensive_narrow_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5204648Z   test_comprehensive_narrow_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5204805Z   test_comprehensive_narrow_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5204974Z   test_comprehensive_narrow_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5205142Z   test_comprehensive_narrow_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5205305Z   test_comprehensive_narrow_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5205473Z   test_comprehensive_narrow_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5205636Z   test_comprehensive_narrow_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5205798Z   test_comprehensive_narrow_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5205960Z   test_comprehensive_narrow_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5206127Z   test_comprehensive_narrow_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5206294Z   test_comprehensive_native_batch_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5206473Z   test_comprehensive_native_batch_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5206652Z   test_comprehensive_native_batch_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5206846Z   test_comprehensive_native_dropout_backward_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5207033Z   test_comprehensive_native_dropout_backward_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5207218Z   test_comprehensive_native_dropout_backward_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5207407Z   test_comprehensive_native_dropout_backward_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5207589Z   test_comprehensive_native_dropout_backward_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5207773Z   test_comprehensive_native_dropout_backward_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5207951Z   test_comprehensive_native_dropout_backward_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5208144Z   test_comprehensive_native_dropout_backward_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5208333Z   test_comprehensive_native_dropout_backward_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5208518Z   test_comprehensive_native_dropout_backward_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5208695Z   test_comprehensive_native_layer_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5208871Z   test_comprehensive_native_layer_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5209051Z   test_comprehensive_native_layer_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5209214Z   test_comprehensive_ne_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5209375Z   test_comprehensive_ne_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5209568Z   test_comprehensive_ne_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5209723Z   test_comprehensive_ne_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5209887Z   test_comprehensive_ne_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5210044Z   test_comprehensive_ne_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5210198Z   test_comprehensive_ne_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5210362Z   test_comprehensive_ne_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5210521Z   test_comprehensive_ne_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5210680Z   test_comprehensive_ne_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5210840Z   test_comprehensive_ne_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5210999Z   test_comprehensive_ne_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5211153Z   test_comprehensive_neg_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5211324Z   test_comprehensive_neg_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5211489Z   test_comprehensive_neg_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5211649Z   test_comprehensive_neg_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5211812Z   test_comprehensive_neg_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5211972Z   test_comprehensive_neg_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5212132Z   test_comprehensive_neg_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5212292Z   test_comprehensive_neg_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5212451Z   test_comprehensive_neg_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5212599Z   test_comprehensive_neg_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5212760Z   test_comprehensive_neg_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5212917Z   test_comprehensive_neg_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5213092Z   test_comprehensive_new_empty_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5213301Z   test_comprehensive_new_empty_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5213479Z   test_comprehensive_new_empty_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5213653Z   test_comprehensive_new_empty_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5213827Z   test_comprehensive_new_empty_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5213999Z   test_comprehensive_new_empty_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5214157Z   test_comprehensive_new_empty_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5214326Z   test_comprehensive_new_empty_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5214492Z   test_comprehensive_new_empty_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5214660Z   test_comprehensive_new_empty_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5214851Z   test_comprehensive_new_empty_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5215022Z   test_comprehensive_new_empty_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5215193Z   test_comprehensive_new_empty_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5215405Z   test_comprehensive_new_empty_strided_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5215612Z   test_comprehensive_new_empty_strided_cpu_bool (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5215812Z   test_comprehensive_new_empty_strided_cpu_complex128 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5216027Z   test_comprehensive_new_empty_strided_cpu_complex32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5216237Z   test_comprehensive_new_empty_strided_cpu_complex64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5216444Z   test_comprehensive_new_empty_strided_cpu_float16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5216649Z   test_comprehensive_new_empty_strided_cpu_float32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5216848Z   test_comprehensive_new_empty_strided_cpu_float64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5217050Z   test_comprehensive_new_empty_strided_cpu_int16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5217258Z   test_comprehensive_new_empty_strided_cpu_int32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5217464Z   test_comprehensive_new_empty_strided_cpu_int64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5217664Z   test_comprehensive_new_empty_strided_cpu_int8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5217868Z   test_comprehensive_new_empty_strided_cpu_uint8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5218027Z   test_comprehensive_new_full_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5218194Z   test_comprehensive_new_full_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5218398Z   test_comprehensive_new_full_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5218575Z   test_comprehensive_new_full_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5218746Z   test_comprehensive_new_full_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5218916Z   test_comprehensive_new_full_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5219085Z   test_comprehensive_new_full_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5219331Z   test_comprehensive_new_full_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5219502Z   test_comprehensive_new_full_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5219656Z   test_comprehensive_new_full_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5219822Z   test_comprehensive_new_full_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5220028Z   test_comprehensive_new_full_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5220196Z   test_comprehensive_new_full_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5220367Z   test_comprehensive_new_ones_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5220532Z   test_comprehensive_new_ones_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5220710Z   test_comprehensive_new_ones_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5220884Z   test_comprehensive_new_ones_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5221058Z   test_comprehensive_new_ones_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5221217Z   test_comprehensive_new_ones_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5221385Z   test_comprehensive_new_ones_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5221550Z   test_comprehensive_new_ones_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5221716Z   test_comprehensive_new_ones_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5221878Z   test_comprehensive_new_ones_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5222043Z   test_comprehensive_new_ones_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5222207Z   test_comprehensive_new_ones_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5222372Z   test_comprehensive_new_ones_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5222545Z   test_comprehensive_new_zeros_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5222695Z   test_comprehensive_new_zeros_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5222869Z   test_comprehensive_new_zeros_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5223043Z   test_comprehensive_new_zeros_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5223215Z   test_comprehensive_new_zeros_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5223385Z   test_comprehensive_new_zeros_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5223553Z   test_comprehensive_new_zeros_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5223750Z   test_comprehensive_new_zeros_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5223919Z   test_comprehensive_new_zeros_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5224085Z   test_comprehensive_new_zeros_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5224239Z   test_comprehensive_new_zeros_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5224403Z   test_comprehensive_new_zeros_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5224568Z   test_comprehensive_new_zeros_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5224742Z   test_comprehensive_nextafter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5224915Z   test_comprehensive_nextafter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5225112Z   test_comprehensive_nextafter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5225313Z   test_comprehensive_nn_functional__scaled_dot_product_attention_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped (0.000s)
2023-01-11T20:52:32.5225513Z   test_comprehensive_nn_functional__scaled_dot_product_attention_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped (0.000s)
2023-01-11T20:52:32.5225708Z   test_comprehensive_nn_functional__scaled_dot_product_attention_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped (0.000s)
2023-01-11T20:52:32.5225895Z   test_comprehensive_nn_functional_adaptive_avg_pool1d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5226090Z   test_comprehensive_nn_functional_adaptive_avg_pool1d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5226291Z   test_comprehensive_nn_functional_adaptive_avg_pool1d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5226489Z   test_comprehensive_nn_functional_adaptive_avg_pool2d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5226683Z   test_comprehensive_nn_functional_adaptive_avg_pool2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5226873Z   test_comprehensive_nn_functional_adaptive_avg_pool2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5227066Z   test_comprehensive_nn_functional_adaptive_avg_pool3d_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5227254Z   test_comprehensive_nn_functional_adaptive_avg_pool3d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5227442Z   test_comprehensive_nn_functional_adaptive_avg_pool3d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5227644Z   test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5227823Z   test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5228011Z   test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5228205Z   test_comprehensive_nn_functional_adaptive_max_pool2d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5228394Z   test_comprehensive_nn_functional_adaptive_max_pool2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5228582Z   test_comprehensive_nn_functional_adaptive_max_pool2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5228802Z   test_comprehensive_nn_functional_adaptive_max_pool3d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5228992Z   test_comprehensive_nn_functional_adaptive_max_pool3d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5229186Z   test_comprehensive_nn_functional_alpha_dropout_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5229378Z   test_comprehensive_nn_functional_alpha_dropout_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5229569Z   test_comprehensive_nn_functional_alpha_dropout_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5229742Z   test_comprehensive_nn_functional_avg_pool1d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5229928Z   test_comprehensive_nn_functional_avg_pool1d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5230141Z   test_comprehensive_nn_functional_avg_pool1d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5230327Z   test_comprehensive_nn_functional_avg_pool1d_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5230514Z   test_comprehensive_nn_functional_avg_pool2d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5230698Z   test_comprehensive_nn_functional_avg_pool2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5230877Z   test_comprehensive_nn_functional_avg_pool2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5231062Z   test_comprehensive_nn_functional_avg_pool2d_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5231249Z   test_comprehensive_nn_functional_avg_pool3d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5231437Z   test_comprehensive_nn_functional_avg_pool3d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5231606Z   test_comprehensive_nn_functional_avg_pool3d_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5231791Z   test_comprehensive_nn_functional_batch_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5231973Z   test_comprehensive_nn_functional_batch_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5232153Z   test_comprehensive_nn_functional_batch_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5232337Z   test_comprehensive_nn_functional_bilinear_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5232522Z   test_comprehensive_nn_functional_bilinear_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5232706Z   test_comprehensive_nn_functional_bilinear_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5232889Z   test_comprehensive_nn_functional_bilinear_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5233068Z   test_comprehensive_nn_functional_bilinear_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5233244Z   test_comprehensive_nn_functional_bilinear_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5233411Z   test_comprehensive_nn_functional_bilinear_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5233586Z   test_comprehensive_nn_functional_bilinear_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5233814Z   test_comprehensive_nn_functional_binary_cross_entropy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5234008Z   test_comprehensive_nn_functional_binary_cross_entropy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5234217Z   test_comprehensive_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5234427Z   test_comprehensive_nn_functional_binary_cross_entropy_with_logits_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5234633Z   test_comprehensive_nn_functional_binary_cross_entropy_with_logits_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5234816Z   test_comprehensive_nn_functional_celu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5234998Z   test_comprehensive_nn_functional_celu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5235198Z   test_comprehensive_nn_functional_celu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5235370Z   test_comprehensive_nn_functional_conv1d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5235553Z   test_comprehensive_nn_functional_conv1d_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5235738Z   test_comprehensive_nn_functional_conv1d_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5235919Z   test_comprehensive_nn_functional_conv1d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5236099Z   test_comprehensive_nn_functional_conv1d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5236276Z   test_comprehensive_nn_functional_conv1d_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5236459Z   test_comprehensive_nn_functional_conv2d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5236646Z   test_comprehensive_nn_functional_conv2d_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5261598Z   test_comprehensive_nn_functional_interpolate_nearest_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5261793Z   test_comprehensive_nn_functional_interpolate_nearest_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5261996Z   test_comprehensive_nn_functional_interpolate_trilinear_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5262197Z   test_comprehensive_nn_functional_interpolate_trilinear_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5262377Z   test_comprehensive_nn_functional_interpolate_trilinear_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5262561Z   test_comprehensive_nn_functional_kl_div_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5262742Z   test_comprehensive_nn_functional_kl_div_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5262918Z   test_comprehensive_nn_functional_kl_div_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5263099Z   test_comprehensive_nn_functional_l1_loss_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5263283Z   test_comprehensive_nn_functional_l1_loss_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5263468Z   test_comprehensive_nn_functional_l1_loss_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5263649Z   test_comprehensive_nn_functional_l1_loss_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5263823Z   test_comprehensive_nn_functional_l1_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5263989Z   test_comprehensive_nn_functional_l1_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5264175Z   test_comprehensive_nn_functional_layer_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5264358Z   test_comprehensive_nn_functional_layer_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5264575Z   test_comprehensive_nn_functional_layer_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5264763Z   test_comprehensive_nn_functional_leaky_relu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5264946Z   test_comprehensive_nn_functional_leaky_relu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5265130Z   test_comprehensive_nn_functional_leaky_relu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5265312Z   test_comprehensive_nn_functional_linear_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5265496Z   test_comprehensive_nn_functional_linear_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5265680Z   test_comprehensive_nn_functional_linear_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5265853Z   test_comprehensive_nn_functional_linear_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5266062Z   test_comprehensive_nn_functional_linear_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5266242Z   test_comprehensive_nn_functional_linear_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5266416Z   test_comprehensive_nn_functional_linear_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5266586Z   test_comprehensive_nn_functional_linear_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5266762Z   test_comprehensive_nn_functional_linear_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5266940Z   test_comprehensive_nn_functional_linear_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5267136Z   test_comprehensive_nn_functional_local_response_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5267332Z   test_comprehensive_nn_functional_local_response_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5267527Z   test_comprehensive_nn_functional_local_response_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5267706Z   test_comprehensive_nn_functional_local_response_norm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5267891Z   test_comprehensive_nn_functional_logsigmoid_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5268081Z   test_comprehensive_nn_functional_logsigmoid_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5268272Z   test_comprehensive_nn_functional_logsigmoid_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5268469Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5268660Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5268854Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5269044Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5269234Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5269420Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5269636Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5269827Z   test_comprehensive_nn_functional_margin_ranking_loss_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5270009Z   test_comprehensive_nn_functional_max_pool1d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5270197Z   test_comprehensive_nn_functional_max_pool1d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5270381Z   test_comprehensive_nn_functional_max_pool1d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5270568Z   test_comprehensive_nn_functional_max_pool2d_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5270754Z   test_comprehensive_nn_functional_max_pool2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5270938Z   test_comprehensive_nn_functional_max_pool2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5271148Z   test_comprehensive_nn_functional_max_pool3d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5271318Z   test_comprehensive_nn_functional_max_pool3d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5271504Z   test_comprehensive_nn_functional_max_unpool1d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5271689Z   test_comprehensive_nn_functional_max_unpool1d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5271879Z   test_comprehensive_nn_functional_max_unpool1d_grad_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5272074Z   test_comprehensive_nn_functional_max_unpool1d_grad_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5272262Z   test_comprehensive_nn_functional_max_unpool2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5272448Z   test_comprehensive_nn_functional_max_unpool2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5272640Z   test_comprehensive_nn_functional_max_unpool2d_grad_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5272828Z   test_comprehensive_nn_functional_max_unpool2d_grad_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5273014Z   test_comprehensive_nn_functional_max_unpool3d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5273185Z   test_comprehensive_nn_functional_max_unpool3d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5273377Z   test_comprehensive_nn_functional_max_unpool3d_grad_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5273571Z   test_comprehensive_nn_functional_max_unpool3d_grad_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5273752Z   test_comprehensive_nn_functional_mish_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5273931Z   test_comprehensive_nn_functional_mish_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5274105Z   test_comprehensive_nn_functional_mish_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5274287Z   test_comprehensive_nn_functional_mse_loss_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5274465Z   test_comprehensive_nn_functional_mse_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5274677Z   test_comprehensive_nn_functional_mse_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5274868Z   test_comprehensive_nn_functional_multi_margin_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5275044Z   test_comprehensive_nn_functional_multi_margin_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5275242Z   test_comprehensive_nn_functional_multilabel_margin_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5275434Z   test_comprehensive_nn_functional_multilabel_margin_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5275639Z   test_comprehensive_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5275842Z   test_comprehensive_nn_functional_multilabel_soft_margin_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5276072Z   test_comprehensive_nn_functional_multilabel_soft_margin_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5276259Z   test_comprehensive_nn_functional_nll_loss_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5276442Z   test_comprehensive_nn_functional_nll_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5276621Z   test_comprehensive_nn_functional_nll_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5276807Z   test_comprehensive_nn_functional_normalize_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5276981Z   test_comprehensive_nn_functional_normalize_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5277171Z   test_comprehensive_nn_functional_normalize_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5277357Z   test_comprehensive_nn_functional_normalize_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5277537Z   test_comprehensive_nn_functional_normalize_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5277715Z   test_comprehensive_nn_functional_one_hot_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5277935Z   test_comprehensive_nn_functional_pad_circular_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5278148Z   test_comprehensive_nn_functional_pad_circular_cpu_bool (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5278371Z   test_comprehensive_nn_functional_pad_circular_cpu_complex128 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5278595Z   test_comprehensive_nn_functional_pad_circular_cpu_complex64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5278811Z   test_comprehensive_nn_functional_pad_circular_cpu_float16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5279026Z   test_comprehensive_nn_functional_pad_circular_cpu_float32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5279221Z   test_comprehensive_nn_functional_pad_circular_cpu_float64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5279433Z   test_comprehensive_nn_functional_pad_circular_cpu_int16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5279679Z   test_comprehensive_nn_functional_pad_circular_cpu_int32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5279895Z   test_comprehensive_nn_functional_pad_circular_cpu_int64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5280109Z   test_comprehensive_nn_functional_pad_circular_cpu_int8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5280318Z   test_comprehensive_nn_functional_pad_circular_cpu_uint8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5280509Z   test_comprehensive_nn_functional_pad_constant_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5280803Z   test_comprehensive_nn_functional_pad_constant_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5281004Z   test_comprehensive_nn_functional_pad_constant_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5281243Z   test_comprehensive_nn_functional_pad_constant_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5281418Z   test_comprehensive_nn_functional_pad_constant_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5281607Z   test_comprehensive_nn_functional_pad_constant_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5281793Z   test_comprehensive_nn_functional_pad_constant_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5281981Z   test_comprehensive_nn_functional_pad_constant_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5282172Z   test_comprehensive_nn_functional_pad_constant_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5282360Z   test_comprehensive_nn_functional_pad_constant_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5282549Z   test_comprehensive_nn_functional_pad_constant_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5282736Z   test_comprehensive_nn_functional_pad_constant_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5282928Z   test_comprehensive_nn_functional_pad_reflect_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5283118Z   test_comprehensive_nn_functional_pad_reflect_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5283291Z   test_comprehensive_nn_functional_pad_reflect_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5283477Z   test_comprehensive_nn_functional_pad_reflect_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5283673Z   test_comprehensive_nn_functional_pad_replicate_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5283868Z   test_comprehensive_nn_functional_pad_replicate_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5284060Z   test_comprehensive_nn_functional_pad_replicate_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5284247Z   test_comprehensive_nn_functional_pad_replicate_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5284444Z   test_comprehensive_nn_functional_pairwise_distance_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5284644Z   test_comprehensive_nn_functional_pairwise_distance_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5284922Z   test_comprehensive_nn_functional_pairwise_distance_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5285121Z   test_comprehensive_nn_functional_pairwise_distance_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5285305Z   test_comprehensive_nn_functional_pairwise_distance_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5285499Z   test_comprehensive_nn_functional_pairwise_distance_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5285691Z   test_comprehensive_nn_functional_pairwise_distance_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5285881Z   test_comprehensive_nn_functional_pairwise_distance_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5286068Z   test_comprehensive_nn_functional_pairwise_distance_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5286265Z   test_comprehensive_nn_functional_pairwise_distance_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5286487Z   test_comprehensive_nn_functional_pairwise_distance_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5286671Z   test_comprehensive_nn_functional_pdist_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5286849Z   test_comprehensive_nn_functional_pdist_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5324804Z   test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5325051Z   test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5325256Z   test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5325461Z   test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5325659Z   test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5325864Z   test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5326066Z   test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5326238Z   test_comprehensive_nn_functional_unfold_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5326792Z   test_comprehensive_nn_functional_unfold_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5327349Z   test_comprehensive_nn_functional_upsample_bilinear_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5328944Z   test_comprehensive_nonzero_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5329117Z   test_comprehensive_nonzero_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5329293Z   test_comprehensive_nonzero_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5329636Z   test_comprehensive_nonzero_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5330825Z   test_comprehensive_nonzero_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5330988Z   test_comprehensive_nonzero_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5331146Z   test_comprehensive_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5331313Z   test_comprehensive_norm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5332128Z   test_comprehensive_norm_fro_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5332301Z   test_comprehensive_norm_fro_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5333140Z   test_comprehensive_norm_inf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5333312Z   test_comprehensive_norm_inf_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5333483Z   test_comprehensive_norm_inf_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5333653Z   test_comprehensive_norm_inf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5334199Z   test_comprehensive_norm_nuc_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5334371Z   test_comprehensive_norm_nuc_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5334524Z   test_comprehensive_norm_nuc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5334691Z   test_comprehensive_norm_nuc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5334860Z   test_comprehensive_normal_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5335577Z   test_comprehensive_normal_number_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5336259Z   test_comprehensive_ones_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5336420Z   test_comprehensive_ones_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5336593Z   test_comprehensive_ones_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5336766Z   test_comprehensive_ones_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5336934Z   test_comprehensive_ones_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5337100Z   test_comprehensive_ones_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5337264Z   test_comprehensive_ones_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5337572Z   test_comprehensive_ones_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5338053Z   test_comprehensive_ones_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5338216Z   test_comprehensive_ones_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5338387Z   test_comprehensive_ones_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5338554Z   test_comprehensive_ones_like_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5338731Z   test_comprehensive_ones_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5338904Z   test_comprehensive_ones_like_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5339065Z   test_comprehensive_ones_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5339341Z   test_comprehensive_ones_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5339516Z   test_comprehensive_ones_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5339681Z   test_comprehensive_ones_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5339849Z   test_comprehensive_ones_like_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5340518Z   test_comprehensive_ones_like_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5340676Z   test_comprehensive_ormqr_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5340875Z   test_comprehensive_ormqr_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5341042Z   test_comprehensive_ormqr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5341204Z   test_comprehensive_ormqr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5341369Z   test_comprehensive_outer_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5341534Z   test_comprehensive_outer_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5341706Z   test_comprehensive_outer_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5341875Z   test_comprehensive_outer_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5342044Z   test_comprehensive_outer_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5342996Z   test_comprehensive_outer_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5343161Z   test_comprehensive_outer_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5343337Z   test_comprehensive_pca_lowrank_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.5343514Z   test_comprehensive_pca_lowrank_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5343674Z   test_comprehensive_permute_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5343841Z   test_comprehensive_permute_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5344015Z   test_comprehensive_permute_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5344357Z   test_comprehensive_permute_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5344555Z   test_comprehensive_permute_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5345702Z   test_comprehensive_permute_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5345878Z   test_comprehensive_pinverse_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5346086Z   test_comprehensive_pinverse_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5346259Z   test_comprehensive_pinverse_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5346598Z   test_comprehensive_polar_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5346936Z   test_comprehensive_polygamma_polygamma_n_0_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5348592Z   test_comprehensive_polygamma_polygamma_n_1_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5348773Z   test_comprehensive_polygamma_polygamma_n_1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5349322Z   test_comprehensive_polygamma_polygamma_n_1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5350238Z   test_comprehensive_polygamma_polygamma_n_2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5350421Z   test_comprehensive_polygamma_polygamma_n_2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5353498Z   test_comprehensive_polygamma_polygamma_n_4_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5355137Z   test_comprehensive_positive_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5355311Z   test_comprehensive_positive_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5355828Z   test_comprehensive_positive_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5356850Z   test_comprehensive_positive_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5357015Z   test_comprehensive_positive_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5357183Z   test_comprehensive_pow_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5357357Z   test_comprehensive_pow_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5357693Z   test_comprehensive_pow_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5358486Z   test_comprehensive_pow_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5358651Z   test_comprehensive_pow_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5358981Z   test_comprehensive_prod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5359130Z   test_comprehensive_prod_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5359304Z   test_comprehensive_prod_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5359472Z   test_comprehensive_prod_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5359638Z   test_comprehensive_prod_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5359994Z   test_comprehensive_prod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5360317Z   test_comprehensive_prod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5360738Z   test_comprehensive_prod_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5360907Z   test_comprehensive_put_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5361071Z   test_comprehensive_put_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5361241Z   test_comprehensive_put_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5361411Z   test_comprehensive_put_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5361628Z   test_comprehensive_put_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5361789Z   test_comprehensive_put_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5361953Z   test_comprehensive_put_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5362112Z   test_comprehensive_put_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5362260Z   test_comprehensive_put_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5362420Z   test_comprehensive_put_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5362581Z   test_comprehensive_put_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5362743Z   test_comprehensive_put_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5362913Z   test_comprehensive_qr_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5363081Z   test_comprehensive_qr_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5363244Z   test_comprehensive_qr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5363403Z   test_comprehensive_qr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5363576Z   test_comprehensive_quantile_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5363908Z   test_comprehensive_rad2deg_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5364077Z   test_comprehensive_rad2deg_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5364249Z   test_comprehensive_rad2deg_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5364413Z   test_comprehensive_rad2deg_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5364585Z   test_comprehensive_rad2deg_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5364751Z   test_comprehensive_rad2deg_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5364912Z   test_comprehensive_rad2deg_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5365070Z   test_comprehensive_rad2deg_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5365275Z   test_comprehensive_rad2deg_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5365443Z   test_comprehensive_rad2deg_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5365617Z   test_comprehensive_rand_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5365793Z   test_comprehensive_rand_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5365967Z   test_comprehensive_rand_like_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5366142Z   test_comprehensive_rand_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5366311Z   test_comprehensive_rand_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5366481Z   test_comprehensive_rand_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5366653Z   test_comprehensive_rand_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5366842Z   test_comprehensive_randint_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5367013Z   test_comprehensive_randint_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5367181Z   test_comprehensive_randint_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5367349Z   test_comprehensive_randint_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5367515Z   test_comprehensive_randint_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5367679Z   test_comprehensive_randint_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5367842Z   test_comprehensive_randint_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5368012Z   test_comprehensive_randint_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5368177Z   test_comprehensive_randint_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5368340Z   test_comprehensive_randint_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5368514Z   test_comprehensive_randint_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5368689Z   test_comprehensive_randint_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5368863Z   test_comprehensive_randint_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5369036Z   test_comprehensive_randint_like_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5369381Z   test_comprehensive_randint_like_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5369553Z   test_comprehensive_randint_like_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5369721Z   test_comprehensive_randint_like_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5369876Z   test_comprehensive_randn_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5370048Z   test_comprehensive_randn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5370218Z   test_comprehensive_randn_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5370382Z   test_comprehensive_randn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5370593Z   test_comprehensive_randn_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5370758Z   test_comprehensive_randn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5370918Z   test_comprehensive_randn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5371094Z   test_comprehensive_randn_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5371271Z   test_comprehensive_randn_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5371433Z   test_comprehensive_randn_like_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5371607Z   test_comprehensive_randn_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5371781Z   test_comprehensive_randn_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5371977Z   test_comprehensive_randn_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5372149Z   test_comprehensive_randn_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5372318Z   test_comprehensive_ravel_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5372484Z   test_comprehensive_ravel_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5372659Z   test_comprehensive_ravel_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5372829Z   test_comprehensive_ravel_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5372981Z   test_comprehensive_ravel_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5373151Z   test_comprehensive_ravel_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5373315Z   test_comprehensive_ravel_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5373474Z   test_comprehensive_ravel_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5373638Z   test_comprehensive_ravel_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5373801Z   test_comprehensive_ravel_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5373959Z   test_comprehensive_ravel_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5374123Z   test_comprehensive_ravel_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5374281Z   test_comprehensive_ravel_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5374436Z   test_comprehensive_real_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5374600Z   test_comprehensive_real_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5374770Z   test_comprehensive_real_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5374939Z   test_comprehensive_real_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5375110Z   test_comprehensive_real_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5375275Z   test_comprehensive_real_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5375439Z   test_comprehensive_real_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5375601Z   test_comprehensive_real_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5375798Z   test_comprehensive_real_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5375945Z   test_comprehensive_real_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5376099Z   test_comprehensive_real_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5376259Z   test_comprehensive_real_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5376420Z   test_comprehensive_real_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5376595Z   test_comprehensive_reciprocal_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5376766Z   test_comprehensive_reciprocal_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5376944Z   test_comprehensive_reciprocal_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5377124Z   test_comprehensive_reciprocal_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5377362Z   test_comprehensive_reciprocal_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5377524Z   test_comprehensive_reciprocal_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5377696Z   test_comprehensive_reciprocal_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5377868Z   test_comprehensive_reciprocal_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5378036Z   test_comprehensive_reciprocal_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5378207Z   test_comprehensive_reciprocal_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5378381Z   test_comprehensive_reciprocal_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5378558Z   test_comprehensive_reciprocal_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5378731Z   test_comprehensive_remainder_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5378906Z   test_comprehensive_remainder_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5379065Z   test_comprehensive_remainder_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5379336Z   test_comprehensive_remainder_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5379512Z   test_comprehensive_remainder_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5379678Z   test_comprehensive_remainder_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5379850Z   test_comprehensive_remainder_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5380022Z   test_comprehensive_remainder_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5380193Z   test_comprehensive_remainder_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5380359Z   test_comprehensive_renorm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5380534Z   test_comprehensive_renorm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5380690Z   test_comprehensive_renorm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5380864Z   test_comprehensive_renorm_cpu_float16 (__main__.TestDecompCPU) ... skip: Inconsistent accuracy (0.000s)
2023-01-11T20:52:32.5381070Z   test_comprehensive_renorm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5381239Z   test_comprehensive_renorm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5381409Z   test_comprehensive_repeat_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5381578Z   test_comprehensive_repeat_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5381753Z   test_comprehensive_repeat_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5381927Z   test_comprehensive_repeat_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5382095Z   test_comprehensive_repeat_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5382245Z   test_comprehensive_repeat_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5382416Z   test_comprehensive_repeat_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5382613Z   test_comprehensive_repeat_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5382776Z   test_comprehensive_repeat_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5382934Z   test_comprehensive_repeat_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5383098Z   test_comprehensive_repeat_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5383262Z   test_comprehensive_repeat_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5383444Z   test_comprehensive_repeat_interleave_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5383623Z   test_comprehensive_repeat_interleave_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5383801Z   test_comprehensive_repeat_interleave_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5383989Z   test_comprehensive_repeat_interleave_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5384167Z   test_comprehensive_repeat_interleave_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5384347Z   test_comprehensive_repeat_interleave_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5384527Z   test_comprehensive_repeat_interleave_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5384707Z   test_comprehensive_repeat_interleave_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5384885Z   test_comprehensive_repeat_interleave_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5385067Z   test_comprehensive_repeat_interleave_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5385245Z   test_comprehensive_repeat_interleave_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5385412Z   test_comprehensive_repeat_interleave_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5385588Z   test_comprehensive_repeat_interleave_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5385761Z   test_comprehensive_reshape_as_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5385935Z   test_comprehensive_reshape_as_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5386110Z   test_comprehensive_reshape_as_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5386314Z   test_comprehensive_reshape_as_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5386491Z   test_comprehensive_reshape_as_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5386662Z   test_comprehensive_reshape_as_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5386832Z   test_comprehensive_reshape_as_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5386986Z   test_comprehensive_reshape_as_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5387155Z   test_comprehensive_reshape_as_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5387322Z   test_comprehensive_reshape_as_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5387490Z   test_comprehensive_reshape_as_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5387661Z   test_comprehensive_reshape_as_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5387856Z   test_comprehensive_reshape_as_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5388024Z   test_comprehensive_reshape_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5388190Z   test_comprehensive_reshape_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5388362Z   test_comprehensive_reshape_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5388520Z   test_comprehensive_reshape_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5388691Z   test_comprehensive_reshape_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5388858Z   test_comprehensive_reshape_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5389028Z   test_comprehensive_reshape_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5389198Z   test_comprehensive_reshape_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5389367Z   test_comprehensive_reshape_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5389529Z   test_comprehensive_reshape_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5389694Z   test_comprehensive_reshape_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5389860Z   test_comprehensive_reshape_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5390008Z   test_comprehensive_reshape_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5390181Z   test_comprehensive_resize__cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5390347Z   test_comprehensive_resize__cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5390521Z   test_comprehensive_resize__cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5390688Z   test_comprehensive_resize__cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5390854Z   test_comprehensive_resize__cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5391021Z   test_comprehensive_resize__cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5391187Z   test_comprehensive_resize__cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5391348Z   test_comprehensive_resize__cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5391523Z   test_comprehensive_resize__cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5391682Z   test_comprehensive_resize__cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5391845Z   test_comprehensive_resize__cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5392003Z   test_comprehensive_resize__cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5392174Z   test_comprehensive_resize_as__cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5392344Z   test_comprehensive_resize_as__cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5392519Z   test_comprehensive_resize_as__cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5392692Z   test_comprehensive_resize_as__cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5392866Z   test_comprehensive_resize_as__cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5393051Z   test_comprehensive_resize_as__cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5393222Z   test_comprehensive_resize_as__cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5393389Z   test_comprehensive_resize_as__cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5393557Z   test_comprehensive_resize_as__cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5393725Z   test_comprehensive_resize_as__cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5393895Z   test_comprehensive_resize_as__cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5394063Z   test_comprehensive_resize_as__cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5394241Z   test_comprehensive_resolve_conj_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5394413Z   test_comprehensive_resolve_conj_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5394577Z   test_comprehensive_resolve_conj_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5394755Z   test_comprehensive_resolve_conj_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5394928Z   test_comprehensive_resolve_conj_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5395098Z   test_comprehensive_resolve_conj_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5395269Z   test_comprehensive_resolve_conj_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5395439Z   test_comprehensive_resolve_conj_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5395611Z   test_comprehensive_resolve_conj_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5395778Z   test_comprehensive_resolve_conj_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5395947Z   test_comprehensive_resolve_conj_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5396104Z   test_comprehensive_resolve_conj_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5396277Z   test_comprehensive_resolve_neg_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5396444Z   test_comprehensive_resolve_neg_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5396652Z   test_comprehensive_resolve_neg_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5396830Z   test_comprehensive_resolve_neg_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5397003Z   test_comprehensive_resolve_neg_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5397176Z   test_comprehensive_resolve_neg_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5397349Z   test_comprehensive_resolve_neg_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5397517Z   test_comprehensive_resolve_neg_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5397674Z   test_comprehensive_resolve_neg_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5397837Z   test_comprehensive_resolve_neg_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5398005Z   test_comprehensive_resolve_neg_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5398207Z   test_comprehensive_resolve_neg_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5398376Z   test_comprehensive_resolve_neg_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5398543Z   test_comprehensive_roll_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5398704Z   test_comprehensive_roll_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5398873Z   test_comprehensive_roll_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5399369Z   test_comprehensive_roll_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5399859Z   test_comprehensive_roll_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5400333Z   test_comprehensive_roll_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5400495Z   test_comprehensive_roll_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5400765Z   test_comprehensive_rot90_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5400930Z   test_comprehensive_rot90_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5402771Z   test_comprehensive_round_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5403262Z   test_comprehensive_round_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5403922Z   test_comprehensive_round_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5404101Z   test_comprehensive_round_decimals_0_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5404281Z   test_comprehensive_round_decimals_0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5404638Z   test_comprehensive_round_decimals_3_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5405170Z   test_comprehensive_round_decimals_neg_3_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5405513Z   test_comprehensive_round_decimals_neg_3_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5405679Z   test_comprehensive_rsqrt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5405845Z   test_comprehensive_rsqrt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5406018Z   test_comprehensive_rsqrt_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5406187Z   test_comprehensive_rsqrt_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5406352Z   test_comprehensive_rsqrt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5406672Z   test_comprehensive_rsqrt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5406977Z   test_comprehensive_rsqrt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5407179Z   test_comprehensive_rsqrt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5407340Z   test_comprehensive_rsqrt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5407503Z   test_comprehensive_rsub_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5407670Z   test_comprehensive_rsub_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5407837Z   test_comprehensive_rsub_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5408002Z   test_comprehensive_rsub_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5408161Z   test_comprehensive_rsub_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5408306Z   test_comprehensive_rsub_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5408469Z   test_comprehensive_rsub_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5408655Z   test_comprehensive_rsub_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5408812Z   test_comprehensive_rsub_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5409134Z   test_comprehensive_rsub_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5409312Z   test_comprehensive_scalar_tensor_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5409488Z   test_comprehensive_scalar_tensor_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5409672Z   test_comprehensive_scalar_tensor_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5409839Z   test_comprehensive_scalar_tensor_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5410019Z   test_comprehensive_scalar_tensor_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5410193Z   test_comprehensive_scalar_tensor_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5410369Z   test_comprehensive_scalar_tensor_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5410537Z   test_comprehensive_scalar_tensor_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5410707Z   test_comprehensive_scalar_tensor_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5410878Z   test_comprehensive_scalar_tensor_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5411052Z   test_comprehensive_scalar_tensor_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5411227Z   test_comprehensive_scalar_tensor_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5411386Z   test_comprehensive_scalar_tensor_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5411563Z   test_comprehensive_scatter_add_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5411736Z   test_comprehensive_scatter_add_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5411913Z   test_comprehensive_scatter_add_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5412089Z   test_comprehensive_scatter_add_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5412291Z   test_comprehensive_scatter_add_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5413469Z   test_comprehensive_scatter_add_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5413642Z   test_comprehensive_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5413836Z   test_comprehensive_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5414011Z   test_comprehensive_scatter_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5414182Z   test_comprehensive_scatter_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5414349Z   test_comprehensive_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5414500Z   test_comprehensive_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5414667Z   test_comprehensive_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5414837Z   test_comprehensive_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5414998Z   test_comprehensive_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5415159Z   test_comprehensive_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5415324Z   test_comprehensive_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5415489Z   test_comprehensive_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5415674Z   test_comprehensive_scatter_reduce_amax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5415853Z   test_comprehensive_scatter_reduce_amax_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5416024Z   test_comprehensive_scatter_reduce_amax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5416384Z   test_comprehensive_scatter_reduce_amax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5416563Z   test_comprehensive_scatter_reduce_amax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5416743Z   test_comprehensive_scatter_reduce_amax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5416922Z   test_comprehensive_scatter_reduce_amax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5417101Z   test_comprehensive_scatter_reduce_amax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5417281Z   test_comprehensive_scatter_reduce_amax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5417498Z   test_comprehensive_scatter_reduce_amin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5417682Z   test_comprehensive_scatter_reduce_amin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5417848Z   test_comprehensive_scatter_reduce_amin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5418022Z   test_comprehensive_scatter_reduce_amin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5418195Z   test_comprehensive_scatter_reduce_amin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5418372Z   test_comprehensive_scatter_reduce_amin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5418732Z   test_comprehensive_scatter_reduce_amin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5419118Z   test_comprehensive_scatter_reduce_amin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5419387Z   test_comprehensive_scatter_reduce_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5419564Z   test_comprehensive_scatter_reduce_mean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5419917Z   test_comprehensive_scatter_reduce_mean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5420099Z   test_comprehensive_scatter_reduce_mean_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5420279Z   test_comprehensive_scatter_reduce_mean_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5420457Z   test_comprehensive_scatter_reduce_mean_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5420636Z   test_comprehensive_scatter_reduce_mean_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5420811Z   test_comprehensive_scatter_reduce_mean_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5420995Z   test_comprehensive_scatter_reduce_prod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5421176Z   test_comprehensive_scatter_reduce_prod_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5421701Z   test_comprehensive_scatter_reduce_prod_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5421879Z   test_comprehensive_scatter_reduce_prod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5422233Z   test_comprehensive_scatter_reduce_prod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5422803Z   test_comprehensive_scatter_reduce_sum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5423330Z   test_comprehensive_scatter_reduce_sum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5423509Z   test_comprehensive_scatter_reduce_sum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5423691Z   test_comprehensive_scatter_reduce_sum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5423867Z   test_comprehensive_scatter_reduce_sum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5424049Z   test_comprehensive_scatter_reduce_sum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5424254Z   test_comprehensive_scatter_reduce_sum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5424431Z   test_comprehensive_scatter_reduce_sum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5424598Z   test_comprehensive_searchsorted_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5424774Z   test_comprehensive_searchsorted_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5424955Z   test_comprehensive_searchsorted_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5425130Z   test_comprehensive_searchsorted_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5425306Z   test_comprehensive_searchsorted_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5425483Z   test_comprehensive_searchsorted_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5425650Z   test_comprehensive_searchsorted_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5425823Z   test_comprehensive_searchsorted_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5425993Z   test_comprehensive_searchsorted_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5426181Z   test_comprehensive_segment_reduce_lengths_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5426356Z   test_comprehensive_segment_reduce_lengths_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5426540Z   test_comprehensive_segment_reduce_lengths_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5426728Z   test_comprehensive_segment_reduce_lengths_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5426915Z   test_comprehensive_segment_reduce_offsets_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5427101Z   test_comprehensive_segment_reduce_offsets_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5427279Z   test_comprehensive_segment_reduce_offsets_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5427460Z   test_comprehensive_segment_reduce_offsets_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5427631Z   test_comprehensive_select_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5427828Z   test_comprehensive_select_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5427988Z   test_comprehensive_select_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5428161Z   test_comprehensive_select_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5428331Z   test_comprehensive_select_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5428500Z   test_comprehensive_select_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5428665Z   test_comprehensive_select_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5428829Z   test_comprehensive_select_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5428994Z   test_comprehensive_select_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5429156Z   test_comprehensive_select_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5429343Z   test_comprehensive_select_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5429493Z   test_comprehensive_select_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5429659Z   test_comprehensive_select_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5429837Z   test_comprehensive_select_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5430012Z   test_comprehensive_select_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5430189Z   test_comprehensive_select_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5430366Z   test_comprehensive_select_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5430543Z   test_comprehensive_select_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5430720Z   test_comprehensive_select_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5430894Z   test_comprehensive_select_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5431049Z   test_comprehensive_select_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5431221Z   test_comprehensive_select_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5431396Z   test_comprehensive_select_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5431561Z   test_comprehensive_sgn_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5431725Z   test_comprehensive_sgn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5431895Z   test_comprehensive_sgn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5432064Z   test_comprehensive_sgn_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5432229Z   test_comprehensive_sgn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5432392Z   test_comprehensive_sgn_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5432536Z   test_comprehensive_sgn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5432690Z   test_comprehensive_sgn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5432848Z   test_comprehensive_sgn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5433036Z   test_comprehensive_sgn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5433197Z   test_comprehensive_sgn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5433356Z   test_comprehensive_sgn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5433518Z   test_comprehensive_sgn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5433685Z   test_comprehensive_short_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5433848Z   test_comprehensive_short_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5434004Z   test_comprehensive_short_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5434173Z   test_comprehensive_short_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5434340Z   test_comprehensive_short_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5434529Z   test_comprehensive_short_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5434698Z   test_comprehensive_short_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5434860Z   test_comprehensive_short_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5435019Z   test_comprehensive_short_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5435174Z   test_comprehensive_short_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5435334Z   test_comprehensive_short_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5435484Z   test_comprehensive_short_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5435656Z   test_comprehensive_sigmoid_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5435825Z   test_comprehensive_sigmoid_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5436000Z   test_comprehensive_sigmoid_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5436174Z   test_comprehensive_sigmoid_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5436342Z   test_comprehensive_sigmoid_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5436508Z   test_comprehensive_sigmoid_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5436674Z   test_comprehensive_sigmoid_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5436834Z   test_comprehensive_sigmoid_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5436982Z   test_comprehensive_sigmoid_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5437149Z   test_comprehensive_sigmoid_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5437314Z   test_comprehensive_sigmoid_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5437476Z   test_comprehensive_sign_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5437639Z   test_comprehensive_sign_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5437807Z   test_comprehensive_sign_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5437970Z   test_comprehensive_sign_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5438127Z   test_comprehensive_sign_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5438319Z   test_comprehensive_sign_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5438466Z   test_comprehensive_sign_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5438622Z   test_comprehensive_sign_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5438781Z   test_comprehensive_sign_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5438942Z   test_comprehensive_sign_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5439129Z   test_comprehensive_signal_windows_bartlett_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5439316Z   test_comprehensive_signal_windows_bartlett_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5439505Z   test_comprehensive_signal_windows_blackman_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5439712Z   test_comprehensive_signal_windows_blackman_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5439897Z   test_comprehensive_signal_windows_cosine_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5440062Z   test_comprehensive_signal_windows_cosine_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5440252Z   test_comprehensive_signal_windows_exponential_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5440443Z   test_comprehensive_signal_windows_exponential_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5440729Z   test_comprehensive_signal_windows_gaussian_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5440919Z   test_comprehensive_signal_windows_gaussian_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5441114Z   test_comprehensive_signal_windows_general_cosine_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5441307Z   test_comprehensive_signal_windows_general_cosine_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5441502Z   test_comprehensive_signal_windows_general_hamming_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5441696Z   test_comprehensive_signal_windows_general_hamming_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5441883Z   test_comprehensive_signal_windows_hamming_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5442049Z   test_comprehensive_signal_windows_hamming_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5442235Z   test_comprehensive_signal_windows_hann_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5442416Z   test_comprehensive_signal_windows_hann_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5442602Z   test_comprehensive_signal_windows_kaiser_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5442784Z   test_comprehensive_signal_windows_kaiser_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5442965Z   test_comprehensive_signal_windows_nuttall_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5443149Z   test_comprehensive_signal_windows_nuttall_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5443321Z   test_comprehensive_signbit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5443553Z   test_comprehensive_signbit_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5443710Z   test_comprehensive_signbit_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5443881Z   test_comprehensive_signbit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5444049Z   test_comprehensive_signbit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5444216Z   test_comprehensive_signbit_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5444381Z   test_comprehensive_signbit_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5444542Z   test_comprehensive_signbit_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5444707Z   test_comprehensive_signbit_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5444872Z   test_comprehensive_signbit_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5445077Z   test_comprehensive_sin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5445227Z   test_comprehensive_sin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5445396Z   test_comprehensive_sin_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5445563Z   test_comprehensive_sin_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5445729Z   test_comprehensive_sin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5445886Z   test_comprehensive_sin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5446045Z   test_comprehensive_sin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5446211Z   test_comprehensive_sin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5446371Z   test_comprehensive_sin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5446531Z   test_comprehensive_sin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5446676Z   test_comprehensive_sin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5446839Z   test_comprehensive_sinc_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5446997Z   test_comprehensive_sinc_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5447166Z   test_comprehensive_sinc_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5447334Z   test_comprehensive_sinc_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5447501Z   test_comprehensive_sinc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5447666Z   test_comprehensive_sinc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5447827Z   test_comprehensive_sinc_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5447984Z   test_comprehensive_sinc_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5448125Z   test_comprehensive_sinc_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5448281Z   test_comprehensive_sinc_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5448443Z   test_comprehensive_sinc_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5448638Z   test_comprehensive_sinh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5448798Z   test_comprehensive_sinh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5448969Z   test_comprehensive_sinh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5449138Z   test_comprehensive_sinh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5449301Z   test_comprehensive_sinh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5449449Z   test_comprehensive_sinh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5449611Z   test_comprehensive_sinh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5449769Z   test_comprehensive_sinh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5449926Z   test_comprehensive_sinh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5450088Z   test_comprehensive_sinh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5450273Z   test_comprehensive_sinh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5450441Z   test_comprehensive_slice_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5450605Z   test_comprehensive_slice_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5450775Z   test_comprehensive_slice_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5450932Z   test_comprehensive_slice_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5451100Z   test_comprehensive_slice_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5451270Z   test_comprehensive_slice_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5451432Z   test_comprehensive_slice_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5451591Z   test_comprehensive_slice_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5451754Z   test_comprehensive_slice_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5451911Z   test_comprehensive_slice_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5452066Z   test_comprehensive_slice_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5452225Z   test_comprehensive_slice_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5452370Z   test_comprehensive_slice_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5452550Z   test_comprehensive_slice_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5452725Z   test_comprehensive_slice_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5452902Z   test_comprehensive_slice_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5453075Z   test_comprehensive_slice_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5453245Z   test_comprehensive_slice_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5453417Z   test_comprehensive_slice_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5453590Z   test_comprehensive_slice_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5453756Z   test_comprehensive_slice_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5453943Z   test_comprehensive_slice_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5454114Z   test_comprehensive_slice_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5454285Z   test_comprehensive_softmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5454455Z   test_comprehensive_softmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5454622Z   test_comprehensive_softmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5454805Z   test_comprehensive_softmax_with_dtype_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5454984Z   test_comprehensive_softmax_with_dtype_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5455170Z   test_comprehensive_softmax_with_dtype_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5455378Z   test_comprehensive_softmax_with_dtype_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5455559Z   test_comprehensive_softmax_with_dtype_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5455723Z   test_comprehensive_softmax_with_dtype_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5455902Z   test_comprehensive_softmax_with_dtype_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5456079Z   test_comprehensive_softmax_with_dtype_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5456256Z   test_comprehensive_softmax_with_dtype_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5456434Z   test_comprehensive_softmax_with_dtype_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5456612Z   test_comprehensive_softmax_with_dtype_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5456786Z   test_comprehensive_softmax_with_dtype_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5456955Z   test_comprehensive_sort_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5457118Z   test_comprehensive_sort_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5457272Z   test_comprehensive_sort_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5457437Z   test_comprehensive_sort_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5457597Z   test_comprehensive_sort_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5457761Z   test_comprehensive_sort_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5457921Z   test_comprehensive_sort_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5458075Z   test_comprehensive_sort_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5458235Z   test_comprehensive_sort_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5458396Z   test_comprehensive_sort_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5458586Z   test_comprehensive_sparse_sampled_addmm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5458762Z   test_comprehensive_sparse_sampled_addmm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5459023Z   test_comprehensive_sparse_sampled_addmm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5459208Z   test_comprehensive_sparse_sampled_addmm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5459457Z   test_comprehensive_special_airy_ai_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5459638Z   test_comprehensive_special_airy_ai_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5459817Z   test_comprehensive_special_airy_ai_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5459992Z   test_comprehensive_special_airy_ai_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5460168Z   test_comprehensive_special_airy_ai_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5460343Z   test_comprehensive_special_airy_ai_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5460506Z   test_comprehensive_special_airy_ai_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5460711Z   test_comprehensive_special_airy_ai_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5460895Z   test_comprehensive_special_bessel_j0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5461075Z   test_comprehensive_special_bessel_j0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5461253Z   test_comprehensive_special_bessel_j0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5461430Z   test_comprehensive_special_bessel_j0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5461606Z   test_comprehensive_special_bessel_j0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5461782Z   test_comprehensive_special_bessel_j0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5461961Z   test_comprehensive_special_bessel_j0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5462122Z   test_comprehensive_special_bessel_j0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5462299Z   test_comprehensive_special_bessel_j1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5462477Z   test_comprehensive_special_bessel_j1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5462657Z   test_comprehensive_special_bessel_j1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5462829Z   test_comprehensive_special_bessel_j1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5463004Z   test_comprehensive_special_bessel_j1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5463177Z   test_comprehensive_special_bessel_j1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5463353Z   test_comprehensive_special_bessel_j1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5463527Z   test_comprehensive_special_bessel_j1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5463700Z   test_comprehensive_special_bessel_y0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5463865Z   test_comprehensive_special_bessel_y0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5464041Z   test_comprehensive_special_bessel_y0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5464257Z   test_comprehensive_special_bessel_y0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5464430Z   test_comprehensive_special_bessel_y0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5464602Z   test_comprehensive_special_bessel_y0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5464775Z   test_comprehensive_special_bessel_y0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5464948Z   test_comprehensive_special_bessel_y0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5465122Z   test_comprehensive_special_bessel_y1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5465299Z   test_comprehensive_special_bessel_y1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5465464Z   test_comprehensive_special_bessel_y1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5465638Z   test_comprehensive_special_bessel_y1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5465839Z   test_comprehensive_special_bessel_y1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5466017Z   test_comprehensive_special_bessel_y1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5466191Z   test_comprehensive_special_bessel_y1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5466365Z   test_comprehensive_special_bessel_y1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5466560Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5466756Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5466954Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5467150Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5467328Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5467518Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5467707Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5467896Z   test_comprehensive_special_chebyshev_polynomial_t_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5468089Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5468284Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5468478Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5468669Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5468859Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5469032Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5469220Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5469438Z   test_comprehensive_special_chebyshev_polynomial_u_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5469899Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_bool (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5470254Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5470604Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5470947Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5471281Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5471645Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5471987Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5472331Z   test_comprehensive_special_chebyshev_polynomial_v_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5472664Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_bool (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5472997Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5473350Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5473686Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5474024Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5474358Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5474691Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5475035Z   test_comprehensive_special_chebyshev_polynomial_w_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5475214Z   test_comprehensive_special_entr_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5475390Z   test_comprehensive_special_entr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5475568Z   test_comprehensive_special_entr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5475743Z   test_comprehensive_special_entr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5475903Z   test_comprehensive_special_entr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5476075Z   test_comprehensive_special_entr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5476273Z   test_comprehensive_special_entr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5476445Z   test_comprehensive_special_entr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5476614Z   test_comprehensive_special_entr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5476782Z   test_comprehensive_special_erfcx_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5476959Z   test_comprehensive_special_erfcx_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5477135Z   test_comprehensive_special_erfcx_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5477311Z   test_comprehensive_special_erfcx_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5477472Z   test_comprehensive_special_erfcx_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5477651Z   test_comprehensive_special_erfcx_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5477850Z   test_comprehensive_special_erfcx_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5478021Z   test_comprehensive_special_erfcx_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5478211Z   test_comprehensive_special_hermite_polynomial_h_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5478403Z   test_comprehensive_special_hermite_polynomial_h_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5478597Z   test_comprehensive_special_hermite_polynomial_h_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5478784Z   test_comprehensive_special_hermite_polynomial_h_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5478977Z   test_comprehensive_special_hermite_polynomial_h_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5479166Z   test_comprehensive_special_hermite_polynomial_h_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5479342Z   test_comprehensive_special_hermite_polynomial_h_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5479528Z   test_comprehensive_special_hermite_polynomial_h_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5479718Z   test_comprehensive_special_hermite_polynomial_he_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5479909Z   test_comprehensive_special_hermite_polynomial_he_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5480102Z   test_comprehensive_special_hermite_polynomial_he_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5480291Z   test_comprehensive_special_hermite_polynomial_he_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5480478Z   test_comprehensive_special_hermite_polynomial_he_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5480799Z   test_comprehensive_special_hermite_polynomial_he_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5480989Z   test_comprehensive_special_hermite_polynomial_he_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5481163Z   test_comprehensive_special_hermite_polynomial_he_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5481339Z   test_comprehensive_special_i0e_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5481581Z   test_comprehensive_special_i0e_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5481758Z   test_comprehensive_special_i0e_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5481928Z   test_comprehensive_special_i0e_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5482099Z   test_comprehensive_special_i0e_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5482268Z   test_comprehensive_special_i0e_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5482435Z   test_comprehensive_special_i0e_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5482607Z   test_comprehensive_special_i0e_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5482776Z   test_comprehensive_special_i0e_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5482934Z   test_comprehensive_special_i1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5483144Z   test_comprehensive_special_i1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5483319Z   test_comprehensive_special_i1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5483488Z   test_comprehensive_special_i1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5483652Z   test_comprehensive_special_i1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5483820Z   test_comprehensive_special_i1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5483986Z   test_comprehensive_special_i1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5484157Z   test_comprehensive_special_i1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5484328Z   test_comprehensive_special_i1e_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5484488Z   test_comprehensive_special_i1e_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5484659Z   test_comprehensive_special_i1e_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5484827Z   test_comprehensive_special_i1e_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5484995Z   test_comprehensive_special_i1e_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5485160Z   test_comprehensive_special_i1e_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5485327Z   test_comprehensive_special_i1e_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5485495Z   test_comprehensive_special_i1e_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5485688Z   test_comprehensive_special_laguerre_polynomial_l_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5485882Z   test_comprehensive_special_laguerre_polynomial_l_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5486061Z   test_comprehensive_special_laguerre_polynomial_l_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5486253Z   test_comprehensive_special_laguerre_polynomial_l_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5486445Z   test_comprehensive_special_laguerre_polynomial_l_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5486636Z   test_comprehensive_special_laguerre_polynomial_l_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5486859Z   test_comprehensive_special_laguerre_polynomial_l_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5487048Z   test_comprehensive_special_laguerre_polynomial_l_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5487407Z   test_comprehensive_special_legendre_polynomial_p_cpu_bool (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5487756Z   test_comprehensive_special_legendre_polynomial_p_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5488095Z   test_comprehensive_special_legendre_polynomial_p_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5488437Z   test_comprehensive_special_legendre_polynomial_p_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5488789Z   test_comprehensive_special_legendre_polynomial_p_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5489121Z   test_comprehensive_special_legendre_polynomial_p_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5489462Z   test_comprehensive_special_legendre_polynomial_p_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5489801Z   test_comprehensive_special_legendre_polynomial_p_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5489977Z   test_comprehensive_special_log_ndtr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5490159Z   test_comprehensive_special_log_ndtr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5490339Z   test_comprehensive_special_log_ndtr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5490516Z   test_comprehensive_special_log_ndtr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5490690Z   test_comprehensive_special_log_ndtr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5490867Z   test_comprehensive_special_log_ndtr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5491040Z   test_comprehensive_special_log_ndtr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5491199Z   test_comprehensive_special_log_ndtr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5491389Z   test_comprehensive_special_modified_bessel_i0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5491580Z   test_comprehensive_special_modified_bessel_i0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5491767Z   test_comprehensive_special_modified_bessel_i0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5491952Z   test_comprehensive_special_modified_bessel_i0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5492137Z   test_comprehensive_special_modified_bessel_i0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5492322Z   test_comprehensive_special_modified_bessel_i0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5492504Z   test_comprehensive_special_modified_bessel_i0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5492720Z   test_comprehensive_special_modified_bessel_i0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5492894Z   test_comprehensive_special_modified_bessel_i1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5493083Z   test_comprehensive_special_modified_bessel_i1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5493268Z   test_comprehensive_special_modified_bessel_i1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5493449Z   test_comprehensive_special_modified_bessel_i1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5493631Z   test_comprehensive_special_modified_bessel_i1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5493814Z   test_comprehensive_special_modified_bessel_i1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5493997Z   test_comprehensive_special_modified_bessel_i1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5494214Z   test_comprehensive_special_modified_bessel_i1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5494397Z   test_comprehensive_special_modified_bessel_k0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5494585Z   test_comprehensive_special_modified_bessel_k0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5494759Z   test_comprehensive_special_modified_bessel_k0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5494939Z   test_comprehensive_special_modified_bessel_k0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5495119Z   test_comprehensive_special_modified_bessel_k0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5495301Z   test_comprehensive_special_modified_bessel_k0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5495483Z   test_comprehensive_special_modified_bessel_k0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5495666Z   test_comprehensive_special_modified_bessel_k0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5495849Z   test_comprehensive_special_modified_bessel_k1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5496035Z   test_comprehensive_special_modified_bessel_k1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5496220Z   test_comprehensive_special_modified_bessel_k1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5496401Z   test_comprehensive_special_modified_bessel_k1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5496572Z   test_comprehensive_special_modified_bessel_k1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5496754Z   test_comprehensive_special_modified_bessel_k1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5496934Z   test_comprehensive_special_modified_bessel_k1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5497116Z   test_comprehensive_special_modified_bessel_k1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5497291Z   test_comprehensive_special_ndtr_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5497462Z   test_comprehensive_special_ndtr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5497637Z   test_comprehensive_special_ndtr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5497840Z   test_comprehensive_special_ndtr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5498014Z   test_comprehensive_special_ndtr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5498173Z   test_comprehensive_special_ndtr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5498339Z   test_comprehensive_special_ndtr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5498510Z   test_comprehensive_special_ndtr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5498678Z   test_comprehensive_special_ndtr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5498844Z   test_comprehensive_special_ndtri_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5499020Z   test_comprehensive_special_ndtri_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5499220Z   test_comprehensive_special_ndtri_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5499472Z   test_comprehensive_special_ndtri_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5499647Z   test_comprehensive_special_ndtri_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5499802Z   test_comprehensive_special_ndtri_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5499977Z   test_comprehensive_special_ndtri_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5500145Z   test_comprehensive_special_ndtri_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5500354Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5500567Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5500774Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5500980Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5501180Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5501375Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5501566Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5501755Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5501959Z   test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5502155Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5502353Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5502548Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.5502745Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5502975Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5503170Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5503364Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5503552Z   test_comprehensive_special_scaled_modified_bessel_k0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5503732Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5503927Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5504120Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5504312Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5504534Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5504724Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5504915Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5505106Z   test_comprehensive_special_scaled_modified_bessel_k1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5505477Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_bool (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5505841Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5506201Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5506545Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5506901Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5507255Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5507607Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5507959Z   test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5508316Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_bool (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5508675Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5509027Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5509404Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5509754Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5510101Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5510451Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5510806Z   test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5511145Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_bool (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5511527Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5511888Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5512237Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5512587Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5512940Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5513291Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5513641Z   test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5513996Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_bool (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5514349Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5514709Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5515062Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5515413Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5515748Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5516096Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5516442Z   test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipping - testing takes an unreasonably long time, #79528 (0.000s)
2023-01-11T20:52:32.5516667Z   test_comprehensive_special_spherical_bessel_j0_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5516859Z   test_comprehensive_special_spherical_bessel_j0_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5517048Z   test_comprehensive_special_spherical_bessel_j0_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5517234Z   test_comprehensive_special_spherical_bessel_j0_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5517418Z   test_comprehensive_special_spherical_bessel_j0_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5517605Z   test_comprehensive_special_spherical_bessel_j0_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5517794Z   test_comprehensive_special_spherical_bessel_j0_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5517995Z   test_comprehensive_special_spherical_bessel_j0_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5518175Z   test_comprehensive_special_xlog1py_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5518352Z   test_comprehensive_special_xlog1py_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5518530Z   test_comprehensive_special_xlog1py_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5518705Z   test_comprehensive_special_xlog1py_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5518878Z   test_comprehensive_special_xlog1py_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5519055Z   test_comprehensive_special_xlog1py_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5519232Z   test_comprehensive_special_xlog1py_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5519403Z   test_comprehensive_special_xlog1py_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5519563Z   test_comprehensive_special_xlog1py_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5519737Z   test_comprehensive_special_xlog1py_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5519907Z   test_comprehensive_special_zeta_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5520079Z   test_comprehensive_special_zeta_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5520255Z   test_comprehensive_special_zeta_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5520430Z   test_comprehensive_special_zeta_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5520720Z   test_comprehensive_special_zeta_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5520893Z   test_comprehensive_special_zeta_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5521066Z   test_comprehensive_special_zeta_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5521239Z   test_comprehensive_special_zeta_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5521396Z   test_comprehensive_split_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5521562Z   test_comprehensive_split_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5521797Z   test_comprehensive_split_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5521972Z   test_comprehensive_split_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5522144Z   test_comprehensive_split_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5522313Z   test_comprehensive_split_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5522482Z   test_comprehensive_split_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5522645Z   test_comprehensive_split_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5522813Z   test_comprehensive_split_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5522958Z   test_comprehensive_split_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5523118Z   test_comprehensive_split_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5523318Z   test_comprehensive_split_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5523487Z   test_comprehensive_split_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5523667Z   test_comprehensive_split_list_args_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5523853Z   test_comprehensive_split_list_args_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5524034Z   test_comprehensive_split_list_args_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5524214Z   test_comprehensive_split_list_args_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5524394Z   test_comprehensive_split_list_args_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5524559Z   test_comprehensive_split_list_args_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5524736Z   test_comprehensive_split_list_args_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5524910Z   test_comprehensive_split_list_args_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5525081Z   test_comprehensive_split_list_args_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5525249Z   test_comprehensive_split_list_args_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5525420Z   test_comprehensive_split_list_args_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5525588Z   test_comprehensive_split_list_args_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5525766Z   test_comprehensive_split_with_sizes_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5525935Z   test_comprehensive_split_with_sizes_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5526102Z   test_comprehensive_split_with_sizes_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5526279Z   test_comprehensive_split_with_sizes_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5526458Z   test_comprehensive_split_with_sizes_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5526633Z   test_comprehensive_split_with_sizes_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5526810Z   test_comprehensive_split_with_sizes_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5527014Z   test_comprehensive_split_with_sizes_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5527190Z   test_comprehensive_split_with_sizes_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5527364Z   test_comprehensive_split_with_sizes_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5527537Z   test_comprehensive_split_with_sizes_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5527696Z   test_comprehensive_split_with_sizes_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5527866Z   test_comprehensive_split_with_sizes_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5528034Z   test_comprehensive_sqrt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5528196Z   test_comprehensive_sqrt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5528397Z   test_comprehensive_sqrt_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5528570Z   test_comprehensive_sqrt_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5528732Z   test_comprehensive_sqrt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5528893Z   test_comprehensive_sqrt_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5529054Z   test_comprehensive_sqrt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5529197Z   test_comprehensive_sqrt_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5529349Z   test_comprehensive_sqrt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5529512Z   test_comprehensive_sqrt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5529677Z   test_comprehensive_sqrt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5529848Z   test_comprehensive_square_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5530014Z   test_comprehensive_square_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5530187Z   test_comprehensive_square_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5530358Z   test_comprehensive_square_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5530525Z   test_comprehensive_square_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5530680Z   test_comprehensive_square_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5530853Z   test_comprehensive_square_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5531020Z   test_comprehensive_square_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5531185Z   test_comprehensive_square_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5531345Z   test_comprehensive_square_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5531509Z   test_comprehensive_square_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5531670Z   test_comprehensive_square_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5531840Z   test_comprehensive_squeeze_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5532000Z   test_comprehensive_squeeze_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5532190Z   test_comprehensive_squeeze_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5532365Z   test_comprehensive_squeeze_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5532537Z   test_comprehensive_squeeze_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5532708Z   test_comprehensive_squeeze_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5532872Z   test_comprehensive_squeeze_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5533041Z   test_comprehensive_squeeze_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5533209Z   test_comprehensive_squeeze_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5533369Z   test_comprehensive_squeeze_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5533529Z   test_comprehensive_squeeze_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5533711Z   test_comprehensive_squeeze_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5533878Z   test_comprehensive_squeeze_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5534045Z   test_comprehensive_stack_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5534210Z   test_comprehensive_stack_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5534380Z   test_comprehensive_stack_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5534548Z   test_comprehensive_stack_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5534721Z   test_comprehensive_stack_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5534888Z   test_comprehensive_stack_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5535051Z   test_comprehensive_stack_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5535196Z   test_comprehensive_stack_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5535361Z   test_comprehensive_stack_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5535520Z   test_comprehensive_stack_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5535677Z   test_comprehensive_stack_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5535840Z   test_comprehensive_stack_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5535999Z   test_comprehensive_stack_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5536168Z   test_comprehensive_std_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5536336Z   test_comprehensive_std_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5536502Z   test_comprehensive_std_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5536649Z   test_comprehensive_std_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5536808Z   test_comprehensive_std_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5536970Z   test_comprehensive_std_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5537139Z   test_comprehensive_std_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5537349Z   test_comprehensive_std_mean_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5537526Z   test_comprehensive_std_mean_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5537694Z   test_comprehensive_std_mean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5537858Z   test_comprehensive_std_mean_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5538029Z   test_comprehensive_std_mean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5538198Z   test_comprehensive_std_mean_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5538385Z   test_comprehensive_std_mean_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5538568Z   test_comprehensive_std_mean_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5538776Z   test_comprehensive_std_mean_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5538958Z   test_comprehensive_std_mean_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5539136Z   test_comprehensive_std_mean_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5539381Z   test_comprehensive_std_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5539564Z   test_comprehensive_std_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5539743Z   test_comprehensive_std_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5539906Z   test_comprehensive_std_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5540084Z   test_comprehensive_std_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5540260Z   test_comprehensive_std_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5540431Z   test_comprehensive_stft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5540600Z   test_comprehensive_stft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5540769Z   test_comprehensive_stft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5540934Z   test_comprehensive_stft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5541095Z   test_comprehensive_sub_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5541265Z   test_comprehensive_sub_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5541419Z   test_comprehensive_sub_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5541588Z   test_comprehensive_sub_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5541752Z   test_comprehensive_sub_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5541910Z   test_comprehensive_sub_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5542066Z   test_comprehensive_sub_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5542225Z   test_comprehensive_sub_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5542384Z   test_comprehensive_sub_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5542576Z   test_comprehensive_sub_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5542735Z   test_comprehensive_sub_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5542883Z   test_comprehensive_sub_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5543050Z   test_comprehensive_sum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5543210Z   test_comprehensive_sum_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5543376Z   test_comprehensive_sum_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5543541Z   test_comprehensive_sum_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5543703Z   test_comprehensive_sum_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5543866Z   test_comprehensive_sum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5544085Z   test_comprehensive_sum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5544245Z   test_comprehensive_sum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5544390Z   test_comprehensive_sum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5544547Z   test_comprehensive_sum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5544707Z   test_comprehensive_sum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5544863Z   test_comprehensive_sum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5545036Z   test_comprehensive_sum_to_size_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5545206Z   test_comprehensive_sum_to_size_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5545386Z   test_comprehensive_sum_to_size_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5545562Z   test_comprehensive_sum_to_size_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5545734Z   test_comprehensive_sum_to_size_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5545892Z   test_comprehensive_sum_to_size_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5546065Z   test_comprehensive_sum_to_size_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5546235Z   test_comprehensive_sum_to_size_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5546401Z   test_comprehensive_sum_to_size_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5546572Z   test_comprehensive_sum_to_size_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5546743Z   test_comprehensive_sum_to_size_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5546911Z   test_comprehensive_sum_to_size_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5547080Z   test_comprehensive_svd_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5547247Z   test_comprehensive_svd_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5547396Z   test_comprehensive_svd_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5547561Z   test_comprehensive_svd_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5547766Z   test_comprehensive_svd_lowrank_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5547938Z   test_comprehensive_svd_lowrank_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5548110Z   test_comprehensive_symeig_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5548280Z   test_comprehensive_symeig_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5548447Z   test_comprehensive_symeig_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5548616Z   test_comprehensive_symeig_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5548779Z   test_comprehensive_t_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5548927Z   test_comprehensive_t_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5549096Z   test_comprehensive_t_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5549288Z   test_comprehensive_t_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5549449Z   test_comprehensive_t_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5549609Z   test_comprehensive_t_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5549772Z   test_comprehensive_t_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5549929Z   test_comprehensive_t_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5550086Z   test_comprehensive_t_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5550230Z   test_comprehensive_t_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5550389Z   test_comprehensive_t_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5550546Z   test_comprehensive_t_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5550726Z   test_comprehensive_take_along_dim_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5550899Z   test_comprehensive_take_along_dim_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5551078Z   test_comprehensive_take_along_dim_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5551261Z   test_comprehensive_take_along_dim_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5551435Z   test_comprehensive_take_along_dim_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5551608Z   test_comprehensive_take_along_dim_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5551771Z   test_comprehensive_take_along_dim_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5551945Z   test_comprehensive_take_along_dim_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5552118Z   test_comprehensive_take_along_dim_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5552291Z   test_comprehensive_take_along_dim_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5552463Z   test_comprehensive_take_along_dim_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5552632Z   test_comprehensive_take_along_dim_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5552798Z   test_comprehensive_take_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5552987Z   test_comprehensive_take_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5553160Z   test_comprehensive_take_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5553316Z   test_comprehensive_take_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5553481Z   test_comprehensive_take_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5553641Z   test_comprehensive_take_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5553799Z   test_comprehensive_take_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5553961Z   test_comprehensive_take_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5554119Z   test_comprehensive_take_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5554276Z   test_comprehensive_take_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5554462Z   test_comprehensive_take_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5554626Z   test_comprehensive_take_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5554777Z   test_comprehensive_tan_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5554937Z   test_comprehensive_tan_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5555105Z   test_comprehensive_tan_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5555270Z   test_comprehensive_tan_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5555434Z   test_comprehensive_tan_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5555598Z   test_comprehensive_tan_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5555759Z   test_comprehensive_tan_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5555916Z   test_comprehensive_tan_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5556076Z   test_comprehensive_tan_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5556222Z   test_comprehensive_tan_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5556377Z   test_comprehensive_tan_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5556544Z   test_comprehensive_tanh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5556702Z   test_comprehensive_tanh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5556873Z   test_comprehensive_tanh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5557043Z   test_comprehensive_tanh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5557208Z   test_comprehensive_tanh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5557370Z   test_comprehensive_tanh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5557531Z   test_comprehensive_tanh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5557675Z   test_comprehensive_tanh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5557829Z   test_comprehensive_tanh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5557988Z   test_comprehensive_tanh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5558196Z   test_comprehensive_tanh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5558374Z   test_comprehensive_tensor_split_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5558546Z   test_comprehensive_tensor_split_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5558725Z   test_comprehensive_tensor_split_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5558902Z   test_comprehensive_tensor_split_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5559076Z   test_comprehensive_tensor_split_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5559238Z   test_comprehensive_tensor_split_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5559411Z   test_comprehensive_tensor_split_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5559581Z   test_comprehensive_tensor_split_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5559782Z   test_comprehensive_tensor_split_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5559953Z   test_comprehensive_tensor_split_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5560122Z   test_comprehensive_tensor_split_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5560292Z   test_comprehensive_tensor_split_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5560464Z   test_comprehensive_tensordot_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5560754Z   test_comprehensive_tensordot_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5560918Z   test_comprehensive_tensordot_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5561092Z   test_comprehensive_tensordot_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5561264Z   test_comprehensive_tensordot_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5561435Z   test_comprehensive_tensordot_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5561600Z   test_comprehensive_tensordot_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5561774Z   test_comprehensive_tensordot_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5561944Z   test_comprehensive_tensordot_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5562114Z   test_comprehensive_tensordot_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5562284Z   test_comprehensive_tile_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5562433Z   test_comprehensive_tile_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5562603Z   test_comprehensive_tile_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5562771Z   test_comprehensive_tile_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5562937Z   test_comprehensive_tile_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5563103Z   test_comprehensive_tile_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5563263Z   test_comprehensive_tile_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5563479Z   test_comprehensive_tile_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5563641Z   test_comprehensive_tile_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5563801Z   test_comprehensive_tile_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5563949Z   test_comprehensive_tile_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5564111Z   test_comprehensive_tile_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5564275Z   test_comprehensive_to_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5564435Z   test_comprehensive_to_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5564604Z   test_comprehensive_to_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5564771Z   test_comprehensive_to_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5564930Z   test_comprehensive_to_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5565122Z   test_comprehensive_to_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5565278Z   test_comprehensive_to_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5565425Z   test_comprehensive_to_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5565586Z   test_comprehensive_to_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5565745Z   test_comprehensive_to_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5565905Z   test_comprehensive_to_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5566067Z   test_comprehensive_to_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5566243Z   test_comprehensive_to_sparse_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5566418Z   test_comprehensive_to_sparse_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5566593Z   test_comprehensive_to_sparse_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5566767Z   test_comprehensive_to_sparse_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5566924Z   test_comprehensive_to_sparse_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5567094Z   test_comprehensive_to_sparse_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5567257Z   test_comprehensive_to_sparse_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5567428Z   test_comprehensive_to_sparse_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5567598Z   test_comprehensive_to_sparse_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5567764Z   test_comprehensive_to_sparse_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5567932Z   test_comprehensive_to_sparse_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5568097Z   test_comprehensive_to_sparse_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5568263Z   test_comprehensive_topk_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5568414Z   test_comprehensive_topk_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5568575Z   test_comprehensive_topk_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5568774Z   test_comprehensive_topk_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5568941Z   test_comprehensive_topk_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5569100Z   test_comprehensive_topk_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5569259Z   test_comprehensive_topk_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5569420Z   test_comprehensive_topk_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5569592Z   test_comprehensive_trace_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5569748Z   test_comprehensive_trace_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5569915Z   test_comprehensive_trace_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5570079Z   test_comprehensive_trace_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5570270Z   test_comprehensive_trace_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5570430Z   test_comprehensive_trace_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5570591Z   test_comprehensive_trace_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5570751Z   test_comprehensive_trace_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5570911Z   test_comprehensive_trace_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5571084Z   test_comprehensive_transpose_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5571240Z   test_comprehensive_transpose_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5571419Z   test_comprehensive_transpose_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5571593Z   test_comprehensive_transpose_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5571767Z   test_comprehensive_transpose_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5571937Z   test_comprehensive_transpose_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5572106Z   test_comprehensive_transpose_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5572273Z   test_comprehensive_transpose_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5572441Z   test_comprehensive_transpose_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5572611Z   test_comprehensive_transpose_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5572767Z   test_comprehensive_transpose_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5572939Z   test_comprehensive_transpose_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5573105Z   test_comprehensive_transpose_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5573278Z   test_comprehensive_trapezoid_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5573455Z   test_comprehensive_trapezoid_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5573627Z   test_comprehensive_trapezoid_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5573797Z   test_comprehensive_trapezoid_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5573994Z   test_comprehensive_trapezoid_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5574166Z   test_comprehensive_trapezoid_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5574322Z   test_comprehensive_trapezoid_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5574488Z   test_comprehensive_trapezoid_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5574655Z   test_comprehensive_trapezoid_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5574821Z   test_comprehensive_trapezoid_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5574989Z   test_comprehensive_trapezoid_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5575154Z   test_comprehensive_trapz_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5575328Z   test_comprehensive_trapz_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5575519Z   test_comprehensive_trapz_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5575689Z   test_comprehensive_trapz_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5575838Z   test_comprehensive_trapz_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5575997Z   test_comprehensive_trapz_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5576163Z   test_comprehensive_trapz_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5576324Z   test_comprehensive_trapz_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5576488Z   test_comprehensive_trapz_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5576653Z   test_comprehensive_trapz_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5576813Z   test_comprehensive_trapz_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5576998Z   test_comprehensive_triangular_solve_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5577183Z   test_comprehensive_triangular_solve_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5577350Z   test_comprehensive_triangular_solve_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5577530Z   test_comprehensive_triangular_solve_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5577697Z   test_comprehensive_tril_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5577862Z   test_comprehensive_tril_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5578033Z   test_comprehensive_tril_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5578203Z   test_comprehensive_tril_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5578369Z   test_comprehensive_tril_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5578532Z   test_comprehensive_tril_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5578697Z   test_comprehensive_tril_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5578846Z   test_comprehensive_tril_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5579002Z   test_comprehensive_tril_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5579188Z   test_comprehensive_tril_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5579422Z   test_comprehensive_tril_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5579588Z   test_comprehensive_tril_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5579763Z   test_comprehensive_tril_indices_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5579935Z   test_comprehensive_tril_indices_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5580101Z   test_comprehensive_triu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5580261Z   test_comprehensive_triu_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5580417Z   test_comprehensive_triu_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5580590Z   test_comprehensive_triu_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5580794Z   test_comprehensive_triu_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5580956Z   test_comprehensive_triu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5581119Z   test_comprehensive_triu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5581279Z   test_comprehensive_triu_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5581437Z   test_comprehensive_triu_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5581591Z   test_comprehensive_triu_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5581751Z   test_comprehensive_triu_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5581897Z   test_comprehensive_triu_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5582069Z   test_comprehensive_triu_indices_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5582240Z   test_comprehensive_triu_indices_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5582414Z   test_comprehensive_true_divide_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5582584Z   test_comprehensive_true_divide_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5582761Z   test_comprehensive_true_divide_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.5582939Z   test_comprehensive_true_divide_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5583116Z   test_comprehensive_true_divide_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5583291Z   test_comprehensive_true_divide_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5583447Z   test_comprehensive_true_divide_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5583616Z   test_comprehensive_true_divide_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5583784Z   test_comprehensive_true_divide_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5583956Z   test_comprehensive_true_divide_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5584125Z   test_comprehensive_true_divide_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5584292Z   test_comprehensive_true_divide_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5584495Z   test_comprehensive_trunc_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5584664Z   test_comprehensive_trunc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5584828Z   test_comprehensive_trunc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5584979Z   test_comprehensive_trunc_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5585137Z   test_comprehensive_trunc_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5585298Z   test_comprehensive_trunc_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5585460Z   test_comprehensive_trunc_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5585621Z   test_comprehensive_trunc_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5585790Z   test_comprehensive_unbind_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5585978Z   test_comprehensive_unbind_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5586149Z   test_comprehensive_unbind_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5586321Z   test_comprehensive_unbind_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5586477Z   test_comprehensive_unbind_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5586645Z   test_comprehensive_unbind_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5586810Z   test_comprehensive_unbind_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5586976Z   test_comprehensive_unbind_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5587144Z   test_comprehensive_unbind_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5587305Z   test_comprehensive_unbind_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5587463Z   test_comprehensive_unbind_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5587623Z   test_comprehensive_unbind_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5587785Z   test_comprehensive_unbind_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5587944Z   test_comprehensive_unflatten_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5588112Z   test_comprehensive_unflatten_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5588292Z   test_comprehensive_unflatten_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5588471Z   test_comprehensive_unflatten_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5588645Z   test_comprehensive_unflatten_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5588814Z   test_comprehensive_unflatten_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5588986Z   test_comprehensive_unflatten_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5589155Z   test_comprehensive_unflatten_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5589324Z   test_comprehensive_unflatten_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5589476Z   test_comprehensive_unflatten_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5589675Z   test_comprehensive_unflatten_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5589844Z   test_comprehensive_unflatten_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5590013Z   test_comprehensive_unflatten_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5590187Z   test_comprehensive_unfold_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5590359Z   test_comprehensive_unfold_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5590539Z   test_comprehensive_unfold_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5590716Z   test_comprehensive_unfold_copy_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5590893Z   test_comprehensive_unfold_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5591087Z   test_comprehensive_unfold_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5591262Z   test_comprehensive_unfold_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5591433Z   test_comprehensive_unfold_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5591603Z   test_comprehensive_unfold_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5591770Z   test_comprehensive_unfold_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5591939Z   test_comprehensive_unfold_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5592109Z   test_comprehensive_unfold_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5592278Z   test_comprehensive_unfold_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5592450Z   test_comprehensive_unfold_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5592602Z   test_comprehensive_unfold_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5592771Z   test_comprehensive_unfold_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5592940Z   test_comprehensive_unfold_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5593108Z   test_comprehensive_unfold_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5593273Z   test_comprehensive_unfold_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5593443Z   test_comprehensive_unfold_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5593610Z   test_comprehensive_unfold_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5593774Z   test_comprehensive_unfold_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5593935Z   test_comprehensive_unfold_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5594080Z   test_comprehensive_unfold_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5594243Z   test_comprehensive_unfold_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5594402Z   test_comprehensive_unfold_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5594571Z   test_comprehensive_uniform_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5594791Z   test_comprehensive_uniform_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5594964Z   test_comprehensive_uniform_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5595132Z   test_comprehensive_uniform_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5595298Z   test_comprehensive_uniform_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5595460Z   test_comprehensive_uniform_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5595633Z   test_comprehensive_unique_consecutive_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5595812Z   test_comprehensive_unique_consecutive_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5595995Z   test_comprehensive_unique_consecutive_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5596179Z   test_comprehensive_unique_consecutive_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5596388Z   test_comprehensive_unique_consecutive_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5596567Z   test_comprehensive_unique_consecutive_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5596745Z   test_comprehensive_unique_consecutive_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5596923Z   test_comprehensive_unique_consecutive_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5597098Z   test_comprehensive_unique_consecutive_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5597254Z   test_comprehensive_unique_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5597419Z   test_comprehensive_unique_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5597587Z   test_comprehensive_unique_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5597751Z   test_comprehensive_unique_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5597918Z   test_comprehensive_unique_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5598079Z   test_comprehensive_unique_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5598237Z   test_comprehensive_unique_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5598398Z   test_comprehensive_unique_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5598559Z   test_comprehensive_unique_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5598722Z   test_comprehensive_unsqueeze_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5598891Z   test_comprehensive_unsqueeze_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5599065Z   test_comprehensive_unsqueeze_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5599241Z   test_comprehensive_unsqueeze_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5599416Z   test_comprehensive_unsqueeze_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5599588Z   test_comprehensive_unsqueeze_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5599758Z   test_comprehensive_unsqueeze_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5599963Z   test_comprehensive_unsqueeze_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5600133Z   test_comprehensive_unsqueeze_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5600284Z   test_comprehensive_unsqueeze_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5600451Z   test_comprehensive_unsqueeze_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5600730Z   test_comprehensive_unsqueeze_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5600900Z   test_comprehensive_unsqueeze_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5601068Z   test_comprehensive_var_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5601237Z   test_comprehensive_var_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5601408Z   test_comprehensive_var_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5601624Z   test_comprehensive_var_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5601784Z   test_comprehensive_var_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5601933Z   test_comprehensive_var_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5602104Z   test_comprehensive_var_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5602279Z   test_comprehensive_var_mean_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5602451Z   test_comprehensive_var_mean_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5602620Z   test_comprehensive_var_mean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5602794Z   test_comprehensive_var_mean_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5602963Z   test_comprehensive_var_mean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5603150Z   test_comprehensive_var_mean_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5603332Z   test_comprehensive_var_mean_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5603499Z   test_comprehensive_var_mean_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5603679Z   test_comprehensive_var_mean_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5603857Z   test_comprehensive_var_mean_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5604039Z   test_comprehensive_var_mean_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5604216Z   test_comprehensive_var_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5604397Z   test_comprehensive_var_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5604576Z   test_comprehensive_var_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5604750Z   test_comprehensive_var_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5604925Z   test_comprehensive_var_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5605100Z   test_comprehensive_var_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5605305Z   test_comprehensive_vdot_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5605476Z   test_comprehensive_vdot_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5605644Z   test_comprehensive_vdot_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5605809Z   test_comprehensive_vdot_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5605973Z   test_comprehensive_vdot_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5606137Z   test_comprehensive_vdot_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5606297Z   test_comprehensive_vdot_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5606452Z   test_comprehensive_vdot_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5606601Z   test_comprehensive_vdot_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5606797Z   test_comprehensive_vdot_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5606976Z   test_comprehensive_view_as_complex_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5607152Z   test_comprehensive_view_as_complex_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5607328Z   test_comprehensive_view_as_complex_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5607499Z   test_comprehensive_view_as_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5607663Z   test_comprehensive_view_as_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5607835Z   test_comprehensive_view_as_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5608008Z   test_comprehensive_view_as_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5608169Z   test_comprehensive_view_as_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5608335Z   test_comprehensive_view_as_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5608497Z   test_comprehensive_view_as_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5608664Z   test_comprehensive_view_as_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5608827Z   test_comprehensive_view_as_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5608986Z   test_comprehensive_view_as_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5609143Z   test_comprehensive_view_as_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5609316Z   test_comprehensive_view_as_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5609484Z   test_comprehensive_view_as_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5609648Z   test_comprehensive_view_as_real_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5609826Z   test_comprehensive_view_as_real_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5609999Z   test_comprehensive_view_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5610168Z   test_comprehensive_view_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5610340Z   test_comprehensive_view_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5610551Z   test_comprehensive_view_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5610722Z   test_comprehensive_view_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5610887Z   test_comprehensive_view_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5611051Z   test_comprehensive_view_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5611216Z   test_comprehensive_view_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5611372Z   test_comprehensive_view_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5611538Z   test_comprehensive_view_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5611705Z   test_comprehensive_view_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5611868Z   test_comprehensive_view_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5612073Z   test_comprehensive_view_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5612244Z   test_comprehensive_view_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5612411Z   test_comprehensive_view_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5612577Z   test_comprehensive_view_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5612728Z   test_comprehensive_view_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5612885Z   test_comprehensive_view_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5613047Z   test_comprehensive_view_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5613206Z   test_comprehensive_view_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5613360Z   test_comprehensive_view_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5613520Z   test_comprehensive_view_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5613681Z   test_comprehensive_view_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5613850Z   test_comprehensive_vsplit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5614015Z   test_comprehensive_vsplit_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5614174Z   test_comprehensive_vsplit_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5614343Z   test_comprehensive_vsplit_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5614515Z   test_comprehensive_vsplit_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5614684Z   test_comprehensive_vsplit_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5614851Z   test_comprehensive_vsplit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5615018Z   test_comprehensive_vsplit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5615182Z   test_comprehensive_vsplit_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5615344Z   test_comprehensive_vsplit_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5615502Z   test_comprehensive_vsplit_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5615687Z   test_comprehensive_vsplit_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5615851Z   test_comprehensive_vsplit_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5616019Z   test_comprehensive_vstack_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5616181Z   test_comprehensive_vstack_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5616353Z   test_comprehensive_vstack_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5616524Z   test_comprehensive_vstack_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5616694Z   test_comprehensive_vstack_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5616860Z   test_comprehensive_vstack_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5617028Z   test_comprehensive_vstack_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5617218Z   test_comprehensive_vstack_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5617385Z   test_comprehensive_vstack_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5617546Z   test_comprehensive_vstack_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5617708Z   test_comprehensive_vstack_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5617869Z   test_comprehensive_vstack_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5618029Z   test_comprehensive_vstack_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5618195Z   test_comprehensive_where_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5618360Z   test_comprehensive_where_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5618533Z   test_comprehensive_where_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5618689Z   test_comprehensive_where_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5618858Z   test_comprehensive_where_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5619024Z   test_comprehensive_where_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5619184Z   test_comprehensive_where_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5619423Z   test_comprehensive_where_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5619587Z   test_comprehensive_where_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5619750Z   test_comprehensive_where_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5619909Z   test_comprehensive_where_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5620070Z   test_comprehensive_where_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5620219Z   test_comprehensive_where_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5620387Z   test_comprehensive_xlogy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5620547Z   test_comprehensive_xlogy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5620713Z   test_comprehensive_xlogy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5620877Z   test_comprehensive_xlogy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5621089Z   test_comprehensive_xlogy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5621252Z   test_comprehensive_xlogy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5621410Z   test_comprehensive_xlogy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5621564Z   test_comprehensive_xlogy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5621712Z   test_comprehensive_xlogy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5621875Z   test_comprehensive_xlogy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5622042Z   test_comprehensive_zero__cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5622202Z   test_comprehensive_zero__cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5622374Z   test_comprehensive_zero__cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5622572Z   test_comprehensive_zero__cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5622734Z   test_comprehensive_zero__cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5622894Z   test_comprehensive_zero__cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5623055Z   test_comprehensive_zero__cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5623202Z   test_comprehensive_zero__cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5623358Z   test_comprehensive_zero__cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5623520Z   test_comprehensive_zero__cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5623679Z   test_comprehensive_zero__cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5623838Z   test_comprehensive_zero__cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5624005Z   test_comprehensive_zeros_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5624166Z   test_comprehensive_zeros_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5624336Z   test_comprehensive_zeros_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5624490Z   test_comprehensive_zeros_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5624660Z   test_comprehensive_zeros_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5624826Z   test_comprehensive_zeros_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5624991Z   test_comprehensive_zeros_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5625156Z   test_comprehensive_zeros_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5625318Z   test_comprehensive_zeros_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5625478Z   test_comprehensive_zeros_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5625635Z   test_comprehensive_zeros_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5625797Z   test_comprehensive_zeros_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5625943Z   test_comprehensive_zeros_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5626182Z   test_comprehensive_zeros_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5626351Z   test_comprehensive_zeros_like_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5626529Z   test_comprehensive_zeros_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5626705Z   test_comprehensive_zeros_like_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5626881Z   test_comprehensive_zeros_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5627055Z   test_comprehensive_zeros_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5627227Z   test_comprehensive_zeros_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5627395Z   test_comprehensive_zeros_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5627553Z   test_comprehensive_zeros_like_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5627748Z   test_comprehensive_zeros_like_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5627915Z   test_comprehensive_zeros_like_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5628083Z   test_comprehensive_zeros_like_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5628254Z   test_comprehensive_zeros_like_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5628433Z   test_quick__native_batch_norm_legit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5628609Z   test_quick__native_batch_norm_legit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5628785Z   test_quick__native_batch_norm_legit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5628961Z   test_quick__softmax_backward_data_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5629138Z   test_quick__softmax_backward_data_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5629301Z   test_quick__softmax_backward_data_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5629455Z   test_quick_abs_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5629610Z   test_quick_abs_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5629766Z   test_quick_abs_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5629922Z   test_quick_abs_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5630076Z   test_quick_abs_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5630230Z   test_quick_abs_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5630382Z   test_quick_abs_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5630516Z   test_quick_abs_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5630668Z   test_quick_abs_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5630816Z   test_quick_abs_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5630964Z   test_quick_abs_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5631113Z   test_quick_abs_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5631318Z   test_quick_acos_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5631467Z   test_quick_acos_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5631627Z   test_quick_acos_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5631785Z   test_quick_acos_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5631925Z   test_quick_acos_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5632077Z   test_quick_acos_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5632225Z   test_quick_acos_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5632372Z   test_quick_acos_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5632523Z   test_quick_acos_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5632673Z   test_quick_acos_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5632851Z   test_quick_acos_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5633011Z   test_quick_acosh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5633146Z   test_quick_acosh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5633306Z   test_quick_acosh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5633463Z   test_quick_acosh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5633618Z   test_quick_acosh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5633771Z   test_quick_acosh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5633926Z   test_quick_acosh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5634078Z   test_quick_acosh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5634223Z   test_quick_acosh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5634371Z   test_quick_acosh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5634503Z   test_quick_acosh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5634659Z   test_quick_add_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5634810Z   test_quick_add_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5634966Z   test_quick_add_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5635121Z   test_quick_add_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5635276Z   test_quick_add_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5635428Z   test_quick_add_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5635576Z   test_quick_add_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5635710Z   test_quick_add_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5635859Z   test_quick_add_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5636008Z   test_quick_add_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5636152Z   test_quick_add_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5636301Z   test_quick_add_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5636486Z   test_quick_add_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5636647Z   test_quick_addcdiv_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5636811Z   test_quick_addcdiv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5636972Z   test_quick_addcdiv_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5637117Z   test_quick_addcdiv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5637273Z   test_quick_addcdiv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5637430Z   test_quick_addcmul_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5637592Z   test_quick_addcmul_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5637757Z   test_quick_addcmul_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5637910Z   test_quick_addcmul_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5638092Z   test_quick_addcmul_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5638248Z   test_quick_addcmul_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5638402Z   test_quick_addcmul_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5638541Z   test_quick_addcmul_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5638696Z   test_quick_addcmul_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5638846Z   test_quick_addcmul_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5639001Z   test_quick_addmm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5639161Z   test_quick_addmm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5639317Z   test_quick_addmm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5639469Z   test_quick_addmm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5639618Z   test_quick_addmm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5639755Z   test_quick_addmm_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5639905Z   test_quick_addmm_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5640056Z   test_quick_addmm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5640205Z   test_quick_addmm_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5640355Z   test_quick_addmm_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5640526Z   test_quick_addmm_decomposed_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5640808Z   test_quick_addmm_decomposed_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5640983Z   test_quick_addmm_decomposed_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5641153Z   test_quick_addmm_decomposed_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5641310Z   test_quick_addmm_decomposed_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5641479Z   test_quick_addmm_decomposed_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5641642Z   test_quick_addmm_decomposed_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5641857Z   test_quick_addmm_decomposed_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5642023Z   test_quick_addmm_decomposed_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5642190Z   test_quick_addmm_decomposed_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5642342Z   test_quick_addr_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5642493Z   test_quick_addr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5642649Z   test_quick_addr_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5642793Z   test_quick_addr_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5642947Z   test_quick_addr_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5643101Z   test_quick_addr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5643291Z   test_quick_addr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5643444Z   test_quick_addr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5643596Z   test_quick_addr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5643743Z   test_quick_addr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5643892Z   test_quick_addr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5644026Z   test_quick_addr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5644182Z   test_quick_all_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5644333Z   test_quick_all_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5644487Z   test_quick_all_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5644641Z   test_quick_all_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5644790Z   test_quick_all_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5644940Z   test_quick_all_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5645086Z   test_quick_all_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5645232Z   test_quick_all_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5645366Z   test_quick_all_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5645509Z   test_quick_all_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5645659Z   test_quick_all_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5645811Z   test_quick_all_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5645963Z   test_quick_amax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5646111Z   test_quick_amax_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5646262Z   test_quick_amax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5646412Z   test_quick_amax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5646547Z   test_quick_amax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5646696Z   test_quick_amax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5646881Z   test_quick_amax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5647029Z   test_quick_amax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5647177Z   test_quick_amax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5647327Z   test_quick_amax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5647479Z   test_quick_amin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5647627Z   test_quick_amin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5647777Z   test_quick_amin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5647913Z   test_quick_amin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5648059Z   test_quick_amin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5648209Z   test_quick_amin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5648385Z   test_quick_amin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5648533Z   test_quick_amin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5648679Z   test_quick_amin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5648824Z   test_quick_amin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5648973Z   test_quick_any_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5649122Z   test_quick_any_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5649263Z   test_quick_any_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5649421Z   test_quick_any_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5649574Z   test_quick_any_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5649726Z   test_quick_any_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5649874Z   test_quick_any_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5650021Z   test_quick_any_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5650167Z   test_quick_any_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5650313Z   test_quick_any_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5650447Z   test_quick_any_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5650592Z   test_quick_any_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5650750Z   test_quick_arange_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5650907Z   test_quick_arange_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5651064Z   test_quick_arange_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5651222Z   test_quick_arange_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5651373Z   test_quick_arange_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5651526Z   test_quick_arange_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5651678Z   test_quick_arange_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5651814Z   test_quick_arange_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5652002Z   test_quick_arange_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5652178Z   test_quick_as_strided_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5652344Z   test_quick_as_strided_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5652519Z   test_quick_as_strided_scatter_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5652693Z   test_quick_as_strided_scatter_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5652864Z   test_quick_as_strided_scatter_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5653033Z   test_quick_as_strided_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5653201Z   test_quick_as_strided_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5653358Z   test_quick_as_strided_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5653550Z   test_quick_as_strided_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5653719Z   test_quick_as_strided_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5653880Z   test_quick_as_strided_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5654047Z   test_quick_as_strided_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5654211Z   test_quick_as_strided_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.5654365Z   test_quick_asin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5654516Z   test_quick_asin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5654659Z   test_quick_asin_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5654816Z   test_quick_asin_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5654968Z   test_quick_asin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5655119Z   test_quick_asin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5655269Z   test_quick_asin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5655417Z   test_quick_asin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5655565Z   test_quick_asin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5655714Z   test_quick_asin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5655865Z   test_quick_asin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5656008Z   test_quick_asinh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5656155Z   test_quick_asinh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5656312Z   test_quick_asinh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5656468Z   test_quick_asinh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5656622Z   test_quick_asinh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5656774Z   test_quick_asinh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5656923Z   test_quick_asinh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5657101Z   test_quick_asinh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5657250Z   test_quick_asinh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5657385Z   test_quick_asinh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5657531Z   test_quick_asinh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5657688Z   test_quick_atan2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5657835Z   test_quick_atan2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5657987Z   test_quick_atan2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5658142Z   test_quick_atan2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5658288Z   test_quick_atan2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5658439Z   test_quick_atan2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5658606Z   test_quick_atan2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5658756Z   test_quick_atan2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5658901Z   test_quick_atan2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5659053Z   test_quick_atan_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5659201Z   test_quick_atan_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5659437Z   test_quick_atan_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5659596Z   test_quick_atan_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5659752Z   test_quick_atan_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5659909Z   test_quick_atan_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5660044Z   test_quick_atan_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5660194Z   test_quick_atan_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5660340Z   test_quick_atan_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5660488Z   test_quick_atan_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5660638Z   test_quick_atan_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5660799Z   test_quick_atanh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5660951Z   test_quick_atanh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5661113Z   test_quick_atanh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5661259Z   test_quick_atanh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5661419Z   test_quick_atanh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5661572Z   test_quick_atanh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5661722Z   test_quick_atanh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5661872Z   test_quick_atanh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5662020Z   test_quick_atanh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5662169Z   test_quick_atanh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5662359Z   test_quick_atanh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5662519Z   test_quick_bitwise_and_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5662664Z   test_quick_bitwise_and_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5662821Z   test_quick_bitwise_and_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5662977Z   test_quick_bitwise_and_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5663134Z   test_quick_bitwise_and_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5663288Z   test_quick_bitwise_and_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5663455Z   test_quick_bitwise_left_shift_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5663622Z   test_quick_bitwise_left_shift_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5663813Z   test_quick_bitwise_left_shift_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5663979Z   test_quick_bitwise_left_shift_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5664132Z   test_quick_bitwise_left_shift_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5664289Z   test_quick_bitwise_not_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5664445Z   test_quick_bitwise_not_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5664600Z   test_quick_bitwise_not_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5664755Z   test_quick_bitwise_not_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5664913Z   test_quick_bitwise_not_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5665067Z   test_quick_bitwise_not_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5665224Z   test_quick_bitwise_or_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5665367Z   test_quick_bitwise_or_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5665520Z   test_quick_bitwise_or_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5665674Z   test_quick_bitwise_or_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5665827Z   test_quick_bitwise_or_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5665981Z   test_quick_bitwise_or_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5666154Z   test_quick_bitwise_right_shift_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5666317Z   test_quick_bitwise_right_shift_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5666482Z   test_quick_bitwise_right_shift_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5666647Z   test_quick_bitwise_right_shift_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5666799Z   test_quick_bitwise_right_shift_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5666956Z   test_quick_bitwise_xor_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5667112Z   test_quick_bitwise_xor_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5667267Z   test_quick_bitwise_xor_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5667467Z   test_quick_bitwise_xor_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5667622Z   test_quick_bitwise_xor_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5667776Z   test_quick_bitwise_xor_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5667939Z   test_quick_bucketize_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5668095Z   test_quick_bucketize_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5668240Z   test_quick_bucketize_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5668397Z   test_quick_bucketize_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5668551Z   test_quick_bucketize_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5668709Z   test_quick_bucketize_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5668892Z   test_quick_bucketize_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5669046Z   test_quick_bucketize_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5669201Z   test_quick_bucketize_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5669356Z   test_quick_cat_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5669508Z   test_quick_cat_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5669649Z   test_quick_cat_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5669806Z   test_quick_cat_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5669963Z   test_quick_cat_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5670116Z   test_quick_cat_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5670268Z   test_quick_cat_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5670416Z   test_quick_cat_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5670565Z   test_quick_cat_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5670713Z   test_quick_cat_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5670844Z   test_quick_cat_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5670993Z   test_quick_cat_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5671139Z   test_quick_cat_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5671296Z   test_quick_ceil_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5671450Z   test_quick_ceil_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5671604Z   test_quick_ceil_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5671753Z   test_quick_ceil_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5671903Z   test_quick_ceil_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5672050Z   test_quick_ceil_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5672184Z   test_quick_ceil_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5672330Z   test_quick_ceil_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5672518Z   test_quick_clamp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5672672Z   test_quick_clamp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5672825Z   test_quick_clamp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5672976Z   test_quick_clamp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5673128Z   test_quick_clamp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5673279Z   test_quick_clamp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5673413Z   test_quick_clamp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5673559Z   test_quick_clamp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5673723Z   test_quick_clamp_max_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5673878Z   test_quick_clamp_max_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5674063Z   test_quick_clamp_max_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5674223Z   test_quick_clamp_max_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5674385Z   test_quick_clamp_max_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5674541Z   test_quick_clamp_max_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5674698Z   test_quick_clamp_max_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5674839Z   test_quick_clamp_max_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5674991Z   test_quick_clamp_max_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5675149Z   test_quick_clamp_max_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5675311Z   test_quick_clamp_min_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5675468Z   test_quick_clamp_min_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5675625Z   test_quick_clamp_min_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5675781Z   test_quick_clamp_min_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5675939Z   test_quick_clamp_min_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5676091Z   test_quick_clamp_min_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5676231Z   test_quick_clamp_min_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5676387Z   test_quick_clamp_min_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5676543Z   test_quick_clamp_min_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5676697Z   test_quick_clamp_min_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5676852Z   test_quick_clone_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5677002Z   test_quick_clone_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5677160Z   test_quick_clone_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5677313Z   test_quick_clone_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5677454Z   test_quick_clone_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5677633Z   test_quick_clone_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5677787Z   test_quick_clone_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5677941Z   test_quick_clone_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5678091Z   test_quick_clone_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5678240Z   test_quick_clone_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5678387Z   test_quick_clone_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5678536Z   test_quick_clone_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5678685Z   test_quick_clone_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5678830Z   test_quick_complex_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5678984Z   test_quick_complex_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5679167Z   test_quick_complex_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5679334Z   test_quick_conj_physical_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5679495Z   test_quick_conj_physical_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5679664Z   test_quick_conj_physical_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5679829Z   test_quick_conj_physical_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5679996Z   test_quick_conj_physical_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5680161Z   test_quick_conj_physical_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5680310Z   test_quick_conj_physical_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5680466Z   test_quick_conj_physical_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5680734Z   test_quick_conj_physical_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5680892Z   test_quick_conj_physical_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5681053Z   test_quick_conj_physical_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5681215Z   test_quick_conj_physical_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5681375Z   test_quick_conj_physical_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5681553Z   test_quick_constant_pad_nd_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5681721Z   test_quick_constant_pad_nd_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5681878Z   test_quick_constant_pad_nd_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5682048Z   test_quick_constant_pad_nd_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5682218Z   test_quick_constant_pad_nd_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5682382Z   test_quick_constant_pad_nd_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5682549Z   test_quick_constant_pad_nd_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5682710Z   test_quick_constant_pad_nd_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5682918Z   test_quick_constant_pad_nd_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5683077Z   test_quick_constant_pad_nd_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5683241Z   test_quick_constant_pad_nd_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5683384Z   test_quick_constant_pad_nd_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5683543Z   test_quick_copysign_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5683700Z   test_quick_copysign_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5683860Z   test_quick_copysign_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5684018Z   test_quick_copysign_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5684181Z   test_quick_copysign_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5684371Z   test_quick_copysign_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5684532Z   test_quick_copysign_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5684673Z   test_quick_copysign_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5684829Z   test_quick_copysign_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5684982Z   test_quick_copysign_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5685136Z   test_quick_cos_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5685283Z   test_quick_cos_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5685441Z   test_quick_cos_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5685595Z   test_quick_cos_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5685746Z   test_quick_cos_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5685897Z   test_quick_cos_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5686031Z   test_quick_cos_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5686176Z   test_quick_cos_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5686322Z   test_quick_cos_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5686469Z   test_quick_cos_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5686615Z   test_quick_cos_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5686771Z   test_quick_cosh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5686919Z   test_quick_cosh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5687075Z   test_quick_cosh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5687219Z   test_quick_cosh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5687371Z   test_quick_cosh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5687520Z   test_quick_cosh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5687669Z   test_quick_cosh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5687816Z   test_quick_cosh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5687988Z   test_quick_cosh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5688138Z   test_quick_cosh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5688283Z   test_quick_cosh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5688438Z   test_quick_cumsum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5688584Z   test_quick_cumsum_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5688742Z   test_quick_cumsum_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5688893Z   test_quick_cumsum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5689048Z   test_quick_cumsum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5689200Z   test_quick_cumsum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5689350Z   test_quick_cumsum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5689548Z   test_quick_cumsum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5689700Z   test_quick_cumsum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5689851Z   test_quick_cumsum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5689993Z   test_quick_diag_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5690143Z   test_quick_diag_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5690301Z   test_quick_diag_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5690454Z   test_quick_diag_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5690610Z   test_quick_diag_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5690766Z   test_quick_diag_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5690914Z   test_quick_diag_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5691063Z   test_quick_diag_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5691198Z   test_quick_diag_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5691346Z   test_quick_diag_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5691494Z   test_quick_diag_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5691644Z   test_quick_diag_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5691808Z   test_quick_diag_embed_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5691968Z   test_quick_diag_embed_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5692135Z   test_quick_diag_embed_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5692298Z   test_quick_diag_embed_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5692455Z   test_quick_diag_embed_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5692604Z   test_quick_diag_embed_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5692764Z   test_quick_diag_embed_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5692917Z   test_quick_diag_embed_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5693103Z   test_quick_diag_embed_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5693262Z   test_quick_diag_embed_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5693418Z   test_quick_diag_embed_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5693573Z   test_quick_diag_embed_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5693727Z   test_quick_diag_embed_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5693881Z   test_quick_diagonal_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5694041Z   test_quick_diagonal_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5694209Z   test_quick_diagonal_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5694377Z   test_quick_diagonal_copy_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5694574Z   test_quick_diagonal_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5694739Z   test_quick_diagonal_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5694898Z   test_quick_diagonal_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5695056Z   test_quick_diagonal_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5695217Z   test_quick_diagonal_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5695360Z   test_quick_diagonal_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5695513Z   test_quick_diagonal_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5695674Z   test_quick_diagonal_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5695831Z   test_quick_diagonal_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5695989Z   test_quick_diagonal_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5696144Z   test_quick_diagonal_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5696300Z   test_quick_diagonal_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5696459Z   test_quick_diagonal_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5696614Z   test_quick_diagonal_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5696760Z   test_quick_diagonal_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5696921Z   test_quick_diagonal_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5697082Z   test_quick_diagonal_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5697237Z   test_quick_diagonal_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5697390Z   test_quick_diagonal_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5697542Z   test_quick_diagonal_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5697696Z   test_quick_diagonal_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5697851Z   test_quick_diagonal_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5698020Z   test_quick_diagonal_scatter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5698203Z   test_quick_diagonal_scatter_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5698378Z   test_quick_diagonal_scatter_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5698548Z   test_quick_diagonal_scatter_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5698715Z   test_quick_diagonal_scatter_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5698881Z   test_quick_diagonal_scatter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5699047Z   test_quick_diagonal_scatter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5699210Z   test_quick_diagonal_scatter_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5699459Z   test_quick_diagonal_scatter_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5699629Z   test_quick_diagonal_scatter_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5699815Z   test_quick_diagonal_scatter_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5699982Z   test_quick_diagonal_scatter_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5700143Z   test_quick_digamma_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5700297Z   test_quick_digamma_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5700455Z   test_quick_digamma_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5700613Z   test_quick_digamma_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5700768Z   test_quick_digamma_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5700925Z   test_quick_digamma_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5701082Z   test_quick_digamma_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5701222Z   test_quick_digamma_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5701373Z   test_quick_digamma_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5701544Z   test_quick_div_floor_rounding_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5701714Z   test_quick_div_floor_rounding_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5701882Z   test_quick_div_floor_rounding_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5702047Z   test_quick_div_floor_rounding_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5702216Z   test_quick_div_floor_rounding_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5702382Z   test_quick_div_floor_rounding_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5702529Z   test_quick_div_floor_rounding_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5702693Z   test_quick_div_floor_rounding_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5702857Z   test_quick_div_floor_rounding_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5703029Z   test_quick_div_no_rounding_mode_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5703196Z   test_quick_div_no_rounding_mode_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5703369Z   test_quick_div_no_rounding_mode_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5703585Z   test_quick_div_no_rounding_mode_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5703759Z   test_quick_div_no_rounding_mode_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5703929Z   test_quick_div_no_rounding_mode_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5704098Z   test_quick_div_no_rounding_mode_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5704253Z   test_quick_div_no_rounding_mode_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5704419Z   test_quick_div_no_rounding_mode_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5704580Z   test_quick_div_no_rounding_mode_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5704748Z   test_quick_div_no_rounding_mode_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5704994Z   test_quick_div_no_rounding_mode_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5705166Z   test_quick_div_trunc_rounding_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5705330Z   test_quick_div_trunc_rounding_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5705495Z   test_quick_div_trunc_rounding_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5705656Z   test_quick_div_trunc_rounding_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5705807Z   test_quick_div_trunc_rounding_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5705977Z   test_quick_div_trunc_rounding_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5706142Z   test_quick_div_trunc_rounding_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5706308Z   test_quick_div_trunc_rounding_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5706474Z   test_quick_div_trunc_rounding_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5706628Z   test_quick_dot_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5706785Z   test_quick_dot_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5706941Z   test_quick_dot_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5707093Z   test_quick_dot_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5707236Z   test_quick_dot_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5707384Z   test_quick_dot_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5707536Z   test_quick_dot_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5707685Z   test_quick_dot_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5707832Z   test_quick_dot_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5707980Z   test_quick_dot_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5708136Z   test_quick_empty_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5708289Z   test_quick_empty_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5708434Z   test_quick_empty_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5708621Z   test_quick_empty_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5708779Z   test_quick_empty_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5708931Z   test_quick_empty_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5709081Z   test_quick_empty_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5709230Z   test_quick_empty_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5709379Z   test_quick_empty_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5709529Z   test_quick_empty_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5709677Z   test_quick_empty_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5709817Z   test_quick_empty_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5709964Z   test_quick_empty_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5710150Z   test_quick_empty_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5710308Z   test_quick_empty_like_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5710474Z   test_quick_empty_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5710638Z   test_quick_empty_like_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5710798Z   test_quick_empty_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5710957Z   test_quick_empty_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5711106Z   test_quick_empty_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5711260Z   test_quick_empty_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5711418Z   test_quick_empty_like_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5711574Z   test_quick_empty_like_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5711731Z   test_quick_empty_like_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5711885Z   test_quick_empty_like_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5712038Z   test_quick_empty_like_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5712189Z   test_quick_eq_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5712336Z   test_quick_eq_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5712480Z   test_quick_eq_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5712634Z   test_quick_eq_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5712787Z   test_quick_eq_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5712936Z   test_quick_eq_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5713084Z   test_quick_eq_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5713228Z   test_quick_eq_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5713374Z   test_quick_eq_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5713520Z   test_quick_eq_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5713688Z   test_quick_eq_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5713836Z   test_quick_eq_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5713982Z   test_quick_eq_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5714133Z   test_quick_erf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5714278Z   test_quick_erf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5714428Z   test_quick_erf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5714578Z   test_quick_erf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5714724Z   test_quick_erf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5714866Z   test_quick_erf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5715004Z   test_quick_erf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5715175Z   test_quick_erf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5715324Z   test_quick_erf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5715478Z   test_quick_erfc_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5715624Z   test_quick_erfc_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5715775Z   test_quick_erfc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5715927Z   test_quick_erfc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5716076Z   test_quick_erfc_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5716213Z   test_quick_erfc_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5716361Z   test_quick_erfc_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5716509Z   test_quick_erfc_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5716655Z   test_quick_erfc_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5716811Z   test_quick_erfinv_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5716964Z   test_quick_erfinv_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5717120Z   test_quick_erfinv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5717275Z   test_quick_erfinv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5717428Z   test_quick_erfinv_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5717567Z   test_quick_erfinv_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5717716Z   test_quick_erfinv_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5717866Z   test_quick_erfinv_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5718019Z   test_quick_erfinv_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5718173Z   test_quick_exp2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5718321Z   test_quick_exp2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5718473Z   test_quick_exp2_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5718621Z   test_quick_exp2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5718805Z   test_quick_exp2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5718940Z   test_quick_exp2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5719088Z   test_quick_exp2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5719233Z   test_quick_exp2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5719381Z   test_quick_exp2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5719527Z   test_quick_exp2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5719677Z   test_quick_exp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5719826Z   test_quick_exp_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5719984Z   test_quick_exp_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5720126Z   test_quick_exp_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5720307Z   test_quick_exp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5720458Z   test_quick_exp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5720812Z   test_quick_exp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5720961Z   test_quick_exp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5721104Z   test_quick_exp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5721254Z   test_quick_exp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5721403Z   test_quick_exp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5721565Z   test_quick_expand_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5721707Z   test_quick_expand_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5721868Z   test_quick_expand_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5722029Z   test_quick_expand_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5722185Z   test_quick_expand_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5722342Z   test_quick_expand_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5722498Z   test_quick_expand_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5722654Z   test_quick_expand_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.5722810Z   test_quick_expand_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5722945Z   test_quick_expand_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5723099Z   test_quick_expand_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5723249Z   test_quick_expand_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5723403Z   test_quick_expm1_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5723553Z   test_quick_expm1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5723711Z   test_quick_expm1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5723864Z   test_quick_expm1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5724012Z   test_quick_expm1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5724213Z   test_quick_expm1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5724351Z   test_quick_expm1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5724503Z   test_quick_expm1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5724651Z   test_quick_expm1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5724800Z   test_quick_eye_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5724956Z   test_quick_eye_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5725110Z   test_quick_eye_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5725262Z   test_quick_eye_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5725414Z   test_quick_eye_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5725582Z   test_quick_eye_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5725735Z   test_quick_eye_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5725883Z   test_quick_eye_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5726029Z   test_quick_eye_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5726177Z   test_quick_eye_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5726324Z   test_quick_eye_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5726475Z   test_quick_fft_fft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5726635Z   test_quick_fft_fft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5726796Z   test_quick_fft_fft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5726940Z   test_quick_fft_fft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5727096Z   test_quick_fft_fft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5727247Z   test_quick_fft_fft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5727396Z   test_quick_fft_fft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5727547Z   test_quick_fft_fft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5727696Z   test_quick_fft_fft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5727844Z   test_quick_fft_fft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5727997Z   test_quick_fft_fft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5728145Z   test_quick_fft_fft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5728301Z   test_quick_fft_fft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5728455Z   test_quick_fft_fft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5728610Z   test_quick_fft_fft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5728763Z   test_quick_fft_fft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5728910Z   test_quick_fft_fft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5729056Z   test_quick_fft_fft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5729236Z   test_quick_fft_fft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5729384Z   test_quick_fft_fft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5729523Z   test_quick_fft_fftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5729685Z   test_quick_fft_fftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5729842Z   test_quick_fft_fftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5729997Z   test_quick_fft_fftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5730151Z   test_quick_fft_fftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5730303Z   test_quick_fft_fftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5730458Z   test_quick_fft_fftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5730609Z   test_quick_fft_fftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5730789Z   test_quick_fft_fftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5730926Z   test_quick_fft_fftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5731080Z   test_quick_fft_hfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5731243Z   test_quick_fft_hfft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5731401Z   test_quick_fft_hfft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5731557Z   test_quick_fft_hfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5731713Z   test_quick_fft_hfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5731865Z   test_quick_fft_hfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5732019Z   test_quick_fft_hfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5732170Z   test_quick_fft_hfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5732309Z   test_quick_fft_hfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5732458Z   test_quick_fft_hfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5732610Z   test_quick_fft_hfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5732768Z   test_quick_fft_hfft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5732925Z   test_quick_fft_hfft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5733083Z   test_quick_fft_hfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5733240Z   test_quick_fft_hfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5733390Z   test_quick_fft_hfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5733529Z   test_quick_fft_hfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5733680Z   test_quick_fft_hfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5733829Z   test_quick_fft_hfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5733978Z   test_quick_fft_hfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5734130Z   test_quick_fft_hfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5734324Z   test_quick_fft_hfftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5734487Z   test_quick_fft_hfftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5734642Z   test_quick_fft_hfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5734797Z   test_quick_fft_hfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5734936Z   test_quick_fft_hfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5735089Z   test_quick_fft_hfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5735240Z   test_quick_fft_hfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5735392Z   test_quick_fft_hfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5735545Z   test_quick_fft_hfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5735721Z   test_quick_fft_ifft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5735885Z   test_quick_fft_ifft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5736044Z   test_quick_fft_ifft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5736198Z   test_quick_fft_ifft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5736340Z   test_quick_fft_ifft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5736491Z   test_quick_fft_ifft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5736643Z   test_quick_fft_ifft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5736793Z   test_quick_fft_ifft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5736946Z   test_quick_fft_ifft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5737095Z   test_quick_fft_ifft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5737246Z   test_quick_fft_ifft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5737404Z   test_quick_fft_ifft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5737549Z   test_quick_fft_ifft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5737704Z   test_quick_fft_ifft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5737860Z   test_quick_fft_ifft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5738014Z   test_quick_fft_ifft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5738166Z   test_quick_fft_ifft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5738317Z   test_quick_fft_ifft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5738468Z   test_quick_fft_ifft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5738618Z   test_quick_fft_ifft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5738769Z   test_quick_fft_ifftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5738918Z   test_quick_fft_ifftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5739077Z   test_quick_fft_ifftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5739352Z   test_quick_fft_ifftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5739515Z   test_quick_fft_ifftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5739669Z   test_quick_fft_ifftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5739824Z   test_quick_fft_ifftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5739977Z   test_quick_fft_ifftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5740128Z   test_quick_fft_ifftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5740280Z   test_quick_fft_ifftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5740421Z   test_quick_fft_ihfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5740583Z   test_quick_fft_ihfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5740739Z   test_quick_fft_ihfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5740928Z   test_quick_fft_ihfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5741086Z   test_quick_fft_ihfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5741239Z   test_quick_fft_ihfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5741391Z   test_quick_fft_ihfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5741543Z   test_quick_fft_ihfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5741682Z   test_quick_fft_ihfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5741839Z   test_quick_fft_ihfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5741997Z   test_quick_fft_ihfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5742151Z   test_quick_fft_ihfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5742305Z   test_quick_fft_ihfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5742454Z   test_quick_fft_ihfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5742608Z   test_quick_fft_ihfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5742758Z   test_quick_fft_ihfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5742907Z   test_quick_fft_ihfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5743054Z   test_quick_fft_ihfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5743209Z   test_quick_fft_ihfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5743365Z   test_quick_fft_ihfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5743520Z   test_quick_fft_ihfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5743674Z   test_quick_fft_ihfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5743826Z   test_quick_fft_ihfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5743983Z   test_quick_fft_ihfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5744135Z   test_quick_fft_irfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5744296Z   test_quick_fft_irfft2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5744473Z   test_quick_fft_irfft2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5744631Z   test_quick_fft_irfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5744781Z   test_quick_fft_irfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5744935Z   test_quick_fft_irfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5745087Z   test_quick_fft_irfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5745238Z   test_quick_fft_irfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5745390Z   test_quick_fft_irfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5745545Z   test_quick_fft_irfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5745701Z   test_quick_fft_irfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5745878Z   test_quick_fft_irfft_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5766226Z   test_quick_fft_irfft_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5766482Z   test_quick_fft_irfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5766638Z   test_quick_fft_irfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5766783Z   test_quick_fft_irfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5766930Z   test_quick_fft_irfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5767073Z   test_quick_fft_irfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5767223Z   test_quick_fft_irfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5767370Z   test_quick_fft_irfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5767514Z   test_quick_fft_irfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5767668Z   test_quick_fft_irfftn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5767833Z   test_quick_fft_irfftn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5767981Z   test_quick_fft_irfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5768138Z   test_quick_fft_irfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5768295Z   test_quick_fft_irfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5768454Z   test_quick_fft_irfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5768612Z   test_quick_fft_irfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5768769Z   test_quick_fft_irfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5768923Z   test_quick_fft_irfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5769078Z   test_quick_fft_rfft2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5769234Z   test_quick_fft_rfft2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5769378Z   test_quick_fft_rfft2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5769533Z   test_quick_fft_rfft2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5769822Z   test_quick_fft_rfft2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5769978Z   test_quick_fft_rfft2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5770135Z   test_quick_fft_rfft2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5770285Z   test_quick_fft_rfft2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5770442Z   test_quick_fft_rfft_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5770600Z   test_quick_fft_rfft_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5770755Z   test_quick_fft_rfft_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5770896Z   test_quick_fft_rfft_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5771051Z   test_quick_fft_rfft_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5771202Z   test_quick_fft_rfft_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5771399Z   test_quick_fft_rfft_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5771553Z   test_quick_fft_rfft_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5771704Z   test_quick_fft_rfftn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5771859Z   test_quick_fft_rfftn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5772017Z   test_quick_fft_rfftn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5772157Z   test_quick_fft_rfftn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5772312Z   test_quick_fft_rfftn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5772466Z   test_quick_fft_rfftn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5772619Z   test_quick_fft_rfftn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5772769Z   test_quick_fft_rfftn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5772924Z   test_quick_fill_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5773074Z   test_quick_fill_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5773227Z   test_quick_fill_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5773382Z   test_quick_fill_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5773523Z   test_quick_fill_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5773679Z   test_quick_fill_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5773836Z   test_quick_fill_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5773985Z   test_quick_fill_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5774135Z   test_quick_fill_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5774285Z   test_quick_fill_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5774430Z   test_quick_fill_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5774581Z   test_quick_fill_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5774731Z   test_quick_fill_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5774901Z   test_quick_flip_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5775051Z   test_quick_flip_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5775210Z   test_quick_flip_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5775365Z   test_quick_flip_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5775516Z   test_quick_flip_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5775667Z   test_quick_flip_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5775816Z   test_quick_flip_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5775962Z   test_quick_flip_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5776098Z   test_quick_flip_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5776246Z   test_quick_flip_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5776424Z   test_quick_flip_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5776574Z   test_quick_flip_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5776727Z   test_quick_floor_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5776881Z   test_quick_floor_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5777035Z   test_quick_floor_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5777185Z   test_quick_floor_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5777335Z   test_quick_floor_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5777470Z   test_quick_floor_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5777619Z   test_quick_floor_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5777771Z   test_quick_floor_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5777935Z   test_quick_floor_divide_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5778098Z   test_quick_floor_divide_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5778254Z   test_quick_floor_divide_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5778409Z   test_quick_floor_divide_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5778567Z   test_quick_floor_divide_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5778709Z   test_quick_floor_divide_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5778862Z   test_quick_floor_divide_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5779020Z   test_quick_floor_divide_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5779179Z   test_quick_floor_divide_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5779430Z   test_quick_fmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5779582Z   test_quick_fmax_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5779737Z   test_quick_fmax_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5779890Z   test_quick_fmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5780040Z   test_quick_fmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5780210Z   test_quick_fmax_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5780362Z   test_quick_fmax_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5780512Z   test_quick_fmax_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5780663Z   test_quick_fmax_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5780813Z   test_quick_fmax_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5780965Z   test_quick_fmin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5781116Z   test_quick_fmin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5781267Z   test_quick_fmin_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5781418Z   test_quick_fmin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5781578Z   test_quick_fmin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5781727Z   test_quick_fmin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5781873Z   test_quick_fmin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5782020Z   test_quick_fmin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5782166Z   test_quick_fmin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5782315Z   test_quick_fmin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5782469Z   test_quick_fmod_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5782623Z   test_quick_fmod_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5782762Z   test_quick_fmod_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5782914Z   test_quick_fmod_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5783063Z   test_quick_fmod_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5783211Z   test_quick_fmod_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5783357Z   test_quick_fmod_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5783504Z   test_quick_fmod_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5783651Z   test_quick_fmod_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5783805Z   test_quick_frac_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5783957Z   test_quick_frac_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5784097Z   test_quick_frac_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5784246Z   test_quick_frac_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5784394Z   test_quick_full_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5784543Z   test_quick_full_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5784702Z   test_quick_full_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5784856Z   test_quick_full_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5785012Z   test_quick_full_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5785191Z   test_quick_full_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5785327Z   test_quick_full_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5785475Z   test_quick_full_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5785622Z   test_quick_full_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5785769Z   test_quick_full_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5785914Z   test_quick_full_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5786061Z   test_quick_full_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5786208Z   test_quick_full_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5786354Z   test_quick_gcd_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5786501Z   test_quick_gcd_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5786670Z   test_quick_gcd_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5786821Z   test_quick_gcd_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5786967Z   test_quick_gcd_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5787119Z   test_quick_ge_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5787268Z   test_quick_ge_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5787417Z   test_quick_ge_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5787563Z   test_quick_ge_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5787709Z   test_quick_ge_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5787845Z   test_quick_ge_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5787993Z   test_quick_ge_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5788139Z   test_quick_ge_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5788287Z   test_quick_ge_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5788435Z   test_quick_ge_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5788603Z   test_quick_grid_sampler_2d_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5788771Z   test_quick_grid_sampler_2d_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5788924Z   test_quick_gt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5789074Z   test_quick_gt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5789212Z   test_quick_gt_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5789360Z   test_quick_gt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5789504Z   test_quick_gt_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5789649Z   test_quick_gt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5789794Z   test_quick_gt_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5789939Z   test_quick_gt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5790087Z   test_quick_gt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5790260Z   test_quick_gt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5790412Z   test_quick_heaviside_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5790573Z   test_quick_heaviside_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5790733Z   test_quick_heaviside_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5790894Z   test_quick_heaviside_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5791053Z   test_quick_heaviside_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5791210Z   test_quick_heaviside_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5791367Z   test_quick_heaviside_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5791522Z   test_quick_heaviside_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5791680Z   test_quick_heaviside_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5791850Z   test_quick_heaviside_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5792008Z   test_quick_hypot_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5792162Z   test_quick_hypot_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5792314Z   test_quick_hypot_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5792470Z   test_quick_igamma_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5792624Z   test_quick_igamma_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5792779Z   test_quick_igamma_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5792939Z   test_quick_igamma_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5793101Z   test_quick_igammac_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5793247Z   test_quick_igammac_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5793402Z   test_quick_igammac_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5793556Z   test_quick_igammac_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5793719Z   test_quick_index_add_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5793873Z   test_quick_index_add_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5794039Z   test_quick_index_add_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5794203Z   test_quick_index_add_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5794360Z   test_quick_index_add_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5794504Z   test_quick_index_add_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5794662Z   test_quick_index_add_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5794819Z   test_quick_index_add_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5794972Z   test_quick_index_add_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5795126Z   test_quick_index_add_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5795280Z   test_quick_index_add_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5795819Z   test_quick_index_add_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5795974Z   test_quick_index_add_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5796136Z   test_quick_index_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5796278Z   test_quick_index_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5796446Z   test_quick_index_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5796610Z   test_quick_index_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5796770Z   test_quick_index_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5796925Z   test_quick_index_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5797079Z   test_quick_index_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5797265Z   test_quick_index_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5797425Z   test_quick_index_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5797581Z   test_quick_index_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5797722Z   test_quick_index_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5797876Z   test_quick_index_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5798038Z   test_quick_index_fill_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5798193Z   test_quick_index_fill_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5798361Z   test_quick_index_fill_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5798525Z   test_quick_index_fill_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5798685Z   test_quick_index_fill_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5798840Z   test_quick_index_fill_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5798994Z   test_quick_index_fill_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5799136Z   test_quick_index_fill_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5799290Z   test_quick_index_fill_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5799443Z   test_quick_index_fill_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5799600Z   test_quick_index_fill_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5799755Z   test_quick_index_fill_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5799920Z   test_quick_index_select_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5800080Z   test_quick_index_select_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5800247Z   test_quick_index_select_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5800402Z   test_quick_index_select_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5800567Z   test_quick_index_select_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5800857Z   test_quick_index_select_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5801065Z   test_quick_index_select_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5801225Z   test_quick_index_select_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5801384Z   test_quick_index_select_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5801540Z   test_quick_index_select_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5801691Z   test_quick_index_select_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5801848Z   test_quick_index_select_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5801994Z   test_quick_index_select_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5802148Z   test_quick_isinf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5802307Z   test_quick_isinf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5802499Z   test_quick_isinf_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5802660Z   test_quick_isinf_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5802820Z   test_quick_isinf_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5802974Z   test_quick_isinf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5803125Z   test_quick_isinf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5803277Z   test_quick_isinf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5803415Z   test_quick_isinf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5803567Z   test_quick_isinf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5803712Z   test_quick_isinf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5803865Z   test_quick_isinf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5804010Z   test_quick_isinf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5804168Z   test_quick_isnan_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5804319Z   test_quick_isnan_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5804478Z   test_quick_isnan_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5804623Z   test_quick_isnan_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5804779Z   test_quick_isnan_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5804934Z   test_quick_isnan_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5805086Z   test_quick_isnan_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5805238Z   test_quick_isnan_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5805388Z   test_quick_isnan_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5805534Z   test_quick_isnan_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5805685Z   test_quick_isnan_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5805831Z   test_quick_isnan_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5805978Z   test_quick_isneginf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5806191Z   test_quick_isneginf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5806353Z   test_quick_isneginf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5806510Z   test_quick_isneginf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5806667Z   test_quick_isneginf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5806821Z   test_quick_isneginf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5806975Z   test_quick_isneginf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5807128Z   test_quick_isneginf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5807282Z   test_quick_isneginf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5807424Z   test_quick_isneginf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5807582Z   test_quick_isposinf_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5807760Z   test_quick_isposinf_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5807920Z   test_quick_isposinf_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5808076Z   test_quick_isposinf_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5808237Z   test_quick_isposinf_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5808396Z   test_quick_isposinf_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5808548Z   test_quick_isposinf_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5808689Z   test_quick_isposinf_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5808848Z   test_quick_isposinf_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5809002Z   test_quick_isposinf_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5809154Z   test_quick_lcm_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5809301Z   test_quick_lcm_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5809438Z   test_quick_lcm_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5809584Z   test_quick_lcm_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5809729Z   test_quick_lcm_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5809880Z   test_quick_le_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5810028Z   test_quick_le_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5810180Z   test_quick_le_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5810330Z   test_quick_le_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5810479Z   test_quick_le_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5810626Z   test_quick_le_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5810758Z   test_quick_le_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.5810909Z   test_quick_le_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5811056Z   test_quick_le_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5811200Z   test_quick_le_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5811381Z   test_quick_lerp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5811538Z   test_quick_lerp_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5811696Z   test_quick_lerp_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5811849Z   test_quick_lerp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5811988Z   test_quick_lerp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5812144Z   test_quick_lgamma_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5812295Z   test_quick_lgamma_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5812448Z   test_quick_lgamma_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5812605Z   test_quick_lgamma_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5812782Z   test_quick_lgamma_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5812934Z   test_quick_lgamma_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5813079Z   test_quick_lgamma_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5813230Z   test_quick_lgamma_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5813363Z   test_quick_lgamma_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5813533Z   test_quick_linalg_vector_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5813707Z   test_quick_linalg_vector_norm_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5813880Z   test_quick_linalg_vector_norm_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5814049Z   test_quick_linalg_vector_norm_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5814216Z   test_quick_linalg_vector_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5814378Z   test_quick_linalg_vector_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5814539Z   test_quick_linspace_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5814703Z   test_quick_linspace_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5814852Z   test_quick_linspace_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5815010Z   test_quick_linspace_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5815169Z   test_quick_linspace_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5815328Z   test_quick_linspace_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5815483Z   test_quick_linspace_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5815636Z   test_quick_linspace_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5815790Z   test_quick_linspace_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5815944Z   test_quick_linspace_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5816083Z   test_quick_linspace_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5816234Z   test_quick_log10_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5816416Z   test_quick_log10_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5816574Z   test_quick_log10_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5816729Z   test_quick_log10_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5816880Z   test_quick_log10_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5817032Z   test_quick_log10_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5817180Z   test_quick_log10_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5817326Z   test_quick_log10_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5817457Z   test_quick_log10_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5817604Z   test_quick_log10_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5817750Z   test_quick_log10_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5817932Z   test_quick_log1p_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5818087Z   test_quick_log1p_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5818244Z   test_quick_log1p_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5818399Z   test_quick_log1p_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5818551Z   test_quick_log1p_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5818701Z   test_quick_log1p_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5818836Z   test_quick_log1p_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5818987Z   test_quick_log1p_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5819133Z   test_quick_log1p_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5819355Z   test_quick_log1p_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5819505Z   test_quick_log1p_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5819657Z   test_quick_log2_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5819806Z   test_quick_log2_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5819962Z   test_quick_log2_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5820103Z   test_quick_log2_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5820259Z   test_quick_log2_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5820412Z   test_quick_log2_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5820562Z   test_quick_log2_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5820711Z   test_quick_log2_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5820860Z   test_quick_log2_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5821009Z   test_quick_log2_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5821157Z   test_quick_log2_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5821310Z   test_quick_log_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5821444Z   test_quick_log_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5821647Z   test_quick_log_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5821800Z   test_quick_log_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5821949Z   test_quick_log_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5822099Z   test_quick_log_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5822244Z   test_quick_log_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5822391Z   test_quick_log_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5822536Z   test_quick_log_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5822672Z   test_quick_log_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5822820Z   test_quick_log_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5822982Z   test_quick_log_softmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5823169Z   test_quick_log_softmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5823326Z   test_quick_log_softmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5823488Z   test_quick_logical_and_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5823647Z   test_quick_logical_and_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5823811Z   test_quick_logical_and_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5823976Z   test_quick_logical_and_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5824123Z   test_quick_logical_and_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5824281Z   test_quick_logical_and_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5824435Z   test_quick_logical_and_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5824594Z   test_quick_logical_and_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5824752Z   test_quick_logical_and_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5824912Z   test_quick_logical_and_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5825073Z   test_quick_logical_and_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5825229Z   test_quick_logical_and_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5825393Z   test_quick_logical_not_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5825537Z   test_quick_logical_not_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5825705Z   test_quick_logical_not_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5825868Z   test_quick_logical_not_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5826027Z   test_quick_logical_not_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5826182Z   test_quick_logical_not_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5826335Z   test_quick_logical_not_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5826491Z   test_quick_logical_not_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5826674Z   test_quick_logical_not_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5826833Z   test_quick_logical_not_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5826976Z   test_quick_logical_not_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5827131Z   test_quick_logical_not_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5827291Z   test_quick_logical_or_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5827447Z   test_quick_logical_or_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5827612Z   test_quick_logical_or_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5827775Z   test_quick_logical_or_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5827937Z   test_quick_logical_or_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5828120Z   test_quick_logical_or_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5828261Z   test_quick_logical_or_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5828417Z   test_quick_logical_or_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5828570Z   test_quick_logical_or_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5828727Z   test_quick_logical_or_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5828880Z   test_quick_logical_or_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5829033Z   test_quick_logical_or_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5829199Z   test_quick_logical_xor_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5829354Z   test_quick_logical_xor_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5829522Z   test_quick_logical_xor_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5829671Z   test_quick_logical_xor_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5829830Z   test_quick_logical_xor_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5829985Z   test_quick_logical_xor_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5830137Z   test_quick_logical_xor_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5830293Z   test_quick_logical_xor_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5830451Z   test_quick_logical_xor_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5830607Z   test_quick_logical_xor_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5830763Z   test_quick_logical_xor_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5830915Z   test_quick_logical_xor_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5831058Z   test_quick_logit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5831209Z   test_quick_logit_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5831360Z   test_quick_logit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5831512Z   test_quick_logit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5831661Z   test_quick_logit_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5831850Z   test_quick_logit_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5831998Z   test_quick_logit_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5832148Z   test_quick_logit_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5832281Z   test_quick_logit_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5832438Z   test_quick_logspace_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5832601Z   test_quick_logspace_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5832763Z   test_quick_logspace_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5832923Z   test_quick_logspace_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5833085Z   test_quick_logspace_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5833268Z   test_quick_logspace_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5833424Z   test_quick_logspace_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5833578Z   test_quick_logspace_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5833717Z   test_quick_logspace_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5833870Z   test_quick_logspace_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5834031Z   test_quick_logsumexp_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5834187Z   test_quick_logsumexp_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5834350Z   test_quick_logsumexp_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5834512Z   test_quick_logsumexp_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5834668Z   test_quick_logsumexp_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5834825Z   test_quick_logsumexp_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5834979Z   test_quick_logsumexp_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5835121Z   test_quick_logsumexp_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5835274Z   test_quick_logsumexp_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5835428Z   test_quick_lt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5835581Z   test_quick_lt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5835731Z   test_quick_lt_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5835883Z   test_quick_lt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5836029Z   test_quick_lt_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5836178Z   test_quick_lt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5836314Z   test_quick_lt_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5836463Z   test_quick_lt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5836611Z   test_quick_lt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5836756Z   test_quick_lt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5836958Z   test_quick_masked_fill_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5837121Z   test_quick_masked_fill_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5837290Z   test_quick_masked_fill_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5837459Z   test_quick_masked_fill_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5837622Z   test_quick_masked_fill_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5837770Z   test_quick_masked_fill_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5837927Z   test_quick_masked_fill_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5838081Z   test_quick_masked_fill_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5838242Z   test_quick_masked_fill_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5838430Z   test_quick_masked_fill_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5838591Z   test_quick_masked_fill_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5838750Z   test_quick_masked_fill_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5838907Z   test_quick_masked_fill_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5839066Z   test_quick_maximum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5839210Z   test_quick_maximum_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5839368Z   test_quick_maximum_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5839526Z   test_quick_maximum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5839686Z   test_quick_maximum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5839840Z   test_quick_maximum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5839994Z   test_quick_maximum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5840142Z   test_quick_maximum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5840297Z   test_quick_maximum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5840435Z   test_quick_maximum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5840588Z   test_quick_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5840863Z   test_quick_mean_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5841027Z   test_quick_mean_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5841181Z   test_quick_mean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5841336Z   test_quick_mean_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5841488Z   test_quick_mean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5841670Z   test_quick_meshgrid_list_of_tensors_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5841848Z   test_quick_meshgrid_list_of_tensors_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5842019Z   test_quick_meshgrid_list_of_tensors_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5842252Z   test_quick_meshgrid_list_of_tensors_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5842432Z   test_quick_meshgrid_list_of_tensors_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5842611Z   test_quick_meshgrid_list_of_tensors_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5842788Z   test_quick_meshgrid_list_of_tensors_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5842964Z   test_quick_meshgrid_list_of_tensors_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5843139Z   test_quick_meshgrid_list_of_tensors_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5843314Z   test_quick_meshgrid_list_of_tensors_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5843493Z   test_quick_meshgrid_list_of_tensors_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5843686Z   test_quick_meshgrid_list_of_tensors_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5843873Z   test_quick_meshgrid_variadic_tensors_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5844050Z   test_quick_meshgrid_variadic_tensors_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5844235Z   test_quick_meshgrid_variadic_tensors_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5844418Z   test_quick_meshgrid_variadic_tensors_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5844596Z   test_quick_meshgrid_variadic_tensors_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5844777Z   test_quick_meshgrid_variadic_tensors_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5844962Z   test_quick_meshgrid_variadic_tensors_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5845141Z   test_quick_meshgrid_variadic_tensors_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5845317Z   test_quick_meshgrid_variadic_tensors_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5845481Z   test_quick_meshgrid_variadic_tensors_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5845656Z   test_quick_meshgrid_variadic_tensors_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5845829Z   test_quick_meshgrid_variadic_tensors_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5845989Z   test_quick_minimum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5846149Z   test_quick_minimum_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5846310Z   test_quick_minimum_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5846467Z   test_quick_minimum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5846626Z   test_quick_minimum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5846778Z   test_quick_minimum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5846919Z   test_quick_minimum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5847070Z   test_quick_minimum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5847223Z   test_quick_minimum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5847405Z   test_quick_minimum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5847557Z   test_quick_mul_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5847709Z   test_quick_mul_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5847868Z   test_quick_mul_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5848019Z   test_quick_mul_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5848159Z   test_quick_mul_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5848312Z   test_quick_mul_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5848464Z   test_quick_mul_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5848612Z   test_quick_mul_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5848761Z   test_quick_mul_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5848938Z   test_quick_mul_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5849090Z   test_quick_mul_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5849240Z   test_quick_mul_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5849386Z   test_quick_mul_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5849526Z   test_quick_mv_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5849681Z   test_quick_mv_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5849834Z   test_quick_mv_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5849988Z   test_quick_mv_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5850139Z   test_quick_mv_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5850289Z   test_quick_mv_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5850437Z   test_quick_mv_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5850584Z   test_quick_mv_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5850719Z   test_quick_mv_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5850867Z   test_quick_mv_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5851047Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5851224Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5851398Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5851571Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5851742Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5851908Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5852078Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5852232Z   test_quick_mvlgamma_mvlgamma_p_1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5852404Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5852608Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5852784Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5852956Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5853126Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5853291Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5853461Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5853626Z   test_quick_mvlgamma_mvlgamma_p_3_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5853791Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5853994Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5854168Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5854338Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5854506Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5854671Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5854840Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5855004Z   test_quick_mvlgamma_mvlgamma_p_5_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5855169Z   test_quick_nan_to_num_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5855328Z   test_quick_nan_to_num_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5855478Z   test_quick_nan_to_num_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5855635Z   test_quick_nan_to_num_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5855789Z   test_quick_nan_to_num_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5855946Z   test_quick_nan_to_num_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5856105Z   test_quick_nan_to_num_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5856264Z   test_quick_nan_to_num_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5856422Z   test_quick_nan_to_num_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5856581Z   test_quick_nan_to_num_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5856733Z   test_quick_narrow_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5856894Z   test_quick_narrow_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5857063Z   test_quick_narrow_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5857230Z   test_quick_narrow_copy_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5857391Z   test_quick_narrow_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5857556Z   test_quick_narrow_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5857747Z   test_quick_narrow_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5857906Z   test_quick_narrow_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5858066Z   test_quick_narrow_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5858213Z   test_quick_narrow_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5858371Z   test_quick_narrow_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5858529Z   test_quick_narrow_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5858687Z   test_quick_narrow_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5858861Z   test_quick_native_batch_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5859033Z   test_quick_native_batch_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5859230Z   test_quick_native_batch_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5859480Z   test_quick_native_dropout_backward_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5859659Z   test_quick_native_dropout_backward_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5859823Z   test_quick_native_dropout_backward_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5860002Z   test_quick_native_dropout_backward_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5860178Z   test_quick_native_dropout_backward_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5860358Z   test_quick_native_dropout_backward_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5860535Z   test_quick_native_dropout_backward_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5860709Z   test_quick_native_dropout_backward_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5860885Z   test_quick_native_dropout_backward_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5861055Z   test_quick_native_dropout_backward_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5861228Z   test_quick_native_layer_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5861383Z   test_quick_native_layer_norm_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5861553Z   test_quick_native_layer_norm_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5861711Z   test_quick_ne_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5861865Z   test_quick_ne_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5862024Z   test_quick_ne_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5862180Z   test_quick_ne_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5862332Z   test_quick_ne_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5862482Z   test_quick_ne_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5862628Z   test_quick_ne_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5862763Z   test_quick_ne_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5862948Z   test_quick_ne_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5863098Z   test_quick_ne_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5863249Z   test_quick_ne_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5863394Z   test_quick_ne_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5863550Z   test_quick_neg_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5863710Z   test_quick_neg_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5863865Z   test_quick_neg_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5864008Z   test_quick_neg_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5864163Z   test_quick_neg_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5864316Z   test_quick_neg_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5864492Z   test_quick_neg_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5864643Z   test_quick_neg_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5864795Z   test_quick_neg_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5864943Z   test_quick_neg_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5865094Z   test_quick_neg_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5865241Z   test_quick_neg_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5865391Z   test_quick_new_empty_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5865553Z   test_quick_new_empty_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5865720Z   test_quick_new_empty_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5865884Z   test_quick_new_empty_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5866041Z   test_quick_new_empty_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5866202Z   test_quick_new_empty_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5866363Z   test_quick_new_empty_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5866520Z   test_quick_new_empty_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5866678Z   test_quick_new_empty_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5866823Z   test_quick_new_empty_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5866981Z   test_quick_new_empty_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5867136Z   test_quick_new_empty_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5867291Z   test_quick_new_empty_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5867494Z   test_quick_new_empty_strided_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5867693Z   test_quick_new_empty_strided_cpu_bool (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5867899Z   test_quick_new_empty_strided_cpu_complex128 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5868212Z   test_quick_new_empty_strided_cpu_complex32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5868417Z   test_quick_new_empty_strided_cpu_complex64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5868605Z   test_quick_new_empty_strided_cpu_float16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5868798Z   test_quick_new_empty_strided_cpu_float32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5868998Z   test_quick_new_empty_strided_cpu_float64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5869194Z   test_quick_new_empty_strided_cpu_int16 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5869384Z   test_quick_new_empty_strided_cpu_int32 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5869600Z   test_quick_new_empty_strided_cpu_int64 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5869795Z   test_quick_new_empty_strided_cpu_int8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5869990Z   test_quick_new_empty_strided_cpu_uint8 (__main__.TestDecompCPU) ... skip: Expected: new_empty_strided is not comparable (0.000s)
2023-01-11T20:52:32.5870154Z   test_quick_new_full_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5870315Z   test_quick_new_full_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5870465Z   test_quick_new_full_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5870628Z   test_quick_new_full_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5870786Z   test_quick_new_full_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5870947Z   test_quick_new_full_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5871104Z   test_quick_new_full_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5871261Z   test_quick_new_full_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5871418Z   test_quick_new_full_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5871574Z   test_quick_new_full_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5871727Z   test_quick_new_full_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5871869Z   test_quick_new_full_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5872023Z   test_quick_new_full_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5872182Z   test_quick_new_ones_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5872336Z   test_quick_new_ones_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5872498Z   test_quick_new_ones_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5872656Z   test_quick_new_ones_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5872812Z   test_quick_new_ones_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5872971Z   test_quick_new_ones_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5873114Z   test_quick_new_ones_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5873304Z   test_quick_new_ones_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5873459Z   test_quick_new_ones_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5873613Z   test_quick_new_ones_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5873768Z   test_quick_new_ones_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5873925Z   test_quick_new_ones_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5874079Z   test_quick_new_ones_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5874238Z   test_quick_new_zeros_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5874393Z   test_quick_new_zeros_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5874550Z   test_quick_new_zeros_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5874740Z   test_quick_new_zeros_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5874899Z   test_quick_new_zeros_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5875058Z   test_quick_new_zeros_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5875217Z   test_quick_new_zeros_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5875376Z   test_quick_new_zeros_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5875533Z   test_quick_new_zeros_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5875690Z   test_quick_new_zeros_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5875848Z   test_quick_new_zeros_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5875991Z   test_quick_new_zeros_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5876150Z   test_quick_new_zeros_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5876317Z   test_quick_nextafter_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5876477Z   test_quick_nextafter_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5876641Z   test_quick_nextafter_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5876832Z   test_quick_nn_functional_binary_cross_entropy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5877022Z   test_quick_nn_functional_binary_cross_entropy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5877228Z   test_quick_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5877431Z   test_quick_nn_functional_binary_cross_entropy_with_logits_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5877619Z   test_quick_nn_functional_binary_cross_entropy_with_logits_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5877791Z   test_quick_nn_functional_elu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5877961Z   test_quick_nn_functional_elu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5878132Z   test_quick_nn_functional_elu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5878314Z   test_quick_nn_functional_embedding_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5878520Z   test_quick_nn_functional_embedding_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5878699Z   test_quick_nn_functional_embedding_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5878878Z   test_quick_nn_functional_embedding_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5879051Z   test_quick_nn_functional_gelu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5879208Z   test_quick_nn_functional_gelu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5879378Z   test_quick_nn_functional_gelu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5879545Z   test_quick_nn_functional_glu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5879717Z   test_quick_nn_functional_glu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5879915Z   test_quick_nn_functional_glu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5880099Z   test_quick_nn_functional_hardshrink_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5880282Z   test_quick_nn_functional_hardshrink_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5880461Z   test_quick_nn_functional_hardshrink_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5880753Z   test_quick_nn_functional_hardsigmoid_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5880937Z   test_quick_nn_functional_hardsigmoid_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5881101Z   test_quick_nn_functional_hardsigmoid_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5881282Z   test_quick_nn_functional_hardswish_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5881465Z   test_quick_nn_functional_hardswish_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5884312Z   test_quick_nn_functional_logsigmoid_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5884838Z   test_quick_nn_functional_mish_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5885948Z   test_quick_nn_functional_pad_constant_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5888079Z   test_quick_nn_functional_prelu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5888600Z   test_quick_nn_functional_relu6_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5894449Z   test_quick_norm_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5897293Z   test_quick_norm_nuc_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5897924Z   test_quick_ones_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
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2023-01-11T20:52:32.5905470Z   test_quick_randn_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5905631Z   test_quick_randn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5905789Z   test_quick_randn_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5905951Z   test_quick_randn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5906133Z   test_quick_randn_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5906275Z   test_quick_randn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5906425Z   test_quick_randn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5906593Z   test_quick_reciprocal_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5906753Z   test_quick_reciprocal_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5906923Z   test_quick_reciprocal_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5907094Z   test_quick_reciprocal_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5907260Z   test_quick_reciprocal_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5907422Z   test_quick_reciprocal_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5907579Z   test_quick_reciprocal_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5907726Z   test_quick_reciprocal_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5907888Z   test_quick_reciprocal_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5908048Z   test_quick_reciprocal_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5908208Z   test_quick_reciprocal_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5908365Z   test_quick_reciprocal_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5908530Z   test_quick_remainder_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5908693Z   test_quick_remainder_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5908853Z   test_quick_remainder_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5909013Z   test_quick_remainder_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5909158Z   test_quick_remainder_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5909315Z   test_quick_remainder_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5909473Z   test_quick_remainder_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5909631Z   test_quick_remainder_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5909818Z   test_quick_remainder_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5909977Z   test_quick_repeat_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5910134Z   test_quick_repeat_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5910295Z   test_quick_repeat_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5910443Z   test_quick_repeat_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5910601Z   test_quick_repeat_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5910755Z   test_quick_repeat_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5910910Z   test_quick_repeat_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5911065Z   test_quick_repeat_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5911248Z   test_quick_repeat_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5911401Z   test_quick_repeat_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5911556Z   test_quick_repeat_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5911706Z   test_quick_repeat_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5911847Z   test_quick_roll_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5911998Z   test_quick_roll_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5912153Z   test_quick_roll_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5912312Z   test_quick_roll_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5912467Z   test_quick_roll_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5912623Z   test_quick_roll_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5912776Z   test_quick_roll_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5912926Z   test_quick_roll_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5913062Z   test_quick_roll_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5913212Z   test_quick_roll_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5913359Z   test_quick_roll_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5913509Z   test_quick_roll_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5913661Z   test_quick_roll_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5913816Z   test_quick_rot90_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5913963Z   test_quick_rot90_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5914122Z   test_quick_rot90_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5914278Z   test_quick_rot90_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5914420Z   test_quick_rot90_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5914571Z   test_quick_rot90_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5914720Z   test_quick_rot90_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5914904Z   test_quick_rot90_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5915054Z   test_quick_rot90_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5915202Z   test_quick_rot90_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5915350Z   test_quick_rot90_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5915496Z   test_quick_rot90_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5915653Z   test_quick_rsqrt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5915792Z   test_quick_rsqrt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5915951Z   test_quick_rsqrt_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5916109Z   test_quick_rsqrt_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5916264Z   test_quick_rsqrt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5916444Z   test_quick_rsqrt_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5916598Z   test_quick_rsqrt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5916749Z   test_quick_rsqrt_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5916899Z   test_quick_rsqrt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5917037Z   test_quick_rsqrt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5917185Z   test_quick_rsqrt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5917339Z   test_quick_rsub_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5917499Z   test_quick_rsub_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5917659Z   test_quick_rsub_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5917814Z   test_quick_rsub_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5917967Z   test_quick_rsub_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5918117Z   test_quick_rsub_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5918268Z   test_quick_rsub_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5918403Z   test_quick_rsub_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5918553Z   test_quick_rsub_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5918702Z   test_quick_rsub_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5918853Z   test_quick_rsub_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5919015Z   test_quick_select_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5919167Z   test_quick_select_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5919330Z   test_quick_select_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5919490Z   test_quick_select_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5919637Z   test_quick_select_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5919794Z   test_quick_select_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5919951Z   test_quick_select_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5920139Z   test_quick_select_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5920294Z   test_quick_select_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5920446Z   test_quick_select_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5920721Z   test_quick_select_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5920951Z   test_quick_select_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5921130Z   test_quick_select_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5921267Z   test_quick_sgn_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5921420Z   test_quick_sgn_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5921579Z   test_quick_sgn_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5921792Z   test_quick_sgn_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5921946Z   test_quick_sgn_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5922097Z   test_quick_sgn_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5922247Z   test_quick_sgn_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5922394Z   test_quick_sgn_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5922528Z   test_quick_sgn_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5922679Z   test_quick_sgn_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5922825Z   test_quick_sgn_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5922978Z   test_quick_sgn_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5923125Z   test_quick_sgn_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5923284Z   test_quick_sigmoid_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5923439Z   test_quick_sigmoid_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5923601Z   test_quick_sigmoid_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5923761Z   test_quick_sigmoid_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5923906Z   test_quick_sigmoid_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5924063Z   test_quick_sigmoid_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5924220Z   test_quick_sigmoid_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5924378Z   test_quick_sigmoid_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5924530Z   test_quick_sigmoid_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5924684Z   test_quick_sigmoid_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5924833Z   test_quick_sigmoid_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5924983Z   test_quick_sign_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5925132Z   test_quick_sign_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5925270Z   test_quick_sign_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5925457Z   test_quick_sign_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5925605Z   test_quick_sign_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5925757Z   test_quick_sign_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5925906Z   test_quick_sign_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5926052Z   test_quick_sign_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5926200Z   test_quick_sign_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5926354Z   test_quick_sign_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5926498Z   test_quick_signbit_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5926650Z   test_quick_signbit_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5926810Z   test_quick_signbit_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5926993Z   test_quick_signbit_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5927149Z   test_quick_signbit_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5927300Z   test_quick_signbit_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5927449Z   test_quick_signbit_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5927597Z   test_quick_signbit_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5927748Z   test_quick_signbit_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5927882Z   test_quick_signbit_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5928033Z   test_quick_sin_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5928184Z   test_quick_sin_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5928341Z   test_quick_sin_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5928489Z   test_quick_sin_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5928637Z   test_quick_sin_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5928785Z   test_quick_sin_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5928932Z   test_quick_sin_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5929065Z   test_quick_sin_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5929212Z   test_quick_sin_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5929359Z   test_quick_sin_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5929506Z   test_quick_sin_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5929657Z   test_quick_sinc_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5929803Z   test_quick_sinc_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5929959Z   test_quick_sinc_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5930116Z   test_quick_sinc_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5930267Z   test_quick_sinc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5930404Z   test_quick_sinc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5930584Z   test_quick_sinc_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5930734Z   test_quick_sinc_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5930881Z   test_quick_sinc_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5931026Z   test_quick_sinc_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5931175Z   test_quick_sinc_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5931326Z   test_quick_sinh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5931474Z   test_quick_sinh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5931616Z   test_quick_sinh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5931776Z   test_quick_sinh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5931925Z   test_quick_sinh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5932105Z   test_quick_sinh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5932253Z   test_quick_sinh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5932402Z   test_quick_sinh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5932546Z   test_quick_sinh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5932692Z   test_quick_sinh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5932840Z   test_quick_sinh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5932982Z   test_quick_slice_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5933129Z   test_quick_slice_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5933288Z   test_quick_slice_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5933446Z   test_quick_slice_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5933601Z   test_quick_slice_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5933754Z   test_quick_slice_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5933906Z   test_quick_slice_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5934053Z   test_quick_slice_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5934189Z   test_quick_slice_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5934340Z   test_quick_slice_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5934489Z   test_quick_slice_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5934636Z   test_quick_slice_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5934781Z   test_quick_slice_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5934939Z   test_quick_softmax_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5935094Z   test_quick_softmax_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5935249Z   test_quick_softmax_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5935412Z   test_quick_special_entr_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5935591Z   test_quick_special_entr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5935755Z   test_quick_special_entr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5935916Z   test_quick_special_entr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5936075Z   test_quick_special_entr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5936231Z   test_quick_special_entr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5936383Z   test_quick_special_entr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5936540Z   test_quick_special_entr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5936701Z   test_quick_special_entr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5936860Z   test_quick_special_erfcx_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5937009Z   test_quick_special_erfcx_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5937194Z   test_quick_special_erfcx_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5937354Z   test_quick_special_erfcx_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5937509Z   test_quick_special_erfcx_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5937662Z   test_quick_special_erfcx_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5937817Z   test_quick_special_erfcx_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5937971Z   test_quick_special_erfcx_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5938136Z   test_quick_special_i0e_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5938292Z   test_quick_special_i0e_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5938444Z   test_quick_special_i0e_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5938600Z   test_quick_special_i0e_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5938758Z   test_quick_special_i0e_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5938914Z   test_quick_special_i0e_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5939068Z   test_quick_special_i0e_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5939221Z   test_quick_special_i0e_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5939446Z   test_quick_special_i0e_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5939605Z   test_quick_special_i1_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5939753Z   test_quick_special_i1_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5939912Z   test_quick_special_i1_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5940068Z   test_quick_special_i1_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5940224Z   test_quick_special_i1_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5940381Z   test_quick_special_i1_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5940536Z   test_quick_special_i1_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5940736Z   test_quick_special_i1_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5940893Z   test_quick_special_i1e_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5941057Z   test_quick_special_i1e_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5941202Z   test_quick_special_i1e_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5941360Z   test_quick_special_i1e_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5941516Z   test_quick_special_i1e_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5941669Z   test_quick_special_i1e_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5941823Z   test_quick_special_i1e_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5941977Z   test_quick_special_i1e_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5942143Z   test_quick_special_log_ndtr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5942342Z   test_quick_special_log_ndtr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5942507Z   test_quick_special_log_ndtr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5942659Z   test_quick_special_log_ndtr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5942823Z   test_quick_special_log_ndtr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5942987Z   test_quick_special_log_ndtr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5943148Z   test_quick_special_log_ndtr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5943311Z   test_quick_special_log_ndtr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5943477Z   test_quick_special_ndtr_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5943635Z   test_quick_special_ndtr_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5943799Z   test_quick_special_ndtr_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5943960Z   test_quick_special_ndtr_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5944105Z   test_quick_special_ndtr_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5944259Z   test_quick_special_ndtr_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5944410Z   test_quick_special_ndtr_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5944567Z   test_quick_special_ndtr_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5944727Z   test_quick_special_ndtr_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5944884Z   test_quick_special_ndtri_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5945050Z   test_quick_special_ndtri_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5945210Z   test_quick_special_ndtri_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5945369Z   test_quick_special_ndtri_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5945510Z   test_quick_special_ndtri_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5945662Z   test_quick_special_ndtri_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5945848Z   test_quick_special_ndtri_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5946004Z   test_quick_special_ndtri_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5946172Z   test_quick_special_xlog1py_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5946334Z   test_quick_special_xlog1py_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5946500Z   test_quick_special_xlog1py_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5946665Z   test_quick_special_xlog1py_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5946830Z   test_quick_special_xlog1py_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5946979Z   test_quick_special_xlog1py_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5947140Z   test_quick_special_xlog1py_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5947328Z   test_quick_special_xlog1py_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5947492Z   test_quick_special_xlog1py_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5947648Z   test_quick_special_xlog1py_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5947805Z   test_quick_special_zeta_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5947967Z   test_quick_special_zeta_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5948127Z   test_quick_special_zeta_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5948288Z   test_quick_special_zeta_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5948433Z   test_quick_special_zeta_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5948587Z   test_quick_special_zeta_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5948744Z   test_quick_special_zeta_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5948902Z   test_quick_special_zeta_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5949057Z   test_quick_split_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5949206Z   test_quick_split_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5949364Z   test_quick_split_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5949521Z   test_quick_split_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5949668Z   test_quick_split_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5949823Z   test_quick_split_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5949975Z   test_quick_split_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5950125Z   test_quick_split_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5950274Z   test_quick_split_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5950424Z   test_quick_split_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5950570Z   test_quick_split_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5950720Z   test_quick_split_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5950906Z   test_quick_split_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5951060Z   test_quick_split_list_args_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.003s)
2023-01-11T20:52:32.5951226Z   test_quick_split_list_args_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5951396Z   test_quick_split_list_args_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5951564Z   test_quick_split_list_args_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5951728Z   test_quick_split_list_args_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5951892Z   test_quick_split_list_args_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5952057Z   test_quick_split_list_args_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5952221Z   test_quick_split_list_args_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5952406Z   test_quick_split_list_args_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5952548Z   test_quick_split_list_args_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5952709Z   test_quick_split_list_args_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5952865Z   test_quick_split_list_args_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5953031Z   test_quick_split_with_sizes_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5953192Z   test_quick_split_with_sizes_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5953361Z   test_quick_split_with_sizes_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5953535Z   test_quick_split_with_sizes_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5953703Z   test_quick_split_with_sizes_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5953866Z   test_quick_split_with_sizes_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5954018Z   test_quick_split_with_sizes_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5954181Z   test_quick_split_with_sizes_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5954346Z   test_quick_split_with_sizes_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5954506Z   test_quick_split_with_sizes_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5954668Z   test_quick_split_with_sizes_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5954829Z   test_quick_split_with_sizes_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5954990Z   test_quick_split_with_sizes_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5955142Z   test_quick_sqrt_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5955290Z   test_quick_sqrt_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5955434Z   test_quick_sqrt_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5955588Z   test_quick_sqrt_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5955740Z   test_quick_sqrt_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5955890Z   test_quick_sqrt_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5956070Z   test_quick_sqrt_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5956219Z   test_quick_sqrt_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5956363Z   test_quick_sqrt_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5956510Z   test_quick_sqrt_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5956646Z   test_quick_sqrt_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5956802Z   test_quick_squeeze_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5956953Z   test_quick_squeeze_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5957114Z   test_quick_squeeze_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5957273Z   test_quick_squeeze_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5957474Z   test_quick_squeeze_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5957633Z   test_quick_squeeze_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5957790Z   test_quick_squeeze_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5957945Z   test_quick_squeeze_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5958085Z   test_quick_squeeze_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5958235Z   test_quick_squeeze_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5958386Z   test_quick_squeeze_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5958539Z   test_quick_squeeze_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5958690Z   test_quick_squeeze_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5958846Z   test_quick_stack_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5958994Z   test_quick_stack_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5959153Z   test_quick_stack_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5959296Z   test_quick_stack_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5959452Z   test_quick_stack_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5959604Z   test_quick_stack_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5959757Z   test_quick_stack_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5959908Z   test_quick_stack_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5960060Z   test_quick_stack_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5960211Z   test_quick_stack_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5960356Z   test_quick_stack_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5960505Z   test_quick_stack_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5960751Z   test_quick_stack_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5960908Z   test_quick_std_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5961065Z   test_quick_std_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5961270Z   test_quick_std_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5961422Z   test_quick_std_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5961576Z   test_quick_std_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5961724Z   test_quick_std_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5961883Z   test_quick_std_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5962046Z   test_quick_std_mean_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5962193Z   test_quick_std_mean_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5962352Z   test_quick_std_mean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5962513Z   test_quick_std_mean_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5962705Z   test_quick_std_mean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5962878Z   test_quick_std_mean_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5963052Z   test_quick_std_mean_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5963223Z   test_quick_std_mean_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5963389Z   test_quick_std_mean_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5963556Z   test_quick_std_mean_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5963709Z   test_quick_std_mean_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5963875Z   test_quick_std_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5964043Z   test_quick_std_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5964209Z   test_quick_std_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5964373Z   test_quick_std_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5964532Z   test_quick_std_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5964688Z   test_quick_std_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5964839Z   test_quick_sub_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5964981Z   test_quick_sub_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5965136Z   test_quick_sub_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5965290Z   test_quick_sub_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5965439Z   test_quick_sub_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5965588Z   test_quick_sub_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5965736Z   test_quick_sub_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5965884Z   test_quick_sub_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5966032Z   test_quick_sub_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5966178Z   test_quick_sub_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5966370Z   test_quick_sub_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5966514Z   test_quick_sub_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5966667Z   test_quick_sum_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5966814Z   test_quick_sum_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5966971Z   test_quick_sum_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5967127Z   test_quick_sum_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5967278Z   test_quick_sum_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5967429Z   test_quick_sum_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5967562Z   test_quick_sum_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5967712Z   test_quick_sum_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5967885Z   test_quick_sum_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5968032Z   test_quick_sum_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5968179Z   test_quick_sum_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5968326Z   test_quick_sum_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5968478Z   test_quick_t_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5968625Z   test_quick_t_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5968779Z   test_quick_t_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5968918Z   test_quick_t_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5969064Z   test_quick_t_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5969213Z   test_quick_t_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5969358Z   test_quick_t_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5969504Z   test_quick_t_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5969649Z   test_quick_t_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5969795Z   test_quick_t_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5969940Z   test_quick_t_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5970073Z   test_quick_t_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5970228Z   test_quick_tan_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5970379Z   test_quick_tan_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5970538Z   test_quick_tan_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5970691Z   test_quick_tan_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5970842Z   test_quick_tan_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5970995Z   test_quick_tan_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5971141Z   test_quick_tan_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5971287Z   test_quick_tan_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5971451Z   test_quick_tan_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5971601Z   test_quick_tan_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5971747Z   test_quick_tan_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5971903Z   test_quick_tanh_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5972053Z   test_quick_tanh_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5972208Z   test_quick_tanh_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5972365Z   test_quick_tanh_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5972519Z   test_quick_tanh_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5972658Z   test_quick_tanh_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5972808Z   test_quick_tanh_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5972983Z   test_quick_tanh_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5973128Z   test_quick_tanh_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5973273Z   test_quick_tanh_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5973424Z   test_quick_tanh_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5973582Z   test_quick_trace_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5973739Z   test_quick_trace_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5973893Z   test_quick_trace_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5974035Z   test_quick_trace_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5974186Z   test_quick_trace_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5974339Z   test_quick_trace_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5974488Z   test_quick_trace_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5974637Z   test_quick_trace_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5974783Z   test_quick_trace_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5974942Z   test_quick_transpose_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5975102Z   test_quick_transpose_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5975269Z   test_quick_transpose_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5975419Z   test_quick_transpose_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5975577Z   test_quick_transpose_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5975734Z   test_quick_transpose_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5975891Z   test_quick_transpose_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5976049Z   test_quick_transpose_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5976205Z   test_quick_transpose_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5976358Z   test_quick_transpose_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5976513Z   test_quick_transpose_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5976699Z   test_quick_transpose_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5976856Z   test_quick_transpose_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5977010Z   test_quick_tril_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5977157Z   test_quick_tril_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5977312Z   test_quick_tril_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5977466Z   test_quick_tril_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5977620Z   test_quick_tril_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5977770Z   test_quick_tril_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5977921Z   test_quick_tril_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5978084Z   test_quick_tril_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5978233Z   test_quick_tril_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5978378Z   test_quick_tril_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5978526Z   test_quick_tril_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5978673Z   test_quick_tril_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5978831Z   test_quick_tril_indices_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5978988Z   test_quick_tril_indices_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5979143Z   test_quick_triu_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5979375Z   test_quick_triu_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5979524Z   test_quick_triu_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5979679Z   test_quick_triu_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5979830Z   test_quick_triu_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5979981Z   test_quick_triu_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5980130Z   test_quick_triu_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5980278Z   test_quick_triu_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5980427Z   test_quick_triu_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5980574Z   test_quick_triu_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5980711Z   test_quick_triu_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5980859Z   test_quick_triu_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5981016Z   test_quick_triu_indices_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5981171Z   test_quick_triu_indices_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5981326Z   test_quick_trunc_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5981478Z   test_quick_trunc_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5981632Z   test_quick_trunc_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5981824Z   test_quick_trunc_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5981975Z   test_quick_trunc_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5982109Z   test_quick_trunc_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5982257Z   test_quick_trunc_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5982401Z   test_quick_trunc_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5982557Z   test_quick_unbind_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5982707Z   test_quick_unbind_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5982864Z   test_quick_unbind_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5983022Z   test_quick_unbind_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5983181Z   test_quick_unbind_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5983357Z   test_quick_unbind_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5983514Z   test_quick_unbind_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5983669Z   test_quick_unbind_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5983820Z   test_quick_unbind_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5983969Z   test_quick_unbind_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5984118Z   test_quick_unbind_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5984268Z   test_quick_unbind_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5984415Z   test_quick_unbind_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5984579Z   test_quick_unfold_copy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5984724Z   test_quick_unfold_copy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5984890Z   test_quick_unfold_copy_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5985053Z   test_quick_unfold_copy_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5985217Z   test_quick_unfold_copy_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5985376Z   test_quick_unfold_copy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5985534Z   test_quick_unfold_copy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5985694Z   test_quick_unfold_copy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5985851Z   test_quick_unfold_copy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5986009Z   test_quick_unfold_copy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5986151Z   test_quick_unfold_copy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5986307Z   test_quick_unfold_copy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5986461Z   test_quick_unfold_copy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5986617Z   test_quick_unfold_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5986768Z   test_quick_unfold_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5986952Z   test_quick_unfold_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5987111Z   test_quick_unfold_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5987270Z   test_quick_unfold_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5987412Z   test_quick_unfold_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5987567Z   test_quick_unfold_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5987719Z   test_quick_unfold_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5987870Z   test_quick_unfold_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5988021Z   test_quick_unfold_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5988171Z   test_quick_unfold_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5988347Z   test_quick_unfold_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5988496Z   test_quick_unfold_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5988653Z   test_quick_uniform_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5988799Z   test_quick_uniform_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5988954Z   test_quick_uniform_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5989109Z   test_quick_uniform_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5989264Z   test_quick_uniform_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5989420Z   test_quick_uniform_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5989581Z   test_quick_unsqueeze_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5989737Z   test_quick_unsqueeze_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5989902Z   test_quick_unsqueeze_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5990064Z   test_quick_unsqueeze_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5990208Z   test_quick_unsqueeze_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5990366Z   test_quick_unsqueeze_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5990522Z   test_quick_unsqueeze_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5990682Z   test_quick_unsqueeze_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5990838Z   test_quick_unsqueeze_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5990995Z   test_quick_unsqueeze_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5991150Z   test_quick_unsqueeze_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5991305Z   test_quick_unsqueeze_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5991443Z   test_quick_unsqueeze_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5991596Z   test_quick_var_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5991748Z   test_quick_var_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5991900Z   test_quick_var_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5992080Z   test_quick_var_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5992231Z   test_quick_var_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5992378Z   test_quick_var_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5992533Z   test_quick_var_mean_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5992694Z   test_quick_var_mean_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5992840Z   test_quick_var_mean_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5992996Z   test_quick_var_mean_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5993153Z   test_quick_var_mean_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5993311Z   test_quick_var_mean_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5993506Z   test_quick_var_mean_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5993680Z   test_quick_var_mean_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5993849Z   test_quick_var_mean_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5994014Z   test_quick_var_mean_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5994180Z   test_quick_var_mean_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5994333Z   test_quick_var_mean_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5994497Z   test_quick_var_unbiased_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5994666Z   test_quick_var_unbiased_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5994835Z   test_quick_var_unbiased_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5995000Z   test_quick_var_unbiased_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5995159Z   test_quick_var_unbiased_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5995315Z   test_quick_var_unbiased_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5995468Z   test_quick_view_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5995616Z   test_quick_view_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5995762Z   test_quick_view_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5995917Z   test_quick_view_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5996073Z   test_quick_view_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5996226Z   test_quick_view_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5996376Z   test_quick_view_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5996524Z   test_quick_view_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5996671Z   test_quick_view_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5996819Z   test_quick_view_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5996951Z   test_quick_view_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5997130Z   test_quick_view_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5997281Z   test_quick_view_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5997436Z   test_quick_where_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5997582Z   test_quick_where_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5997741Z   test_quick_where_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5997898Z   test_quick_where_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5998052Z   test_quick_where_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5998204Z   test_quick_where_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5998346Z   test_quick_where_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5998494Z   test_quick_where_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5998669Z   test_quick_where_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5998819Z   test_quick_where_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5998964Z   test_quick_where_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5999111Z   test_quick_where_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5999255Z   test_quick_where_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5999409Z   test_quick_xlogy_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5999557Z   test_quick_xlogy_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5999699Z   test_quick_xlogy_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.5999853Z   test_quick_xlogy_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6000001Z   test_quick_xlogy_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6000149Z   test_quick_xlogy_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6000297Z   test_quick_xlogy_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6000441Z   test_quick_xlogy_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6000588Z   test_quick_xlogy_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6000847Z   test_quick_xlogy_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6000991Z   test_quick_zero__cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6001146Z   test_quick_zero__cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6001304Z   test_quick_zero__cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6001459Z   test_quick_zero__cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6001612Z   test_quick_zero__cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6001764Z   test_quick_zero__cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6001917Z   test_quick_zero__cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6002066Z   test_quick_zero__cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6002263Z   test_quick_zero__cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6002396Z   test_quick_zero__cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6002547Z   test_quick_zero__cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6002694Z   test_quick_zero__cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6002849Z   test_quick_zeros_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6003000Z   test_quick_zeros_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6003159Z   test_quick_zeros_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6003316Z   test_quick_zeros_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6003473Z   test_quick_zeros_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6003614Z   test_quick_zeros_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6003800Z   test_quick_zeros_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6003951Z   test_quick_zeros_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6004098Z   test_quick_zeros_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6004245Z   test_quick_zeros_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6004389Z   test_quick_zeros_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6004537Z   test_quick_zeros_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6004681Z   test_quick_zeros_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6004843Z   test_quick_zeros_like_cpu_bfloat16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6004987Z   test_quick_zeros_like_cpu_bool (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6005150Z   test_quick_zeros_like_cpu_complex128 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6005310Z   test_quick_zeros_like_cpu_complex32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6005466Z   test_quick_zeros_like_cpu_complex64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6005624Z   test_quick_zeros_like_cpu_float16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6005777Z   test_quick_zeros_like_cpu_float32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6005929Z   test_quick_zeros_like_cpu_float64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6006085Z   test_quick_zeros_like_cpu_int16 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6006241Z   test_quick_zeros_like_cpu_int32 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6006383Z   test_quick_zeros_like_cpu_int64 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6006537Z   test_quick_zeros_like_cpu_int8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6006690Z   test_quick_zeros_like_cpu_uint8 (__main__.TestDecompCPU) ... skip: Skipped under ASAN (0.000s)
2023-01-11T20:52:32.6006810Z   test_uniform_cpu (__main__.TestDecompCPU) ... ok (0.027s)
2023-01-11T20:52:32.6006823Z 
2023-01-11T20:52:32.6007133Z ----------------------------------------------------------------------
2023-01-11T20:52:32.6007213Z Ran 7557 tests in 2.958s
2023-01-11T20:52:32.6007219Z 
2023-01-11T20:52:32.6007292Z OK (skipped=7556)
2023-01-11T20:52:32.6007329Z 
2023-01-11T20:52:32.6007417Z Generating XML reports...
2023-01-11T20:52:32.6007702Z Generated XML report: test-reports/python-unittest/test_decomp/TEST-TestDecompCPU-20230111205227.xml
2023-01-11T20:52:32.6007998Z Generated XML report: test-reports/python-unittest/test_decomp/TEST-DecompAmpTestsCPU-20230111205227.xml
2023-01-11T20:52:32.6008312Z Generated XML report: test-reports/python-unittest/test_decomp/TEST-DecompContiguousTestsCPU-20230111205227.xml
2023-01-11T20:52:32.6008318Z 
2023-01-11T20:52:32.6008799Z ##[endgroup]
2023-01-11T20:52:32.6009075Z FINISHED PRINTING LOG FILE of test_decomp (/var/lib/jenkins/workspace/test/test-reports/test_decomp_0t79n_gg)
2023-01-11T20:52:32.6009080Z 
2023-01-11T20:52:37.2992229Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:52:37.4635436Z Ignoring disabled issues:  ['91003']
2023-01-11T20:52:37.4846420Z Running test_dlpack ... [2023-01-11 20:52:37.484327]
2023-01-11T20:52:37.4848446Z Executing ['/opt/conda/bin/python', '-bb', 'test_dlpack.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:52:37.484623]
2023-01-11T20:52:42.4534246Z 
2023-01-11T20:52:42.4534784Z Expand the folded group to see the log file of test_dlpack
2023-01-11T20:52:42.4536266Z ##[group]PRINTING LOG FILE of test_dlpack (/var/lib/jenkins/workspace/test/test-reports/test_dlpack_dc8d7bir)
2023-01-11T20:52:42.4537181Z Test results will be stored in test-reports/python-unittest/test_dlpack
2023-01-11T20:52:42.4537486Z 
2023-01-11T20:52:42.4537613Z Running tests...
2023-01-11T20:52:42.4538144Z ----------------------------------------------------------------------
2023-01-11T20:52:42.4538686Z   test_dlpack_capsule_conversion_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... ok (0.005s)
2023-01-11T20:52:42.4539743Z   test_dlpack_capsule_conversion_cpu_complex128 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4540509Z   test_dlpack_capsule_conversion_cpu_complex64 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4541145Z   test_dlpack_capsule_conversion_cpu_float16 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4541741Z   test_dlpack_capsule_conversion_cpu_float32 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4542276Z   test_dlpack_capsule_conversion_cpu_float64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4542852Z   test_dlpack_capsule_conversion_cpu_int16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4543480Z   test_dlpack_capsule_conversion_cpu_int32 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4544061Z   test_dlpack_capsule_conversion_cpu_int64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4544663Z   test_dlpack_capsule_conversion_cpu_int8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4545252Z   test_dlpack_capsule_conversion_cpu_uint8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4545900Z   test_dlpack_conversion_with_diff_streams_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4546572Z   test_dlpack_conversion_with_diff_streams_cpu_complex128 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4547276Z   test_dlpack_conversion_with_diff_streams_cpu_complex64 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4547981Z   test_dlpack_conversion_with_diff_streams_cpu_float16 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4548641Z   test_dlpack_conversion_with_diff_streams_cpu_float32 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4549318Z   test_dlpack_conversion_with_diff_streams_cpu_float64 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4550018Z   test_dlpack_conversion_with_diff_streams_cpu_int16 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4550716Z   test_dlpack_conversion_with_diff_streams_cpu_int32 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4551485Z   test_dlpack_conversion_with_diff_streams_cpu_int64 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4552077Z   test_dlpack_conversion_with_diff_streams_cpu_int8 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4552750Z   test_dlpack_conversion_with_diff_streams_cpu_uint8 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4553457Z   test_dlpack_conversion_with_streams_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4554173Z   test_dlpack_conversion_with_streams_cpu_complex128 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4554833Z   test_dlpack_conversion_with_streams_cpu_complex64 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4555531Z   test_dlpack_conversion_with_streams_cpu_float16 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4556226Z   test_dlpack_conversion_with_streams_cpu_float32 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4556994Z   test_dlpack_conversion_with_streams_cpu_float64 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4557671Z   test_dlpack_conversion_with_streams_cpu_int16 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4558176Z   test_dlpack_conversion_with_streams_cpu_int32 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4558548Z   test_dlpack_conversion_with_streams_cpu_int64 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4558946Z   test_dlpack_conversion_with_streams_cpu_int8 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4559313Z   test_dlpack_conversion_with_streams_cpu_uint8 (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4559663Z   test_dlpack_default_stream_cpu (__main__.TestTorchDlPackCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T20:52:42.4559993Z   test_dlpack_error_on_bool_tensor_cpu (__main__.TestTorchDlPackCPU) ... ok (0.004s)
2023-01-11T20:52:42.4560304Z   test_dlpack_export_is_conj_cpu (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4560780Z   test_dlpack_export_non_strided_cpu (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4561091Z   test_dlpack_export_requires_grad_cpu (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4561406Z   test_dlpack_normalize_strides_cpu (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4561731Z   test_dlpack_protocol_conversion_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4562060Z   test_dlpack_protocol_conversion_cpu_complex128 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4562401Z   test_dlpack_protocol_conversion_cpu_complex64 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4562737Z   test_dlpack_protocol_conversion_cpu_float16 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4563067Z   test_dlpack_protocol_conversion_cpu_float32 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4563382Z   test_dlpack_protocol_conversion_cpu_float64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4563710Z   test_dlpack_protocol_conversion_cpu_int16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4564031Z   test_dlpack_protocol_conversion_cpu_int32 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4564342Z   test_dlpack_protocol_conversion_cpu_int64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4564666Z   test_dlpack_protocol_conversion_cpu_int8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4564986Z   test_dlpack_protocol_conversion_cpu_uint8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4565380Z   test_dlpack_shared_storage_cpu (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4565695Z   test_dlpack_tensor_invalid_stream_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4566030Z   test_dlpack_tensor_invalid_stream_cpu_complex128 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4566370Z   test_dlpack_tensor_invalid_stream_cpu_complex64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4566697Z   test_dlpack_tensor_invalid_stream_cpu_float16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4567032Z   test_dlpack_tensor_invalid_stream_cpu_float32 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4567367Z   test_dlpack_tensor_invalid_stream_cpu_float64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4567694Z   test_dlpack_tensor_invalid_stream_cpu_int16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4568012Z   test_dlpack_tensor_invalid_stream_cpu_int32 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4568336Z   test_dlpack_tensor_invalid_stream_cpu_int64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4568708Z   test_dlpack_tensor_invalid_stream_cpu_int8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4569036Z   test_dlpack_tensor_invalid_stream_cpu_uint8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4569333Z   test_from_dlpack_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4569634Z   test_from_dlpack_cpu_complex128 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4569940Z   test_from_dlpack_cpu_complex64 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4570229Z   test_from_dlpack_cpu_float16 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4570527Z   test_from_dlpack_cpu_float32 (__main__.TestTorchDlPackCPU) ... ok (0.002s)
2023-01-11T20:52:42.4570825Z   test_from_dlpack_cpu_float64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4571115Z   test_from_dlpack_cpu_int16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4571396Z   test_from_dlpack_cpu_int32 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4571688Z   test_from_dlpack_cpu_int64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4571976Z   test_from_dlpack_cpu_int8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4572256Z   test_from_dlpack_cpu_uint8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4572557Z   test_from_dlpack_dtype_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4572875Z   test_from_dlpack_dtype_cpu_complex128 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4573191Z   test_from_dlpack_dtype_cpu_complex64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4573494Z   test_from_dlpack_dtype_cpu_float16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4573807Z   test_from_dlpack_dtype_cpu_float32 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4574118Z   test_from_dlpack_dtype_cpu_float64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4574411Z   test_from_dlpack_dtype_cpu_int16 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4574712Z   test_from_dlpack_dtype_cpu_int32 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4575012Z   test_from_dlpack_dtype_cpu_int64 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4575314Z   test_from_dlpack_dtype_cpu_int8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4575607Z   test_from_dlpack_dtype_cpu_uint8 (__main__.TestTorchDlPackCPU) ... ok (0.001s)
2023-01-11T20:52:42.4575929Z   test_from_dlpack_noncontinguous_cpu_bfloat16 (__main__.TestTorchDlPackCPU) ... ok (0.005s)
2023-01-11T20:52:42.4576270Z   test_from_dlpack_noncontinguous_cpu_complex128 (__main__.TestTorchDlPackCPU) ... ok (0.005s)
2023-01-11T20:52:42.4576635Z   test_from_dlpack_noncontinguous_cpu_complex64 (__main__.TestTorchDlPackCPU) ... ok (0.005s)
2023-01-11T20:52:42.4576973Z   test_from_dlpack_noncontinguous_cpu_float16 (__main__.TestTorchDlPackCPU) ... ok (0.005s)
2023-01-11T20:52:42.4577303Z   test_from_dlpack_noncontinguous_cpu_float32 (__main__.TestTorchDlPackCPU) ... ok (0.005s)
2023-01-11T20:52:42.4577631Z   test_from_dlpack_noncontinguous_cpu_float64 (__main__.TestTorchDlPackCPU) ... ok (0.004s)
2023-01-11T20:52:42.4577939Z   test_from_dlpack_noncontinguous_cpu_int16 (__main__.TestTorchDlPackCPU) ... ok (0.003s)
2023-01-11T20:52:42.4578266Z   test_from_dlpack_noncontinguous_cpu_int32 (__main__.TestTorchDlPackCPU) ... ok (0.018s)
2023-01-11T20:52:42.4578588Z   test_from_dlpack_noncontinguous_cpu_int64 (__main__.TestTorchDlPackCPU) ... ok (0.003s)
2023-01-11T20:52:42.4578903Z   test_from_dlpack_noncontinguous_cpu_int8 (__main__.TestTorchDlPackCPU) ... ok (0.003s)
2023-01-11T20:52:42.4579302Z   test_from_dlpack_noncontinguous_cpu_uint8 (__main__.TestTorchDlPackCPU) ... ok (0.003s)
2023-01-11T20:52:42.4579481Z 
2023-01-11T20:52:42.4579739Z ----------------------------------------------------------------------
2023-01-11T20:52:42.4580038Z Ran 95 tests in 0.175s
2023-01-11T20:52:42.4580141Z 
2023-01-11T20:52:42.4580213Z OK (skipped=23)
2023-01-11T20:52:42.4580321Z 
2023-01-11T20:52:42.4580408Z Generating XML reports...
2023-01-11T20:52:42.4580834Z Generated XML report: test-reports/python-unittest/test_dlpack/TEST-TestTorchDlPackCPU-20230111205241.xml
2023-01-11T20:52:42.4581066Z 
2023-01-11T20:52:42.4581336Z ##[endgroup]
2023-01-11T20:52:42.4581706Z FINISHED PRINTING LOG FILE of test_dlpack (/var/lib/jenkins/workspace/test/test-reports/test_dlpack_dc8d7bir)
2023-01-11T20:52:42.4581914Z 
2023-01-11T20:52:47.3650456Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:52:47.5491572Z Ignoring disabled issues:  ['91003']
2023-01-11T20:52:47.5710811Z Running test_jit_llga_fuser ... [2023-01-11 20:52:47.570706]
2023-01-11T20:52:47.5712221Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_llga_fuser.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:52:47.571015]
2023-01-11T20:52:52.6545191Z 
2023-01-11T20:52:52.6545744Z Expand the folded group to see the log file of test_jit_llga_fuser
2023-01-11T20:52:52.6546915Z ##[group]PRINTING LOG FILE of test_jit_llga_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_llga_fuser_ljnanlup)
2023-01-11T20:52:52.6547407Z 
2023-01-11T20:52:52.6547541Z Running tests...
2023-01-11T20:52:52.6548226Z ----------------------------------------------------------------------
2023-01-11T20:52:52.6548881Z   test_dynamo_aot_ts_onednn (__main__.TestDynamoAOT) ... Test results will be stored in test-reports/python-unittest/test_jit_llga_fuser
2023-01-11T20:52:52.6549247Z skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6549644Z   test_context_manager (__main__.TestEnableDisableLlgaFuser) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6550117Z   test_bn2d_eltwise_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6550565Z   test_bn2d_eltwise_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6551013Z   test_conv2d_bn_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6551453Z   test_conv2d_bn_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6551900Z   test_conv2d_bn_relu_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6552343Z   test_conv2d_bn_relu_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6552794Z   test_conv2d_clamp_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6553453Z   test_conv2d_clamp_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6553911Z   test_conv2d_eltwise_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6554351Z   test_conv2d_eltwise_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6554803Z   test_conv2d_silu_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6555252Z   test_conv2d_silu_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6555699Z   test_conv2d_sum_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6556126Z   test_conv2d_sum_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6556599Z   test_ensure_tensor_is_rewrapped_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6557085Z   test_ensure_tensor_is_rewrapped_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6557596Z   test_linear_eltwise_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6558057Z   test_linear_eltwise_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6558541Z   test_rewrap_tensor_input_to_pytorch_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6559034Z   test_rewrap_tensor_input_to_pytorch_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:52:52.6559481Z   test_wildcard_cpu_bfloat16 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6559931Z   test_wildcard_cpu_float32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6560407Z   test_wildcard_unsupported_dtype_cpu_int32 (__main__.TestFusionPatternCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6561023Z   test_vision_alexnet_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6561414Z   test_vision_alexnet_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6561829Z   test_vision_densenet121_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6562246Z   test_vision_densenet121_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6562662Z   test_vision_densenet161_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6563061Z   test_vision_densenet161_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6563478Z   test_vision_densenet169_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6563895Z   test_vision_densenet169_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6564295Z   test_vision_densenet201_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6564703Z   test_vision_densenet201_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6565126Z   test_vision_efficientnet_b0_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6565547Z   test_vision_efficientnet_b0_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6565958Z   test_vision_efficientnet_b1_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6566379Z   test_vision_efficientnet_b1_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6566853Z   test_vision_efficientnet_b2_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6567272Z   test_vision_efficientnet_b2_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6567683Z   test_vision_efficientnet_b3_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6568105Z   test_vision_efficientnet_b3_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6568526Z   test_vision_efficientnet_b4_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6568934Z   test_vision_efficientnet_b4_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6569356Z   test_vision_efficientnet_b5_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6569777Z   test_vision_efficientnet_b5_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6570199Z   test_vision_efficientnet_b6_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6570654Z   test_vision_efficientnet_b6_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6571082Z   test_vision_efficientnet_b7_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6571504Z   test_vision_efficientnet_b7_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6571917Z   test_vision_googlenet_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6572314Z   test_vision_googlenet_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6572726Z   test_vision_mnasnet1_0_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6573129Z   test_vision_mnasnet1_0_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6573543Z   test_vision_mobilenet_v2_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6573946Z   test_vision_mobilenet_v2_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6574375Z   test_vision_mobilenet_v3_large_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6574802Z   test_vision_mobilenet_v3_large_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6575211Z   test_vision_regnet_y_400mf_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6575624Z   test_vision_regnet_y_400mf_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6576031Z   test_vision_resnet50_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6576432Z   test_vision_resnet50_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6576837Z   test_vision_resnext101_32x8d_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6577258Z   test_vision_resnext101_32x8d_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6577681Z   test_vision_resnext50_32x4d_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6578099Z   test_vision_resnext50_32x4d_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6578511Z   test_vision_shufflenet_v2_x1_0_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6578933Z   test_vision_shufflenet_v2_x1_0_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6579471Z   test_vision_squeezenet1_0_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6579926Z   test_vision_squeezenet1_0_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6580331Z   test_vision_vgg16_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6580725Z   test_vision_vgg16_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6581139Z   test_vision_wide_resnet50_2_bfloat16 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6581546Z   test_vision_wide_resnet50_2_float32 (__main__.TestModel) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:52:52.6581943Z   test_add_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6582332Z   test_add_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6582733Z   test_add_scalar_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6583126Z   test_add_scalar_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6583523Z   test_addmm_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6583951Z   test_addmm_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6584342Z   test_avg_pool2d_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6584745Z   test_avg_pool2d_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6585136Z   test_bn2d_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6585521Z   test_bn2d_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6585888Z   test_cat_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6586268Z   test_cat_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6586656Z   test_conv2d_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6587048Z   test_conv2d_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6587426Z   test_eltwise_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6587817Z   test_eltwise_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6588224Z   test_identity_binary_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6588630Z   test_identity_binary_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6589035Z   test_layer_norm_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6589434Z   test_layer_norm_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6589836Z   test_linear_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6590214Z   test_linear_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6590618Z   test_max_pool2d_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6591020Z   test_max_pool2d_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6591401Z   test_mul_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6591783Z   test_mul_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6592178Z   test_softmax_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6592571Z   test_softmax_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6592996Z   test_typecheck_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6593398Z   test_typecheck_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6593837Z   test_variable_kernel_avg_pool2d_cpu_bfloat16 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6594287Z   test_variable_kernel_avg_pool2d_cpu_float32 (__main__.TestOpCPU) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:52:52.6594470Z 
2023-01-11T20:52:52.6594668Z ----------------------------------------------------------------------
2023-01-11T20:52:52.6594913Z Ran 107 tests in 0.077s
2023-01-11T20:52:52.6595027Z 
2023-01-11T20:52:52.6595100Z OK (skipped=107)
2023-01-11T20:52:52.6595209Z 
2023-01-11T20:52:52.6595279Z Generating XML reports...
2023-01-11T20:52:52.6595689Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestDynamoAOT-20230111205251.xml
2023-01-11T20:52:52.6596240Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestEnableDisableLlgaFuser-20230111205251.xml
2023-01-11T20:52:52.6596836Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestFusionPatternCPU-20230111205251.xml
2023-01-11T20:52:52.6597334Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestModel-20230111205251.xml
2023-01-11T20:52:52.6597809Z Generated XML report: test-reports/python-unittest/test_jit_llga_fuser/TEST-TestOpCPU-20230111205251.xml
2023-01-11T20:52:52.6598025Z 
2023-01-11T20:52:52.6598304Z ##[endgroup]
2023-01-11T20:52:52.6598687Z FINISHED PRINTING LOG FILE of test_jit_llga_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_llga_fuser_ljnanlup)
2023-01-11T20:52:52.6598906Z 
2023-01-11T20:52:57.7470776Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:52:57.9372082Z Ignoring disabled issues:  ['91003']
2023-01-11T20:52:57.9670513Z Running test_mkldnn ... [2023-01-11 20:52:57.966537]
2023-01-11T20:52:57.9671363Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:52:57.966848]
2023-01-11T20:53:02.4088590Z 
2023-01-11T20:53:02.4089356Z Expand the folded group to see the log file of test_mkldnn
2023-01-11T20:53:02.4090476Z ##[group]PRINTING LOG FILE of test_mkldnn (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_3_0m5gp6)
2023-01-11T20:53:02.4090806Z 
2023-01-11T20:53:02.4090916Z Running tests...
2023-01-11T20:53:02.4094819Z ----------------------------------------------------------------------
2023-01-11T20:53:02.4095714Z   test_0_dimension_tensor (__main__.TestMkldnn) ... Test results will be stored in test-reports/python-unittest/test_mkldnn
2023-01-11T20:53:02.4096382Z skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:02.4097076Z   test_adaptive_avg_pool2d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4097869Z   test_adaptive_avg_pool2d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4098615Z   test_add (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:02.4099519Z   test_autograd_from_mkldnn (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4100265Z   test_autograd_to_mkldnn (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4100994Z   test_avg_pool2d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4101857Z   test_avg_pool2d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4102609Z   test_avg_pool2d_stride_none (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4103346Z   test_avg_pool3d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4104085Z   test_avg_pool3d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4105105Z   test_batch_norm_2d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4105837Z   test_batch_norm_2d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4106581Z   test_batch_norm_3d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4107318Z   test_batch_norm_3d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4108033Z   test_clone (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4108702Z   test_conv1d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4109412Z   test_conv1d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4110145Z   test_conv1d_functional (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4110835Z   test_conv2d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4111535Z   test_conv2d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4112046Z   test_conv2d_legacy_jit_model (__main__.TestMkldnn)
2023-01-11T20:53:02.4112961Z MKLDNN integration used to serialize models with 5d weight for grouped ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4113748Z   test_conv2d_nhwc (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4114484Z   test_conv2d_nhwc_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4115197Z   test_conv3d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4115893Z   test_conv3d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4116610Z   test_conversion (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.003s)
2023-01-11T20:53:02.4117312Z   test_copy (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4118000Z   test_detach (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4118672Z   test_empty (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4119360Z   test_gelu (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4120061Z   test_gelu_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4120935Z   test_is_mkldnn (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4121648Z   test_is_mkldnn_jit (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4122392Z   test_legacy_new_failure (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4123099Z   test_linear (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4123800Z   test_linear_backward (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4124529Z   test_linear_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4125300Z   test_linear_non_contiguous_weight (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4126042Z   test_max_pool2d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4126782Z   test_max_pool2d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4127532Z   test_max_pool2d_stride_none (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4128266Z   test_max_pool3d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4128969Z   test_max_pool3d_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4129724Z   test_max_pool_unsupported (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4130501Z   test_mkldnn_conv_shapecheck (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:02.4131381Z   test_mul (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:02.4132051Z   test_prelu (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4132753Z   test_prelu_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4133446Z   test_relu (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4134100Z   test_relu_ (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4134789Z   test_relu_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4135519Z   test_relu_inplace_bf16 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4136225Z   test_repr (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4136906Z   test_reshape (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4137644Z   test_reshape_backward (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4138563Z   test_reshape_blocked_format (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4139384Z   test_resnet18 (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4140116Z   test_resnext50_32x4d (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4140883Z   test_set_data_tensorimpl_type (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4141617Z   test_sigmoid (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4142301Z   test_softmax (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4142989Z   test_tanh (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4143698Z   test_transpose (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4144436Z   test_transpose_invalid_dime (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4145179Z   test_unsupported (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:02.4145874Z   test_view (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4146557Z   test_zero_ (__main__.TestMkldnn) ... skip: MKL-DNN build is disabled (0.000s)
2023-01-11T20:53:02.4146866Z 
2023-01-11T20:53:02.4147226Z ----------------------------------------------------------------------
2023-01-11T20:53:02.4147673Z Ran 68 tests in 0.053s
2023-01-11T20:53:02.4147887Z 
2023-01-11T20:53:02.4148018Z OK (skipped=68)
2023-01-11T20:53:02.4148220Z 
2023-01-11T20:53:02.4148363Z Generating XML reports...
2023-01-11T20:53:02.4149090Z Generated XML report: test-reports/python-unittest/test_mkldnn/TEST-TestMkldnn-20230111205301.xml
2023-01-11T20:53:02.4149499Z 
2023-01-11T20:53:02.4150011Z ##[endgroup]
2023-01-11T20:53:02.4150700Z FINISHED PRINTING LOG FILE of test_mkldnn (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_3_0m5gp6)
2023-01-11T20:53:02.4151095Z 
2023-01-11T20:53:07.3584950Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:53:07.5462831Z Ignoring disabled issues:  ['91003']
2023-01-11T20:53:07.5826415Z Running test_nvfuser_frontend ... [2023-01-11 20:53:07.582241]
2023-01-11T20:53:07.5828502Z Executing ['/opt/conda/bin/python', '-bb', 'test_nvfuser_frontend.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:53:07.582631]
2023-01-11T20:53:12.4325335Z 
2023-01-11T20:53:12.4325880Z Expand the folded group to see the log file of test_nvfuser_frontend
2023-01-11T20:53:12.4326991Z ##[group]PRINTING LOG FILE of test_nvfuser_frontend (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_frontend_et1uaoj8)
2023-01-11T20:53:12.4327441Z 
2023-01-11T20:53:12.4327569Z Running tests...
2023-01-11T20:53:12.4328481Z ----------------------------------------------------------------------
2023-01-11T20:53:12.4329361Z   test_basic (__main__.TestNvFuserFrontend) ... Test results will be stored in test-reports/python-unittest/test_nvfuser_frontend
2023-01-11T20:53:12.4329941Z skip: requires CUDA (0.003s)
2023-01-11T20:53:12.4330410Z   test_basic_fp16 (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T20:53:12.4331029Z   test_broadcast_mixing (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T20:53:12.4331646Z   test_cast_double_to_half (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T20:53:12.4332283Z   test_explicit_broadcast_input (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T20:53:12.4332915Z   test_implicit_broadcast_input (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T20:53:12.4333529Z   test_ops_broadcast (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T20:53:12.4334142Z   test_prim_layer_norm_fwd (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.005s)
2023-01-11T20:53:12.4334754Z   test_prim_rms_norm_fwd (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.003s)
2023-01-11T20:53:12.4335489Z   test_promote_to_double (__main__.TestNvFuserFrontend) ... skip: requires CUDA (0.001s)
2023-01-11T20:53:12.4335834Z 
2023-01-11T20:53:12.4336217Z ----------------------------------------------------------------------
2023-01-11T20:53:12.4336648Z Ran 10 tests in 0.019s
2023-01-11T20:53:12.4336850Z 
2023-01-11T20:53:12.4336962Z OK (skipped=10)
2023-01-11T20:53:12.4337156Z 
2023-01-11T20:53:12.4337307Z Generating XML reports...
2023-01-11T20:53:12.4338124Z Generated XML report: test-reports/python-unittest/test_nvfuser_frontend/TEST-TestNvFuserFrontend-20230111205311.xml
2023-01-11T20:53:12.4338580Z 
2023-01-11T20:53:12.4338971Z ##[endgroup]
2023-01-11T20:53:12.4339803Z FINISHED PRINTING LOG FILE of test_nvfuser_frontend (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_frontend_et1uaoj8)
2023-01-11T20:53:12.4340230Z 
2023-01-11T20:53:17.6792723Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:53:17.8572539Z Ignoring disabled issues:  ['91003']
2023-01-11T20:53:17.8785969Z Running test_mkldnn_fusion ... [2023-01-11 20:53:17.878185]
2023-01-11T20:53:17.8786848Z Executing ['/opt/conda/bin/python', '-bb', 'test_mkldnn_fusion.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:53:17.878471]
2023-01-11T20:53:22.2894034Z 
2023-01-11T20:53:22.2894563Z Expand the folded group to see the log file of test_mkldnn_fusion
2023-01-11T20:53:22.2895436Z ##[group]PRINTING LOG FILE of test_mkldnn_fusion (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_fusion_c9b6chnw)
2023-01-11T20:53:22.2895874Z 
2023-01-11T20:53:22.2896009Z Running tests...
2023-01-11T20:53:22.2896456Z ----------------------------------------------------------------------
2023-01-11T20:53:22.2896935Z   test_conv_binary_fusion_ops (__main__.TestMkldnnFusion) ... Test results will be stored in test-reports/python-unittest/test_mkldnn_fusion
2023-01-11T20:53:22.2897331Z skip: MKL-DNN build is disabled (0.003s)
2023-01-11T20:53:22.2897704Z   test_conv_unary_fusion_nnc (__main__.TestMkldnnFusion) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:22.2898129Z   test_conv_unary_fusion_ops (__main__.TestMkldnnFusion) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:22.2898558Z   test_linear_binary_fusion_ops (__main__.TestMkldnnFusion) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:22.2899055Z   test_linear_unary_fusion_ops (__main__.TestMkldnnFusion) ... skip: MKL-DNN build is disabled (0.001s)
2023-01-11T20:53:22.2899457Z   test_single_conv (__main__.TestMkldnnFusion) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:22.2899871Z   test_unsupported_conv (__main__.TestMkldnnFusion) ... skip: MKL-DNN build is disabled (0.002s)
2023-01-11T20:53:22.2900060Z 
2023-01-11T20:53:22.2900257Z ----------------------------------------------------------------------
2023-01-11T20:53:22.2900793Z Ran 7 tests in 0.013s
2023-01-11T20:53:22.2900977Z 
2023-01-11T20:53:22.2901107Z OK (skipped=7)
2023-01-11T20:53:22.2901301Z 
2023-01-11T20:53:22.2901459Z Generating XML reports...
2023-01-11T20:53:22.2901986Z Generated XML report: test-reports/python-unittest/test_mkldnn_fusion/TEST-TestMkldnnFusion-20230111205321.xml
2023-01-11T20:53:22.2902220Z 
2023-01-11T20:53:22.2902447Z ##[endgroup]
2023-01-11T20:53:22.2902836Z FINISHED PRINTING LOG FILE of test_mkldnn_fusion (/var/lib/jenkins/workspace/test/test-reports/test_mkldnn_fusion_c9b6chnw)
2023-01-11T20:53:22.2903053Z 
2023-01-11T20:53:27.1964610Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:53:27.3609609Z Ignoring disabled issues:  ['91003']
2023-01-11T20:53:27.3825340Z Running test_numba_integration ... [2023-01-11 20:53:27.382134]
2023-01-11T20:53:27.3826836Z Executing ['/opt/conda/bin/python', '-bb', 'test_numba_integration.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:53:27.382446]
2023-01-11T20:53:32.1236195Z 
2023-01-11T20:53:32.1237150Z Expand the folded group to see the log file of test_numba_integration
2023-01-11T20:53:32.1238475Z ##[group]PRINTING LOG FILE of test_numba_integration (/var/lib/jenkins/workspace/test/test-reports/test_numba_integration_3pk7e6s2)
2023-01-11T20:53:32.1239344Z Test results will be stored in test-reports/python-unittest/test_numba_integration
2023-01-11T20:53:32.1239654Z 
2023-01-11T20:53:32.1239777Z Running tests...
2023-01-11T20:53:32.1240282Z ----------------------------------------------------------------------
2023-01-11T20:53:32.1240996Z   test_active_device (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1241701Z 'as_cuda_array' tensor device must match active numba context. ... skip: No cuda (0.001s)
2023-01-11T20:53:32.1242262Z   test_array_adaptor (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1242821Z Torch __cuda_array_adaptor__ exposes tensor data to numba.cuda. ... skip: No cuda (0.002s)
2023-01-11T20:53:32.1243391Z   test_conversion_errors (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1243992Z Numba properly detects array interface for tensor.Tensor variants. ... skip: No cuda (0.002s)
2023-01-11T20:53:32.1244599Z   test_cuda_array_interface (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1247589Z torch.Tensor exposes __cuda_array_interface__ for cuda tensors. ... skip: No cuda (0.002s)
2023-01-11T20:53:32.1248724Z   test_from_cuda_array_interface (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1250341Z torch.as_tensor() and torch.tensor() supports the __cuda_array_interface__ protocol. ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.003s)
2023-01-11T20:53:32.1251446Z   test_from_cuda_array_interface_active_device (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1252771Z torch.as_tensor() tensor device must match active numba context. ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.001s)
2023-01-11T20:53:32.1253666Z   test_from_cuda_array_interface_inferred_strides (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1254456Z torch.as_tensor(numba_ary) should have correct inferred (contiguous) strides ... skip: No cuda (0.001s)
2023-01-11T20:53:32.1255178Z   test_from_cuda_array_interface_lifetime (__main__.TestNumbaIntegration)
2023-01-11T20:53:32.1256133Z torch.as_tensor(obj) tensor grabs a reference to obj so that the lifetime of obj exceeds the tensor ... skip: Test is temporary disabled, see https://github.com/pytorch/pytorch/issues/54418 (0.001s)
2023-01-11T20:53:32.1256749Z 
2023-01-11T20:53:32.1257269Z ----------------------------------------------------------------------
2023-01-11T20:53:32.1257774Z Ran 8 tests in 0.013s
2023-01-11T20:53:32.1258014Z 
2023-01-11T20:53:32.1258165Z OK (skipped=8)
2023-01-11T20:53:32.1258394Z 
2023-01-11T20:53:32.1258571Z Generating XML reports...
2023-01-11T20:53:32.1259595Z Generated XML report: test-reports/python-unittest/test_numba_integration/TEST-TestNumbaIntegration-20230111205331.xml
2023-01-11T20:53:32.1260450Z 
2023-01-11T20:53:32.1261010Z ##[endgroup]
2023-01-11T20:53:32.1261924Z FINISHED PRINTING LOG FILE of test_numba_integration (/var/lib/jenkins/workspace/test/test-reports/test_numba_integration_3pk7e6s2)
2023-01-11T20:53:32.1262413Z 
2023-01-11T20:53:36.9775581Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:53:37.1387026Z Ignoring disabled issues:  ['91003']
2023-01-11T20:53:37.1600436Z Running dynamo/test_aot_cudagraphs ... [2023-01-11 20:53:37.159646]
2023-01-11T20:53:37.1601820Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_aot_cudagraphs.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:53:37.159930]
2023-01-11T20:53:41.6602561Z 
2023-01-11T20:53:41.6603126Z Expand the folded group to see the log file of dynamo/test_aot_cudagraphs
2023-01-11T20:53:41.6604394Z ##[group]PRINTING LOG FILE of dynamo/test_aot_cudagraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_cudagraphs_jezoiuh4)
2023-01-11T20:53:41.6604885Z 
2023-01-11T20:53:41.6605005Z Running tests...
2023-01-11T20:53:41.6605628Z ----------------------------------------------------------------------
2023-01-11T20:53:41.6606875Z   test_basic (__main__.TestAotCudagraphs) ... Test results will be stored in test-reports/python-unittest/dynamo.test_aot_cudagraphs
2023-01-11T20:53:41.6607459Z skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6607934Z   test_dead_fill (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6608499Z   test_dtoh (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6609081Z   test_factory (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6609581Z   test_htod (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6610119Z   test_mutate_constant (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6610708Z   test_mutate_input (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6611280Z   test_mutated_metadata (__main__.TestAotCudagraphs) ... skip: these tests require cuda (0.001s)
2023-01-11T20:53:41.6611610Z 
2023-01-11T20:53:41.6611991Z ----------------------------------------------------------------------
2023-01-11T20:53:41.6612376Z Ran 8 tests in 0.007s
2023-01-11T20:53:41.6612570Z 
2023-01-11T20:53:41.6612688Z OK (skipped=8)
2023-01-11T20:53:41.6612870Z 
2023-01-11T20:53:41.6613013Z Generating XML reports...
2023-01-11T20:53:41.6613776Z Generated XML report: test-reports/python-unittest/dynamo.test_aot_cudagraphs/TEST-TestAotCudagraphs-20230111205341.xml
2023-01-11T20:53:41.6614183Z 
2023-01-11T20:53:41.6614617Z ##[endgroup]
2023-01-11T20:53:41.6615373Z FINISHED PRINTING LOG FILE of dynamo/test_aot_cudagraphs (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_aot_cudagraphs_jezoiuh4)
2023-01-11T20:53:41.6615813Z 
2023-01-11T20:53:46.5152765Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:53:46.6813595Z Ignoring disabled issues:  ['91003']
2023-01-11T20:53:46.7032327Z Running dynamo/test_replay_record ... [2023-01-11 20:53:46.702717]
2023-01-11T20:53:46.7033081Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_replay_record.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:53:46.703028]
2023-01-11T20:53:51.1531711Z 
2023-01-11T20:53:51.1532424Z Expand the folded group to see the log file of dynamo/test_replay_record
2023-01-11T20:53:51.1533891Z ##[group]PRINTING LOG FILE of dynamo/test_replay_record (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_replay_record__y_jny86)
2023-01-11T20:53:51.1534982Z Test results will be stored in test-reports/python-unittest/dynamo.test_replay_record
2023-01-11T20:53:51.1535338Z 
2023-01-11T20:53:51.1535455Z Running tests...
2023-01-11T20:53:51.1536012Z ----------------------------------------------------------------------
2023-01-11T20:53:51.1536835Z   test_fn_call_args (__main__.ReplayRecordTests) ... skip: requires dill (0.001s)
2023-01-11T20:53:51.1537285Z   test_local_module (__main__.ReplayRecordTests) ... skip: requires dill (0.001s)
2023-01-11T20:53:51.1537789Z   test_nonlocal_fn_call (__main__.ReplayRecordTests) ... skip: requires dill (0.001s)
2023-01-11T20:53:51.1538295Z   test_nonlocal_module_class (__main__.ReplayRecordTests) ... skip: requires dill (0.001s)
2023-01-11T20:53:51.1538748Z   test_nonlocal_module_fn_call (__main__.ReplayRecordTests) ... skip: requires dill (0.001s)
2023-01-11T20:53:51.1539131Z   test_successful_inline (__main__.ReplayRecordTests) ... skip: requires dill (0.001s)
2023-01-11T20:53:51.1539460Z   test_unsuccessful_inline (__main__.ReplayRecordTests) ... skip: requires dill (0.001s)
2023-01-11T20:53:51.1539642Z 
2023-01-11T20:53:51.1539860Z ----------------------------------------------------------------------
2023-01-11T20:53:51.1540097Z Ran 7 tests in 0.005s
2023-01-11T20:53:51.1540201Z 
2023-01-11T20:53:51.1540289Z OK (skipped=7)
2023-01-11T20:53:51.1540396Z 
2023-01-11T20:53:51.1540480Z Generating XML reports...
2023-01-11T20:53:51.1541039Z Generated XML report: test-reports/python-unittest/dynamo.test_replay_record/TEST-ReplayRecordTests-20230111205350.xml
2023-01-11T20:53:51.1541285Z 
2023-01-11T20:53:51.1541548Z ##[endgroup]
2023-01-11T20:53:51.1541967Z FINISHED PRINTING LOG FILE of dynamo/test_replay_record (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_replay_record__y_jny86)
2023-01-11T20:53:51.1542206Z 
2023-01-11T20:53:55.9299857Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:53:56.0945510Z Ignoring disabled issues:  ['91003']
2023-01-11T20:53:56.1160995Z Running test_complex ... [2023-01-11 20:53:56.115795]
2023-01-11T20:53:56.1163126Z Executing ['/opt/conda/bin/python', '-bb', 'test_complex.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:53:56.116100]
2023-01-11T20:54:00.8400042Z 
2023-01-11T20:54:00.8400792Z Expand the folded group to see the log file of test_complex
2023-01-11T20:54:00.8401642Z ##[group]PRINTING LOG FILE of test_complex (/var/lib/jenkins/workspace/test/test-reports/test_complex_4xy57yw0)
2023-01-11T20:54:00.8402157Z Test results will be stored in test-reports/python-unittest/test_complex
2023-01-11T20:54:00.8402353Z 
2023-01-11T20:54:00.8402427Z Running tests...
2023-01-11T20:54:00.8402740Z ----------------------------------------------------------------------
2023-01-11T20:54:00.8403046Z   test_dtype_inference_cpu_float32 (__main__.TestComplexTensorCPU) ... ok (0.003s)
2023-01-11T20:54:00.8403377Z   test_dtype_inference_cpu_float64 (__main__.TestComplexTensorCPU) ... ok (0.001s)
2023-01-11T20:54:00.8403691Z   test_to_list_cpu_complex128 (__main__.TestComplexTensorCPU) ... ok (0.001s)
2023-01-11T20:54:00.8403996Z   test_to_list_cpu_complex64 (__main__.TestComplexTensorCPU) ... ok (0.001s)
2023-01-11T20:54:00.8404164Z 
2023-01-11T20:54:00.8404348Z ----------------------------------------------------------------------
2023-01-11T20:54:00.8404587Z Ran 4 tests in 0.007s
2023-01-11T20:54:00.8404700Z 
2023-01-11T20:54:00.8404758Z OK
2023-01-11T20:54:00.8404846Z 
2023-01-11T20:54:00.8404928Z Generating XML reports...
2023-01-11T20:54:00.8405348Z Generated XML report: test-reports/python-unittest/test_complex/TEST-TestComplexTensorCPU-20230111205400.xml
2023-01-11T20:54:00.8405591Z 
2023-01-11T20:54:00.8405804Z ##[endgroup]
2023-01-11T20:54:00.8406163Z FINISHED PRINTING LOG FILE of test_complex (/var/lib/jenkins/workspace/test/test-reports/test_complex_4xy57yw0)
2023-01-11T20:54:00.8406369Z 
2023-01-11T20:54:05.6647689Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:54:05.8853733Z Ignoring disabled issues:  ['91003']
2023-01-11T20:54:05.9070017Z Running test_nvfuser_dynamo ... [2023-01-11 20:54:05.906728]
2023-01-11T20:54:05.9072385Z Executing ['/opt/conda/bin/python', '-bb', 'test_nvfuser_dynamo.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:54:05.907013]
2023-01-11T20:54:10.4962348Z 
2023-01-11T20:54:10.4962883Z Expand the folded group to see the log file of test_nvfuser_dynamo
2023-01-11T20:54:10.4964009Z ##[group]PRINTING LOG FILE of test_nvfuser_dynamo (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_dynamo_fzg50xqh)
2023-01-11T20:54:10.4964457Z 
2023-01-11T20:54:10.4964589Z Running tests...
2023-01-11T20:54:10.4965258Z ----------------------------------------------------------------------
2023-01-11T20:54:10.4966114Z   test_basic (__main__.TestNvFuserDynamo) ... Test results will be stored in test-reports/python-unittest/test_nvfuser_dynamo
2023-01-11T20:54:10.4966668Z skip: requires CUDA (0.001s)
2023-01-11T20:54:10.4967189Z   test_batch_norm_implicit_dtype_promotion (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.001s)
2023-01-11T20:54:10.4967633Z   test_dtype_correctness (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.001s)
2023-01-11T20:54:10.4967949Z   test_min_cut (__main__.TestNvFuserDynamo) ... skip: requires CUDA (0.002s)
2023-01-11T20:54:10.4968126Z 
2023-01-11T20:54:10.4968324Z ----------------------------------------------------------------------
2023-01-11T20:54:10.4968549Z Ran 4 tests in 0.005s
2023-01-11T20:54:10.4968660Z 
2023-01-11T20:54:10.4968920Z OK (skipped=4)
2023-01-11T20:54:10.4969036Z 
2023-01-11T20:54:10.4969121Z Generating XML reports...
2023-01-11T20:54:10.4969557Z Generated XML report: test-reports/python-unittest/test_nvfuser_dynamo/TEST-TestNvFuserDynamo-20230111205409.xml
2023-01-11T20:54:10.4969784Z 
2023-01-11T20:54:10.4970019Z ##[endgroup]
2023-01-11T20:54:10.4970411Z FINISHED PRINTING LOG FILE of test_nvfuser_dynamo (/var/lib/jenkins/workspace/test/test-reports/test_nvfuser_dynamo_fzg50xqh)
2023-01-11T20:54:10.4970634Z 
2023-01-11T20:54:15.3279886Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:54:15.4895812Z Ignoring disabled issues:  ['91003']
2023-01-11T20:54:15.5109034Z Running dynamo/test_model_output ... [2023-01-11 20:54:15.510511]
2023-01-11T20:54:15.5110185Z Executing ['/opt/conda/bin/python', '-bb', 'dynamo/test_model_output.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:54:15.510771]
2023-01-11T20:54:19.9178351Z 
2023-01-11T20:54:19.9178931Z Expand the folded group to see the log file of dynamo/test_model_output
2023-01-11T20:54:19.9180148Z ##[group]PRINTING LOG FILE of dynamo/test_model_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_model_output_5xd6_tpm)
2023-01-11T20:54:19.9181131Z Test results will be stored in test-reports/python-unittest/dynamo.test_model_output
2023-01-11T20:54:19.9181456Z 
2023-01-11T20:54:19.9181579Z Running tests...
2023-01-11T20:54:19.9182069Z ----------------------------------------------------------------------
2023-01-11T20:54:19.9182611Z   test_pretrained (__main__.TestHFPretrained) ... skip: requires HuggingFace (0.001s)
2023-01-11T20:54:19.9183167Z   test_mo_assign (__main__.TestModelOutput) ... skip: requires HuggingFace (0.001s)
2023-01-11T20:54:19.9183703Z   test_mo_create (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T20:54:19.9184239Z   test_mo_getattr (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T20:54:19.9184794Z   test_mo_getitem (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T20:54:19.9185333Z   test_mo_index (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T20:54:19.9185856Z   test_mo_init (__main__.TestModelOutput) ... skip: requires HuggingFace (0.002s)
2023-01-11T20:54:19.9186392Z   test_mo_tuple (__main__.TestModelOutput) ... skip: requires HuggingFace (0.000s)
2023-01-11T20:54:19.9186686Z 
2023-01-11T20:54:19.9187022Z ----------------------------------------------------------------------
2023-01-11T20:54:19.9187414Z Ran 8 tests in 0.006s
2023-01-11T20:54:19.9187601Z 
2023-01-11T20:54:19.9187721Z OK (skipped=8)
2023-01-11T20:54:19.9187901Z 
2023-01-11T20:54:19.9188040Z Generating XML reports...
2023-01-11T20:54:19.9188792Z Generated XML report: test-reports/python-unittest/dynamo.test_model_output/TEST-TestHFPretrained-20230111205419.xml
2023-01-11T20:54:19.9189982Z Generated XML report: test-reports/python-unittest/dynamo.test_model_output/TEST-TestModelOutput-20230111205419.xml
2023-01-11T20:54:19.9190403Z 
2023-01-11T20:54:19.9190794Z ##[endgroup]
2023-01-11T20:54:19.9191501Z FINISHED PRINTING LOG FILE of dynamo/test_model_output (/var/lib/jenkins/workspace/test/test-reports/dynamo-test_model_output_5xd6_tpm)
2023-01-11T20:54:19.9191890Z 
2023-01-11T20:54:24.7689040Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:54:24.9391289Z Ignoring disabled issues:  ['91003']
2023-01-11T20:54:24.9613534Z Running test_vulkan ... [2023-01-11 20:54:24.961003]
2023-01-11T20:54:24.9615219Z Executing ['/opt/conda/bin/python', '-bb', 'test_vulkan.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:54:24.961306]
2023-01-11T20:54:29.2418443Z 
2023-01-11T20:54:29.2420628Z Expand the folded group to see the log file of test_vulkan
2023-01-11T20:54:29.2421753Z ##[group]PRINTING LOG FILE of test_vulkan (/var/lib/jenkins/workspace/test/test-reports/test_vulkan_k1h27kn7)
2023-01-11T20:54:29.2422065Z 
2023-01-11T20:54:29.2422187Z Running tests...
2023-01-11T20:54:29.2423101Z ----------------------------------------------------------------------
2023-01-11T20:54:29.2423958Z   test_conv (__main__.TestVulkanRewritePass) ... Test results will be stored in test-reports/python-unittest/test_vulkan
2023-01-11T20:54:29.2424619Z skip: Vulkan backend must be available for these tests. (0.004s)
2023-01-11T20:54:29.2424923Z 
2023-01-11T20:54:29.2425293Z ----------------------------------------------------------------------
2023-01-11T20:54:29.2425752Z Ran 1 test in 0.004s
2023-01-11T20:54:29.2425950Z 
2023-01-11T20:54:29.2426086Z OK (skipped=1)
2023-01-11T20:54:29.2426293Z 
2023-01-11T20:54:29.2426453Z Generating XML reports...
2023-01-11T20:54:29.2427275Z Generated XML report: test-reports/python-unittest/test_vulkan/TEST-TestVulkanRewritePass-20230111205428.xml
2023-01-11T20:54:29.2427741Z 
2023-01-11T20:54:29.2428194Z ##[endgroup]
2023-01-11T20:54:29.2428897Z FINISHED PRINTING LOG FILE of test_vulkan (/var/lib/jenkins/workspace/test/test-reports/test_vulkan_k1h27kn7)
2023-01-11T20:54:29.2429284Z 
2023-01-11T20:54:33.9883503Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:54:34.1581596Z Ignoring disabled issues:  ['91003']
2023-01-11T20:54:34.1804989Z Running test_type_hints ... [2023-01-11 20:54:34.180136]
2023-01-11T20:54:34.1807098Z Executing ['/opt/conda/bin/python', '-bb', 'test_type_hints.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:54:34.180452]
2023-01-11T20:54:38.6338587Z 
2023-01-11T20:54:38.6339241Z Expand the folded group to see the log file of test_type_hints
2023-01-11T20:54:38.6340280Z ##[group]PRINTING LOG FILE of test_type_hints (/var/lib/jenkins/workspace/test/test-reports/test_type_hints_j_elmfq4)
2023-01-11T20:54:38.6341237Z Test results will be stored in test-reports/python-unittest/test_type_hints
2023-01-11T20:54:38.6341605Z 
2023-01-11T20:54:38.6341727Z Running tests...
2023-01-11T20:54:38.6342037Z ----------------------------------------------------------------------
2023-01-11T20:54:38.6342315Z   test_doc_examples (__main__.TestTypeHints)
2023-01-11T20:54:38.6342646Z Run documentation examples through mypy. ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T20:54:38.6342858Z 
2023-01-11T20:54:38.6343052Z ----------------------------------------------------------------------
2023-01-11T20:54:38.6343275Z Ran 1 test in 0.002s
2023-01-11T20:54:38.6343384Z 
2023-01-11T20:54:38.6343454Z OK (skipped=1)
2023-01-11T20:54:38.6343559Z 
2023-01-11T20:54:38.6343641Z Generating XML reports...
2023-01-11T20:54:38.6344044Z Generated XML report: test-reports/python-unittest/test_type_hints/TEST-TestTypeHints-20230111205438.xml
2023-01-11T20:54:38.6344266Z 
2023-01-11T20:54:38.6344485Z ##[endgroup]
2023-01-11T20:54:38.6345082Z FINISHED PRINTING LOG FILE of test_type_hints (/var/lib/jenkins/workspace/test/test-reports/test_type_hints_j_elmfq4)
2023-01-11T20:54:38.6345294Z 
2023-01-11T20:54:43.5796856Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:54:43.7657124Z Ignoring disabled issues:  ['91003']
2023-01-11T20:54:43.7962894Z Running test_openmp ... [2023-01-11 20:54:43.795791]
2023-01-11T20:54:43.7964800Z Executing ['/opt/conda/bin/python', '-bb', 'test_openmp.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 20:54:43.796109]
2023-01-11T20:54:48.0891559Z 
2023-01-11T20:54:48.0892129Z Expand the folded group to see the log file of test_openmp
2023-01-11T20:54:48.0893111Z ##[group]PRINTING LOG FILE of test_openmp (/var/lib/jenkins/workspace/test/test-reports/test_openmp_w_gnyf0k)
2023-01-11T20:54:48.0893463Z 
2023-01-11T20:54:48.0893570Z Running tests...
2023-01-11T20:54:48.0894179Z ----------------------------------------------------------------------
2023-01-11T20:54:48.0894641Z   test_n_threads (__main__.TestOpenMP_ParallelFor)
2023-01-11T20:54:48.0895154Z Make sure there is no memory leak with many threads ... Test results will be stored in test-reports/python-unittest/test_openmp
2023-01-11T20:54:48.0895660Z skip: Cannot test with ASAN (0.001s)
2023-01-11T20:54:48.0895901Z   test_one_thread (__main__.TestOpenMP_ParallelFor)
2023-01-11T20:54:48.0896316Z Make sure there is no memory leak with one thread: issue gh-32284 ... skip: Cannot test with ASAN (0.000s)
2023-01-11T20:54:48.0896514Z 
2023-01-11T20:54:48.0896712Z ----------------------------------------------------------------------
2023-01-11T20:54:48.0896937Z Ran 2 tests in 0.001s
2023-01-11T20:54:48.0897050Z 
2023-01-11T20:54:48.0897123Z OK (skipped=2)
2023-01-11T20:54:48.0897228Z 
2023-01-11T20:54:48.0897310Z Generating XML reports...
2023-01-11T20:54:48.0897700Z Generated XML report: test-reports/python-unittest/test_openmp/TEST-TestOpenMP_ParallelFor-20230111205447.xml
2023-01-11T20:54:48.0897926Z 
2023-01-11T20:54:48.0898180Z ##[endgroup]
2023-01-11T20:54:48.0898542Z FINISHED PRINTING LOG FILE of test_openmp (/var/lib/jenkins/workspace/test/test-reports/test_openmp_w_gnyf0k)
2023-01-11T20:54:48.0898749Z 
2023-01-11T20:54:52.8544029Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T20:54:53.0211130Z Ignoring disabled issues:  ['91003']
2023-01-11T21:05:10.2331154Z 
2023-01-11T21:05:10.2331623Z Expand the folded group to see the log file of inductor/test_torchinductor
2023-01-11T21:05:10.2332575Z ##[group]PRINTING LOG FILE of inductor/test_torchinductor (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_1hiiowhq)
2023-01-11T21:05:10.2382535Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:05:10.2383954Z Test results will be stored in test-reports/python-unittest/inductor.test_torchinductor
2023-01-11T21:05:10.2384413Z 
2023-01-11T21:05:10.2384497Z Running tests...
2023-01-11T21:05:10.2384972Z ----------------------------------------------------------------------
2023-01-11T21:05:10.2385521Z   test_auto_simd (__main__.CPUReproTests) ... ok (0.386s)
2023-01-11T21:05:10.2385970Z   test_complex_memory_overlap (__main__.CPUReproTests) ... ok (0.013s)
2023-01-11T21:05:10.2386888Z   test_conv_stride_constraints (__main__.CPUReproTests) ... [2023-01-11 20:43:34,314] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2387703Z [2023-01-11 20:43:37,052] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2388412Z [2023-01-11 20:43:37,104] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2389123Z [2023-01-11 20:43:37,145] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2389417Z 
2023-01-11T21:05:10.2389563Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2389866Z import torch
2023-01-11T21:05:10.2390123Z import random
2023-01-11T21:05:10.2390729Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2391141Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2391384Z 
2023-01-11T21:05:10.2391508Z aten = torch.ops.aten
2023-01-11T21:05:10.2391865Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2392253Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2392435Z 
2023-01-11T21:05:10.2392441Z 
2023-01-11T21:05:10.2392638Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2393157Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2393701Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2394414Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2394725Z {
2023-01-11T21:05:10.2395037Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2395537Z     {
2023-01-11T21:05:10.2395858Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.2396190Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.2396476Z         {
2023-01-11T21:05:10.2396771Z             for(long i1=0; i1<5; i1+=1)
2023-01-11T21:05:10.2397042Z             {
2023-01-11T21:05:10.2397525Z                 #pragma GCC ivdep
2023-01-11T21:05:10.2397885Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:05:10.2398164Z                 {
2023-01-11T21:05:10.2398422Z                     {
2023-01-11T21:05:10.2398708Z                         {
2023-01-11T21:05:10.2399039Z                             auto tmp0 = in_ptr0[i2 + (256*i1) + (1280*i0)];
2023-01-11T21:05:10.2399439Z                             out_ptr0[i1 + (5*i2) + (1280*i0)] = tmp0;
2023-01-11T21:05:10.2399769Z                         }
2023-01-11T21:05:10.2400059Z                     }
2023-01-11T21:05:10.2400314Z                 }
2023-01-11T21:05:10.2400751Z             }
2023-01-11T21:05:10.2401026Z         }
2023-01-11T21:05:10.2401262Z     }
2023-01-11T21:05:10.2401523Z }
2023-01-11T21:05:10.2401848Z ''')
2023-01-11T21:05:10.2401995Z 
2023-01-11T21:05:10.2402002Z 
2023-01-11T21:05:10.2402162Z async_compile.wait(globals())
2023-01-11T21:05:10.2402465Z del async_compile
2023-01-11T21:05:10.2402654Z 
2023-01-11T21:05:10.2402782Z def call(args):
2023-01-11T21:05:10.2403055Z     inp_1, weight_1 = args
2023-01-11T21:05:10.2403357Z     args.clear()
2023-01-11T21:05:10.2403907Z     buf0 = empty_strided((2, 5, 16, 16), (1280, 1, 80, 5), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2404397Z     kernel_cpp_0(c_void_p(inp_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2404771Z     del inp_1
2023-01-11T21:05:10.2405151Z     buf1 = aten.convolution(buf0, weight_1, None, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.2405570Z     assert_size_stride(buf1, (2, 6, 14, 14), (1176, 1, 84, 6))
2023-01-11T21:05:10.2405845Z     del buf0
2023-01-11T21:05:10.2406110Z     del weight_1
2023-01-11T21:05:10.2406386Z     return (buf1, )
2023-01-11T21:05:10.2406550Z 
2023-01-11T21:05:10.2406565Z 
2023-01-11T21:05:10.2406691Z if __name__ == "__main__":
2023-01-11T21:05:10.2407053Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2407507Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2408159Z     inp_1 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2408817Z     weight_1 = rand_strided((6, 5, 3, 3), (45, 1, 15, 5), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2409270Z     print_performance(lambda: call([inp_1, weight_1]))
2023-01-11T21:05:10.2409513Z 
2023-01-11T21:05:10.2409520Z 
2023-01-11T21:05:10.2409675Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2409976Z import torch
2023-01-11T21:05:10.2410251Z import random
2023-01-11T21:05:10.2410608Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2411031Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2411285Z 
2023-01-11T21:05:10.2411413Z aten = torch.ops.aten
2023-01-11T21:05:10.2411958Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2412337Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2412538Z 
2023-01-11T21:05:10.2412545Z 
2023-01-11T21:05:10.2412700Z async_compile.wait(globals())
2023-01-11T21:05:10.2413029Z del async_compile
2023-01-11T21:05:10.2413215Z 
2023-01-11T21:05:10.2413327Z def call(args):
2023-01-11T21:05:10.2413611Z     inp_1, weight_1 = args
2023-01-11T21:05:10.2413898Z     args.clear()
2023-01-11T21:05:10.2414273Z     buf0 = aten.convolution(inp_1, weight_1, None, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.2414694Z     assert_size_stride(buf0, (2, 6, 14, 14), (1176, 196, 14, 1))
2023-01-11T21:05:10.2415055Z     del inp_1
2023-01-11T21:05:10.2415354Z     del weight_1
2023-01-11T21:05:10.2415585Z     return (buf0, )
2023-01-11T21:05:10.2415748Z 
2023-01-11T21:05:10.2415754Z 
2023-01-11T21:05:10.2415862Z if __name__ == "__main__":
2023-01-11T21:05:10.2416178Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2416550Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2417155Z     inp_1 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2417872Z     weight_1 = rand_strided((6, 5, 3, 3), (45, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2418275Z     print_performance(lambda: call([inp_1, weight_1]))
2023-01-11T21:05:10.2418575Z 
2023-01-11T21:05:10.2418668Z ok (2.958s)
2023-01-11T21:05:10.2419377Z   test_cpp_kernel_profile (__main__.CPUReproTests) ... STAGE:2023-01-11 20:43:37 744:744 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:05:10.2420259Z [2023-01-11 20:43:37,193] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 0
2023-01-11T21:05:10.2420934Z [2023-01-11 20:43:43,248] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 0
2023-01-11T21:05:10.2421725Z STAGE:2023-01-11 20:43:43 744:744 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:05:10.2422381Z STAGE:2023-01-11 20:43:43 744:744 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:05:10.2422709Z 
2023-01-11T21:05:10.2422875Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2423220Z import torch
2023-01-11T21:05:10.2423473Z import random
2023-01-11T21:05:10.2423815Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2424207Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2424441Z 
2023-01-11T21:05:10.2424559Z aten = torch.ops.aten
2023-01-11T21:05:10.2424927Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2425309Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2425531Z 
2023-01-11T21:05:10.2425538Z 
2023-01-11T21:05:10.2425788Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2426166Z #include <ATen/record_function.h>
2023-01-11T21:05:10.2426775Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2427369Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2427757Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2428132Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2428427Z {
2023-01-11T21:05:10.2428797Z     RECORD_FUNCTION("graph_0_kernel_cpp_0", c10::ArrayRef<c10::IValue>({}));
2023-01-11T21:05:10.2429233Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2429536Z     {
2023-01-11T21:05:10.2429789Z         #pragma omp for 
2023-01-11T21:05:10.2430090Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.2430365Z         {
2023-01-11T21:05:10.2430710Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2431164Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.2431576Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2431944Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2432357Z         }
2023-01-11T21:05:10.2432677Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2433039Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.2433313Z         {
2023-01-11T21:05:10.2433598Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2433960Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.2434277Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2434588Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.2434891Z         }
2023-01-11T21:05:10.2435137Z     }
2023-01-11T21:05:10.2435387Z }
2023-01-11T21:05:10.2435662Z ''')
2023-01-11T21:05:10.2435815Z 
2023-01-11T21:05:10.2435822Z 
2023-01-11T21:05:10.2435947Z async_compile.wait(globals())
2023-01-11T21:05:10.2436440Z del async_compile
2023-01-11T21:05:10.2436624Z 
2023-01-11T21:05:10.2436739Z def call(args):
2023-01-11T21:05:10.2437010Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.2437296Z     args.clear()
2023-01-11T21:05:10.2437786Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2438302Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2438807Z     del arg0_1
2023-01-11T21:05:10.2439110Z     del arg1_1
2023-01-11T21:05:10.2439410Z     return (buf0, )
2023-01-11T21:05:10.2443120Z 
2023-01-11T21:05:10.2443130Z 
2023-01-11T21:05:10.2443306Z if __name__ == "__main__":
2023-01-11T21:05:10.2443666Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2444094Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2444694Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2445318Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2445802Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.2446067Z 
2023-01-11T21:05:10.2446187Z ok (6.132s)
2023-01-11T21:05:10.2446560Z   test_cpu_vec_cosim (__main__.CPUReproTests) ... ok (0.002s)
2023-01-11T21:05:10.2447473Z   test_inplace_add_alpha (__main__.CPUReproTests) ... [2023-01-11 20:43:43,329] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2448104Z [2023-01-11 20:43:46,062] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2448302Z 
2023-01-11T21:05:10.2448395Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2448575Z import torch
2023-01-11T21:05:10.2448743Z import random
2023-01-11T21:05:10.2448958Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2449204Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2449355Z 
2023-01-11T21:05:10.2449432Z aten = torch.ops.aten
2023-01-11T21:05:10.2449676Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2449969Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2450131Z 
2023-01-11T21:05:10.2450140Z 
2023-01-11T21:05:10.2450346Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2450799Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2451280Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2451612Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2451917Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2452176Z {
2023-01-11T21:05:10.2452433Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2452712Z     {
2023-01-11T21:05:10.2452939Z         #pragma omp for 
2023-01-11T21:05:10.2453190Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.2453376Z         {
2023-01-11T21:05:10.2453622Z             {
2023-01-11T21:05:10.2453842Z                 {
2023-01-11T21:05:10.2454125Z                     auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.2454434Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.2454753Z                     auto tmp2 = static_cast<float>(0.55);
2023-01-11T21:05:10.2455222Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.2455558Z                     auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.2455877Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.2456182Z                 }
2023-01-11T21:05:10.2456461Z             }
2023-01-11T21:05:10.2456694Z         }
2023-01-11T21:05:10.2456952Z     }
2023-01-11T21:05:10.2457182Z }
2023-01-11T21:05:10.2457457Z ''')
2023-01-11T21:05:10.2457608Z 
2023-01-11T21:05:10.2457615Z 
2023-01-11T21:05:10.2457770Z async_compile.wait(globals())
2023-01-11T21:05:10.2458101Z del async_compile
2023-01-11T21:05:10.2458279Z 
2023-01-11T21:05:10.2458399Z def call(args):
2023-01-11T21:05:10.2458725Z     x_1, y_1 = args
2023-01-11T21:05:10.2459007Z     args.clear()
2023-01-11T21:05:10.2459431Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(y_1.data_ptr()), c_void_p(x_1.data_ptr()))
2023-01-11T21:05:10.2459778Z     del y_1
2023-01-11T21:05:10.2460051Z     return (x_1, )
2023-01-11T21:05:10.2460220Z 
2023-01-11T21:05:10.2460227Z 
2023-01-11T21:05:10.2460357Z if __name__ == "__main__":
2023-01-11T21:05:10.2460714Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2461260Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2461817Z     x_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2462391Z     y_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2462763Z     print_performance(lambda: call([x_1, y_1]))
2023-01-11T21:05:10.2462957Z 
2023-01-11T21:05:10.2463052Z ok (2.783s)
2023-01-11T21:05:10.2463748Z   test_inplace_squeeze_needed (__main__.CPUReproTests) ... [2023-01-11 20:43:46,738] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 1
2023-01-11T21:05:10.2464596Z [2023-01-11 20:43:49,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 1
2023-01-11T21:05:10.2464920Z 
2023-01-11T21:05:10.2465067Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2465378Z import torch
2023-01-11T21:05:10.2465694Z import random
2023-01-11T21:05:10.2466008Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2466437Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2466690Z 
2023-01-11T21:05:10.2466818Z aten = torch.ops.aten
2023-01-11T21:05:10.2467246Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2467650Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2467833Z 
2023-01-11T21:05:10.2467839Z 
2023-01-11T21:05:10.2468026Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2468492Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2468969Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.2469323Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.2469656Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2469991Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.2470327Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:05:10.2470658Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.2470984Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.2471348Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.2471714Z                        bool* __restrict__ out_ptr4)
2023-01-11T21:05:10.2471993Z {
2023-01-11T21:05:10.2472292Z     auto in_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.2472619Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:05:10.2472921Z     {
2023-01-11T21:05:10.2473171Z         {
2023-01-11T21:05:10.2473645Z             float tmp3 = 0;
2023-01-11T21:05:10.2474023Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2474340Z             {
2023-01-11T21:05:10.2474668Z                 #pragma omp for reduction(+:tmp3) 
2023-01-11T21:05:10.2475138Z                 for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.2475428Z                 {
2023-01-11T21:05:10.2475693Z                     {
2023-01-11T21:05:10.2476003Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2476332Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.2476666Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2476988Z                         tmp3 += tmp2;
2023-01-11T21:05:10.2477244Z                     }
2023-01-11T21:05:10.2477507Z                 }
2023-01-11T21:05:10.2477769Z             }
2023-01-11T21:05:10.2478041Z             out_ptr0[0] = tmp3;
2023-01-11T21:05:10.2478331Z         }
2023-01-11T21:05:10.2478575Z     }
2023-01-11T21:05:10.2478788Z     {
2023-01-11T21:05:10.2479038Z         {
2023-01-11T21:05:10.2479337Z             float tmp8 = 0;
2023-01-11T21:05:10.2479651Z             float tmp9 = 0;
2023-01-11T21:05:10.2480016Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2480357Z             {
2023-01-11T21:05:10.2480805Z                 #pragma omp for reduction(+:tmp8) reduction(+:tmp9) 
2023-01-11T21:05:10.2481232Z                 for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.2481667Z                 {
2023-01-11T21:05:10.2481939Z                     {
2023-01-11T21:05:10.2482288Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2482614Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.2482945Z                         auto tmp3 = out_ptr0[0];
2023-01-11T21:05:10.2483266Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2483621Z                         auto tmp4 = static_cast<float>(10);
2023-01-11T21:05:10.2483973Z                         auto tmp5 = tmp3 / tmp4;
2023-01-11T21:05:10.2484402Z                         auto tmp6 = tmp2 - tmp5;
2023-01-11T21:05:10.2484735Z                         auto tmp7 = tmp6 * tmp6;
2023-01-11T21:05:10.2485069Z                         tmp8 += tmp7;
2023-01-11T21:05:10.2485365Z                         tmp9 += tmp2;
2023-01-11T21:05:10.2485632Z                     }
2023-01-11T21:05:10.2485883Z                 }
2023-01-11T21:05:10.2486113Z             }
2023-01-11T21:05:10.2486405Z             out_ptr1[0] = tmp8;
2023-01-11T21:05:10.2486710Z             out_ptr2[0] = tmp9;
2023-01-11T21:05:10.2486971Z         }
2023-01-11T21:05:10.2487219Z     }
2023-01-11T21:05:10.2487537Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2487851Z     {
2023-01-11T21:05:10.2488120Z         #pragma omp for 
2023-01-11T21:05:10.2488449Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.2488729Z         {
2023-01-11T21:05:10.2488974Z             {
2023-01-11T21:05:10.2489235Z                 {
2023-01-11T21:05:10.2489540Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.2489903Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.2490278Z                     auto tmp3 = out_ptr2[0];
2023-01-11T21:05:10.2490638Z                     auto tmp7 = out_ptr1[0];
2023-01-11T21:05:10.2490982Z                     auto tmp13 = in_ptr2[i0];
2023-01-11T21:05:10.2491348Z                     auto tmp15 = in_ptr3[i0];
2023-01-11T21:05:10.2491710Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2492088Z                     auto tmp4 = static_cast<float>(10);
2023-01-11T21:05:10.2492470Z                     auto tmp5 = tmp3 / tmp4;
2023-01-11T21:05:10.2492928Z                     auto tmp6 = tmp2 - tmp5;
2023-01-11T21:05:10.2493282Z                     auto tmp8 = tmp7 / tmp4;
2023-01-11T21:05:10.2493780Z                     auto tmp9 = static_cast<float>(1e-05);
2023-01-11T21:05:10.2494173Z                     auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.2494695Z                     auto tmp11 = 1 / std::sqrt(tmp10);
2023-01-11T21:05:10.2495073Z                     auto tmp12 = tmp6 * tmp11;
2023-01-11T21:05:10.2495448Z                     auto tmp14 = tmp12 * tmp13;
2023-01-11T21:05:10.2495802Z                     auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.2496277Z                     auto tmp17 = tmp16 * (tmp16>0);
2023-01-11T21:05:10.2496646Z                     auto tmp18 = static_cast<float>(0);
2023-01-11T21:05:10.2497008Z                     auto tmp19 = tmp17 <= tmp18;
2023-01-11T21:05:10.2497339Z                     in_out_ptr0[i0] = tmp12;
2023-01-11T21:05:10.2497667Z                     out_ptr3[i0] = tmp17;
2023-01-11T21:05:10.2497976Z                     out_ptr4[i0] = tmp19;
2023-01-11T21:05:10.2498237Z                 }
2023-01-11T21:05:10.2498555Z             }
2023-01-11T21:05:10.2498818Z         }
2023-01-11T21:05:10.2499087Z         #pragma omp single
2023-01-11T21:05:10.2499365Z         {
2023-01-11T21:05:10.2499613Z             {
2023-01-11T21:05:10.2499849Z                 {
2023-01-11T21:05:10.2500145Z                     auto tmp0 = out_ptr1[0];
2023-01-11T21:05:10.2500511Z                     auto tmp1 = static_cast<float>(10);
2023-01-11T21:05:10.2500873Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.2501391Z                     auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:05:10.2501779Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.2502135Z                     auto tmp5 = 1 / std::sqrt(tmp4);
2023-01-11T21:05:10.2502566Z                     auto tmp6 = tmp5 / tmp1;
2023-01-11T21:05:10.2502945Z                     in_out_ptr1[0] = tmp6;
2023-01-11T21:05:10.2503260Z                 }
2023-01-11T21:05:10.2503520Z             }
2023-01-11T21:05:10.2503786Z         }
2023-01-11T21:05:10.2504034Z     }
2023-01-11T21:05:10.2504282Z }
2023-01-11T21:05:10.2504564Z ''')
2023-01-11T21:05:10.2504721Z 
2023-01-11T21:05:10.2504730Z 
2023-01-11T21:05:10.2504882Z async_compile.wait(globals())
2023-01-11T21:05:10.2505183Z del async_compile
2023-01-11T21:05:10.2505363Z 
2023-01-11T21:05:10.2505467Z def call(args):
2023-01-11T21:05:10.2505844Z     primals_1, primals_2, primals_3, primals_4, primals_5 = args
2023-01-11T21:05:10.2506186Z     args.clear()
2023-01-11T21:05:10.2506682Z     buf0 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2507187Z     aten.mm.out(as_strided(primals_5, (1, 10), (10, 1)), as_strided(primals_1, (10, 10), (1, 10)), out=buf0)
2023-01-11T21:05:10.2507578Z     del primals_1
2023-01-11T21:05:10.2508103Z     buf1 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2508691Z     buf2 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2509301Z     buf3 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2509697Z     buf4 = as_strided(buf0, (10, ), (1, )); del buf0  # reuse
2023-01-11T21:05:10.2510251Z     buf5 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2510799Z     buf6 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.2511188Z     buf7 = buf2; del buf2  # reuse
2023-01-11T21:05:10.2511891Z     kernel_cpp_0(c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:05:10.2512559Z     del buf1
2023-01-11T21:05:10.2512860Z     del buf3
2023-01-11T21:05:10.2513102Z     del primals_2
2023-01-11T21:05:10.2513382Z     del primals_4
2023-01-11T21:05:10.2513761Z     return (buf5, primals_3, as_strided(primals_5, (1, 10), (10, 1)), buf4, buf6, buf7, )
2023-01-11T21:05:10.2514044Z 
2023-01-11T21:05:10.2514050Z 
2023-01-11T21:05:10.2514164Z if __name__ == "__main__":
2023-01-11T21:05:10.2514529Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2514956Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2515577Z     primals_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2516159Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2516829Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2517436Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2518016Z     primals_5 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2518530Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]))
2023-01-11T21:05:10.2518833Z 
2023-01-11T21:05:10.2518954Z ok (3.578s)
2023-01-11T21:05:10.2519716Z   test_load_same_bool_tensor_twice (__main__.CPUReproTests) ... [2023-01-11 20:43:49,753] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 2
2023-01-11T21:05:10.2520516Z [2023-01-11 20:43:52,564] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 2
2023-01-11T21:05:10.2520937Z 
2023-01-11T21:05:10.2521107Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2521426Z import torch
2023-01-11T21:05:10.2521700Z import random
2023-01-11T21:05:10.2522077Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2522513Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2522773Z 
2023-01-11T21:05:10.2522903Z aten = torch.ops.aten
2023-01-11T21:05:10.2523427Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2523855Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2524055Z 
2023-01-11T21:05:10.2524062Z 
2023-01-11T21:05:10.2524306Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2524827Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2525379Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.2525783Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2526155Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.2526505Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.2526801Z {
2023-01-11T21:05:10.2527109Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2527400Z     {
2023-01-11T21:05:10.2527667Z         #pragma omp for 
2023-01-11T21:05:10.2527963Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.2528235Z         {
2023-01-11T21:05:10.2528550Z             float g_tmp_buffer_in_ptr0[16] = {0};
2023-01-11T21:05:10.2528953Z             flag_to_float(in_ptr0 + 16*i0, g_tmp_buffer_in_ptr0, 16);
2023-01-11T21:05:10.2529470Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:05:10.2529942Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.2530386Z             flag_to_float(in_ptr0 + 16*i0, g_tmp_buffer_in_ptr0, 16);
2023-01-11T21:05:10.2530980Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:05:10.2531424Z             auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:05:10.2531824Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2532176Z             tmp3.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.2532447Z         }
2023-01-11T21:05:10.2532742Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2533084Z         for(long i0=32; i0<34; i0+=1)
2023-01-11T21:05:10.2533356Z         {
2023-01-11T21:05:10.2533638Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2533963Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.2534385Z             auto tmp1 = static_cast<float>(-33.0);
2023-01-11T21:05:10.2534758Z             auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:05:10.2535088Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.2535403Z             out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.2535675Z         }
2023-01-11T21:05:10.2535918Z     }
2023-01-11T21:05:10.2536166Z }
2023-01-11T21:05:10.2536414Z ''')
2023-01-11T21:05:10.2536559Z 
2023-01-11T21:05:10.2536566Z 
2023-01-11T21:05:10.2536719Z async_compile.wait(globals())
2023-01-11T21:05:10.2537027Z del async_compile
2023-01-11T21:05:10.2537208Z 
2023-01-11T21:05:10.2537415Z def call(args):
2023-01-11T21:05:10.2537709Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.2537992Z     args.clear()
2023-01-11T21:05:10.2538538Z     buf0 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2539130Z     buf1 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2539675Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.2540115Z     del arg0_1
2023-01-11T21:05:10.2540373Z     del arg1_1
2023-01-11T21:05:10.2540661Z     return (buf0, buf1, )
2023-01-11T21:05:10.2540847Z 
2023-01-11T21:05:10.2540854Z 
2023-01-11T21:05:10.2540980Z if __name__ == "__main__":
2023-01-11T21:05:10.2541305Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2541711Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2542267Z     arg0_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2542809Z     arg1_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.2543221Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.2543456Z 
2023-01-11T21:05:10.2543653Z ok (2.924s)
2023-01-11T21:05:10.2544413Z   test_masked_fill_softmax (__main__.CPUReproTests) ... [2023-01-11 20:43:52,656] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 3
2023-01-11T21:05:10.2545209Z [2023-01-11 20:43:55,567] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 3
2023-01-11T21:05:10.2545513Z 
2023-01-11T21:05:10.2545665Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2545956Z import torch
2023-01-11T21:05:10.2546200Z import random
2023-01-11T21:05:10.2546508Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2546937Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2547177Z 
2023-01-11T21:05:10.2547294Z aten = torch.ops.aten
2023-01-11T21:05:10.2547678Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2548084Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2548280Z 
2023-01-11T21:05:10.2548287Z 
2023-01-11T21:05:10.2548525Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2549032Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2549577Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.2549998Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:05:10.2550385Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2550704Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.2551044Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.2551325Z {
2023-01-11T21:05:10.2551573Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.2551893Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2552195Z     {
2023-01-11T21:05:10.2552439Z         #pragma omp for 
2023-01-11T21:05:10.2552719Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.2552989Z         {
2023-01-11T21:05:10.2553208Z             {
2023-01-11T21:05:10.2553986Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:05:10.2554772Z                 float tmp5 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.2555194Z                 auto tmp5_vec = at::vec::Vectorized<float>(tmp5);
2023-01-11T21:05:10.2555531Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.2555817Z                 {
2023-01-11T21:05:10.2556116Z                     float g_tmp_buffer_in_ptr0[16] = {0};
2023-01-11T21:05:10.2556510Z                     flag_to_float(in_ptr0 + (16*i1) + (17*i0), g_tmp_buffer_in_ptr0, 16);
2023-01-11T21:05:10.2556971Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:05:10.2557541Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i1) + (17*i0));
2023-01-11T21:05:10.2557912Z                     auto tmp1 = (tmp0);
2023-01-11T21:05:10.2558408Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:05:10.2558862Z                     auto tmp4 = decltype(tmp2)::blendv(tmp3, tmp2, tmp1);
2023-01-11T21:05:10.2559266Z                     tmp5_vec = at::vec::maximum(tmp5_vec, tmp4);
2023-01-11T21:05:10.2559599Z                 }
2023-01-11T21:05:10.2560109Z                 tmp5 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp5_vec);
2023-01-11T21:05:10.2560807Z                 #pragma omp simd simdlen(8)  reduction(max:tmp5)
2023-01-11T21:05:10.2561174Z                 for(long i1=16; i1<17; i1+=1)
2023-01-11T21:05:10.2561468Z                 {
2023-01-11T21:05:10.2561807Z                     auto tmp0 = in_ptr0[i1 + (17*i0)];
2023-01-11T21:05:10.2562201Z                     auto tmp3 = in_ptr1[i1 + (17*i0)];
2023-01-11T21:05:10.2562567Z                     auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:05:10.2563199Z                     auto tmp2 = static_cast<float>(-33.0);
2023-01-11T21:05:10.2563618Z                     auto tmp4 = tmp1 ? tmp2 : tmp3;
2023-01-11T21:05:10.2563984Z                     tmp5 = std::max(tmp5, tmp4);
2023-01-11T21:05:10.2564317Z                 }
2023-01-11T21:05:10.2564643Z                 out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.2564917Z             }
2023-01-11T21:05:10.2565186Z         }
2023-01-11T21:05:10.2565456Z         #pragma omp for 
2023-01-11T21:05:10.2565767Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.2566056Z         {
2023-01-11T21:05:10.2566319Z             {
2023-01-11T21:05:10.2566797Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.2567273Z                 float tmp8 = 0;
2023-01-11T21:05:10.2567664Z                 auto tmp8_vec = at::vec::Vectorized<float>(tmp8);
2023-01-11T21:05:10.2568020Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.2568304Z                 {
2023-01-11T21:05:10.2568638Z                     float g_tmp_buffer_in_ptr0[16] = {0};
2023-01-11T21:05:10.2569070Z                     flag_to_float(in_ptr0 + (16*i1) + (17*i0), g_tmp_buffer_in_ptr0, 16);
2023-01-11T21:05:10.2569543Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:05:10.2570071Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i1) + (17*i0));
2023-01-11T21:05:10.2570518Z                     auto tmp5 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:05:10.2570881Z                     auto tmp1 = (tmp0);
2023-01-11T21:05:10.2571422Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(-33.0));
2023-01-11T21:05:10.2571890Z                     auto tmp4 = decltype(tmp2)::blendv(tmp3, tmp2, tmp1);
2023-01-11T21:05:10.2572368Z                     auto tmp6 = tmp4 - tmp5;
2023-01-11T21:05:10.2572688Z                     auto tmp7 = tmp6.exp();
2023-01-11T21:05:10.2573055Z                     tmp7.store(out_ptr1 + (16*i1) + (17*i0));
2023-01-11T21:05:10.2573395Z                     tmp8_vec += tmp7;
2023-01-11T21:05:10.2573691Z                 }
2023-01-11T21:05:10.2574255Z                 tmp8 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp8_vec);
2023-01-11T21:05:10.2574804Z                 #pragma omp simd simdlen(8)  reduction(+:tmp8)
2023-01-11T21:05:10.2575192Z                 for(long i1=16; i1<17; i1+=1)
2023-01-11T21:05:10.2575475Z                 {
2023-01-11T21:05:10.2575783Z                     auto tmp0 = in_ptr0[i1 + (17*i0)];
2023-01-11T21:05:10.2576120Z                     auto tmp3 = in_ptr1[i1 + (17*i0)];
2023-01-11T21:05:10.2576580Z                     auto tmp5 = out_ptr0[i0];
2023-01-11T21:05:10.2576933Z                     auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:05:10.2577422Z                     auto tmp2 = static_cast<float>(-33.0);
2023-01-11T21:05:10.2577785Z                     auto tmp4 = tmp1 ? tmp2 : tmp3;
2023-01-11T21:05:10.2578186Z                     auto tmp6 = tmp4 - tmp5;
2023-01-11T21:05:10.2578575Z                     auto tmp7 = std::exp(tmp6);
2023-01-11T21:05:10.2578917Z                     out_ptr1[i1 + (17*i0)] = tmp7;
2023-01-11T21:05:10.2579220Z                     tmp8 += tmp7;
2023-01-11T21:05:10.2579488Z                 }
2023-01-11T21:05:10.2579768Z                 out_ptr2[i0] = tmp8;
2023-01-11T21:05:10.2580055Z             }
2023-01-11T21:05:10.2580312Z         }
2023-01-11T21:05:10.2580572Z         #pragma omp for 
2023-01-11T21:05:10.2580857Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.2581132Z         {
2023-01-11T21:05:10.2581407Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.2581685Z             {
2023-01-11T21:05:10.2582066Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (16*i1) + (17*i0));
2023-01-11T21:05:10.2582591Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:05:10.2582973Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.2583325Z                 tmp2.store(in_out_ptr0 + (16*i1) + (17*i0));
2023-01-11T21:05:10.2583636Z             }
2023-01-11T21:05:10.2583936Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.2584251Z             for(long i1=16; i1<17; i1+=1)
2023-01-11T21:05:10.2584553Z             {
2023-01-11T21:05:10.2584851Z                 auto tmp0 = out_ptr1[i1 + (17*i0)];
2023-01-11T21:05:10.2585191Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:05:10.2585524Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.2585859Z                 in_out_ptr0[i1 + (17*i0)] = tmp2;
2023-01-11T21:05:10.2586155Z             }
2023-01-11T21:05:10.2586410Z         }
2023-01-11T21:05:10.2586660Z     }
2023-01-11T21:05:10.2586891Z }
2023-01-11T21:05:10.2587174Z ''')
2023-01-11T21:05:10.2587333Z 
2023-01-11T21:05:10.2587341Z 
2023-01-11T21:05:10.2587482Z async_compile.wait(globals())
2023-01-11T21:05:10.2587804Z del async_compile
2023-01-11T21:05:10.2587963Z 
2023-01-11T21:05:10.2588075Z def call(args):
2023-01-11T21:05:10.2588366Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.2588648Z     args.clear()
2023-01-11T21:05:10.2589119Z     buf0 = empty_strided((2, 1), (1, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2589683Z     buf1 = empty_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2590251Z     buf2 = empty_strided((2, 1), (1, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2590627Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.2591148Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.2591625Z     del arg0_1
2023-01-11T21:05:10.2669897Z     del arg1_1
2023-01-11T21:05:10.2670329Z     return (buf3, )
2023-01-11T21:05:10.2670507Z 
2023-01-11T21:05:10.2670512Z 
2023-01-11T21:05:10.2670655Z if __name__ == "__main__":
2023-01-11T21:05:10.2671042Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2671481Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2672034Z     arg0_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2672608Z     arg1_1 = rand_strided((2, 17), (17, 1), device='cpu', dtype=torch.uint8)
2023-01-11T21:05:10.2673013Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.2673249Z 
2023-01-11T21:05:10.2673350Z ok (3.010s)
2023-01-11T21:05:10.2674076Z   test_new_vec_op_cpu_only (__main__.CPUReproTests) ... [2023-01-11 20:43:55,641] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2674778Z [2023-01-11 20:43:58,386] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2675294Z 
2023-01-11T21:05:10.2675419Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2675741Z import torch
2023-01-11T21:05:10.2675953Z import random
2023-01-11T21:05:10.2676257Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2676605Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2676813Z 
2023-01-11T21:05:10.2676923Z aten = torch.ops.aten
2023-01-11T21:05:10.2677213Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2677559Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2677760Z 
2023-01-11T21:05:10.2677765Z 
2023-01-11T21:05:10.2677941Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2678329Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2678858Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2679259Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2679549Z {
2023-01-11T21:05:10.2679848Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2680151Z     {
2023-01-11T21:05:10.2680495Z         #pragma omp for 
2023-01-11T21:05:10.2680866Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.2681123Z         {
2023-01-11T21:05:10.2681462Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2681806Z             auto tmp1 = tmp0.erf();
2023-01-11T21:05:10.2682119Z             auto tmp2 = tmp1.expm1();
2023-01-11T21:05:10.2682378Z             auto tmp3 = tmp2.log1p();
2023-01-11T21:05:10.2682685Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2682983Z         }
2023-01-11T21:05:10.2683209Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2683470Z         for(long i0=16; i0<18; i0+=1)
2023-01-11T21:05:10.2683754Z         {
2023-01-11T21:05:10.2684020Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2684271Z             auto tmp1 = std::erf(tmp0);
2023-01-11T21:05:10.2684597Z             auto tmp2 = std::expm1(tmp1);
2023-01-11T21:05:10.2684934Z             auto tmp3 = std::log1p(tmp2);
2023-01-11T21:05:10.2685285Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.2685585Z         }
2023-01-11T21:05:10.2685854Z     }
2023-01-11T21:05:10.2686089Z }
2023-01-11T21:05:10.2686384Z ''')
2023-01-11T21:05:10.2686528Z 
2023-01-11T21:05:10.2686535Z 
2023-01-11T21:05:10.2686675Z async_compile.wait(globals())
2023-01-11T21:05:10.2686971Z del async_compile
2023-01-11T21:05:10.2687157Z 
2023-01-11T21:05:10.2687244Z def call(args):
2023-01-11T21:05:10.2687493Z     x_1, = args
2023-01-11T21:05:10.2687737Z     args.clear()
2023-01-11T21:05:10.2688253Z     buf0 = empty_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2688737Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2689061Z     del x_1
2023-01-11T21:05:10.2689344Z     return (buf0, )
2023-01-11T21:05:10.2689514Z 
2023-01-11T21:05:10.2689521Z 
2023-01-11T21:05:10.2689647Z if __name__ == "__main__":
2023-01-11T21:05:10.2689975Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2690406Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2690959Z     x_1 = rand_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2691373Z     print_performance(lambda: call([x_1]))
2023-01-11T21:05:10.2691588Z 
2023-01-11T21:05:10.2691689Z ok (2.811s)
2023-01-11T21:05:10.2692423Z   test_no_op_squeeze (__main__.CPUReproTests) ... [2023-01-11 20:43:58,420] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 4
2023-01-11T21:05:10.2693230Z [2023-01-11 20:43:58,424] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 4
2023-01-11T21:05:10.2693548Z 
2023-01-11T21:05:10.2693698Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2694029Z import torch
2023-01-11T21:05:10.2694450Z import random
2023-01-11T21:05:10.2694800Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2695214Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2695459Z 
2023-01-11T21:05:10.2695591Z aten = torch.ops.aten
2023-01-11T21:05:10.2695987Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2696375Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2696572Z 
2023-01-11T21:05:10.2696578Z 
2023-01-11T21:05:10.2696732Z async_compile.wait(globals())
2023-01-11T21:05:10.2697052Z del async_compile
2023-01-11T21:05:10.2697249Z 
2023-01-11T21:05:10.2697358Z def call(args):
2023-01-11T21:05:10.2697656Z     arg0_1, = args
2023-01-11T21:05:10.2697955Z     args.clear()
2023-01-11T21:05:10.2698250Z     return (arg0_1, )
2023-01-11T21:05:10.2698426Z 
2023-01-11T21:05:10.2698431Z 
2023-01-11T21:05:10.2698669Z if __name__ == "__main__":
2023-01-11T21:05:10.2699054Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2699503Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2700081Z     arg0_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2700604Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.2700829Z 
2023-01-11T21:05:10.2700940Z ok (0.036s)
2023-01-11T21:05:10.2701765Z   test_parallel_num_threads (__main__.CPUReproTests) ... [W ParallelNative.cpp:230] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
2023-01-11T21:05:10.2702781Z [2023-01-11 20:43:58,442] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 5
2023-01-11T21:05:10.2703523Z [2023-01-11 20:44:01,169] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 5
2023-01-11T21:05:10.2703802Z 
2023-01-11T21:05:10.2703948Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2704235Z import torch
2023-01-11T21:05:10.2704496Z import random
2023-01-11T21:05:10.2704841Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2705248Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2705492Z 
2023-01-11T21:05:10.2705616Z aten = torch.ops.aten
2023-01-11T21:05:10.2705992Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2706353Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2706523Z 
2023-01-11T21:05:10.2706530Z 
2023-01-11T21:05:10.2706734Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2707243Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2707763Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2708133Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2708471Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2708759Z {
2023-01-11T21:05:10.2709070Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2709413Z     {
2023-01-11T21:05:10.2709708Z         #pragma omp for 
2023-01-11T21:05:10.2710022Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.2710329Z         {
2023-01-11T21:05:10.2710713Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2711200Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.2711571Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2711894Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2712173Z         }
2023-01-11T21:05:10.2712448Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2712772Z         for(long i0=192; i0<200; i0+=1)
2023-01-11T21:05:10.2713058Z         {
2023-01-11T21:05:10.2713323Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2713640Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.2713938Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2714246Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.2714611Z         }
2023-01-11T21:05:10.2714821Z     }
2023-01-11T21:05:10.2715035Z }
2023-01-11T21:05:10.2715325Z ''')
2023-01-11T21:05:10.2715487Z 
2023-01-11T21:05:10.2715495Z 
2023-01-11T21:05:10.2715660Z async_compile.wait(globals())
2023-01-11T21:05:10.2715968Z del async_compile
2023-01-11T21:05:10.2716130Z 
2023-01-11T21:05:10.2716236Z def call(args):
2023-01-11T21:05:10.2716527Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.2716813Z     args.clear()
2023-01-11T21:05:10.2717309Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2717839Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2718240Z     del arg0_1
2023-01-11T21:05:10.2718493Z     del arg1_1
2023-01-11T21:05:10.2718767Z     return (buf0, )
2023-01-11T21:05:10.2718930Z 
2023-01-11T21:05:10.2718936Z 
2023-01-11T21:05:10.2719048Z if __name__ == "__main__":
2023-01-11T21:05:10.2719427Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2719887Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2801839Z     arg0_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2844437Z     arg1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2844752Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.2844991Z 
2023-01-11T21:05:10.2845100Z ok (2.748s)
2023-01-11T21:05:10.2845840Z   test_sign_cpu_only (__main__.CPUReproTests) ... [2023-01-11 20:44:01,208] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2846623Z [2023-01-11 20:44:03,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2846945Z 
2023-01-11T21:05:10.2847099Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2847409Z import torch
2023-01-11T21:05:10.2847683Z import random
2023-01-11T21:05:10.2848010Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2848434Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2848675Z 
2023-01-11T21:05:10.2848801Z aten = torch.ops.aten
2023-01-11T21:05:10.2849177Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2849592Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2849795Z 
2023-01-11T21:05:10.2849800Z 
2023-01-11T21:05:10.2850024Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2850547Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2851077Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2851455Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2851744Z {
2023-01-11T21:05:10.2852020Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2852316Z     {
2023-01-11T21:05:10.2852578Z         #pragma omp for 
2023-01-11T21:05:10.2852861Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.2853157Z         {
2023-01-11T21:05:10.2853526Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2854011Z             auto tmp1 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), decltype(tmp0)(0) < tmp0);
2023-01-11T21:05:10.2854567Z             auto tmp2 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), tmp0 < decltype(tmp0)(0));
2023-01-11T21:05:10.2855057Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.2855388Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2855656Z         }
2023-01-11T21:05:10.2855949Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2856277Z         for(long i0=16; i0<18; i0+=1)
2023-01-11T21:05:10.2856542Z         {
2023-01-11T21:05:10.2856823Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2857136Z             auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:05:10.2857429Z             auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:05:10.2858050Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.2858368Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.2858690Z         }
2023-01-11T21:05:10.2858937Z     }
2023-01-11T21:05:10.2859165Z }
2023-01-11T21:05:10.2859406Z ''')
2023-01-11T21:05:10.2859551Z 
2023-01-11T21:05:10.2859558Z 
2023-01-11T21:05:10.2859701Z async_compile.wait(globals())
2023-01-11T21:05:10.2860002Z del async_compile
2023-01-11T21:05:10.2860178Z 
2023-01-11T21:05:10.2860291Z def call(args):
2023-01-11T21:05:10.2860543Z     x_1, = args
2023-01-11T21:05:10.2860806Z     args.clear()
2023-01-11T21:05:10.2861302Z     buf0 = empty_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2861745Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2862093Z     del x_1
2023-01-11T21:05:10.2862355Z     return (buf0, )
2023-01-11T21:05:10.2862533Z 
2023-01-11T21:05:10.2862542Z 
2023-01-11T21:05:10.2862641Z if __name__ == "__main__":
2023-01-11T21:05:10.2862984Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2863397Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2864033Z     x_1 = rand_strided((2, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2864420Z     print_performance(lambda: call([x_1]))
2023-01-11T21:05:10.2864639Z 
2023-01-11T21:05:10.2864743Z ok (2.737s)
2023-01-11T21:05:10.2865086Z   test_timed_cpu_only (__main__.CPUReproTests) ... ok (0.007s)
2023-01-11T21:05:10.2865859Z   test_vec_dynamic_shapes (__main__.CPUReproTests) ... [2023-01-11 20:44:04,298] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 6
2023-01-11T21:05:10.2866665Z [2023-01-11 20:44:07,015] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 6
2023-01-11T21:05:10.2866991Z 
2023-01-11T21:05:10.2867144Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2867459Z import torch
2023-01-11T21:05:10.2867716Z import random
2023-01-11T21:05:10.2868063Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2868478Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2868720Z 
2023-01-11T21:05:10.2868835Z aten = torch.ops.aten
2023-01-11T21:05:10.2869236Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2869629Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2869836Z 
2023-01-11T21:05:10.2869845Z 
2023-01-11T21:05:10.2870064Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2870567Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2871107Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.2871487Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2871831Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.2872190Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.2872560Z                        const long ks0,
2023-01-11T21:05:10.2872858Z                        const long ks1)
2023-01-11T21:05:10.2873134Z {
2023-01-11T21:05:10.2873403Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.2873750Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2874037Z     {
2023-01-11T21:05:10.2874302Z         #pragma omp for 
2023-01-11T21:05:10.2874599Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:05:10.2874866Z         {
2023-01-11T21:05:10.2875114Z             {
2023-01-11T21:05:10.2875361Z                 {
2023-01-11T21:05:10.2875888Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.2876275Z                     for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:05:10.2876574Z                     {
2023-01-11T21:05:10.2876823Z                         {
2023-01-11T21:05:10.2877153Z                             auto tmp0 = in_ptr0[i1 + (i0*ks1)];
2023-01-11T21:05:10.2877539Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:05:10.2877894Z                         }
2023-01-11T21:05:10.2878146Z                     }
2023-01-11T21:05:10.2878432Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.2878704Z                 }
2023-01-11T21:05:10.2878958Z             }
2023-01-11T21:05:10.2879202Z         }
2023-01-11T21:05:10.2879455Z         #pragma omp for 
2023-01-11T21:05:10.2879757Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:05:10.2880032Z         {
2023-01-11T21:05:10.2880275Z             {
2023-01-11T21:05:10.2880514Z                 {
2023-01-11T21:05:10.2880967Z                     float tmp4 = 0;
2023-01-11T21:05:10.2881295Z                     for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:05:10.2881582Z                     {
2023-01-11T21:05:10.2881836Z                         {
2023-01-11T21:05:10.2882166Z                             auto tmp0 = in_ptr0[i1 + (i0*ks1)];
2023-01-11T21:05:10.2882503Z                             auto tmp1 = out_ptr0[i0];
2023-01-11T21:05:10.2882938Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.2883307Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:05:10.2883648Z                             out_ptr1[i1 + (i0*ks1)] = tmp3;
2023-01-11T21:05:10.2884077Z                             tmp4 += tmp3;
2023-01-11T21:05:10.2884362Z                         }
2023-01-11T21:05:10.2884615Z                     }
2023-01-11T21:05:10.2884908Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.2885200Z                 }
2023-01-11T21:05:10.2885455Z             }
2023-01-11T21:05:10.2885694Z         }
2023-01-11T21:05:10.2885974Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.2886306Z         for(long i0=0; i0<ks0; i0+=1)
2023-01-11T21:05:10.2886569Z         {
2023-01-11T21:05:10.2886848Z             for(long i1=0; i1<ks1; i1+=1)
2023-01-11T21:05:10.2887129Z             {
2023-01-11T21:05:10.2887364Z                 {
2023-01-11T21:05:10.2887633Z                     {
2023-01-11T21:05:10.2887944Z                         auto tmp0 = out_ptr1[i1 + (i0*ks1)];
2023-01-11T21:05:10.2888283Z                         auto tmp1 = out_ptr2[i0];
2023-01-11T21:05:10.2888614Z                         auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.2888963Z                         in_out_ptr0[i1 + (i0*ks1)] = tmp2;
2023-01-11T21:05:10.2889243Z                     }
2023-01-11T21:05:10.2889498Z                 }
2023-01-11T21:05:10.2889733Z             }
2023-01-11T21:05:10.2889985Z         }
2023-01-11T21:05:10.2890259Z     }
2023-01-11T21:05:10.2890517Z }
2023-01-11T21:05:10.2890752Z ''')
2023-01-11T21:05:10.2890844Z 
2023-01-11T21:05:10.2890849Z 
2023-01-11T21:05:10.2890936Z async_compile.wait(globals())
2023-01-11T21:05:10.2891125Z del async_compile
2023-01-11T21:05:10.2891229Z 
2023-01-11T21:05:10.2891295Z def call(args):
2023-01-11T21:05:10.2891450Z     arg0_1, = args
2023-01-11T21:05:10.2891615Z     args.clear()
2023-01-11T21:05:10.2891795Z     arg0_1_size = arg0_1.size()
2023-01-11T21:05:10.2891961Z     s0 = arg0_1_size[0]
2023-01-11T21:05:10.2892132Z     s1 = arg0_1_size[1]
2023-01-11T21:05:10.2892429Z     buf0 = empty_strided((s0, 1), (1, s0), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2892769Z     buf1 = empty_strided((s0, s1), (s1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2893115Z     buf2 = empty_strided((s0, 1), (1, s0), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2893348Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.2893654Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_long(s0), c_long(s1))
2023-01-11T21:05:10.2893934Z     del arg0_1
2023-01-11T21:05:10.2894099Z     return (buf3, )
2023-01-11T21:05:10.2894205Z 
2023-01-11T21:05:10.2894209Z 
2023-01-11T21:05:10.2894283Z if __name__ == "__main__":
2023-01-11T21:05:10.2894486Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2894746Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2895092Z     arg0_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2895415Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.2895552Z 
2023-01-11T21:05:10.2895618Z ok (3.107s)
2023-01-11T21:05:10.2896060Z   test_vec_kernel_cpu_only (__main__.CPUReproTests) ... [2023-01-11 20:44:07,412] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2896560Z [2023-01-11 20:44:08,581] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2896990Z [2023-01-11 20:44:08,979] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2897437Z [2023-01-11 20:44:11,749] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2897877Z [2023-01-11 20:44:12,117] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2898066Z 
2023-01-11T21:05:10.2898151Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2898338Z import torch
2023-01-11T21:05:10.2898593Z import random
2023-01-11T21:05:10.2898813Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2899100Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2899253Z 
2023-01-11T21:05:10.2899331Z aten = torch.ops.aten
2023-01-11T21:05:10.2899578Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2899811Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2899937Z 
2023-01-11T21:05:10.2899941Z 
2023-01-11T21:05:10.2900077Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2900404Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2900729Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2900974Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2901198Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2901387Z {
2023-01-11T21:05:10.2901554Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2901741Z     {
2023-01-11T21:05:10.2901903Z         #pragma omp for 
2023-01-11T21:05:10.2902082Z         for(long i0=0; i0<200; i0+=1)
2023-01-11T21:05:10.2902259Z         {
2023-01-11T21:05:10.2902411Z             {
2023-01-11T21:05:10.2902555Z                 {
2023-01-11T21:05:10.2902743Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2902952Z                     auto tmp12 = in_ptr1[i0];
2023-01-11T21:05:10.2903162Z                     auto tmp1 = std::abs(tmp0);
2023-01-11T21:05:10.2903377Z                     auto tmp2 = std::sin(tmp1);
2023-01-11T21:05:10.2903618Z                     auto tmp3 = -tmp2;
2023-01-11T21:05:10.2903812Z                     auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.2904078Z                     auto tmp5 = std::exp(-tmp4);
2023-01-11T21:05:10.2904287Z                     auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:05:10.2904482Z                     auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:05:10.2904699Z                     auto tmp8 = std::cos(tmp7);
2023-01-11T21:05:10.2904910Z                     auto tmp9 = std::exp(tmp8);
2023-01-11T21:05:10.2905125Z                     auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:05:10.2905325Z                     auto tmp11 = tmp10 + tmp0;
2023-01-11T21:05:10.2905579Z                     auto tmp13 = tmp11 - tmp12;
2023-01-11T21:05:10.2905783Z                     auto tmp14 = tmp13 * tmp0;
2023-01-11T21:05:10.2905977Z                     auto tmp15 = tmp14 / tmp0;
2023-01-11T21:05:10.2906181Z                     auto tmp16 = tmp15 * tmp15;
2023-01-11T21:05:10.2906389Z                     auto tmp17 = tmp16 * tmp16;
2023-01-11T21:05:10.2906579Z                     auto tmp18 = tmp17 * tmp15;
2023-01-11T21:05:10.2906779Z                     auto tmp19 = tmp18 * tmp18;
2023-01-11T21:05:10.2906990Z                     auto tmp20 = std::log(tmp19);
2023-01-11T21:05:10.2907194Z                     auto tmp21 = std::floor(tmp20);
2023-01-11T21:05:10.2907444Z                     auto tmp22 = std::ceil(tmp21);
2023-01-11T21:05:10.2907660Z                     auto tmp23 = std::trunc(tmp22);
2023-01-11T21:05:10.2907881Z                     auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:05:10.2908097Z                     auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:05:10.2908311Z                     auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:05:10.2908516Z                     auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:05:10.2908758Z                     auto tmp28 = tmp26 - tmp27;
2023-01-11T21:05:10.2908962Z                     auto tmp29 = tmp28 + tmp12;
2023-01-11T21:05:10.2909163Z                     out_ptr0[i0] = tmp29;
2023-01-11T21:05:10.2909325Z                 }
2023-01-11T21:05:10.2909479Z             }
2023-01-11T21:05:10.2909633Z         }
2023-01-11T21:05:10.2909767Z     }
2023-01-11T21:05:10.2909907Z }
2023-01-11T21:05:10.2910064Z ''')
2023-01-11T21:05:10.2910155Z 
2023-01-11T21:05:10.2910162Z 
2023-01-11T21:05:10.2910239Z async_compile.wait(globals())
2023-01-11T21:05:10.2910432Z del async_compile
2023-01-11T21:05:10.2910538Z 
2023-01-11T21:05:10.2910605Z def call(args):
2023-01-11T21:05:10.2910768Z     x1_1, x2_1 = args
2023-01-11T21:05:10.2910952Z     args.clear()
2023-01-11T21:05:10.2911251Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2911559Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2911783Z     del x1_1
2023-01-11T21:05:10.2911939Z     del x2_1
2023-01-11T21:05:10.2912100Z     return (buf0, )
2023-01-11T21:05:10.2912193Z 
2023-01-11T21:05:10.2912209Z 
2023-01-11T21:05:10.2912271Z if __name__ == "__main__":
2023-01-11T21:05:10.2912487Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2912744Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2913071Z     x1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2913413Z     x2_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2913668Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:05:10.2913801Z 
2023-01-11T21:05:10.2913808Z 
2023-01-11T21:05:10.2913898Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2914077Z import torch
2023-01-11T21:05:10.2914240Z import random
2023-01-11T21:05:10.2914451Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2914693Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2914838Z 
2023-01-11T21:05:10.2914913Z aten = torch.ops.aten
2023-01-11T21:05:10.2915152Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2915382Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2915506Z 
2023-01-11T21:05:10.2915510Z 
2023-01-11T21:05:10.2915642Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2915965Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2916304Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2916528Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2916748Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2916930Z {
2023-01-11T21:05:10.2917094Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2917277Z     {
2023-01-11T21:05:10.2917433Z         #pragma omp for 
2023-01-11T21:05:10.2917607Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.2917778Z         {
2023-01-11T21:05:10.2917998Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2918266Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.2918499Z             auto tmp1 = tmp0.abs();
2023-01-11T21:05:10.2918693Z             auto tmp2 = tmp1.sin();
2023-01-11T21:05:10.2918881Z             auto tmp3 = tmp2.neg();
2023-01-11T21:05:10.2919060Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.2919351Z             auto tmp5 = decltype(tmp4)(1)/(decltype(tmp4)(1) + tmp4.neg().exp());
2023-01-11T21:05:10.2919629Z             auto tmp6 = at::vec::clamp_min(tmp5, decltype(tmp5)(0));
2023-01-11T21:05:10.2919846Z             auto tmp7 = tmp6.cos();
2023-01-11T21:05:10.2920035Z             auto tmp8 = tmp7.exp();
2023-01-11T21:05:10.2920225Z             auto tmp9 = tmp8.sqrt();
2023-01-11T21:05:10.2920409Z             auto tmp10 = tmp9 + tmp0;
2023-01-11T21:05:10.2920800Z             auto tmp12 = tmp10 - tmp11;
2023-01-11T21:05:10.2921064Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:05:10.2921253Z             auto tmp14 = tmp13 / tmp0;
2023-01-11T21:05:10.2921448Z             auto tmp15 = tmp14 * tmp14;
2023-01-11T21:05:10.2921641Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:05:10.2921822Z             auto tmp17 = tmp16 * tmp14;
2023-01-11T21:05:10.2922018Z             auto tmp18 = tmp17 * tmp17;
2023-01-11T21:05:10.2922211Z             auto tmp19 = tmp18.log();
2023-01-11T21:05:10.2922414Z             auto tmp20 = tmp19.floor();
2023-01-11T21:05:10.2922596Z             auto tmp21 = tmp20.ceil();
2023-01-11T21:05:10.2922794Z             auto tmp22 = tmp21.trunc();
2023-01-11T21:05:10.2923055Z             auto tmp23 = tmp22.lgamma();
2023-01-11T21:05:10.2923254Z             auto tmp24 = tmp23.fmod(tmp11);
2023-01-11T21:05:10.2923546Z             auto tmp25 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), decltype(tmp24)(0) < tmp24);
2023-01-11T21:05:10.2923902Z             auto tmp26 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), tmp24 < decltype(tmp24)(0));
2023-01-11T21:05:10.2924207Z             auto tmp27 = tmp25 - tmp26;
2023-01-11T21:05:10.2924403Z             auto tmp28 = tmp27 + tmp11;
2023-01-11T21:05:10.2924611Z             tmp28.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2924793Z         }
2023-01-11T21:05:10.2924962Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2925167Z         for(long i0=192; i0<200; i0+=1)
2023-01-11T21:05:10.2925338Z         {
2023-01-11T21:05:10.2925497Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2925692Z             auto tmp12 = in_ptr1[i0];
2023-01-11T21:05:10.2925895Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:05:10.2926080Z             auto tmp2 = std::sin(tmp1);
2023-01-11T21:05:10.2926305Z             auto tmp3 = -tmp2;
2023-01-11T21:05:10.2926494Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.2926725Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:05:10.2926923Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:05:10.2927117Z             auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:05:10.2927304Z             auto tmp8 = std::cos(tmp7);
2023-01-11T21:05:10.2927500Z             auto tmp9 = std::exp(tmp8);
2023-01-11T21:05:10.2927703Z             auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:05:10.2927891Z             auto tmp11 = tmp10 + tmp0;
2023-01-11T21:05:10.2928127Z             auto tmp13 = tmp11 - tmp12;
2023-01-11T21:05:10.2928324Z             auto tmp14 = tmp13 * tmp0;
2023-01-11T21:05:10.2928515Z             auto tmp15 = tmp14 / tmp0;
2023-01-11T21:05:10.2928694Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:05:10.2928887Z             auto tmp17 = tmp16 * tmp16;
2023-01-11T21:05:10.2929079Z             auto tmp18 = tmp17 * tmp15;
2023-01-11T21:05:10.2929257Z             auto tmp19 = tmp18 * tmp18;
2023-01-11T21:05:10.2929455Z             auto tmp20 = std::log(tmp19);
2023-01-11T21:05:10.2929664Z             auto tmp21 = std::floor(tmp20);
2023-01-11T21:05:10.2929863Z             auto tmp22 = std::ceil(tmp21);
2023-01-11T21:05:10.2930069Z             auto tmp23 = std::trunc(tmp22);
2023-01-11T21:05:10.2930281Z             auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:05:10.2930489Z             auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:05:10.2930704Z             auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:05:10.2930907Z             auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:05:10.2931192Z             auto tmp28 = tmp26 - tmp27;
2023-01-11T21:05:10.2931377Z             auto tmp29 = tmp28 + tmp12;
2023-01-11T21:05:10.2931574Z             out_ptr0[i0] = tmp29;
2023-01-11T21:05:10.2931746Z         }
2023-01-11T21:05:10.2931886Z     }
2023-01-11T21:05:10.2932035Z }
2023-01-11T21:05:10.2932195Z ''')
2023-01-11T21:05:10.2932273Z 
2023-01-11T21:05:10.2932278Z 
2023-01-11T21:05:10.2932365Z async_compile.wait(globals())
2023-01-11T21:05:10.2932557Z del async_compile
2023-01-11T21:05:10.2932665Z 
2023-01-11T21:05:10.2932735Z def call(args):
2023-01-11T21:05:10.2932894Z     x1_1, x2_1 = args
2023-01-11T21:05:10.2933069Z     args.clear()
2023-01-11T21:05:10.2933372Z     buf0 = empty_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2933673Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2933913Z     del x1_1
2023-01-11T21:05:10.2934072Z     del x2_1
2023-01-11T21:05:10.2934225Z     return (buf0, )
2023-01-11T21:05:10.2934334Z 
2023-01-11T21:05:10.2934338Z 
2023-01-11T21:05:10.2934414Z if __name__ == "__main__":
2023-01-11T21:05:10.2934635Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2934923Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2935255Z     x1_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2935594Z     x2_1 = rand_strided((10, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2935851Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:05:10.2935991Z 
2023-01-11T21:05:10.2935995Z 
2023-01-11T21:05:10.2936073Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2936261Z import torch
2023-01-11T21:05:10.2936430Z import random
2023-01-11T21:05:10.2936627Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2936888Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2937038Z 
2023-01-11T21:05:10.2937116Z aten = torch.ops.aten
2023-01-11T21:05:10.2937356Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2937586Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2937591Z 
2023-01-11T21:05:10.2937595Z 
2023-01-11T21:05:10.2937731Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2937932Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2938050Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2938149Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2938247Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2938306Z {
2023-01-11T21:05:10.2938389Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2938451Z     {
2023-01-11T21:05:10.2938613Z         #pragma omp for 
2023-01-11T21:05:10.2938697Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:05:10.2938759Z         {
2023-01-11T21:05:10.2938837Z             #pragma GCC ivdep
2023-01-11T21:05:10.2938911Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.2938972Z             {
2023-01-11T21:05:10.2939036Z                 {
2023-01-11T21:05:10.2939105Z                     {
2023-01-11T21:05:10.2939211Z                         auto tmp0 = in_ptr0[i0 + (20*i1)];
2023-01-11T21:05:10.2939312Z                         auto tmp12 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.2939411Z                         auto tmp1 = std::abs(tmp0);
2023-01-11T21:05:10.2939495Z                         auto tmp2 = std::sin(tmp1);
2023-01-11T21:05:10.2939624Z                         auto tmp3 = -tmp2;
2023-01-11T21:05:10.2939716Z                         auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.2939865Z                         auto tmp5 = std::exp(-tmp4);
2023-01-11T21:05:10.2939959Z                         auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:05:10.2940054Z                         auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:05:10.2940147Z                         auto tmp8 = std::cos(tmp7);
2023-01-11T21:05:10.2940286Z                         auto tmp9 = std::exp(tmp8);
2023-01-11T21:05:10.2940375Z                         auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:05:10.2940465Z                         auto tmp11 = tmp10 + tmp0;
2023-01-11T21:05:10.2940608Z                         auto tmp13 = tmp11 - tmp12;
2023-01-11T21:05:10.2940697Z                         auto tmp14 = tmp13 * tmp0;
2023-01-11T21:05:10.2940788Z                         auto tmp15 = tmp14 / tmp0;
2023-01-11T21:05:10.2940881Z                         auto tmp16 = tmp15 * tmp15;
2023-01-11T21:05:10.2940969Z                         auto tmp17 = tmp16 * tmp16;
2023-01-11T21:05:10.2941046Z                         auto tmp18 = tmp17 * tmp15;
2023-01-11T21:05:10.2941132Z                         auto tmp19 = tmp18 * tmp18;
2023-01-11T21:05:10.2941229Z                         auto tmp20 = std::log(tmp19);
2023-01-11T21:05:10.2941328Z                         auto tmp21 = std::floor(tmp20);
2023-01-11T21:05:10.2941428Z                         auto tmp22 = std::ceil(tmp21);
2023-01-11T21:05:10.2941530Z                         auto tmp23 = std::trunc(tmp22);
2023-01-11T21:05:10.2941631Z                         auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:05:10.2941765Z                         auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:05:10.2941847Z                         auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:05:10.2941939Z                         auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:05:10.2942080Z                         auto tmp28 = tmp26 - tmp27;
2023-01-11T21:05:10.2942170Z                         auto tmp29 = tmp28 + tmp12;
2023-01-11T21:05:10.2942261Z                         out_ptr0[i0 + (20*i1)] = tmp29;
2023-01-11T21:05:10.2942328Z                     }
2023-01-11T21:05:10.2942391Z                 }
2023-01-11T21:05:10.2942440Z             }
2023-01-11T21:05:10.2942499Z         }
2023-01-11T21:05:10.2942558Z     }
2023-01-11T21:05:10.2942616Z }
2023-01-11T21:05:10.2942692Z ''')
2023-01-11T21:05:10.2942697Z 
2023-01-11T21:05:10.2942704Z 
2023-01-11T21:05:10.2942791Z async_compile.wait(globals())
2023-01-11T21:05:10.2942861Z del async_compile
2023-01-11T21:05:10.2942866Z 
2023-01-11T21:05:10.2942922Z def call(args):
2023-01-11T21:05:10.2942993Z     x1_1, x2_1 = args
2023-01-11T21:05:10.2943067Z     args.clear()
2023-01-11T21:05:10.2943266Z     buf0 = empty_strided((20, 10), (1, 20), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2943421Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2943486Z     del x1_1
2023-01-11T21:05:10.2943548Z     del x2_1
2023-01-11T21:05:10.2943606Z     return (buf0, )
2023-01-11T21:05:10.2943622Z 
2023-01-11T21:05:10.2943626Z 
2023-01-11T21:05:10.2943687Z if __name__ == "__main__":
2023-01-11T21:05:10.2943799Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2943918Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2944110Z     x1_1 = rand_strided((20, 10), (1, 20), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2944302Z     x2_1 = rand_strided((20, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2944408Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:05:10.2944673Z [2023-01-11 20:44:14,836] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2944931Z [2023-01-11 20:44:15,235] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.2945178Z [2023-01-11 20:44:17,945] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.2945183Z 
2023-01-11T21:05:10.2945199Z 
2023-01-11T21:05:10.2945280Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2945347Z import torch
2023-01-11T21:05:10.2945414Z import random
2023-01-11T21:05:10.2945527Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2945645Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2945731Z 
2023-01-11T21:05:10.2945807Z aten = torch.ops.aten
2023-01-11T21:05:10.2945939Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2946017Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2946024Z 
2023-01-11T21:05:10.2946040Z 
2023-01-11T21:05:10.2946160Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2946361Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2946478Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2946584Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.2946681Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2946742Z {
2023-01-11T21:05:10.2946839Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2946887Z     {
2023-01-11T21:05:10.2946961Z         #pragma omp for 
2023-01-11T21:05:10.2947041Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.2947104Z         {
2023-01-11T21:05:10.2947236Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2947367Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.2947477Z             auto tmp1 = tmp0.abs();
2023-01-11T21:05:10.2947545Z             auto tmp2 = tmp1.sin();
2023-01-11T21:05:10.2947622Z             auto tmp3 = tmp2.neg();
2023-01-11T21:05:10.2947704Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.2947837Z             auto tmp5 = decltype(tmp4)(1)/(decltype(tmp4)(1) + tmp4.neg().exp());
2023-01-11T21:05:10.2947963Z             auto tmp6 = at::vec::clamp_min(tmp5, decltype(tmp5)(0));
2023-01-11T21:05:10.2948042Z             auto tmp7 = tmp6.cos();
2023-01-11T21:05:10.2948121Z             auto tmp8 = tmp7.exp();
2023-01-11T21:05:10.2948192Z             auto tmp9 = tmp8.sqrt();
2023-01-11T21:05:10.2948273Z             auto tmp10 = tmp9 + tmp0;
2023-01-11T21:05:10.2948398Z             auto tmp12 = tmp10 - tmp11;
2023-01-11T21:05:10.2948484Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:05:10.2948564Z             auto tmp14 = tmp13 / tmp0;
2023-01-11T21:05:10.2948646Z             auto tmp15 = tmp14 * tmp14;
2023-01-11T21:05:10.2948728Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:05:10.2948796Z             auto tmp17 = tmp16 * tmp14;
2023-01-11T21:05:10.2948875Z             auto tmp18 = tmp17 * tmp17;
2023-01-11T21:05:10.2948957Z             auto tmp19 = tmp18.log();
2023-01-11T21:05:10.2949039Z             auto tmp20 = tmp19.floor();
2023-01-11T21:05:10.2949121Z             auto tmp21 = tmp20.ceil();
2023-01-11T21:05:10.2949201Z             auto tmp22 = tmp21.trunc();
2023-01-11T21:05:10.2949287Z             auto tmp23 = tmp22.lgamma();
2023-01-11T21:05:10.2949368Z             auto tmp24 = tmp23.fmod(tmp11);
2023-01-11T21:05:10.2949536Z             auto tmp25 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), decltype(tmp24)(0) < tmp24);
2023-01-11T21:05:10.2949701Z             auto tmp26 = decltype(tmp24)::blendv(decltype(tmp24)(0), decltype(tmp24)(1), tmp24 < decltype(tmp24)(0));
2023-01-11T21:05:10.2949827Z             auto tmp27 = tmp25 - tmp26;
2023-01-11T21:05:10.2949907Z             auto tmp28 = tmp27 + tmp11;
2023-01-11T21:05:10.2950003Z             tmp28.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2950063Z         }
2023-01-11T21:05:10.2950155Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2950223Z         for(long i0=64; i0<70; i0+=1)
2023-01-11T21:05:10.2950284Z         {
2023-01-11T21:05:10.2950365Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2950447Z             auto tmp12 = in_ptr1[i0];
2023-01-11T21:05:10.2950534Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:05:10.2950621Z             auto tmp2 = std::sin(tmp1);
2023-01-11T21:05:10.2950733Z             auto tmp3 = -tmp2;
2023-01-11T21:05:10.2950803Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.2950932Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:05:10.2951040Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:05:10.2951126Z             auto tmp7 = tmp6 * (tmp6>0);
2023-01-11T21:05:10.2951209Z             auto tmp8 = std::cos(tmp7);
2023-01-11T21:05:10.2951293Z             auto tmp9 = std::exp(tmp8);
2023-01-11T21:05:10.2951385Z             auto tmp10 = std::sqrt(tmp9);
2023-01-11T21:05:10.2951456Z             auto tmp11 = tmp10 + tmp0;
2023-01-11T21:05:10.2951577Z             auto tmp13 = tmp11 - tmp12;
2023-01-11T21:05:10.2951659Z             auto tmp14 = tmp13 * tmp0;
2023-01-11T21:05:10.2951740Z             auto tmp15 = tmp14 / tmp0;
2023-01-11T21:05:10.2951819Z             auto tmp16 = tmp15 * tmp15;
2023-01-11T21:05:10.2951895Z             auto tmp17 = tmp16 * tmp16;
2023-01-11T21:05:10.2951973Z             auto tmp18 = tmp17 * tmp15;
2023-01-11T21:05:10.2952041Z             auto tmp19 = tmp18 * tmp18;
2023-01-11T21:05:10.2952128Z             auto tmp20 = std::log(tmp19);
2023-01-11T21:05:10.2952220Z             auto tmp21 = std::floor(tmp20);
2023-01-11T21:05:10.2952312Z             auto tmp22 = std::ceil(tmp21);
2023-01-11T21:05:10.2952404Z             auto tmp23 = std::trunc(tmp22);
2023-01-11T21:05:10.2952498Z             auto tmp24 = std::lgamma(tmp23);
2023-01-11T21:05:10.2952627Z             auto tmp25 = std::fmod(tmp24, tmp12);
2023-01-11T21:05:10.2952715Z             auto tmp26 = tmp25 > 0 ? 1 : 0;
2023-01-11T21:05:10.2952788Z             auto tmp27 = tmp25 < 0 ? 1 : 0;
2023-01-11T21:05:10.2952911Z             auto tmp28 = tmp26 - tmp27;
2023-01-11T21:05:10.2952991Z             auto tmp29 = tmp28 + tmp12;
2023-01-11T21:05:10.2953069Z             out_ptr0[i0] = tmp29;
2023-01-11T21:05:10.2953128Z         }
2023-01-11T21:05:10.2953187Z     }
2023-01-11T21:05:10.2953244Z }
2023-01-11T21:05:10.2953307Z ''')
2023-01-11T21:05:10.2953313Z 
2023-01-11T21:05:10.2953317Z 
2023-01-11T21:05:10.2953404Z async_compile.wait(globals())
2023-01-11T21:05:10.2953475Z del async_compile
2023-01-11T21:05:10.2953480Z 
2023-01-11T21:05:10.2953549Z def call(args):
2023-01-11T21:05:10.2953619Z     x1_1, x2_1 = args
2023-01-11T21:05:10.2953687Z     args.clear()
2023-01-11T21:05:10.2953883Z     buf0 = empty_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2954030Z     kernel_cpp_0(c_void_p(x1_1.data_ptr()), c_void_p(x2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2954093Z     del x1_1
2023-01-11T21:05:10.2954156Z     del x2_1
2023-01-11T21:05:10.2954227Z     return (buf0, )
2023-01-11T21:05:10.2954232Z 
2023-01-11T21:05:10.2954236Z 
2023-01-11T21:05:10.2954308Z if __name__ == "__main__":
2023-01-11T21:05:10.2954420Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2954541Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2954735Z     x1_1 = rand_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2954913Z     x2_1 = rand_strided((10, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2955021Z     print_performance(lambda: call([x1_1, x2_1]))
2023-01-11T21:05:10.2955027Z 
2023-01-11T21:05:10.2955095Z ok (10.924s)
2023-01-11T21:05:10.2955534Z   test_abs_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.2955658Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.2955916Z [2023-01-11 20:44:17,996] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 7
2023-01-11T21:05:10.2956175Z [2023-01-11 20:44:20,727] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 7
2023-01-11T21:05:10.2956180Z 
2023-01-11T21:05:10.2956270Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2956337Z import torch
2023-01-11T21:05:10.2956392Z import random
2023-01-11T21:05:10.2956536Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2956653Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2956658Z 
2023-01-11T21:05:10.2956733Z aten = torch.ops.aten
2023-01-11T21:05:10.2956866Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2956954Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2956960Z 
2023-01-11T21:05:10.2956964Z 
2023-01-11T21:05:10.2957093Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2957297Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2957401Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2957499Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.2957557Z {
2023-01-11T21:05:10.2957653Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2957711Z     {
2023-01-11T21:05:10.2957784Z         #pragma omp for 
2023-01-11T21:05:10.2957865Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.2957913Z         {
2023-01-11T21:05:10.2958044Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2958151Z             auto tmp1 = tmp0.abs();
2023-01-11T21:05:10.2958282Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.2958364Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.2958445Z             auto tmp4 = tmp0 / tmp3;
2023-01-11T21:05:10.2958535Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.2958583Z         }
2023-01-11T21:05:10.2958676Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2958755Z         for(long i0=16; i0<17; i0+=1)
2023-01-11T21:05:10.2958813Z         {
2023-01-11T21:05:10.2958894Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2958984Z             auto tmp1 = std::abs(tmp0);
2023-01-11T21:05:10.2959081Z             auto tmp2 = static_cast<float>(1);
2023-01-11T21:05:10.2959149Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.2959230Z             auto tmp4 = tmp0 / tmp3;
2023-01-11T21:05:10.2959307Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.2959366Z         }
2023-01-11T21:05:10.2959426Z     }
2023-01-11T21:05:10.2959487Z }
2023-01-11T21:05:10.2959564Z ''')
2023-01-11T21:05:10.2959569Z 
2023-01-11T21:05:10.2959573Z 
2023-01-11T21:05:10.2959649Z async_compile.wait(globals())
2023-01-11T21:05:10.2959719Z del async_compile
2023-01-11T21:05:10.2959724Z 
2023-01-11T21:05:10.2959791Z def call(args):
2023-01-11T21:05:10.2959859Z     arg0_1, = args
2023-01-11T21:05:10.2959928Z     args.clear()
2023-01-11T21:05:10.2960121Z     buf0 = empty_strided((17, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2960253Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.2960307Z     del arg0_1
2023-01-11T21:05:10.2960375Z     return (buf0, )
2023-01-11T21:05:10.2960380Z 
2023-01-11T21:05:10.2960384Z 
2023-01-11T21:05:10.2960457Z if __name__ == "__main__":
2023-01-11T21:05:10.2960569Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2960857Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2961071Z     arg0_1 = rand_strided((17, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2961178Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.2961183Z 
2023-01-11T21:05:10.2961248Z ok (2.787s)
2023-01-11T21:05:10.2961706Z   test_adaptive_avg_pool2d1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.2961821Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.2962081Z [2023-01-11 20:44:20,810] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 8
2023-01-11T21:05:10.2962390Z [2023-01-11 20:44:20,832] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._adaptive_avg_pool2d
2023-01-11T21:05:10.2962396Z 
2023-01-11T21:05:10.2962493Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2962562Z import torch
2023-01-11T21:05:10.2962633Z import random
2023-01-11T21:05:10.2962746Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2962869Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2962874Z 
2023-01-11T21:05:10.2962939Z aten = torch.ops.aten
2023-01-11T21:05:10.2963075Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2963165Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2963170Z 
2023-01-11T21:05:10.2963174Z 
2023-01-11T21:05:10.2963308Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2963512Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2963634Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2963733Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.2963831Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.2963920Z {
2023-01-11T21:05:10.2964021Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2964084Z     {
2023-01-11T21:05:10.2964162Z         #pragma omp for 
2023-01-11T21:05:10.2964243Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.2964306Z         {
2023-01-11T21:05:10.2964385Z             #pragma GCC ivdep
2023-01-11T21:05:10.2964453Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:05:10.2964518Z             {
2023-01-11T21:05:10.2964600Z                 #pragma GCC ivdep
2023-01-11T21:05:10.2964688Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:05:10.2964752Z                 {
2023-01-11T21:05:10.2964819Z                     {
2023-01-11T21:05:10.2964887Z                         {
2023-01-11T21:05:10.2964991Z                             auto tmp0 = static_cast<long>(((8*i1) / 3));
2023-01-11T21:05:10.2965111Z                             auto tmp1 = static_cast<long>(((21 + (16*i1)) / 6));
2023-01-11T21:05:10.2965210Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.2965321Z                             auto tmp3 = static_cast<long>(((8*i2) / 3));
2023-01-11T21:05:10.2965434Z                             auto tmp4 = static_cast<long>(((21 + (16*i2)) / 6));
2023-01-11T21:05:10.2965560Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:05:10.2965691Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:05:10.2965795Z                             float tmp7 = 0.0;
2023-01-11T21:05:10.2965909Z                             if(tmp6)
2023-01-11T21:05:10.2965991Z                             {
2023-01-11T21:05:10.2966117Z                                 auto tmp8 = in_ptr0[(16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2966204Z                                 tmp7 = tmp8;
2023-01-11T21:05:10.2966277Z                             }
2023-01-11T21:05:10.2966440Z                             auto tmp9 = static_cast<long>(1 + (((8*i2) / 3)));
2023-01-11T21:05:10.2966550Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:05:10.2966636Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:05:10.2966724Z                             float tmp12 = 0.0;
2023-01-11T21:05:10.2966803Z                             if(tmp11)
2023-01-11T21:05:10.2966871Z                             {
2023-01-11T21:05:10.2966996Z                                 auto tmp13 = in_ptr0[1 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2967083Z                                 tmp12 = tmp13;
2023-01-11T21:05:10.2967150Z                             }
2023-01-11T21:05:10.2967232Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:05:10.2967346Z                             auto tmp15 = static_cast<long>(2 + (((8*i2) / 3)));
2023-01-11T21:05:10.2967480Z                             auto tmp16 = tmp15 < tmp4;
2023-01-11T21:05:10.2967574Z                             auto tmp17 = tmp2 & tmp16;
2023-01-11T21:05:10.2967661Z                             float tmp18 = 0.0;
2023-01-11T21:05:10.2967739Z                             if(tmp17)
2023-01-11T21:05:10.2967806Z                             {
2023-01-11T21:05:10.2967926Z                                 auto tmp19 = in_ptr0[2 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2967998Z                                 tmp18 = tmp19;
2023-01-11T21:05:10.2968065Z                             }
2023-01-11T21:05:10.2968160Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:05:10.2968276Z                             auto tmp21 = static_cast<long>(3 + (((8*i2) / 3)));
2023-01-11T21:05:10.2968369Z                             auto tmp22 = tmp21 < tmp4;
2023-01-11T21:05:10.2968465Z                             auto tmp23 = tmp2 & tmp22;
2023-01-11T21:05:10.2968556Z                             float tmp24 = 0.0;
2023-01-11T21:05:10.2968617Z                             if(tmp23)
2023-01-11T21:05:10.2968683Z                             {
2023-01-11T21:05:10.2968832Z                                 auto tmp25 = in_ptr0[3 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2968916Z                                 tmp24 = tmp25;
2023-01-11T21:05:10.2968983Z                             }
2023-01-11T21:05:10.2969076Z                             auto tmp26 = tmp24 + tmp20;
2023-01-11T21:05:10.2969189Z                             auto tmp27 = static_cast<long>(1 + (((8*i1) / 3)));
2023-01-11T21:05:10.2969269Z                             auto tmp28 = tmp27 < tmp1;
2023-01-11T21:05:10.2969362Z                             auto tmp29 = tmp28 & tmp5;
2023-01-11T21:05:10.2969447Z                             float tmp30 = 0.0;
2023-01-11T21:05:10.2969526Z                             if(tmp29)
2023-01-11T21:05:10.2969595Z                             {
2023-01-11T21:05:10.2969721Z                                 auto tmp31 = in_ptr0[16 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2969806Z                                 tmp30 = tmp31;
2023-01-11T21:05:10.2969876Z                             }
2023-01-11T21:05:10.2969959Z                             auto tmp32 = tmp30 + tmp26;
2023-01-11T21:05:10.2970052Z                             auto tmp33 = tmp28 & tmp10;
2023-01-11T21:05:10.2970143Z                             float tmp34 = 0.0;
2023-01-11T21:05:10.2970221Z                             if(tmp33)
2023-01-11T21:05:10.2970290Z                             {
2023-01-11T21:05:10.2970411Z                                 auto tmp35 = in_ptr0[17 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2970495Z                                 tmp34 = tmp35;
2023-01-11T21:05:10.2970550Z                             }
2023-01-11T21:05:10.2970642Z                             auto tmp36 = tmp34 + tmp32;
2023-01-11T21:05:10.2970736Z                             auto tmp37 = tmp28 & tmp16;
2023-01-11T21:05:10.2970824Z                             float tmp38 = 0.0;
2023-01-11T21:05:10.2970900Z                             if(tmp37)
2023-01-11T21:05:10.2970967Z                             {
2023-01-11T21:05:10.2971090Z                                 auto tmp39 = in_ptr0[18 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2971163Z                                 tmp38 = tmp39;
2023-01-11T21:05:10.2971229Z                             }
2023-01-11T21:05:10.2971324Z                             auto tmp40 = tmp38 + tmp36;
2023-01-11T21:05:10.2971416Z                             auto tmp41 = tmp28 & tmp22;
2023-01-11T21:05:10.2971500Z                             float tmp42 = 0.0;
2023-01-11T21:05:10.2971575Z                             if(tmp41)
2023-01-11T21:05:10.2971641Z                             {
2023-01-11T21:05:10.2971763Z                                 auto tmp43 = in_ptr0[19 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2971865Z                                 tmp42 = tmp43;
2023-01-11T21:05:10.2971930Z                             }
2023-01-11T21:05:10.2972024Z                             auto tmp44 = tmp42 + tmp40;
2023-01-11T21:05:10.2972141Z                             auto tmp45 = static_cast<long>(2 + (((8*i1) / 3)));
2023-01-11T21:05:10.2972236Z                             auto tmp46 = tmp45 < tmp1;
2023-01-11T21:05:10.2972329Z                             auto tmp47 = tmp46 & tmp5;
2023-01-11T21:05:10.2972414Z                             float tmp48 = 0.0;
2023-01-11T21:05:10.2972475Z                             if(tmp47)
2023-01-11T21:05:10.2972541Z                             {
2023-01-11T21:05:10.2972662Z                                 auto tmp49 = in_ptr0[32 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2972744Z                                 tmp48 = tmp49;
2023-01-11T21:05:10.2972811Z                             }
2023-01-11T21:05:10.2972903Z                             auto tmp50 = tmp48 + tmp44;
2023-01-11T21:05:10.2972998Z                             auto tmp51 = tmp46 & tmp10;
2023-01-11T21:05:10.2973073Z                             float tmp52 = 0.0;
2023-01-11T21:05:10.2973175Z                             if(tmp51)
2023-01-11T21:05:10.2973242Z                             {
2023-01-11T21:05:10.2973365Z                                 auto tmp53 = in_ptr0[33 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2973450Z                                 tmp52 = tmp53;
2023-01-11T21:05:10.2973517Z                             }
2023-01-11T21:05:10.2973609Z                             auto tmp54 = tmp52 + tmp50;
2023-01-11T21:05:10.2973702Z                             auto tmp55 = tmp46 & tmp16;
2023-01-11T21:05:10.2973776Z                             float tmp56 = 0.0;
2023-01-11T21:05:10.2973848Z                             if(tmp55)
2023-01-11T21:05:10.2973915Z                             {
2023-01-11T21:05:10.2974034Z                                 auto tmp57 = in_ptr0[34 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2974119Z                                 tmp56 = tmp57;
2023-01-11T21:05:10.2974185Z                             }
2023-01-11T21:05:10.2974280Z                             auto tmp58 = tmp56 + tmp54;
2023-01-11T21:05:10.2974360Z                             auto tmp59 = tmp46 & tmp22;
2023-01-11T21:05:10.2974444Z                             float tmp60 = 0.0;
2023-01-11T21:05:10.2974518Z                             if(tmp59)
2023-01-11T21:05:10.2974585Z                             {
2023-01-11T21:05:10.2974708Z                                 auto tmp61 = in_ptr0[35 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2974790Z                                 tmp60 = tmp61;
2023-01-11T21:05:10.2974857Z                             }
2023-01-11T21:05:10.2974938Z                             auto tmp62 = tmp60 + tmp58;
2023-01-11T21:05:10.2975051Z                             auto tmp63 = static_cast<long>(3 + (((8*i1) / 3)));
2023-01-11T21:05:10.2975148Z                             auto tmp64 = tmp63 < tmp1;
2023-01-11T21:05:10.2975241Z                             auto tmp65 = tmp64 & tmp5;
2023-01-11T21:05:10.2975328Z                             float tmp66 = 0.0;
2023-01-11T21:05:10.2975402Z                             if(tmp65)
2023-01-11T21:05:10.2975468Z                             {
2023-01-11T21:05:10.2975587Z                                 auto tmp67 = in_ptr0[48 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2975658Z                                 tmp66 = tmp67;
2023-01-11T21:05:10.2975723Z                             }
2023-01-11T21:05:10.2975816Z                             auto tmp68 = tmp66 + tmp62;
2023-01-11T21:05:10.2975908Z                             auto tmp69 = tmp64 & tmp10;
2023-01-11T21:05:10.2975993Z                             float tmp70 = 0.0;
2023-01-11T21:05:10.2976067Z                             if(tmp69)
2023-01-11T21:05:10.2976132Z                             {
2023-01-11T21:05:10.2976271Z                                 auto tmp71 = in_ptr0[49 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2976354Z                                 tmp70 = tmp71;
2023-01-11T21:05:10.2976422Z                             }
2023-01-11T21:05:10.2976516Z                             auto tmp72 = tmp70 + tmp68;
2023-01-11T21:05:10.2976607Z                             auto tmp73 = tmp64 & tmp16;
2023-01-11T21:05:10.2976690Z                             float tmp74 = 0.0;
2023-01-11T21:05:10.2976763Z                             if(tmp73)
2023-01-11T21:05:10.2976817Z                             {
2023-01-11T21:05:10.2976938Z                                 auto tmp75 = in_ptr0[50 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2977022Z                                 tmp74 = tmp75;
2023-01-11T21:05:10.2977086Z                             }
2023-01-11T21:05:10.2977179Z                             auto tmp76 = tmp74 + tmp72;
2023-01-11T21:05:10.2977271Z                             auto tmp77 = tmp64 & tmp22;
2023-01-11T21:05:10.2977357Z                             float tmp78 = 0.0;
2023-01-11T21:05:10.2977434Z                             if(tmp77)
2023-01-11T21:05:10.2977518Z                             {
2023-01-11T21:05:10.2977640Z                                 auto tmp79 = in_ptr0[51 + (16*(((8*i1) / 3))) + (256*i0) + (((8*i2) / 3))];
2023-01-11T21:05:10.2977722Z                                 tmp78 = tmp79;
2023-01-11T21:05:10.2977789Z                             }
2023-01-11T21:05:10.2977883Z                             auto tmp80 = tmp78 + tmp76;
2023-01-11T21:05:10.2977966Z                             float tmp81 = 0.0;
2023-01-11T21:05:10.2978040Z                             if(tmp6)
2023-01-11T21:05:10.2978095Z                             {
2023-01-11T21:05:10.2978202Z                                 auto tmp82 = static_cast<float>(1);
2023-01-11T21:05:10.2978285Z                                 tmp81 = tmp82;
2023-01-11T21:05:10.2978352Z                             }
2023-01-11T21:05:10.2978438Z                             float tmp83 = 0.0;
2023-01-11T21:05:10.2978606Z                             if(tmp11)
2023-01-11T21:05:10.2978680Z                             {
2023-01-11T21:05:10.2978775Z                                 auto tmp84 = static_cast<float>(1);
2023-01-11T21:05:10.2978855Z                                 tmp83 = tmp84;
2023-01-11T21:05:10.2978923Z                             }
2023-01-11T21:05:10.2979019Z                             auto tmp85 = tmp83 + tmp81;
2023-01-11T21:05:10.2979104Z                             float tmp86 = 0.0;
2023-01-11T21:05:10.2979178Z                             if(tmp17)
2023-01-11T21:05:10.2979244Z                             {
2023-01-11T21:05:10.2979338Z                                 auto tmp87 = static_cast<float>(1);
2023-01-11T21:05:10.2979421Z                                 tmp86 = tmp87;
2023-01-11T21:05:10.2979491Z                             }
2023-01-11T21:05:10.2979588Z                             auto tmp88 = tmp86 + tmp85;
2023-01-11T21:05:10.2979674Z                             float tmp89 = 0.0;
2023-01-11T21:05:10.2979750Z                             if(tmp23)
2023-01-11T21:05:10.2979820Z                             {
2023-01-11T21:05:10.2979914Z                                 auto tmp90 = static_cast<float>(1);
2023-01-11T21:05:10.2979997Z                                 tmp89 = tmp90;
2023-01-11T21:05:10.2980065Z                             }
2023-01-11T21:05:10.2980159Z                             auto tmp91 = tmp89 + tmp88;
2023-01-11T21:05:10.2980244Z                             float tmp92 = 0.0;
2023-01-11T21:05:10.2980316Z                             if(tmp29)
2023-01-11T21:05:10.2980384Z                             {
2023-01-11T21:05:10.2980478Z                                 auto tmp93 = static_cast<float>(1);
2023-01-11T21:05:10.2980560Z                                 tmp92 = tmp93;
2023-01-11T21:05:10.2980626Z                             }
2023-01-11T21:05:10.2980770Z                             auto tmp94 = tmp92 + tmp91;
2023-01-11T21:05:10.2980853Z                             float tmp95 = 0.0;
2023-01-11T21:05:10.2980927Z                             if(tmp33)
2023-01-11T21:05:10.2980995Z                             {
2023-01-11T21:05:10.2981101Z                                 auto tmp96 = static_cast<float>(1);
2023-01-11T21:05:10.2981171Z                                 tmp95 = tmp96;
2023-01-11T21:05:10.2981236Z                             }
2023-01-11T21:05:10.2981330Z                             auto tmp97 = tmp95 + tmp94;
2023-01-11T21:05:10.2981415Z                             float tmp98 = 0.0;
2023-01-11T21:05:10.2981488Z                             if(tmp37)
2023-01-11T21:05:10.2981556Z                             {
2023-01-11T21:05:10.2981662Z                                 auto tmp99 = static_cast<float>(1);
2023-01-11T21:05:10.2981731Z                                 tmp98 = tmp99;
2023-01-11T21:05:10.2981798Z                             }
2023-01-11T21:05:10.2981898Z                             auto tmp100 = tmp98 + tmp97;
2023-01-11T21:05:10.2981987Z                             float tmp101 = 0.0;
2023-01-11T21:05:10.2982061Z                             if(tmp41)
2023-01-11T21:05:10.2982153Z                             {
2023-01-11T21:05:10.2982263Z                                 auto tmp102 = static_cast<float>(1);
2023-01-11T21:05:10.2982338Z                                 tmp101 = tmp102;
2023-01-11T21:05:10.2982407Z                             }
2023-01-11T21:05:10.2982504Z                             auto tmp103 = tmp101 + tmp100;
2023-01-11T21:05:10.2982594Z                             float tmp104 = 0.0;
2023-01-11T21:05:10.2982667Z                             if(tmp47)
2023-01-11T21:05:10.2982732Z                             {
2023-01-11T21:05:10.2982842Z                                 auto tmp105 = static_cast<float>(1);
2023-01-11T21:05:10.2982916Z                                 tmp104 = tmp105;
2023-01-11T21:05:10.2982983Z                             }
2023-01-11T21:05:10.2983081Z                             auto tmp106 = tmp104 + tmp103;
2023-01-11T21:05:10.2983168Z                             float tmp107 = 0.0;
2023-01-11T21:05:10.2983245Z                             if(tmp51)
2023-01-11T21:05:10.2983310Z                             {
2023-01-11T21:05:10.2983420Z                                 auto tmp108 = static_cast<float>(1);
2023-01-11T21:05:10.2983495Z                                 tmp107 = tmp108;
2023-01-11T21:05:10.2983560Z                             }
2023-01-11T21:05:10.2983660Z                             auto tmp109 = tmp107 + tmp106;
2023-01-11T21:05:10.2983747Z                             float tmp110 = 0.0;
2023-01-11T21:05:10.2983824Z                             if(tmp55)
2023-01-11T21:05:10.2983890Z                             {
2023-01-11T21:05:10.2983997Z                                 auto tmp111 = static_cast<float>(1);
2023-01-11T21:05:10.2984072Z                                 tmp110 = tmp111;
2023-01-11T21:05:10.2984139Z                             }
2023-01-11T21:05:10.2984235Z                             auto tmp112 = tmp110 + tmp109;
2023-01-11T21:05:10.2984323Z                             float tmp113 = 0.0;
2023-01-11T21:05:10.2984401Z                             if(tmp59)
2023-01-11T21:05:10.2984468Z                             {
2023-01-11T21:05:10.2984575Z                                 auto tmp114 = static_cast<float>(1);
2023-01-11T21:05:10.2984649Z                                 tmp113 = tmp114;
2023-01-11T21:05:10.2984716Z                             }
2023-01-11T21:05:10.2984815Z                             auto tmp115 = tmp113 + tmp112;
2023-01-11T21:05:10.2984902Z                             float tmp116 = 0.0;
2023-01-11T21:05:10.2984975Z                             if(tmp65)
2023-01-11T21:05:10.2985042Z                             {
2023-01-11T21:05:10.2985151Z                                 auto tmp117 = static_cast<float>(1);
2023-01-11T21:05:10.2985236Z                                 tmp116 = tmp117;
2023-01-11T21:05:10.2985322Z                             }
2023-01-11T21:05:10.2985417Z                             auto tmp118 = tmp116 + tmp115;
2023-01-11T21:05:10.2985506Z                             float tmp119 = 0.0;
2023-01-11T21:05:10.2985581Z                             if(tmp69)
2023-01-11T21:05:10.2985648Z                             {
2023-01-11T21:05:10.2985756Z                                 auto tmp120 = static_cast<float>(1);
2023-01-11T21:05:10.2985840Z                                 tmp119 = tmp120;
2023-01-11T21:05:10.2985895Z                             }
2023-01-11T21:05:10.2985994Z                             auto tmp121 = tmp119 + tmp118;
2023-01-11T21:05:10.2986080Z                             float tmp122 = 0.0;
2023-01-11T21:05:10.2986155Z                             if(tmp73)
2023-01-11T21:05:10.2986220Z                             {
2023-01-11T21:05:10.2986330Z                                 auto tmp123 = static_cast<float>(1);
2023-01-11T21:05:10.2986420Z                                 tmp122 = tmp123;
2023-01-11T21:05:10.2986475Z                             }
2023-01-11T21:05:10.2986575Z                             auto tmp124 = tmp122 + tmp121;
2023-01-11T21:05:10.2986702Z                             float tmp125 = 0.0;
2023-01-11T21:05:10.2986778Z                             if(tmp77)
2023-01-11T21:05:10.2986844Z                             {
2023-01-11T21:05:10.2986950Z                                 auto tmp126 = static_cast<float>(1);
2023-01-11T21:05:10.2987034Z                                 tmp125 = tmp126;
2023-01-11T21:05:10.2987088Z                             }
2023-01-11T21:05:10.2987184Z                             auto tmp127 = tmp125 + tmp124;
2023-01-11T21:05:10.2987282Z                             auto tmp128 = tmp80 / tmp127;
2023-01-11T21:05:10.2987385Z                             out_ptr0[i2 + (6*i1) + (36*i0)] = tmp128;
2023-01-11T21:05:10.2987452Z                         }
2023-01-11T21:05:10.2987516Z                     }
2023-01-11T21:05:10.2987590Z                 }
2023-01-11T21:05:10.2987640Z             }
2023-01-11T21:05:10.2987701Z         }
2023-01-11T21:05:10.2987780Z         #pragma omp for 
2023-01-11T21:05:10.2987866Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.2987928Z         {
2023-01-11T21:05:10.2988070Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.2988206Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.2988276Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2988370Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.2988433Z         }
2023-01-11T21:05:10.2988529Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.2988614Z         for(long i0=2048; i0<2048; i0+=1)
2023-01-11T21:05:10.2988676Z         {
2023-01-11T21:05:10.2988759Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.2988843Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.2988928Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.2989008Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.2989071Z         }
2023-01-11T21:05:10.2989132Z     }
2023-01-11T21:05:10.2989191Z }
2023-01-11T21:05:10.2989306Z ''')
2023-01-11T21:05:10.2989314Z 
2023-01-11T21:05:10.2989319Z 
2023-01-11T21:05:10.2989395Z async_compile.wait(globals())
2023-01-11T21:05:10.2989466Z del async_compile
2023-01-11T21:05:10.2989471Z 
2023-01-11T21:05:10.2989541Z def call(args):
2023-01-11T21:05:10.2989610Z     arg0_1, = args
2023-01-11T21:05:10.2989681Z     args.clear()
2023-01-11T21:05:10.2989899Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2990121Z     buf1 = empty_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2990284Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.2990340Z     del arg0_1
2023-01-11T21:05:10.2990491Z     buf2 = aten._adaptive_avg_pool2d(buf1, [2, 5])
2023-01-11T21:05:10.2990558Z     del buf1
2023-01-11T21:05:10.2990627Z     buf3 = buf2
2023-01-11T21:05:10.2990733Z     assert_size_stride(buf3, (2, 4, 2, 5), (40, 10, 5, 1))
2023-01-11T21:05:10.2990800Z     del buf2
2023-01-11T21:05:10.2990877Z     return (buf0, buf3, )
2023-01-11T21:05:10.2990882Z 
2023-01-11T21:05:10.2990886Z 
2023-01-11T21:05:10.2990949Z if __name__ == "__main__":
2023-01-11T21:05:10.2991062Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.2991186Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.2991405Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.2991514Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.2991778Z [2023-01-11 20:44:24,404] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 8
2023-01-11T21:05:10.2992215Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.2992345Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.2992600Z [2023-01-11 20:44:24,462] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 9
2023-01-11T21:05:10.2992607Z 
2023-01-11T21:05:10.2992611Z 
2023-01-11T21:05:10.2992706Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.2992762Z import torch
2023-01-11T21:05:10.2992833Z import random
2023-01-11T21:05:10.2992947Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.2993068Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.2993073Z 
2023-01-11T21:05:10.2993151Z aten = torch.ops.aten
2023-01-11T21:05:10.2993288Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.2993381Z async_compile = AsyncCompile()
2023-01-11T21:05:10.2993386Z 
2023-01-11T21:05:10.2993390Z 
2023-01-11T21:05:10.2993510Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.2993716Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.2993833Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.2993934Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.2994028Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.2994087Z {
2023-01-11T21:05:10.2994183Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.2994230Z     {
2023-01-11T21:05:10.2994304Z         #pragma omp for 
2023-01-11T21:05:10.2994382Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.2994444Z         {
2023-01-11T21:05:10.2994522Z             #pragma GCC ivdep
2023-01-11T21:05:10.2994603Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:05:10.2994667Z             {
2023-01-11T21:05:10.2994738Z                 #pragma GCC ivdep
2023-01-11T21:05:10.2994827Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:05:10.2994888Z                 {
2023-01-11T21:05:10.2994956Z                     {
2023-01-11T21:05:10.2995022Z                         {
2023-01-11T21:05:10.2995132Z                             auto tmp0 = static_cast<long>((i1 / 2));
2023-01-11T21:05:10.2995249Z                             auto tmp1 = static_cast<long>(((8 + (3*i1)) / 6));
2023-01-11T21:05:10.2995331Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.2995440Z                             auto tmp3 = static_cast<long>((i2 / 2));
2023-01-11T21:05:10.2995554Z                             auto tmp4 = static_cast<long>(((8 + (3*i2)) / 6));
2023-01-11T21:05:10.2995648Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:05:10.2995738Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:05:10.2995825Z                             float tmp7 = 0.0;
2023-01-11T21:05:10.2995934Z                             if(tmp6)
2023-01-11T21:05:10.2996003Z                             {
2023-01-11T21:05:10.2996108Z                                 auto tmp8 = in_ptr0[(3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:05:10.2996191Z                                 tmp7 = tmp8;
2023-01-11T21:05:10.2996258Z                             }
2023-01-11T21:05:10.2996368Z                             auto tmp9 = static_cast<long>(1 + (i2 / 2));
2023-01-11T21:05:10.2996462Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:05:10.2996557Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:05:10.2996644Z                             float tmp12 = 0.0;
2023-01-11T21:05:10.2996707Z                             if(tmp11)
2023-01-11T21:05:10.2996774Z                             {
2023-01-11T21:05:10.2996889Z                                 auto tmp13 = in_ptr0[1 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:05:10.2996976Z                                 tmp12 = tmp13;
2023-01-11T21:05:10.2997045Z                             }
2023-01-11T21:05:10.2997139Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:05:10.2997288Z                             auto tmp15 = static_cast<long>(1 + (i1 / 2));
2023-01-11T21:05:10.2997373Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:05:10.2997466Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:05:10.2997553Z                             float tmp18 = 0.0;
2023-01-11T21:05:10.2997627Z                             if(tmp17)
2023-01-11T21:05:10.2997696Z                             {
2023-01-11T21:05:10.2997810Z                                 auto tmp19 = in_ptr0[3 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:05:10.2997894Z                                 tmp18 = tmp19;
2023-01-11T21:05:10.2997959Z                             }
2023-01-11T21:05:10.2998043Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:05:10.2998140Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:05:10.2998227Z                             float tmp22 = 0.0;
2023-01-11T21:05:10.2998301Z                             if(tmp21)
2023-01-11T21:05:10.2998373Z                             {
2023-01-11T21:05:10.2998486Z                                 auto tmp23 = in_ptr0[4 + (3*(i1 / 2)) + (9*i0) + (i2 / 2)];
2023-01-11T21:05:10.2998570Z                                 tmp22 = tmp23;
2023-01-11T21:05:10.2998624Z                             }
2023-01-11T21:05:10.2998721Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:05:10.2998807Z                             float tmp25 = 0.0;
2023-01-11T21:05:10.2998880Z                             if(tmp6)
2023-01-11T21:05:10.2998947Z                             {
2023-01-11T21:05:10.2999054Z                                 auto tmp26 = static_cast<float>(1);
2023-01-11T21:05:10.2999137Z                                 tmp25 = tmp26;
2023-01-11T21:05:10.2999191Z                             }
2023-01-11T21:05:10.2999279Z                             float tmp27 = 0.0;
2023-01-11T21:05:10.2999353Z                             if(tmp11)
2023-01-11T21:05:10.2999420Z                             {
2023-01-11T21:05:10.2999529Z                                 auto tmp28 = static_cast<float>(1);
2023-01-11T21:05:10.2999610Z                                 tmp27 = tmp28;
2023-01-11T21:05:10.2999676Z                             }
2023-01-11T21:05:10.2999758Z                             auto tmp29 = tmp27 + tmp25;
2023-01-11T21:05:10.2999842Z                             float tmp30 = 0.0;
2023-01-11T21:05:10.2999916Z                             if(tmp17)
2023-01-11T21:05:10.2999983Z                             {
2023-01-11T21:05:10.3000089Z                                 auto tmp31 = static_cast<float>(1);
2023-01-11T21:05:10.3000172Z                                 tmp30 = tmp31;
2023-01-11T21:05:10.3000236Z                             }
2023-01-11T21:05:10.3000318Z                             auto tmp32 = tmp30 + tmp29;
2023-01-11T21:05:10.3000435Z                             float tmp33 = 0.0;
2023-01-11T21:05:10.3000508Z                             if(tmp21)
2023-01-11T21:05:10.3000574Z                             {
2023-01-11T21:05:10.3000845Z                                 auto tmp34 = static_cast<float>(1);
2023-01-11T21:05:10.3000961Z                                 tmp33 = tmp34;
2023-01-11T21:05:10.3001032Z                             }
2023-01-11T21:05:10.3001114Z                             auto tmp35 = tmp33 + tmp32;
2023-01-11T21:05:10.3001210Z                             auto tmp36 = tmp24 / tmp35;
2023-01-11T21:05:10.3001313Z                             out_ptr0[i2 + (6*i1) + (36*i0)] = tmp36;
2023-01-11T21:05:10.3001379Z                         }
2023-01-11T21:05:10.3001444Z                     }
2023-01-11T21:05:10.3001506Z                 }
2023-01-11T21:05:10.3001568Z             }
2023-01-11T21:05:10.3001615Z         }
2023-01-11T21:05:10.3001691Z         #pragma omp for 
2023-01-11T21:05:10.3001774Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3001836Z         {
2023-01-11T21:05:10.3001913Z             #pragma GCC ivdep
2023-01-11T21:05:10.3001994Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.3002112Z             {
2023-01-11T21:05:10.3002182Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3002266Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:05:10.3002330Z                 {
2023-01-11T21:05:10.3002395Z                     {
2023-01-11T21:05:10.3002461Z                         {
2023-01-11T21:05:10.3002574Z                             auto tmp0 = static_cast<long>(((3*i1) / 2));
2023-01-11T21:05:10.3002688Z                             auto tmp1 = static_cast<long>(2 + (((3*i1) / 2)));
2023-01-11T21:05:10.3002772Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.3002881Z                             auto tmp3 = static_cast<long>(((3*i2) / 5));
2023-01-11T21:05:10.3002991Z                             auto tmp4 = static_cast<long>(((7 + (3*i2)) / 5));
2023-01-11T21:05:10.3003085Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:05:10.3003176Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:05:10.3003266Z                             float tmp7 = 0.0;
2023-01-11T21:05:10.3003340Z                             if(tmp6)
2023-01-11T21:05:10.3003396Z                             {
2023-01-11T21:05:10.3003519Z                                 auto tmp8 = in_ptr0[(3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:05:10.3003628Z                                 auto tmp9 = static_cast<float>(1);
2023-01-11T21:05:10.3003726Z                                 auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.3003814Z                                 tmp7 = tmp10;
2023-01-11T21:05:10.3003882Z                             }
2023-01-11T21:05:10.3003999Z                             auto tmp11 = static_cast<long>(1 + (((3*i2) / 5)));
2023-01-11T21:05:10.3004093Z                             auto tmp12 = tmp11 < tmp4;
2023-01-11T21:05:10.3004179Z                             auto tmp13 = tmp2 & tmp12;
2023-01-11T21:05:10.3004268Z                             float tmp14 = 0.0;
2023-01-11T21:05:10.3004344Z                             if(tmp13)
2023-01-11T21:05:10.3004409Z                             {
2023-01-11T21:05:10.3004532Z                                 auto tmp15 = in_ptr0[1 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:05:10.3004640Z                                 auto tmp16 = static_cast<float>(1);
2023-01-11T21:05:10.3004744Z                                 auto tmp17 = tmp15 + tmp16;
2023-01-11T21:05:10.3004818Z                                 tmp14 = tmp17;
2023-01-11T21:05:10.3004885Z                             }
2023-01-11T21:05:10.3004979Z                             auto tmp18 = tmp14 + tmp7;
2023-01-11T21:05:10.3005095Z                             auto tmp19 = static_cast<long>(1 + (((3*i1) / 2)));
2023-01-11T21:05:10.3005188Z                             auto tmp20 = tmp19 < tmp1;
2023-01-11T21:05:10.3005324Z                             auto tmp21 = tmp20 & tmp5;
2023-01-11T21:05:10.3005411Z                             float tmp22 = 0.0;
2023-01-11T21:05:10.3005486Z                             if(tmp21)
2023-01-11T21:05:10.3005542Z                             {
2023-01-11T21:05:10.3005663Z                                 auto tmp23 = in_ptr0[3 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:05:10.3005770Z                                 auto tmp24 = static_cast<float>(1);
2023-01-11T21:05:10.3005868Z                                 auto tmp25 = tmp23 + tmp24;
2023-01-11T21:05:10.3005953Z                                 tmp22 = tmp25;
2023-01-11T21:05:10.3006023Z                             }
2023-01-11T21:05:10.3006118Z                             auto tmp26 = tmp22 + tmp18;
2023-01-11T21:05:10.3006201Z                             auto tmp27 = tmp20 & tmp12;
2023-01-11T21:05:10.3006287Z                             float tmp28 = 0.0;
2023-01-11T21:05:10.3006363Z                             if(tmp27)
2023-01-11T21:05:10.3006429Z                             {
2023-01-11T21:05:10.3006551Z                                 auto tmp29 = in_ptr0[4 + (3*(((3*i1) / 2))) + (9*i0) + (((3*i2) / 5))];
2023-01-11T21:05:10.3006692Z                                 auto tmp30 = static_cast<float>(1);
2023-01-11T21:05:10.3006789Z                                 auto tmp31 = tmp29 + tmp30;
2023-01-11T21:05:10.3006865Z                                 tmp28 = tmp31;
2023-01-11T21:05:10.3006932Z                             }
2023-01-11T21:05:10.3007025Z                             auto tmp32 = tmp28 + tmp26;
2023-01-11T21:05:10.3007113Z                             float tmp33 = 0.0;
2023-01-11T21:05:10.3007188Z                             if(tmp6)
2023-01-11T21:05:10.3007255Z                             {
2023-01-11T21:05:10.3007362Z                                 auto tmp34 = static_cast<float>(1);
2023-01-11T21:05:10.3007444Z                                 tmp33 = tmp34;
2023-01-11T21:05:10.3007502Z                             }
2023-01-11T21:05:10.3007588Z                             float tmp35 = 0.0;
2023-01-11T21:05:10.3007662Z                             if(tmp13)
2023-01-11T21:05:10.3007777Z                             {
2023-01-11T21:05:10.3007937Z                                 auto tmp36 = static_cast<float>(1);
2023-01-11T21:05:10.3008051Z                                 tmp35 = tmp36;
2023-01-11T21:05:10.3008184Z                             }
2023-01-11T21:05:10.3008268Z                             auto tmp37 = tmp35 + tmp33;
2023-01-11T21:05:10.3008386Z                             float tmp38 = 0.0;
2023-01-11T21:05:10.3008590Z                             if(tmp21)
2023-01-11T21:05:10.3008688Z                             {
2023-01-11T21:05:10.3008826Z                                 auto tmp39 = static_cast<float>(1);
2023-01-11T21:05:10.3008898Z                                 tmp38 = tmp39;
2023-01-11T21:05:10.3009003Z                             }
2023-01-11T21:05:10.3009131Z                             auto tmp40 = tmp38 + tmp37;
2023-01-11T21:05:10.3009248Z                             float tmp41 = 0.0;
2023-01-11T21:05:10.3009355Z                             if(tmp27)
2023-01-11T21:05:10.3009459Z                             {
2023-01-11T21:05:10.3009622Z                                 auto tmp42 = static_cast<float>(1);
2023-01-11T21:05:10.3009693Z                                 tmp41 = tmp42;
2023-01-11T21:05:10.3009788Z                             }
2023-01-11T21:05:10.3009922Z                             auto tmp43 = tmp41 + tmp40;
2023-01-11T21:05:10.3010045Z                             auto tmp44 = tmp32 / tmp43;
2023-01-11T21:05:10.3010178Z                             out_ptr1[i2 + (5*i1) + (10*i0)] = tmp44;
2023-01-11T21:05:10.3010304Z                         }
2023-01-11T21:05:10.3010397Z                     }
2023-01-11T21:05:10.3010448Z                 }
2023-01-11T21:05:10.3010538Z             }
2023-01-11T21:05:10.3010642Z         }
2023-01-11T21:05:10.3010772Z     }
2023-01-11T21:05:10.3010860Z }
2023-01-11T21:05:10.3010988Z ''')
2023-01-11T21:05:10.3010994Z 
2023-01-11T21:05:10.3010998Z 
2023-01-11T21:05:10.3011117Z async_compile.wait(globals())
2023-01-11T21:05:10.3011175Z del async_compile
2023-01-11T21:05:10.3011184Z 
2023-01-11T21:05:10.3011281Z def call(args):
2023-01-11T21:05:10.3011376Z     arg0_1, = args
2023-01-11T21:05:10.3011477Z     args.clear()
2023-01-11T21:05:10.3011753Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3011992Z     buf1 = empty_strided((2, 4, 2, 5), (40, 10, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3012188Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3012287Z     del arg0_1
2023-01-11T21:05:10.3012353Z     return (buf0, buf1, )
2023-01-11T21:05:10.3012358Z 
2023-01-11T21:05:10.3012362Z 
2023-01-11T21:05:10.3012497Z if __name__ == "__main__":
2023-01-11T21:05:10.3012640Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3012791Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3013073Z     arg0_1 = rand_strided((2, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3013231Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3013529Z [2023-01-11 20:44:27,435] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 9
2023-01-11T21:05:10.3013962Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3014117Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3014358Z [2023-01-11 20:44:27,525] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 10
2023-01-11T21:05:10.3014366Z 
2023-01-11T21:05:10.3014413Z 
2023-01-11T21:05:10.3014494Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3014600Z import torch
2023-01-11T21:05:10.3014702Z import random
2023-01-11T21:05:10.3014844Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3015018Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3015023Z 
2023-01-11T21:05:10.3015130Z aten = torch.ops.aten
2023-01-11T21:05:10.3015292Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3015368Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3015373Z 
2023-01-11T21:05:10.3015377Z 
2023-01-11T21:05:10.3015571Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3015809Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3015958Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3016085Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3016213Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3016321Z {
2023-01-11T21:05:10.3016448Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3016499Z     {
2023-01-11T21:05:10.3016602Z         #pragma omp for 
2023-01-11T21:05:10.3016720Z         for(long i0=0; i0<18; i0+=1)
2023-01-11T21:05:10.3016810Z         {
2023-01-11T21:05:10.3016978Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3017100Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3017149Z         }
2023-01-11T21:05:10.3017272Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3017399Z         for(long i0=288; i0<288; i0+=1)
2023-01-11T21:05:10.3017489Z         {
2023-01-11T21:05:10.3017608Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3017746Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.3017835Z         }
2023-01-11T21:05:10.3017896Z         #pragma omp for 
2023-01-11T21:05:10.3018039Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3018127Z         {
2023-01-11T21:05:10.3018237Z             #pragma GCC ivdep
2023-01-11T21:05:10.3018394Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.3018588Z             {
2023-01-11T21:05:10.3018700Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3018775Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:05:10.3018868Z                 {
2023-01-11T21:05:10.3018960Z                     {
2023-01-11T21:05:10.3019057Z                         {
2023-01-11T21:05:10.3019198Z                             auto tmp0 = static_cast<long>(3*i1);
2023-01-11T21:05:10.3019339Z                             auto tmp1 = static_cast<long>(3 + (3*i1));
2023-01-11T21:05:10.3019491Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.3019590Z                             auto tmp3 = static_cast<long>(((6*i2) / 5));
2023-01-11T21:05:10.3019737Z                             auto tmp4 = static_cast<long>(2 + (((6*i2) / 5)));
2023-01-11T21:05:10.3019896Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:05:10.3020014Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:05:10.3020161Z                             float tmp7 = 0.0;
2023-01-11T21:05:10.3020266Z                             if(tmp6)
2023-01-11T21:05:10.3020368Z                             {
2023-01-11T21:05:10.3020472Z                                 auto tmp8 = in_ptr0[(18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3020610Z                                 auto tmp9 = static_cast<float>(1);
2023-01-11T21:05:10.3020755Z                                 auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.3020871Z                                 tmp7 = tmp10;
2023-01-11T21:05:10.3020968Z                             }
2023-01-11T21:05:10.3021111Z                             auto tmp11 = static_cast<long>(1 + (((6*i2) / 5)));
2023-01-11T21:05:10.3021235Z                             auto tmp12 = tmp11 < tmp4;
2023-01-11T21:05:10.3021368Z                             auto tmp13 = tmp2 & tmp12;
2023-01-11T21:05:10.3021448Z                             float tmp14 = 0.0;
2023-01-11T21:05:10.3021553Z                             if(tmp13)
2023-01-11T21:05:10.3021653Z                             {
2023-01-11T21:05:10.3021820Z                                 auto tmp15 = in_ptr0[1 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3021986Z                                 auto tmp16 = static_cast<float>(1);
2023-01-11T21:05:10.3022115Z                                 auto tmp17 = tmp15 + tmp16;
2023-01-11T21:05:10.3022236Z                                 tmp14 = tmp17;
2023-01-11T21:05:10.3022291Z                             }
2023-01-11T21:05:10.3022423Z                             auto tmp18 = tmp14 + tmp7;
2023-01-11T21:05:10.3022566Z                             auto tmp19 = static_cast<long>(1 + (3*i1));
2023-01-11T21:05:10.3022689Z                             auto tmp20 = tmp19 < tmp1;
2023-01-11T21:05:10.3022816Z                             auto tmp21 = tmp20 & tmp5;
2023-01-11T21:05:10.3022951Z                             float tmp22 = 0.0;
2023-01-11T21:05:10.3023055Z                             if(tmp21)
2023-01-11T21:05:10.3023112Z                             {
2023-01-11T21:05:10.3023260Z                                 auto tmp23 = in_ptr0[6 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3023404Z                                 auto tmp24 = static_cast<float>(1);
2023-01-11T21:05:10.3023531Z                                 auto tmp25 = tmp23 + tmp24;
2023-01-11T21:05:10.3023646Z                                 tmp22 = tmp25;
2023-01-11T21:05:10.3023741Z                             }
2023-01-11T21:05:10.3023865Z                             auto tmp26 = tmp22 + tmp18;
2023-01-11T21:05:10.3024037Z                             auto tmp27 = tmp20 & tmp12;
2023-01-11T21:05:10.3024114Z                             float tmp28 = 0.0;
2023-01-11T21:05:10.3024218Z                             if(tmp27)
2023-01-11T21:05:10.3024354Z                             {
2023-01-11T21:05:10.3024498Z                                 auto tmp29 = in_ptr0[7 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3024637Z                                 auto tmp30 = static_cast<float>(1);
2023-01-11T21:05:10.3024763Z                                 auto tmp31 = tmp29 + tmp30;
2023-01-11T21:05:10.3024876Z                                 tmp28 = tmp31;
2023-01-11T21:05:10.3024932Z                             }
2023-01-11T21:05:10.3025061Z                             auto tmp32 = tmp28 + tmp26;
2023-01-11T21:05:10.3025219Z                             auto tmp33 = static_cast<long>(2 + (3*i1));
2023-01-11T21:05:10.3025349Z                             auto tmp34 = tmp33 < tmp1;
2023-01-11T21:05:10.3025471Z                             auto tmp35 = tmp34 & tmp5;
2023-01-11T21:05:10.3025584Z                             float tmp36 = 0.0;
2023-01-11T21:05:10.3025688Z                             if(tmp35)
2023-01-11T21:05:10.3025790Z                             {
2023-01-11T21:05:10.3025895Z                                 auto tmp37 = in_ptr0[12 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3026059Z                                 auto tmp38 = static_cast<float>(1);
2023-01-11T21:05:10.3026222Z                                 auto tmp39 = tmp37 + tmp38;
2023-01-11T21:05:10.3026354Z                                 tmp36 = tmp39;
2023-01-11T21:05:10.3026449Z                             }
2023-01-11T21:05:10.3026574Z                             auto tmp40 = tmp36 + tmp32;
2023-01-11T21:05:10.3026699Z                             auto tmp41 = tmp34 & tmp12;
2023-01-11T21:05:10.3026774Z                             float tmp42 = 0.0;
2023-01-11T21:05:10.3026877Z                             if(tmp41)
2023-01-11T21:05:10.3026974Z                             {
2023-01-11T21:05:10.3027128Z                                 auto tmp43 = in_ptr0[13 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3027266Z                                 auto tmp44 = static_cast<float>(1);
2023-01-11T21:05:10.3027410Z                                 auto tmp45 = tmp43 + tmp44;
2023-01-11T21:05:10.3027524Z                                 tmp42 = tmp45;
2023-01-11T21:05:10.3027581Z                             }
2023-01-11T21:05:10.3027703Z                             auto tmp46 = tmp42 + tmp40;
2023-01-11T21:05:10.3027827Z                             auto tmp47 = tmp1 < tmp1;
2023-01-11T21:05:10.3027953Z                             auto tmp48 = tmp47 & tmp5;
2023-01-11T21:05:10.3028078Z                             float tmp49 = 0.0;
2023-01-11T21:05:10.3028210Z                             if(tmp48)
2023-01-11T21:05:10.3028305Z                             {
2023-01-11T21:05:10.3028467Z                                 auto tmp50 = in_ptr0[18 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3028562Z                                 auto tmp51 = static_cast<float>(1);
2023-01-11T21:05:10.3028688Z                                 auto tmp52 = tmp50 + tmp51;
2023-01-11T21:05:10.3028802Z                                 tmp49 = tmp52;
2023-01-11T21:05:10.3028898Z                             }
2023-01-11T21:05:10.3029028Z                             auto tmp53 = tmp49 + tmp46;
2023-01-11T21:05:10.3029151Z                             auto tmp54 = tmp47 & tmp12;
2023-01-11T21:05:10.3029269Z                             float tmp55 = 0.0;
2023-01-11T21:05:10.3029333Z                             if(tmp54)
2023-01-11T21:05:10.3029431Z                             {
2023-01-11T21:05:10.3029593Z                                 auto tmp56 = in_ptr0[19 + (18*i1) + (36*i0) + (((6*i2) / 5))];
2023-01-11T21:05:10.3029734Z                                 auto tmp57 = static_cast<float>(1);
2023-01-11T21:05:10.3029859Z                                 auto tmp58 = tmp56 + tmp57;
2023-01-11T21:05:10.3029978Z                                 tmp55 = tmp58;
2023-01-11T21:05:10.3030077Z                             }
2023-01-11T21:05:10.3030160Z                             auto tmp59 = tmp55 + tmp53;
2023-01-11T21:05:10.3030334Z                             float tmp60 = 0.0;
2023-01-11T21:05:10.3030436Z                             if(tmp6)
2023-01-11T21:05:10.3030531Z                             {
2023-01-11T21:05:10.3030686Z                                 auto tmp61 = static_cast<float>(1);
2023-01-11T21:05:10.3030801Z                                 tmp60 = tmp61;
2023-01-11T21:05:10.3030906Z                             }
2023-01-11T21:05:10.3030981Z                             float tmp62 = 0.0;
2023-01-11T21:05:10.3031086Z                             if(tmp13)
2023-01-11T21:05:10.3031180Z                             {
2023-01-11T21:05:10.3031318Z                                 auto tmp63 = static_cast<float>(1);
2023-01-11T21:05:10.3031428Z                                 tmp62 = tmp63;
2023-01-11T21:05:10.3031529Z                             }
2023-01-11T21:05:10.3031673Z                             auto tmp64 = tmp62 + tmp60;
2023-01-11T21:05:10.3031796Z                             float tmp65 = 0.0;
2023-01-11T21:05:10.3031861Z                             if(tmp21)
2023-01-11T21:05:10.3031954Z                             {
2023-01-11T21:05:10.3032117Z                                 auto tmp66 = static_cast<float>(1);
2023-01-11T21:05:10.3032257Z                                 tmp65 = tmp66;
2023-01-11T21:05:10.3032352Z                             }
2023-01-11T21:05:10.3032475Z                             auto tmp67 = tmp65 + tmp64;
2023-01-11T21:05:10.3032596Z                             float tmp68 = 0.0;
2023-01-11T21:05:10.3032659Z                             if(tmp27)
2023-01-11T21:05:10.3032774Z                             {
2023-01-11T21:05:10.3032914Z                                 auto tmp69 = static_cast<float>(1);
2023-01-11T21:05:10.3033030Z                                 tmp68 = tmp69;
2023-01-11T21:05:10.3033126Z                             }
2023-01-11T21:05:10.3033248Z                             auto tmp70 = tmp68 + tmp67;
2023-01-11T21:05:10.3033365Z                             float tmp71 = 0.0;
2023-01-11T21:05:10.3033431Z                             if(tmp35)
2023-01-11T21:05:10.3033535Z                             {
2023-01-11T21:05:10.3033673Z                                 auto tmp72 = static_cast<float>(1);
2023-01-11T21:05:10.3033806Z                                 tmp71 = tmp72;
2023-01-11T21:05:10.3033904Z                             }
2023-01-11T21:05:10.3034027Z                             auto tmp73 = tmp71 + tmp70;
2023-01-11T21:05:10.3034142Z                             float tmp74 = 0.0;
2023-01-11T21:05:10.3034205Z                             if(tmp41)
2023-01-11T21:05:10.3034330Z                             {
2023-01-11T21:05:10.3034474Z                                 auto tmp75 = static_cast<float>(1);
2023-01-11T21:05:10.3034584Z                                 tmp74 = tmp75;
2023-01-11T21:05:10.3034684Z                             }
2023-01-11T21:05:10.3034826Z                             auto tmp76 = tmp74 + tmp73;
2023-01-11T21:05:10.3034943Z                             float tmp77 = 0.0;
2023-01-11T21:05:10.3035004Z                             if(tmp48)
2023-01-11T21:05:10.3035100Z                             {
2023-01-11T21:05:10.3035238Z                                 auto tmp78 = static_cast<float>(1);
2023-01-11T21:05:10.3035357Z                                 tmp77 = tmp78;
2023-01-11T21:05:10.3035453Z                             }
2023-01-11T21:05:10.3035578Z                             auto tmp79 = tmp77 + tmp76;
2023-01-11T21:05:10.3035691Z                             float tmp80 = 0.0;
2023-01-11T21:05:10.3035752Z                             if(tmp54)
2023-01-11T21:05:10.3035865Z                             {
2023-01-11T21:05:10.3036000Z                                 auto tmp81 = static_cast<float>(1);
2023-01-11T21:05:10.3036113Z                                 tmp80 = tmp81;
2023-01-11T21:05:10.3036244Z                             }
2023-01-11T21:05:10.3036366Z                             auto tmp82 = tmp80 + tmp79;
2023-01-11T21:05:10.3036524Z                             auto tmp83 = tmp59 / tmp82;
2023-01-11T21:05:10.3036655Z                             out_ptr1[i2 + (5*i1) + (10*i0)] = tmp83;
2023-01-11T21:05:10.3036710Z                         }
2023-01-11T21:05:10.3036805Z                     }
2023-01-11T21:05:10.3036918Z                 }
2023-01-11T21:05:10.3037009Z             }
2023-01-11T21:05:10.3037105Z         }
2023-01-11T21:05:10.3037194Z     }
2023-01-11T21:05:10.3037242Z }
2023-01-11T21:05:10.3037366Z ''')
2023-01-11T21:05:10.3037374Z 
2023-01-11T21:05:10.3037379Z 
2023-01-11T21:05:10.3037496Z async_compile.wait(globals())
2023-01-11T21:05:10.3037595Z del async_compile
2023-01-11T21:05:10.3037600Z 
2023-01-11T21:05:10.3037699Z def call(args):
2023-01-11T21:05:10.3037812Z     arg0_1, = args
2023-01-11T21:05:10.3037918Z     args.clear()
2023-01-11T21:05:10.3038194Z     buf0 = empty_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3038392Z     buf1 = empty_strided((2, 4, 2, 5), (40, 10, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3038590Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3038689Z     del arg0_1
2023-01-11T21:05:10.3038838Z     return (buf0, buf1, )
2023-01-11T21:05:10.3038844Z 
2023-01-11T21:05:10.3038848Z 
2023-01-11T21:05:10.3038954Z if __name__ == "__main__":
2023-01-11T21:05:10.3039105Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3039284Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3039528Z     arg0_1 = rand_strided((2, 4, 6, 6), (144, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3039624Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3039920Z [2023-01-11 20:44:30,437] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 10
2023-01-11T21:05:10.3039926Z 
2023-01-11T21:05:10.3040018Z ok (9.710s)
2023-01-11T21:05:10.3040503Z   test_adaptive_avg_pool2d2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3040804Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3041131Z [2023-01-11 20:44:30,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 11
2023-01-11T21:05:10.3041403Z [2023-01-11 20:44:30,486] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._adaptive_avg_pool2d
2023-01-11T21:05:10.3041722Z [2023-01-11 20:44:30,492] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 11
2023-01-11T21:05:10.3041727Z 
2023-01-11T21:05:10.3041886Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3041943Z import torch
2023-01-11T21:05:10.3042046Z import random
2023-01-11T21:05:10.3042188Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3042337Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3042343Z 
2023-01-11T21:05:10.3112891Z aten = torch.ops.aten
2023-01-11T21:05:10.3113143Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3113238Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3113244Z 
2023-01-11T21:05:10.3113249Z 
2023-01-11T21:05:10.3113328Z async_compile.wait(globals())
2023-01-11T21:05:10.3113391Z del async_compile
2023-01-11T21:05:10.3113396Z 
2023-01-11T21:05:10.3113459Z def call(args):
2023-01-11T21:05:10.3113515Z     arg0_1, = args
2023-01-11T21:05:10.3113577Z     args.clear()
2023-01-11T21:05:10.3113681Z     buf0 = aten._adaptive_avg_pool2d(arg0_1, [4, 4])
2023-01-11T21:05:10.3113739Z     del arg0_1
2023-01-11T21:05:10.3113796Z     buf1 = buf0
2023-01-11T21:05:10.3113893Z     assert_size_stride(buf1, (2, 4, 4, 4), (64, 16, 4, 1))
2023-01-11T21:05:10.3114167Z     del buf0
2023-01-11T21:05:10.3114225Z     return (buf1, )
2023-01-11T21:05:10.3114231Z 
2023-01-11T21:05:10.3114235Z 
2023-01-11T21:05:10.3114303Z if __name__ == "__main__":
2023-01-11T21:05:10.3114413Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3114529Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3114793Z     arg0_1 = rand_strided((2, 4, 21, 21), (1764, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3114893Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3114899Z 
2023-01-11T21:05:10.3114956Z ok (0.053s)
2023-01-11T21:05:10.3115394Z   test_add_const_float_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3115513Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3115841Z [2023-01-11 20:44:30,514] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 12
2023-01-11T21:05:10.3116104Z [2023-01-11 20:44:33,210] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 12
2023-01-11T21:05:10.3116110Z 
2023-01-11T21:05:10.3116194Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3116254Z import torch
2023-01-11T21:05:10.3116315Z import random
2023-01-11T21:05:10.3116420Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3116532Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3116537Z 
2023-01-11T21:05:10.3116605Z aten = torch.ops.aten
2023-01-11T21:05:10.3116728Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3116810Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3116816Z 
2023-01-11T21:05:10.3116823Z 
2023-01-11T21:05:10.3116949Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3117143Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3117254Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3117345Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3117397Z {
2023-01-11T21:05:10.3117486Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3117534Z     {
2023-01-11T21:05:10.3117603Z         #pragma omp for 
2023-01-11T21:05:10.3117676Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3117729Z         {
2023-01-11T21:05:10.3117867Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3117994Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1.5));
2023-01-11T21:05:10.3118071Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3118149Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3118208Z         }
2023-01-11T21:05:10.3118295Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3118369Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.3118423Z         {
2023-01-11T21:05:10.3118498Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3118590Z             auto tmp1 = static_cast<float>(1.5);
2023-01-11T21:05:10.3118659Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3118729Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3118781Z         }
2023-01-11T21:05:10.3118834Z     }
2023-01-11T21:05:10.3118884Z }
2023-01-11T21:05:10.3118955Z ''')
2023-01-11T21:05:10.3118960Z 
2023-01-11T21:05:10.3118964Z 
2023-01-11T21:05:10.3119046Z async_compile.wait(globals())
2023-01-11T21:05:10.3119105Z del async_compile
2023-01-11T21:05:10.3119109Z 
2023-01-11T21:05:10.3119170Z def call(args):
2023-01-11T21:05:10.3119230Z     arg0_1, = args
2023-01-11T21:05:10.3119292Z     args.clear()
2023-01-11T21:05:10.3119478Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3119633Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3119693Z     del arg0_1
2023-01-11T21:05:10.3119749Z     return (buf0, )
2023-01-11T21:05:10.3119756Z 
2023-01-11T21:05:10.3119765Z 
2023-01-11T21:05:10.3119827Z if __name__ == "__main__":
2023-01-11T21:05:10.3119929Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3120043Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3120228Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3120325Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3120329Z 
2023-01-11T21:05:10.3120386Z ok (2.718s)
2023-01-11T21:05:10.3121042Z   test_add_const_int_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3121166Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3121456Z [2023-01-11 20:44:33,243] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 13
2023-01-11T21:05:10.3121715Z [2023-01-11 20:44:35,881] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 13
2023-01-11T21:05:10.3121720Z 
2023-01-11T21:05:10.3121805Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3121866Z import torch
2023-01-11T21:05:10.3121928Z import random
2023-01-11T21:05:10.3122033Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3122144Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3122149Z 
2023-01-11T21:05:10.3122217Z aten = torch.ops.aten
2023-01-11T21:05:10.3122337Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3122421Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3122426Z 
2023-01-11T21:05:10.3122431Z 
2023-01-11T21:05:10.3122554Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3122752Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3122862Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3122952Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3123004Z {
2023-01-11T21:05:10.3123093Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3123140Z     {
2023-01-11T21:05:10.3123206Z         #pragma omp for 
2023-01-11T21:05:10.3123277Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3123331Z         {
2023-01-11T21:05:10.3123457Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3123581Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3123659Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3123736Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3123790Z         }
2023-01-11T21:05:10.3123875Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3123950Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.3124004Z         {
2023-01-11T21:05:10.3124078Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3124170Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.3124240Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3124309Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3124362Z         }
2023-01-11T21:05:10.3124414Z     }
2023-01-11T21:05:10.3124464Z }
2023-01-11T21:05:10.3124532Z ''')
2023-01-11T21:05:10.3124538Z 
2023-01-11T21:05:10.3124541Z 
2023-01-11T21:05:10.3124622Z async_compile.wait(globals())
2023-01-11T21:05:10.3124680Z del async_compile
2023-01-11T21:05:10.3124685Z 
2023-01-11T21:05:10.3124745Z def call(args):
2023-01-11T21:05:10.3124804Z     arg0_1, = args
2023-01-11T21:05:10.3124906Z     args.clear()
2023-01-11T21:05:10.3125095Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3125223Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3125282Z     del arg0_1
2023-01-11T21:05:10.3125339Z     return (buf0, )
2023-01-11T21:05:10.3125344Z 
2023-01-11T21:05:10.3125354Z 
2023-01-11T21:05:10.3125415Z if __name__ == "__main__":
2023-01-11T21:05:10.3125519Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3125633Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3125818Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3125916Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3125921Z 
2023-01-11T21:05:10.3125978Z ok (2.670s)
2023-01-11T21:05:10.3126449Z   test_add_inplace_permuted_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3126571Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3126814Z [2023-01-11 20:44:35,911] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 14
2023-01-11T21:05:10.3127068Z [2023-01-11 20:44:38,654] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 14
2023-01-11T21:05:10.3127074Z 
2023-01-11T21:05:10.3127157Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3127218Z import torch
2023-01-11T21:05:10.3127280Z import random
2023-01-11T21:05:10.3127386Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3127496Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3127501Z 
2023-01-11T21:05:10.3127574Z aten = torch.ops.aten
2023-01-11T21:05:10.3127694Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3127777Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3127782Z 
2023-01-11T21:05:10.3127788Z 
2023-01-11T21:05:10.3127913Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3128108Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3128217Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3128311Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3128402Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3128453Z {
2023-01-11T21:05:10.3128535Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3128589Z     {
2023-01-11T21:05:10.3128671Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3128743Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3128797Z         {
2023-01-11T21:05:10.3128876Z             for(long i1=0; i1<12; i1+=1)
2023-01-11T21:05:10.3128931Z             {
2023-01-11T21:05:10.3129005Z                 for(long i2=0; i2<13; i2+=1)
2023-01-11T21:05:10.3129061Z                 {
2023-01-11T21:05:10.3129207Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i2) + (221*i1) + (2652*i0));
2023-01-11T21:05:10.3129343Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i2) + (221*i0));
2023-01-11T21:05:10.3129427Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3129530Z                     tmp2.store(out_ptr0 + (16*i2) + (221*i1) + (2652*i0));
2023-01-11T21:05:10.3129587Z                 }
2023-01-11T21:05:10.3129674Z                 #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.3129751Z                 for(long i2=208; i2<221; i2+=1)
2023-01-11T21:05:10.3129806Z                 {
2023-01-11T21:05:10.3129906Z                     auto tmp0 = out_ptr0[i2 + (221*i1) + (2652*i0)];
2023-01-11T21:05:10.3130029Z                     auto tmp1 = in_ptr1[i2 + (221*i0)];
2023-01-11T21:05:10.3130110Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3130202Z                     out_ptr0[i2 + (221*i1) + (2652*i0)] = tmp2;
2023-01-11T21:05:10.3130260Z                 }
2023-01-11T21:05:10.3130308Z             }
2023-01-11T21:05:10.3130361Z         }
2023-01-11T21:05:10.3130413Z     }
2023-01-11T21:05:10.3130464Z }
2023-01-11T21:05:10.3130535Z ''')
2023-01-11T21:05:10.3130540Z 
2023-01-11T21:05:10.3130544Z 
2023-01-11T21:05:10.3130625Z async_compile.wait(globals())
2023-01-11T21:05:10.3130686Z del async_compile
2023-01-11T21:05:10.3130691Z 
2023-01-11T21:05:10.3130746Z def call(args):
2023-01-11T21:05:10.3130811Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3130874Z     args.clear()
2023-01-11T21:05:10.3131027Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:05:10.3131086Z     del arg1_1
2023-01-11T21:05:10.3131151Z     return (arg0_1, )
2023-01-11T21:05:10.3131158Z 
2023-01-11T21:05:10.3131162Z 
2023-01-11T21:05:10.3131229Z if __name__ == "__main__":
2023-01-11T21:05:10.3131329Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3131471Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3131690Z     arg0_1 = rand_strided((2, 13, 12, 17), (2652, 17, 221, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3131897Z     arg1_1 = rand_strided((2, 13, 1, 17), (221, 17, 17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3132002Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3132007Z 
2023-01-11T21:05:10.3132065Z ok (2.788s)
2023-01-11T21:05:10.3132491Z   test_addmm_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3132611Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3132862Z [2023-01-11 20:44:38,751] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 15
2023-01-11T21:05:10.3133116Z [2023-01-11 20:44:41,595] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 15
2023-01-11T21:05:10.3133122Z 
2023-01-11T21:05:10.3133202Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3133264Z import torch
2023-01-11T21:05:10.3133325Z import random
2023-01-11T21:05:10.3133431Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3133541Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3133546Z 
2023-01-11T21:05:10.3133615Z aten = torch.ops.aten
2023-01-11T21:05:10.3133739Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3133817Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3133829Z 
2023-01-11T21:05:10.3133833Z 
2023-01-11T21:05:10.3133953Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3134147Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3134258Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3134354Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3134447Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.3134537Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3134629Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.3134712Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.3134763Z {
2023-01-11T21:05:10.3134851Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3134903Z     {
2023-01-11T21:05:10.3134971Z         #pragma omp for 
2023-01-11T21:05:10.3135042Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3135127Z         {
2023-01-11T21:05:10.3135247Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3135370Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3135448Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3135534Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3135588Z         }
2023-01-11T21:05:10.3135673Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3135746Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.3135795Z         {
2023-01-11T21:05:10.3135870Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3135958Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.3136034Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3136103Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3136157Z         }
2023-01-11T21:05:10.3136222Z         #pragma omp for 
2023-01-11T21:05:10.3136289Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3136344Z         {
2023-01-11T21:05:10.3136464Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.3136588Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.3136689Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3136771Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.3136824Z         }
2023-01-11T21:05:10.3136904Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3136977Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.3137031Z         {
2023-01-11T21:05:10.3137105Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.3137195Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3137268Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3137338Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.3137388Z         }
2023-01-11T21:05:10.3137454Z         #pragma omp for 
2023-01-11T21:05:10.3137524Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3137581Z         {
2023-01-11T21:05:10.3137704Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i0);
2023-01-11T21:05:10.3137825Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.3137900Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3137983Z             tmp2.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.3138030Z         }
2023-01-11T21:05:10.3138116Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3138188Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.3138240Z         {
2023-01-11T21:05:10.3138313Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:05:10.3138403Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.3138567Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3138648Z             out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.3138701Z         }
2023-01-11T21:05:10.3138754Z     }
2023-01-11T21:05:10.3138804Z }
2023-01-11T21:05:10.3138880Z ''')
2023-01-11T21:05:10.3138887Z 
2023-01-11T21:05:10.3138891Z 
2023-01-11T21:05:10.3139016Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:05:10.3139213Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3139318Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.3139369Z {
2023-01-11T21:05:10.3139455Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3139508Z     {
2023-01-11T21:05:10.3139574Z         #pragma omp for 
2023-01-11T21:05:10.3139648Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3139696Z         {
2023-01-11T21:05:10.3139824Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.3139948Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:05:10.3140023Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3140109Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.3140163Z         }
2023-01-11T21:05:10.3140287Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3140360Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.3140407Z         {
2023-01-11T21:05:10.3140485Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.3140576Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:05:10.3140652Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3140723Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3140776Z         }
2023-01-11T21:05:10.3140823Z     }
2023-01-11T21:05:10.3140872Z }
2023-01-11T21:05:10.3140941Z ''')
2023-01-11T21:05:10.3140945Z 
2023-01-11T21:05:10.3140950Z 
2023-01-11T21:05:10.3141028Z async_compile.wait(globals())
2023-01-11T21:05:10.3141091Z del async_compile
2023-01-11T21:05:10.3141096Z 
2023-01-11T21:05:10.3141157Z def call(args):
2023-01-11T21:05:10.3141229Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.3141291Z     args.clear()
2023-01-11T21:05:10.3141470Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3141655Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3141831Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3142088Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.3142150Z     del arg0_1
2023-01-11T21:05:10.3142206Z     del arg1_1
2023-01-11T21:05:10.3142262Z     del arg2_1
2023-01-11T21:05:10.3142438Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3142552Z     aten.addmm.out(buf0, buf1, buf2, beta=1, alpha=1, out=buf3)
2023-01-11T21:05:10.3142607Z     del buf0
2023-01-11T21:05:10.3142664Z     del buf1
2023-01-11T21:05:10.3142716Z     del buf2
2023-01-11T21:05:10.3142793Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:05:10.3142887Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.3142947Z     return (buf4, )
2023-01-11T21:05:10.3142957Z 
2023-01-11T21:05:10.3142962Z 
2023-01-11T21:05:10.3143024Z if __name__ == "__main__":
2023-01-11T21:05:10.3143129Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3143245Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3143432Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3143615Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3143795Z     arg2_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3143909Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.3143914Z 
2023-01-11T21:05:10.3143971Z ok (2.930s)
2023-01-11T21:05:10.3144402Z   test_alexnet_prefix_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3144523Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3144773Z [2023-01-11 20:44:41,989] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 16
2023-01-11T21:05:10.3144974Z [2023-01-11 20:44:42,126] torch._inductor.scheduler: [DEBUG] removed dead node: buf2
2023-01-11T21:05:10.3145227Z [2023-01-11 20:44:44,800] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 16
2023-01-11T21:05:10.3145232Z 
2023-01-11T21:05:10.3145315Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3145376Z import torch
2023-01-11T21:05:10.3145437Z import random
2023-01-11T21:05:10.3145541Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3145648Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3145697Z 
2023-01-11T21:05:10.3145767Z aten = torch.ops.aten
2023-01-11T21:05:10.3145891Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3145972Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3145979Z 
2023-01-11T21:05:10.3145984Z 
2023-01-11T21:05:10.3146108Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3146305Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3146415Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3146505Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3146552Z {
2023-01-11T21:05:10.3146640Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3146692Z     {
2023-01-11T21:05:10.3146760Z         #pragma omp for 
2023-01-11T21:05:10.3146834Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.3146888Z         {
2023-01-11T21:05:10.3146954Z             #pragma GCC ivdep
2023-01-11T21:05:10.3147031Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:05:10.3147086Z             {
2023-01-11T21:05:10.3147158Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3147239Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:05:10.3147321Z                 {
2023-01-11T21:05:10.3147380Z                     {
2023-01-11T21:05:10.3147434Z                         {
2023-01-11T21:05:10.3147536Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3147643Z                             auto tmp2 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3147749Z                             auto tmp5 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3147855Z                             auto tmp8 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3147960Z                             auto tmp11 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3148061Z                             auto tmp14 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3148168Z                             auto tmp17 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3148271Z                             auto tmp20 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3148376Z                             auto tmp23 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3148473Z                             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.3148564Z                             auto tmp3 = tmp2 * (tmp2>0);
2023-01-11T21:05:10.3148688Z                             auto tmp4 = (tmp1 != tmp1) ? tmp1 : std::max(tmp3, tmp1);
2023-01-11T21:05:10.3148781Z                             auto tmp6 = tmp5 * (tmp5>0);
2023-01-11T21:05:10.3148899Z                             auto tmp7 = (tmp4 != tmp4) ? tmp4 : std::max(tmp6, tmp4);
2023-01-11T21:05:10.3148988Z                             auto tmp9 = tmp8 * (tmp8>0);
2023-01-11T21:05:10.3149097Z                             auto tmp10 = (tmp7 != tmp7) ? tmp7 : std::max(tmp9, tmp7);
2023-01-11T21:05:10.3149194Z                             auto tmp12 = tmp11 * (tmp11>0);
2023-01-11T21:05:10.3149319Z                             auto tmp13 = (tmp10 != tmp10) ? tmp10 : std::max(tmp12, tmp10);
2023-01-11T21:05:10.3149415Z                             auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:05:10.3149533Z                             auto tmp16 = (tmp13 != tmp13) ? tmp13 : std::max(tmp15, tmp13);
2023-01-11T21:05:10.3149631Z                             auto tmp18 = tmp17 * (tmp17>0);
2023-01-11T21:05:10.3149752Z                             auto tmp19 = (tmp16 != tmp16) ? tmp16 : std::max(tmp18, tmp16);
2023-01-11T21:05:10.3149845Z                             auto tmp21 = tmp20 * (tmp20>0);
2023-01-11T21:05:10.3149968Z                             auto tmp22 = (tmp19 != tmp19) ? tmp19 : std::max(tmp21, tmp19);
2023-01-11T21:05:10.3150052Z                             auto tmp24 = tmp23 * (tmp23>0);
2023-01-11T21:05:10.3150168Z                             auto tmp25 = (tmp22 != tmp22) ? tmp22 : std::max(tmp24, tmp22);
2023-01-11T21:05:10.3150303Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp25;
2023-01-11T21:05:10.3150363Z                         }
2023-01-11T21:05:10.3150428Z                     }
2023-01-11T21:05:10.3150483Z                 }
2023-01-11T21:05:10.3150543Z             }
2023-01-11T21:05:10.3150591Z         }
2023-01-11T21:05:10.3150647Z     }
2023-01-11T21:05:10.3150699Z }
2023-01-11T21:05:10.3150775Z ''')
2023-01-11T21:05:10.3150779Z 
2023-01-11T21:05:10.3150783Z 
2023-01-11T21:05:10.3150865Z async_compile.wait(globals())
2023-01-11T21:05:10.3150933Z del async_compile
2023-01-11T21:05:10.3150938Z 
2023-01-11T21:05:10.3150998Z def call(args):
2023-01-11T21:05:10.3151076Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.3151134Z     args.clear()
2023-01-11T21:05:10.3151262Z     buf0 = aten.convolution(arg2_1, arg1_1, arg0_1, (4, 4), (2, 2), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.3151371Z     assert_size_stride(buf0, (16, 64, 55, 55), (193600, 3025, 55, 1))
2023-01-11T21:05:10.3151432Z     del arg0_1
2023-01-11T21:05:10.3151494Z     del arg1_1
2023-01-11T21:05:10.3151554Z     del arg2_1
2023-01-11T21:05:10.3151820Z     buf1 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3151943Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3152010Z     return (buf1, )
2023-01-11T21:05:10.3152015Z 
2023-01-11T21:05:10.3152020Z 
2023-01-11T21:05:10.3152086Z if __name__ == "__main__":
2023-01-11T21:05:10.3152197Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3152312Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3152503Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3152713Z     arg1_1 = rand_strided((64, 3, 11, 11), (363, 121, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3152935Z     arg2_1 = rand_strided((16, 3, 224, 224), (150528, 50176, 224, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3153045Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.3153050Z 
2023-01-11T21:05:10.3153111Z ok (4.208s)
2023-01-11T21:05:10.3153543Z   test_any_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3153664Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3153915Z [2023-01-11 20:44:45,891] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 17
2023-01-11T21:05:10.3154172Z [2023-01-11 20:44:48,544] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 17
2023-01-11T21:05:10.3154577Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3154692Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3154941Z [2023-01-11 20:44:48,658] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 18
2023-01-11T21:05:10.3155191Z [2023-01-11 20:44:51,601] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 18
2023-01-11T21:05:10.3155196Z 
2023-01-11T21:05:10.3155286Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3155341Z import torch
2023-01-11T21:05:10.3155402Z import random
2023-01-11T21:05:10.3155511Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3155655Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3155660Z 
2023-01-11T21:05:10.3155734Z aten = torch.ops.aten
2023-01-11T21:05:10.3155866Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3155951Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3155956Z 
2023-01-11T21:05:10.3155960Z 
2023-01-11T21:05:10.3156089Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3156281Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3156386Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.3156481Z                        bool* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.3156579Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3156670Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:05:10.3156761Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:05:10.3156816Z {
2023-01-11T21:05:10.3156888Z     auto out_ptr2 = in_out_ptr0;
2023-01-11T21:05:10.3156964Z     auto out_ptr3 = in_out_ptr1;
2023-01-11T21:05:10.3157021Z     {
2023-01-11T21:05:10.3157076Z         {
2023-01-11T21:05:10.3157149Z             bool tmp2 = 0;
2023-01-11T21:05:10.3157250Z             bool tmp4 = 0;
2023-01-11T21:05:10.3157309Z             bool tmp8 = 0;
2023-01-11T21:05:10.3157376Z             bool tmp10 = 0;
2023-01-11T21:05:10.3157477Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3157532Z             {
2023-01-11T21:05:10.3157693Z                 #pragma omp for reduction(||:tmp2) reduction(||:tmp4) reduction(||:tmp8) reduction(||:tmp10) 
2023-01-11T21:05:10.3157775Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.3157831Z                 {
2023-01-11T21:05:10.3157883Z                     {
2023-01-11T21:05:10.3157974Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3158075Z                         auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:05:10.3158172Z                         auto tmp3 = std::isinf(tmp0);
2023-01-11T21:05:10.3158261Z                         auto tmp5 = tmp3 == 0;
2023-01-11T21:05:10.3158360Z                         auto tmp6 = static_cast<long>(tmp5);
2023-01-11T21:05:10.3158466Z                         auto tmp7 = static_cast<bool>(tmp6);
2023-01-11T21:05:10.3158550Z                         auto tmp9 = tmp5 == 0;
2023-01-11T21:05:10.3158625Z                         tmp2 = tmp2 || tmp1;
2023-01-11T21:05:10.3158706Z                         tmp4 = tmp4 || tmp3;
2023-01-11T21:05:10.3158782Z                         tmp8 = tmp8 || tmp7;
2023-01-11T21:05:10.3158869Z                         tmp10 = tmp10 || tmp9;
2023-01-11T21:05:10.3158933Z                     }
2023-01-11T21:05:10.3158987Z                 }
2023-01-11T21:05:10.3159045Z             }
2023-01-11T21:05:10.3159109Z             out_ptr0[0] = tmp2;
2023-01-11T21:05:10.3159176Z             out_ptr1[0] = tmp4;
2023-01-11T21:05:10.3159249Z             out_ptr2[0] = tmp8;
2023-01-11T21:05:10.3159326Z             out_ptr3[0] = tmp10;
2023-01-11T21:05:10.3159381Z         }
2023-01-11T21:05:10.3159439Z     }
2023-01-11T21:05:10.3159486Z     {
2023-01-11T21:05:10.3159542Z         {
2023-01-11T21:05:10.3159617Z             auto tmp0 = out_ptr2[0];
2023-01-11T21:05:10.3159694Z             auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.3159765Z             in_out_ptr0[0] = tmp1;
2023-01-11T21:05:10.3159823Z         }
2023-01-11T21:05:10.3159881Z     }
2023-01-11T21:05:10.3159926Z     {
2023-01-11T21:05:10.3159979Z         {
2023-01-11T21:05:10.3160057Z             auto tmp0 = out_ptr3[0];
2023-01-11T21:05:10.3160132Z             auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.3160202Z             in_out_ptr1[0] = tmp1;
2023-01-11T21:05:10.3160257Z         }
2023-01-11T21:05:10.3160307Z     }
2023-01-11T21:05:10.3160352Z }
2023-01-11T21:05:10.3160423Z ''')
2023-01-11T21:05:10.3160428Z 
2023-01-11T21:05:10.3160432Z 
2023-01-11T21:05:10.3160517Z async_compile.wait(globals())
2023-01-11T21:05:10.3160580Z del async_compile
2023-01-11T21:05:10.3160788Z 
2023-01-11T21:05:10.3160888Z def call(args):
2023-01-11T21:05:10.3160981Z     arg0_1, = args
2023-01-11T21:05:10.3161057Z     args.clear()
2023-01-11T21:05:10.3161232Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3161405Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3161571Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3161741Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3161822Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:05:10.3161895Z     buf5 = buf3; del buf3  # reuse
2023-01-11T21:05:10.3162106Z     kernel_cpp_0(c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3162162Z     del arg0_1
2023-01-11T21:05:10.3162242Z     return (buf0, buf1, buf4, buf5, )
2023-01-11T21:05:10.3162247Z 
2023-01-11T21:05:10.3162253Z 
2023-01-11T21:05:10.3162324Z if __name__ == "__main__":
2023-01-11T21:05:10.3162431Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3162545Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3162779Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3162883Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3162888Z 
2023-01-11T21:05:10.3162892Z 
2023-01-11T21:05:10.3162979Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3163042Z import torch
2023-01-11T21:05:10.3163099Z import random
2023-01-11T21:05:10.3163204Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3163323Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3163328Z 
2023-01-11T21:05:10.3163399Z aten = torch.ops.aten
2023-01-11T21:05:10.3163524Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3163611Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3163618Z 
2023-01-11T21:05:10.3163622Z 
2023-01-11T21:05:10.3163751Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3163947Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3164051Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.3164145Z                        bool* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.3164243Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3164335Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:05:10.3164426Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:05:10.3164479Z {
2023-01-11T21:05:10.3164560Z     auto out_ptr3 = in_out_ptr0;
2023-01-11T21:05:10.3164630Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:05:10.3164718Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3164775Z     {
2023-01-11T21:05:10.3164842Z         #pragma omp for 
2023-01-11T21:05:10.3164918Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.3164976Z         {
2023-01-11T21:05:10.3165026Z             {
2023-01-11T21:05:10.3165085Z                 {
2023-01-11T21:05:10.3165154Z                     bool tmp2 = 0;
2023-01-11T21:05:10.3165242Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.3165307Z                     {
2023-01-11T21:05:10.3165367Z                         {
2023-01-11T21:05:10.3165467Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.3165563Z                             auto tmp1 = static_cast<bool>(tmp0);
2023-01-11T21:05:10.3165647Z                             tmp2 = tmp2 || tmp1;
2023-01-11T21:05:10.3165712Z                         }
2023-01-11T21:05:10.3165774Z                     }
2023-01-11T21:05:10.3165849Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3165910Z                 }
2023-01-11T21:05:10.3165964Z             }
2023-01-11T21:05:10.3166011Z         }
2023-01-11T21:05:10.3166067Z     }
2023-01-11T21:05:10.3166120Z     {
2023-01-11T21:05:10.3166208Z         {
2023-01-11T21:05:10.3166274Z             bool tmp2 = 0;
2023-01-11T21:05:10.3166341Z             bool tmp5 = 0;
2023-01-11T21:05:10.3166438Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3166488Z             {
2023-01-11T21:05:10.3166609Z                 #pragma omp for reduction(||:tmp2) reduction(||:tmp5) 
2023-01-11T21:05:10.3166692Z                 for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.3166751Z                 {
2023-01-11T21:05:10.3166807Z                     {
2023-01-11T21:05:10.3166895Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3166993Z                         auto tmp1 = std::isinf(tmp0);
2023-01-11T21:05:10.3167069Z                         auto tmp3 = tmp1 == 0;
2023-01-11T21:05:10.3167156Z                         auto tmp4 = tmp3 == 0;
2023-01-11T21:05:10.3167238Z                         tmp2 = tmp2 || tmp1;
2023-01-11T21:05:10.3167315Z                         tmp5 = tmp5 || tmp4;
2023-01-11T21:05:10.3167380Z                     }
2023-01-11T21:05:10.3167436Z                 }
2023-01-11T21:05:10.3167493Z             }
2023-01-11T21:05:10.3167558Z             out_ptr1[0] = tmp2;
2023-01-11T21:05:10.3167627Z             out_ptr2[0] = tmp5;
2023-01-11T21:05:10.3167714Z         }
2023-01-11T21:05:10.3167770Z     }
2023-01-11T21:05:10.3167858Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3167909Z     {
2023-01-11T21:05:10.3167970Z         #pragma omp for 
2023-01-11T21:05:10.3168042Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3168100Z         {
2023-01-11T21:05:10.3168154Z             {
2023-01-11T21:05:10.3168213Z                 {
2023-01-11T21:05:10.3168282Z                     bool tmp5 = 0;
2023-01-11T21:05:10.3168370Z                     for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.3168422Z                     {
2023-01-11T21:05:10.3168482Z                         {
2023-01-11T21:05:10.3168583Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:05:10.3168684Z                             auto tmp1 = std::isinf(tmp0);
2023-01-11T21:05:10.3168769Z                             auto tmp2 = tmp1 == 0;
2023-01-11T21:05:10.3168874Z                             auto tmp3 = static_cast<long>(tmp2);
2023-01-11T21:05:10.3168978Z                             auto tmp4 = static_cast<bool>(tmp3);
2023-01-11T21:05:10.3169053Z                             tmp5 = tmp5 || tmp4;
2023-01-11T21:05:10.3169114Z                         }
2023-01-11T21:05:10.3169174Z                     }
2023-01-11T21:05:10.3169249Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:05:10.3169309Z                 }
2023-01-11T21:05:10.3169363Z             }
2023-01-11T21:05:10.3169419Z         }
2023-01-11T21:05:10.3169480Z         #pragma omp for 
2023-01-11T21:05:10.3169551Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3169607Z         {
2023-01-11T21:05:10.3169661Z             {
2023-01-11T21:05:10.3169719Z                 {
2023-01-11T21:05:10.3169802Z                     auto tmp0 = out_ptr3[i0];
2023-01-11T21:05:10.3169888Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.3169964Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.3170019Z                 }
2023-01-11T21:05:10.3170077Z             }
2023-01-11T21:05:10.3170130Z         }
2023-01-11T21:05:10.3170206Z         #pragma omp single
2023-01-11T21:05:10.3170257Z         {
2023-01-11T21:05:10.3170305Z             {
2023-01-11T21:05:10.3170365Z                 {
2023-01-11T21:05:10.3170450Z                     auto tmp0 = out_ptr2[0];
2023-01-11T21:05:10.3170528Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.3170606Z                     in_out_ptr1[0] = tmp1;
2023-01-11T21:05:10.3170661Z                 }
2023-01-11T21:05:10.3170715Z             }
2023-01-11T21:05:10.3170761Z         }
2023-01-11T21:05:10.3170813Z     }
2023-01-11T21:05:10.3170867Z }
2023-01-11T21:05:10.3170940Z ''')
2023-01-11T21:05:10.3170945Z 
2023-01-11T21:05:10.3170949Z 
2023-01-11T21:05:10.3171033Z async_compile.wait(globals())
2023-01-11T21:05:10.3171130Z del async_compile
2023-01-11T21:05:10.3171134Z 
2023-01-11T21:05:10.3171198Z def call(args):
2023-01-11T21:05:10.3171253Z     arg0_1, = args
2023-01-11T21:05:10.3171316Z     args.clear()
2023-01-11T21:05:10.3171501Z     buf0 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3171673Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3171837Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3172014Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3172090Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:05:10.3172170Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:05:10.3172369Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3172433Z     del arg0_1
2023-01-11T21:05:10.3172514Z     return (buf0, buf1, buf3, buf5, )
2023-01-11T21:05:10.3172521Z 
2023-01-11T21:05:10.3172525Z 
2023-01-11T21:05:10.3172592Z if __name__ == "__main__":
2023-01-11T21:05:10.3172703Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3172847Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3173036Z     arg0_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3173140Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3173145Z 
2023-01-11T21:05:10.3173198Z ok (5.796s)
2023-01-11T21:05:10.3173627Z   test_arange1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3173751Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3174012Z [2023-01-11 20:44:51,744] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 19
2023-01-11T21:05:10.3174266Z [2023-01-11 20:44:54,478] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 19
2023-01-11T21:05:10.3174271Z 
2023-01-11T21:05:10.3174360Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3174426Z import torch
2023-01-11T21:05:10.3174489Z import random
2023-01-11T21:05:10.3174600Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3174706Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3174711Z 
2023-01-11T21:05:10.3174780Z aten = torch.ops.aten
2023-01-11T21:05:10.3174911Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3174993Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3174998Z 
2023-01-11T21:05:10.3175002Z 
2023-01-11T21:05:10.3175132Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3175333Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3175443Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3175537Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3175619Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3175673Z {
2023-01-11T21:05:10.3175765Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3175820Z     {
2023-01-11T21:05:10.3175888Z         #pragma omp for 
2023-01-11T21:05:10.3175968Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.3176025Z         {
2023-01-11T21:05:10.3176074Z             {
2023-01-11T21:05:10.3176129Z                 {
2023-01-11T21:05:10.3176215Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3176311Z                     auto tmp1 = static_cast<float>(i0);
2023-01-11T21:05:10.3176398Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3176474Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3176592Z                 }
2023-01-11T21:05:10.3176650Z             }
2023-01-11T21:05:10.3176705Z         }
2023-01-11T21:05:10.3176778Z         #pragma omp for 
2023-01-11T21:05:10.3176853Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3176910Z         {
2023-01-11T21:05:10.3176982Z             #pragma GCC ivdep
2023-01-11T21:05:10.3177050Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.3177108Z             {
2023-01-11T21:05:10.3177168Z                 {
2023-01-11T21:05:10.3177225Z                     {
2023-01-11T21:05:10.3177323Z                         auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.3177425Z                         auto tmp1 = static_cast<long>(10 + i1);
2023-01-11T21:05:10.3177531Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.3177610Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.3177700Z                         out_ptr1[i1 + (8*i0)] = tmp3;
2023-01-11T21:05:10.3177760Z                     }
2023-01-11T21:05:10.3177820Z                 }
2023-01-11T21:05:10.3177875Z             }
2023-01-11T21:05:10.3177933Z         }
2023-01-11T21:05:10.3177988Z     }
2023-01-11T21:05:10.3178034Z }
2023-01-11T21:05:10.3178133Z ''')
2023-01-11T21:05:10.3178138Z 
2023-01-11T21:05:10.3178142Z 
2023-01-11T21:05:10.3178228Z async_compile.wait(globals())
2023-01-11T21:05:10.3178291Z del async_compile
2023-01-11T21:05:10.3178296Z 
2023-01-11T21:05:10.3178362Z def call(args):
2023-01-11T21:05:10.3178427Z     arg0_1, = args
2023-01-11T21:05:10.3178584Z     args.clear()
2023-01-11T21:05:10.3178773Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3178959Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3179115Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3179178Z     del arg0_1
2023-01-11T21:05:10.3179253Z     return (buf0, buf1, )
2023-01-11T21:05:10.3179260Z 
2023-01-11T21:05:10.3179264Z 
2023-01-11T21:05:10.3179332Z if __name__ == "__main__":
2023-01-11T21:05:10.3179443Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3179566Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3179748Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3179847Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3179853Z 
2023-01-11T21:05:10.3179917Z ok (2.877s)
2023-01-11T21:05:10.3180346Z   test_arange2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3180467Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3180721Z [2023-01-11 20:44:54,559] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 20
2023-01-11T21:05:10.3180980Z [2023-01-11 20:44:57,262] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 20
2023-01-11T21:05:10.3180986Z 
2023-01-11T21:05:10.3181075Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3181137Z import torch
2023-01-11T21:05:10.3181192Z import random
2023-01-11T21:05:10.3181301Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3181415Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3181420Z 
2023-01-11T21:05:10.3181495Z aten = torch.ops.aten
2023-01-11T21:05:10.3181622Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3181705Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3181712Z 
2023-01-11T21:05:10.3181716Z 
2023-01-11T21:05:10.3181845Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3182089Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3182191Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.3182281Z                        long* __restrict__ out_ptr0)
2023-01-11T21:05:10.3182337Z {
2023-01-11T21:05:10.3182425Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3182486Z     {
2023-01-11T21:05:10.3182557Z         #pragma omp for 
2023-01-11T21:05:10.3182629Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3182677Z         {
2023-01-11T21:05:10.3182752Z             #pragma GCC ivdep
2023-01-11T21:05:10.3182827Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.3182881Z             {
2023-01-11T21:05:10.3182943Z                 {
2023-01-11T21:05:10.3183005Z                     {
2023-01-11T21:05:10.3183101Z                         auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.3183193Z                         auto tmp1 = static_cast<long>(i1);
2023-01-11T21:05:10.3183285Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3183372Z                         out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:05:10.3183436Z                     }
2023-01-11T21:05:10.3183521Z                 }
2023-01-11T21:05:10.3183580Z             }
2023-01-11T21:05:10.3183635Z         }
2023-01-11T21:05:10.3183682Z     }
2023-01-11T21:05:10.3183738Z }
2023-01-11T21:05:10.3183822Z ''')
2023-01-11T21:05:10.3183829Z 
2023-01-11T21:05:10.3183835Z 
2023-01-11T21:05:10.3183956Z async_compile.wait(globals())
2023-01-11T21:05:10.3184039Z del async_compile
2023-01-11T21:05:10.3184046Z 
2023-01-11T21:05:10.3184133Z def call(args):
2023-01-11T21:05:10.3184215Z     arg0_1, = args
2023-01-11T21:05:10.3184299Z     args.clear()
2023-01-11T21:05:10.3184563Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3184739Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3184826Z     del arg0_1
2023-01-11T21:05:10.3184919Z     return (buf0, )
2023-01-11T21:05:10.3184926Z 
2023-01-11T21:05:10.3184933Z 
2023-01-11T21:05:10.3185040Z if __name__ == "__main__":
2023-01-11T21:05:10.3185195Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3185362Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3185634Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3185780Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3185786Z 
2023-01-11T21:05:10.3185866Z ok (2.781s)
2023-01-11T21:05:10.3186505Z   test_arange3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3186696Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3187161Z [2023-01-11 20:44:57,355] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 21
2023-01-11T21:05:10.3187634Z [2023-01-11 20:45:00,117] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 21
2023-01-11T21:05:10.3187643Z 
2023-01-11T21:05:10.3187818Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3187948Z import torch
2023-01-11T21:05:10.3188069Z import random
2023-01-11T21:05:10.3188281Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3188498Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3188505Z 
2023-01-11T21:05:10.3188651Z aten = torch.ops.aten
2023-01-11T21:05:10.3188894Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3189066Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3189073Z 
2023-01-11T21:05:10.3189080Z 
2023-01-11T21:05:10.3189315Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3189816Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3190021Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3190201Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3190319Z {
2023-01-11T21:05:10.3190499Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3190618Z     {
2023-01-11T21:05:10.3190760Z         #pragma omp for 
2023-01-11T21:05:10.3190904Z         for(long i0=0; i0<14; i0+=1)
2023-01-11T21:05:10.3191009Z         {
2023-01-11T21:05:10.3191128Z             {
2023-01-11T21:05:10.3191247Z                 {
2023-01-11T21:05:10.3191409Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3191594Z                     auto tmp1 = static_cast<long>(4*i0);
2023-01-11T21:05:10.3191783Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.3191947Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.3192088Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.3192210Z                 }
2023-01-11T21:05:10.3192329Z             }
2023-01-11T21:05:10.3192446Z         }
2023-01-11T21:05:10.3192561Z     }
2023-01-11T21:05:10.3192677Z }
2023-01-11T21:05:10.3192928Z ''')
2023-01-11T21:05:10.3192939Z 
2023-01-11T21:05:10.3192962Z 
2023-01-11T21:05:10.3193118Z async_compile.wait(globals())
2023-01-11T21:05:10.3193249Z del async_compile
2023-01-11T21:05:10.3193255Z 
2023-01-11T21:05:10.3193390Z def call(args):
2023-01-11T21:05:10.3193520Z     arg0_1, = args
2023-01-11T21:05:10.3193648Z     args.clear()
2023-01-11T21:05:10.3193987Z     buf0 = empty_strided((14, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3194218Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3194335Z     del arg0_1
2023-01-11T21:05:10.3194467Z     return (buf0, )
2023-01-11T21:05:10.3194474Z 
2023-01-11T21:05:10.3194480Z 
2023-01-11T21:05:10.3194619Z if __name__ == "__main__":
2023-01-11T21:05:10.3194835Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3195061Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3195407Z     arg0_1 = rand_strided((14, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3195604Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3195611Z 
2023-01-11T21:05:10.3195731Z ok (2.856s)
2023-01-11T21:05:10.3196549Z   test_arange4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3196769Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3197229Z [2023-01-11 20:45:00,182] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 22
2023-01-11T21:05:10.3197702Z [2023-01-11 20:45:02,891] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 22
2023-01-11T21:05:10.3197710Z 
2023-01-11T21:05:10.3197889Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3198028Z import torch
2023-01-11T21:05:10.3198162Z import random
2023-01-11T21:05:10.3198374Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3198588Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3198609Z 
2023-01-11T21:05:10.3198743Z aten = torch.ops.aten
2023-01-11T21:05:10.3198993Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3199163Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3199170Z 
2023-01-11T21:05:10.3199177Z 
2023-01-11T21:05:10.3199419Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3199791Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3200101Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3200283Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3200405Z {
2023-01-11T21:05:10.3200582Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3200835Z     {
2023-01-11T21:05:10.3200980Z         #pragma omp for 
2023-01-11T21:05:10.3201131Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.3201250Z         {
2023-01-11T21:05:10.3201367Z             {
2023-01-11T21:05:10.3201476Z                 {
2023-01-11T21:05:10.3201639Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3201954Z                     auto tmp1 = static_cast<long>(512 + ((-1)*i0));
2023-01-11T21:05:10.3202147Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.3202382Z                     auto tmp3 = tmp0 - tmp2;
2023-01-11T21:05:10.3202535Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.3202659Z                 }
2023-01-11T21:05:10.3202764Z             }
2023-01-11T21:05:10.3202893Z         }
2023-01-11T21:05:10.3203013Z     }
2023-01-11T21:05:10.3203132Z }
2023-01-11T21:05:10.3203278Z ''')
2023-01-11T21:05:10.3203286Z 
2023-01-11T21:05:10.3203292Z 
2023-01-11T21:05:10.3203582Z async_compile.wait(globals())
2023-01-11T21:05:10.3203725Z del async_compile
2023-01-11T21:05:10.3203733Z 
2023-01-11T21:05:10.3203854Z def call(args):
2023-01-11T21:05:10.3203987Z     arg0_1, = args
2023-01-11T21:05:10.3204117Z     args.clear()
2023-01-11T21:05:10.3204472Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3204712Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3204842Z     del arg0_1
2023-01-11T21:05:10.3204976Z     return (buf0, )
2023-01-11T21:05:10.3204984Z 
2023-01-11T21:05:10.3204991Z 
2023-01-11T21:05:10.3205130Z if __name__ == "__main__":
2023-01-11T21:05:10.3205324Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3205546Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3205891Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3206089Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3206096Z 
2023-01-11T21:05:10.3206229Z ok (2.777s)
2023-01-11T21:05:10.3207054Z   test_argmax_argmin1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3207283Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3207749Z [2023-01-11 20:45:03,016] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 23
2023-01-11T21:05:10.3208225Z [2023-01-11 20:45:05,690] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 23
2023-01-11T21:05:10.3208236Z 
2023-01-11T21:05:10.3208403Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3208536Z import torch
2023-01-11T21:05:10.3208675Z import random
2023-01-11T21:05:10.3208896Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3209120Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3209128Z 
2023-01-11T21:05:10.3209283Z aten = torch.ops.aten
2023-01-11T21:05:10.3209526Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3209693Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3209700Z 
2023-01-11T21:05:10.3209707Z 
2023-01-11T21:05:10.3209937Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3210297Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3210510Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3210683Z                        long* __restrict__ out_ptr0,
2023-01-11T21:05:10.3210973Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.3211091Z {
2023-01-11T21:05:10.3211215Z     {
2023-01-11T21:05:10.3211323Z         {
2023-01-11T21:05:10.3211539Z             struct IndexValue_1 {size_t index; float value;};
2023-01-11T21:05:10.3211991Z             IndexValue_1 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.3212243Z             #pragma omp declare reduction(argmax : struct IndexValue_1 :\
2023-01-11T21:05:10.3212512Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.3212772Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.3213182Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.3213395Z             struct IndexValue_2 {size_t index; float value;};
2023-01-11T21:05:10.3213628Z             IndexValue_2 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.3213874Z             #pragma omp declare reduction(argmin : struct IndexValue_2 :\
2023-01-11T21:05:10.3214213Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.3214481Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.3214739Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.3214931Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3215050Z             {
2023-01-11T21:05:10.3215304Z                 #pragma omp for reduction(argmax:tmp1) reduction(argmin:tmp2) 
2023-01-11T21:05:10.3215459Z                 for(long i0=0; i0<524288; i0+=1)
2023-01-11T21:05:10.3215587Z                 {
2023-01-11T21:05:10.3215710Z                     {
2023-01-11T21:05:10.3215874Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3216040Z                         if (tmp1.value < tmp0) {
2023-01-11T21:05:10.3216234Z                             tmp1.index = i0; tmp1.value = tmp0;
2023-01-11T21:05:10.3216362Z                         }
2023-01-11T21:05:10.3216518Z                         if (tmp2.value > tmp0) {
2023-01-11T21:05:10.3216718Z                             tmp2.index = i0; tmp2.value = tmp0;
2023-01-11T21:05:10.3216843Z                         }
2023-01-11T21:05:10.3216966Z                     }
2023-01-11T21:05:10.3217087Z                 }
2023-01-11T21:05:10.3217203Z             }
2023-01-11T21:05:10.3217360Z             out_ptr0[0] = tmp1.index;
2023-01-11T21:05:10.3217495Z             out_ptr1[0] = tmp2.index;
2023-01-11T21:05:10.3217609Z         }
2023-01-11T21:05:10.3217733Z     }
2023-01-11T21:05:10.3217847Z }
2023-01-11T21:05:10.3218010Z ''')
2023-01-11T21:05:10.3218020Z 
2023-01-11T21:05:10.3218027Z 
2023-01-11T21:05:10.3218195Z async_compile.wait(globals())
2023-01-11T21:05:10.3218334Z del async_compile
2023-01-11T21:05:10.3218341Z 
2023-01-11T21:05:10.3218587Z def call(args):
2023-01-11T21:05:10.3218706Z     arg0_1, = args
2023-01-11T21:05:10.3218842Z     args.clear()
2023-01-11T21:05:10.3219172Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3219498Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3219785Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3219915Z     del arg0_1
2023-01-11T21:05:10.3220060Z     return (buf0, buf1, )
2023-01-11T21:05:10.3220070Z 
2023-01-11T21:05:10.3220076Z 
2023-01-11T21:05:10.3220208Z if __name__ == "__main__":
2023-01-11T21:05:10.3220417Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3220645Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3221014Z     arg0_1 = rand_strided((8, 256, 256), (65536, 256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3221220Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3221317Z 
2023-01-11T21:05:10.3221448Z ok (2.925s)
2023-01-11T21:05:10.3222276Z   test_argmax_argmin2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3222495Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3222960Z [2023-01-11 20:45:05,919] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 24
2023-01-11T21:05:10.3223415Z [2023-01-11 20:45:08,599] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 24
2023-01-11T21:05:10.3223437Z 
2023-01-11T21:05:10.3223595Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3223722Z import torch
2023-01-11T21:05:10.3223854Z import random
2023-01-11T21:05:10.3224063Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3224283Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3224292Z 
2023-01-11T21:05:10.3224443Z aten = torch.ops.aten
2023-01-11T21:05:10.3224753Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3224917Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3224924Z 
2023-01-11T21:05:10.3224945Z 
2023-01-11T21:05:10.3225171Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3225537Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3225755Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3225931Z                        long* __restrict__ out_ptr0,
2023-01-11T21:05:10.3226108Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.3226282Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.3226452Z                        long* __restrict__ out_ptr3)
2023-01-11T21:05:10.3226564Z {
2023-01-11T21:05:10.3226745Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3226860Z     {
2023-01-11T21:05:10.3227002Z         #pragma omp for 
2023-01-11T21:05:10.3227157Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:05:10.3227272Z         {
2023-01-11T21:05:10.3227383Z             {
2023-01-11T21:05:10.3227503Z                 {
2023-01-11T21:05:10.3227727Z                     struct IndexValue_3 {size_t index; float value;};
2023-01-11T21:05:10.3228155Z                     IndexValue_3 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.3228410Z                     #pragma omp declare reduction(argmax : struct IndexValue_3 :\
2023-01-11T21:05:10.3228686Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.3228960Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.3229404Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.3229625Z                     struct IndexValue_4 {size_t index; float value;};
2023-01-11T21:05:10.3229862Z                     IndexValue_4 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.3230111Z                     #pragma omp declare reduction(argmin : struct IndexValue_4 :\
2023-01-11T21:05:10.3230387Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.3230662Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.3230918Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.3231081Z                     for(long i1=0; i1<144; i1+=1)
2023-01-11T21:05:10.3231205Z                     {
2023-01-11T21:05:10.3231333Z                         {
2023-01-11T21:05:10.3231501Z                             auto tmp0 = in_ptr0[i0 + (144*i1)];
2023-01-11T21:05:10.3231763Z                             if (tmp1.value < tmp0) {
2023-01-11T21:05:10.3231958Z                                 tmp1.index = i1; tmp1.value = tmp0;
2023-01-11T21:05:10.3232090Z                             }
2023-01-11T21:05:10.3232261Z                             if (tmp2.value > tmp0) {
2023-01-11T21:05:10.3232455Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:05:10.3232582Z                             }
2023-01-11T21:05:10.3232697Z                         }
2023-01-11T21:05:10.3232819Z                     }
2023-01-11T21:05:10.3232991Z                     out_ptr0[i0] = tmp1.index;
2023-01-11T21:05:10.3233157Z                     out_ptr1[i0] = tmp2.index;
2023-01-11T21:05:10.3233276Z                 }
2023-01-11T21:05:10.3233393Z             }
2023-01-11T21:05:10.3233512Z         }
2023-01-11T21:05:10.3233636Z         #pragma omp for 
2023-01-11T21:05:10.3233783Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:05:10.3233903Z         {
2023-01-11T21:05:10.3234021Z             {
2023-01-11T21:05:10.3234143Z                 {
2023-01-11T21:05:10.3234360Z                     struct IndexValue_5 {size_t index; float value;};
2023-01-11T21:05:10.3234846Z                     IndexValue_5 tmp1{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.3235087Z                     #pragma omp declare reduction(argmax : struct IndexValue_5 :\
2023-01-11T21:05:10.3235358Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.3235621Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.3236057Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.3236278Z                     struct IndexValue_6 {size_t index; float value;};
2023-01-11T21:05:10.3236518Z                     IndexValue_6 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.3236774Z                     #pragma omp declare reduction(argmin : struct IndexValue_6 :\
2023-01-11T21:05:10.3237049Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.3237320Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.3237568Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.3237731Z                     for(long i1=0; i1<144; i1+=1)
2023-01-11T21:05:10.3237853Z                     {
2023-01-11T21:05:10.3237980Z                         {
2023-01-11T21:05:10.3238166Z                             auto tmp0 = in_ptr0[i1 + (144*i0)];
2023-01-11T21:05:10.3238336Z                             if (tmp1.value < tmp0) {
2023-01-11T21:05:10.3238528Z                                 tmp1.index = i1; tmp1.value = tmp0;
2023-01-11T21:05:10.3238643Z                             }
2023-01-11T21:05:10.3238819Z                             if (tmp2.value > tmp0) {
2023-01-11T21:05:10.3239013Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:05:10.3239148Z                             }
2023-01-11T21:05:10.3239271Z                         }
2023-01-11T21:05:10.3239395Z                     }
2023-01-11T21:05:10.3239564Z                     out_ptr2[i0] = tmp1.index;
2023-01-11T21:05:10.3239712Z                     out_ptr3[i0] = tmp2.index;
2023-01-11T21:05:10.3239837Z                 }
2023-01-11T21:05:10.3239959Z             }
2023-01-11T21:05:10.3240076Z         }
2023-01-11T21:05:10.3240192Z     }
2023-01-11T21:05:10.3240307Z }
2023-01-11T21:05:10.3240464Z ''')
2023-01-11T21:05:10.3240473Z 
2023-01-11T21:05:10.3240479Z 
2023-01-11T21:05:10.3240774Z async_compile.wait(globals())
2023-01-11T21:05:10.3240915Z del async_compile
2023-01-11T21:05:10.3240923Z 
2023-01-11T21:05:10.3241054Z def call(args):
2023-01-11T21:05:10.3241184Z     arg0_1, = args
2023-01-11T21:05:10.3241435Z     args.clear()
2023-01-11T21:05:10.3241782Z     buf0 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3242116Z     buf1 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3242444Z     buf2 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3242758Z     buf3 = empty_strided((144, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3243140Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.3243268Z     del arg0_1
2023-01-11T21:05:10.3243436Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:05:10.3243445Z 
2023-01-11T21:05:10.3243451Z 
2023-01-11T21:05:10.3243591Z if __name__ == "__main__":
2023-01-11T21:05:10.3243800Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3244024Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3244379Z     arg0_1 = rand_strided((144, 144), (144, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3244567Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3244575Z 
2023-01-11T21:05:10.3244791Z ok (2.800s)
2023-01-11T21:05:10.3245006Z   test_argmax_argmin3_cpu (__main__.CpuTests) ... skip: 
2023-01-11T21:05:10.3245293Z         FIXME: In the case of having equally max/min elements, our implementation returns
2023-01-11T21:05:10.3245487Z         the last index instead of the first one
2023-01-11T21:05:10.3245611Z          (0.002s)
2023-01-11T21:05:10.3246444Z   test_as_strided_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3246677Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3247147Z [2023-01-11 20:45:08,700] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 25
2023-01-11T21:05:10.3247616Z [2023-01-11 20:45:11,435] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 25
2023-01-11T21:05:10.3247625Z 
2023-01-11T21:05:10.3247808Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3247947Z import torch
2023-01-11T21:05:10.3248085Z import random
2023-01-11T21:05:10.3248304Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3248537Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3248545Z 
2023-01-11T21:05:10.3248692Z aten = torch.ops.aten
2023-01-11T21:05:10.3248922Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3249098Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3249106Z 
2023-01-11T21:05:10.3249113Z 
2023-01-11T21:05:10.3249370Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3249744Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3249957Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.3250151Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:05:10.3250265Z {
2023-01-11T21:05:10.3250447Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3250556Z     {
2023-01-11T21:05:10.3250702Z         #pragma omp for 
2023-01-11T21:05:10.3250855Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.3250968Z         {
2023-01-11T21:05:10.3251214Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3251457Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3251611Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3251847Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.3252068Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.3252245Z             tmp4.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.3252364Z         }
2023-01-11T21:05:10.3252542Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3252702Z         for(long i0=4096; i0<4096; i0+=1)
2023-01-11T21:05:10.3252818Z         {
2023-01-11T21:05:10.3252979Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3253153Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.3253312Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3253491Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.3253641Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.3253792Z             in_out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.3253911Z         }
2023-01-11T21:05:10.3254016Z     }
2023-01-11T21:05:10.3254133Z }
2023-01-11T21:05:10.3254291Z ''')
2023-01-11T21:05:10.3254300Z 
2023-01-11T21:05:10.3254306Z 
2023-01-11T21:05:10.3254473Z async_compile.wait(globals())
2023-01-11T21:05:10.3254616Z del async_compile
2023-01-11T21:05:10.3254624Z 
2023-01-11T21:05:10.3254761Z def call(args):
2023-01-11T21:05:10.3254893Z     arg0_1, = args
2023-01-11T21:05:10.3255023Z     args.clear()
2023-01-11T21:05:10.3255421Z     buf0 = empty_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3255632Z     buf1 = as_strided(buf0, (8, 8, 64), (512, 64, 1)); del buf0  # reuse
2023-01-11T21:05:10.3255873Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:05:10.3256064Z     return (as_strided(arg0_1, (8, 8, 64), (512, 64, 1)), buf1, )
2023-01-11T21:05:10.3256073Z 
2023-01-11T21:05:10.3256079Z 
2023-01-11T21:05:10.3256221Z if __name__ == "__main__":
2023-01-11T21:05:10.3256426Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3256651Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3257000Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3257190Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3257197Z 
2023-01-11T21:05:10.3257319Z ok (2.827s)
2023-01-11T21:05:10.3258155Z   test_as_strided_scatter_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3258378Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3258924Z [2023-01-11 20:45:11,531] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 26
2023-01-11T21:05:10.3259396Z [2023-01-11 20:45:14,229] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 26
2023-01-11T21:05:10.3259404Z 
2023-01-11T21:05:10.3259580Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3259717Z import torch
2023-01-11T21:05:10.3259853Z import random
2023-01-11T21:05:10.3260049Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3260279Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3260290Z 
2023-01-11T21:05:10.3260431Z aten = torch.ops.aten
2023-01-11T21:05:10.3260674Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3260845Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3260852Z 
2023-01-11T21:05:10.3260860Z 
2023-01-11T21:05:10.3261093Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3261460Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3261671Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3261850Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3262029Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3262147Z {
2023-01-11T21:05:10.3262413Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3262532Z     {
2023-01-11T21:05:10.3262676Z         #pragma omp for 
2023-01-11T21:05:10.3262824Z         for(long i0=0; i0<640; i0+=1)
2023-01-11T21:05:10.3262927Z         {
2023-01-11T21:05:10.3263176Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3263418Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:05:10.3263581Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3263826Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:05:10.3263982Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.3264151Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3264272Z         }
2023-01-11T21:05:10.3264430Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3264592Z         for(long i0=10240; i0<10240; i0+=1)
2023-01-11T21:05:10.3264707Z         {
2023-01-11T21:05:10.3264860Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3265041Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:05:10.3265191Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3265356Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:05:10.3265607Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.3265763Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.3265880Z         }
2023-01-11T21:05:10.3266018Z         #pragma omp for 
2023-01-11T21:05:10.3266165Z         for(long i0=0; i0<5120; i0+=1)
2023-01-11T21:05:10.3266281Z         {
2023-01-11T21:05:10.3266388Z             {
2023-01-11T21:05:10.3266508Z                 {
2023-01-11T21:05:10.3266674Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.3266862Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3267026Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3267206Z                     auto tmp3 = static_cast<float>(4);
2023-01-11T21:05:10.3267469Z                     auto tmp4 = tmp2 - tmp3;
2023-01-11T21:05:10.3267619Z                     out_ptr0[2*i0] = tmp4;
2023-01-11T21:05:10.3267740Z                 }
2023-01-11T21:05:10.3267858Z             }
2023-01-11T21:05:10.3267973Z         }
2023-01-11T21:05:10.3268094Z     }
2023-01-11T21:05:10.3268207Z }
2023-01-11T21:05:10.3268366Z ''')
2023-01-11T21:05:10.3268375Z 
2023-01-11T21:05:10.3268381Z 
2023-01-11T21:05:10.3268545Z async_compile.wait(globals())
2023-01-11T21:05:10.3268686Z del async_compile
2023-01-11T21:05:10.3268693Z 
2023-01-11T21:05:10.3268827Z def call(args):
2023-01-11T21:05:10.3268968Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3269105Z     args.clear()
2023-01-11T21:05:10.3269466Z     buf0 = empty_strided((10, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3269749Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3269865Z     del arg0_1
2023-01-11T21:05:10.3269992Z     del arg1_1
2023-01-11T21:05:10.3270135Z     return (buf0, )
2023-01-11T21:05:10.3270143Z 
2023-01-11T21:05:10.3270150Z 
2023-01-11T21:05:10.3270289Z if __name__ == "__main__":
2023-01-11T21:05:10.3270501Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3270735Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3271101Z     arg0_1 = rand_strided((10, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3271451Z     arg1_1 = rand_strided((10, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3271645Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3271668Z 
2023-01-11T21:05:10.3271780Z ok (2.803s)
2023-01-11T21:05:10.3272626Z   test_avg_pool2d1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3272949Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3273416Z [2023-01-11 20:45:14,304] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 27
2023-01-11T21:05:10.3273887Z [2023-01-11 20:45:17,114] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 27
2023-01-11T21:05:10.3273895Z 
2023-01-11T21:05:10.3274073Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3274204Z import torch
2023-01-11T21:05:10.3274342Z import random
2023-01-11T21:05:10.3274542Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3274759Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3274768Z 
2023-01-11T21:05:10.3274911Z aten = torch.ops.aten
2023-01-11T21:05:10.3275158Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3275326Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3275337Z 
2023-01-11T21:05:10.3275343Z 
2023-01-11T21:05:10.3275584Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3275945Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3276226Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3276392Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3276513Z {
2023-01-11T21:05:10.3276695Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3276779Z     {
2023-01-11T21:05:10.3276879Z         #pragma omp for 
2023-01-11T21:05:10.3276984Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3277067Z         {
2023-01-11T21:05:10.3277154Z             #pragma GCC ivdep
2023-01-11T21:05:10.3277259Z             for(long i1=0; i1<7; i1+=1)
2023-01-11T21:05:10.3277342Z             {
2023-01-11T21:05:10.3277446Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3277569Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:05:10.3277671Z                 {
2023-01-11T21:05:10.3277769Z                     {
2023-01-11T21:05:10.3277858Z                         {
2023-01-11T21:05:10.3278017Z                             auto tmp0 = in_ptr0[(2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3278183Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3278336Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3278493Z                             auto tmp5 = in_ptr0[16 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3278652Z                             auto tmp7 = in_ptr0[17 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3278802Z                             auto tmp9 = in_ptr0[18 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3278967Z                             auto tmp11 = in_ptr0[32 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3279109Z                             auto tmp13 = in_ptr0[33 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3279274Z                             auto tmp15 = in_ptr0[34 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.3279414Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:05:10.3279560Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:05:10.3279696Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:05:10.3279832Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:05:10.3279973Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:05:10.3280097Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:05:10.3280238Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:05:10.3280377Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:05:10.3280551Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:05:10.3280853Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:05:10.3281000Z                             out_ptr0[i2 + (7*i1) + (49*i0)] = tmp18;
2023-01-11T21:05:10.3281213Z                         }
2023-01-11T21:05:10.3281305Z                     }
2023-01-11T21:05:10.3281388Z                 }
2023-01-11T21:05:10.3281485Z             }
2023-01-11T21:05:10.3281575Z         }
2023-01-11T21:05:10.3281659Z     }
2023-01-11T21:05:10.3281751Z }
2023-01-11T21:05:10.3281898Z ''')
2023-01-11T21:05:10.3281907Z 
2023-01-11T21:05:10.3281913Z 
2023-01-11T21:05:10.3282048Z async_compile.wait(globals())
2023-01-11T21:05:10.3282142Z del async_compile
2023-01-11T21:05:10.3282150Z 
2023-01-11T21:05:10.3282252Z def call(args):
2023-01-11T21:05:10.3282358Z     arg0_1, = args
2023-01-11T21:05:10.3282459Z     args.clear()
2023-01-11T21:05:10.3282780Z     buf0 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3282981Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3283073Z     del arg0_1
2023-01-11T21:05:10.3283167Z     return (buf0, )
2023-01-11T21:05:10.3283174Z 
2023-01-11T21:05:10.3283180Z 
2023-01-11T21:05:10.3283284Z if __name__ == "__main__":
2023-01-11T21:05:10.3283452Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3283725Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3284061Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3284214Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3284221Z 
2023-01-11T21:05:10.3284321Z ok (2.866s)
2023-01-11T21:05:10.3284966Z   test_avg_pool2d2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3285149Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3285545Z [2023-01-11 20:45:17,331] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 28
2023-01-11T21:05:10.3285934Z [2023-01-11 20:45:20,075] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 28
2023-01-11T21:05:10.3285941Z 
2023-01-11T21:05:10.3286082Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3286181Z import torch
2023-01-11T21:05:10.3286279Z import random
2023-01-11T21:05:10.3286452Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3286629Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3286636Z 
2023-01-11T21:05:10.3286748Z aten = torch.ops.aten
2023-01-11T21:05:10.3286926Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3287057Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3287065Z 
2023-01-11T21:05:10.3287071Z 
2023-01-11T21:05:10.3287269Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3287581Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3287751Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3287895Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3287980Z {
2023-01-11T21:05:10.3288125Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3288200Z     {
2023-01-11T21:05:10.3288317Z         #pragma omp for 
2023-01-11T21:05:10.3288429Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.3288511Z         {
2023-01-11T21:05:10.3288622Z             #pragma GCC ivdep
2023-01-11T21:05:10.3288742Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:05:10.3288816Z             {
2023-01-11T21:05:10.3288935Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3289059Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:05:10.3289148Z                 {
2023-01-11T21:05:10.3289250Z                     {
2023-01-11T21:05:10.3289436Z                         {
2023-01-11T21:05:10.3289599Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3289756Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3289904Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3290064Z                             auto tmp5 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3290229Z                             auto tmp7 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3290392Z                             auto tmp9 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3290557Z                             auto tmp11 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3290717Z                             auto tmp13 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3290884Z                             auto tmp15 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.3291025Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:05:10.3291143Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:05:10.3291331Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:05:10.3291460Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:05:10.3291593Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:05:10.3291732Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:05:10.3291865Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:05:10.3292001Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:05:10.3292158Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:05:10.3292301Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:05:10.3292450Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp18;
2023-01-11T21:05:10.3292550Z                         }
2023-01-11T21:05:10.3292649Z                     }
2023-01-11T21:05:10.3292740Z                 }
2023-01-11T21:05:10.3292832Z             }
2023-01-11T21:05:10.3292903Z         }
2023-01-11T21:05:10.3292989Z     }
2023-01-11T21:05:10.3293078Z }
2023-01-11T21:05:10.3293214Z ''')
2023-01-11T21:05:10.3293222Z 
2023-01-11T21:05:10.3293228Z 
2023-01-11T21:05:10.3293364Z async_compile.wait(globals())
2023-01-11T21:05:10.3293463Z del async_compile
2023-01-11T21:05:10.3293471Z 
2023-01-11T21:05:10.3293569Z def call(args):
2023-01-11T21:05:10.3293656Z     arg0_1, = args
2023-01-11T21:05:10.3293763Z     args.clear()
2023-01-11T21:05:10.3294112Z     buf0 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3294304Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3294399Z     del arg0_1
2023-01-11T21:05:10.3294496Z     return (buf0, )
2023-01-11T21:05:10.3294502Z 
2023-01-11T21:05:10.3294512Z 
2023-01-11T21:05:10.3294622Z if __name__ == "__main__":
2023-01-11T21:05:10.3294790Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3294954Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3295297Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3295453Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3295460Z 
2023-01-11T21:05:10.3295556Z ok (3.846s)
2023-01-11T21:05:10.3296202Z   test_avg_pool2d3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3296389Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3296860Z [2023-01-11 20:45:21,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 29
2023-01-11T21:05:10.3297264Z [2023-01-11 20:45:23,767] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 29
2023-01-11T21:05:10.3297272Z 
2023-01-11T21:05:10.3297410Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3297495Z import torch
2023-01-11T21:05:10.3297588Z import random
2023-01-11T21:05:10.3297758Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3297936Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3297942Z 
2023-01-11T21:05:10.3298052Z aten = torch.ops.aten
2023-01-11T21:05:10.3298228Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3298345Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3298352Z 
2023-01-11T21:05:10.3298358Z 
2023-01-11T21:05:10.3298669Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3298908Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3299066Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3299195Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3299363Z {
2023-01-11T21:05:10.3299506Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3299587Z     {
2023-01-11T21:05:10.3299690Z         #pragma omp for 
2023-01-11T21:05:10.3299795Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3299886Z         {
2023-01-11T21:05:10.3300003Z             #pragma GCC ivdep
2023-01-11T21:05:10.3300123Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.3300216Z             {
2023-01-11T21:05:10.3300306Z                 {
2023-01-11T21:05:10.3300407Z                     {
2023-01-11T21:05:10.3300676Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:05:10.3300824Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3300970Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3301122Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:05:10.3301262Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3301395Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:05:10.3301653Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:05:10.3301774Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:05:10.3301905Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:05:10.3302035Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:05:10.3302177Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:05:10.3302301Z                         float tmp11 = 0.0;
2023-01-11T21:05:10.3302407Z                         if(tmp10)
2023-01-11T21:05:10.3302509Z                         {
2023-01-11T21:05:10.3302759Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3302880Z                             tmp11 = tmp12;
2023-01-11T21:05:10.3302977Z                         }
2023-01-11T21:05:10.3303132Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:05:10.3303269Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:05:10.3303405Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:05:10.3303537Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:05:10.3303674Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:05:10.3303775Z                         float tmp18 = 0.0;
2023-01-11T21:05:10.3303891Z                         if(tmp17)
2023-01-11T21:05:10.3303990Z                         {
2023-01-11T21:05:10.3304247Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3304357Z                             tmp18 = tmp19;
2023-01-11T21:05:10.3304450Z                         }
2023-01-11T21:05:10.3304580Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:05:10.3304813Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:05:10.3304949Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:05:10.3305086Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:05:10.3305226Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:05:10.3305351Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:05:10.3305471Z                         float tmp26 = 0.0;
2023-01-11T21:05:10.3305571Z                         if(tmp25)
2023-01-11T21:05:10.3305652Z                         {
2023-01-11T21:05:10.3305915Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3306038Z                             tmp26 = tmp27;
2023-01-11T21:05:10.3306136Z                         }
2023-01-11T21:05:10.3306257Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:05:10.3306415Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:05:10.3306547Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:05:10.3306675Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:05:10.3306812Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:05:10.3307010Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:05:10.3307135Z                         float tmp34 = 0.0;
2023-01-11T21:05:10.3307250Z                         if(tmp33)
2023-01-11T21:05:10.3307349Z                         {
2023-01-11T21:05:10.3307609Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3307714Z                             tmp34 = tmp35;
2023-01-11T21:05:10.3307813Z                         }
2023-01-11T21:05:10.3307953Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:05:10.3308085Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:05:10.3308206Z                         float tmp38 = 0.0;
2023-01-11T21:05:10.3308312Z                         if(tmp37)
2023-01-11T21:05:10.3308411Z                         {
2023-01-11T21:05:10.3308564Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:05:10.3308673Z                             tmp38 = tmp39;
2023-01-11T21:05:10.3308771Z                         }
2023-01-11T21:05:10.3308905Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:05:10.3309039Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:05:10.3309164Z                         float tmp42 = 0.0;
2023-01-11T21:05:10.3309265Z                         if(tmp41)
2023-01-11T21:05:10.3309353Z                         {
2023-01-11T21:05:10.3309491Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3309607Z                             tmp42 = tmp43;
2023-01-11T21:05:10.3309710Z                         }
2023-01-11T21:05:10.3309841Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:05:10.3309997Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:05:10.3310135Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:05:10.3310262Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:05:10.3310381Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:05:10.3310514Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:05:10.3310627Z                         float tmp50 = 0.0;
2023-01-11T21:05:10.3310727Z                         if(tmp49)
2023-01-11T21:05:10.3310821Z                         {
2023-01-11T21:05:10.3310963Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3311080Z                             tmp50 = tmp51;
2023-01-11T21:05:10.3311154Z                         }
2023-01-11T21:05:10.3311270Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:05:10.3311388Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:05:10.3311497Z                         float tmp54 = 0.0;
2023-01-11T21:05:10.3311594Z                         if(tmp53)
2023-01-11T21:05:10.3311786Z                         {
2023-01-11T21:05:10.3311940Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3312050Z                             tmp54 = tmp55;
2023-01-11T21:05:10.3312163Z                         }
2023-01-11T21:05:10.3312309Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:05:10.3312445Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:05:10.3312568Z                         float tmp58 = 0.0;
2023-01-11T21:05:10.3312674Z                         if(tmp57)
2023-01-11T21:05:10.3312771Z                         {
2023-01-11T21:05:10.3312915Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3313035Z                             tmp58 = tmp59;
2023-01-11T21:05:10.3313129Z                         }
2023-01-11T21:05:10.3313262Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:05:10.3313420Z                         auto tmp61 = static_cast<float>(0.1111111111111111);
2023-01-11T21:05:10.3313562Z                         auto tmp62 = tmp60 * tmp61;
2023-01-11T21:05:10.3313701Z                         out_ptr0[i1 + (4*i0)] = tmp62;
2023-01-11T21:05:10.3313785Z                     }
2023-01-11T21:05:10.3313988Z                 }
2023-01-11T21:05:10.3314086Z             }
2023-01-11T21:05:10.3314180Z         }
2023-01-11T21:05:10.3314273Z     }
2023-01-11T21:05:10.3314362Z }
2023-01-11T21:05:10.3314505Z ''')
2023-01-11T21:05:10.3314514Z 
2023-01-11T21:05:10.3314520Z 
2023-01-11T21:05:10.3314647Z async_compile.wait(globals())
2023-01-11T21:05:10.3314757Z del async_compile
2023-01-11T21:05:10.3314765Z 
2023-01-11T21:05:10.3314872Z def call(args):
2023-01-11T21:05:10.3314975Z     arg0_1, = args
2023-01-11T21:05:10.3315073Z     args.clear()
2023-01-11T21:05:10.3315389Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3315589Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3315693Z     del arg0_1
2023-01-11T21:05:10.3315777Z     return (buf0, )
2023-01-11T21:05:10.3315783Z 
2023-01-11T21:05:10.3315790Z 
2023-01-11T21:05:10.3315893Z if __name__ == "__main__":
2023-01-11T21:05:10.3316062Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3316238Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3316564Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3316722Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3316731Z 
2023-01-11T21:05:10.3316830Z ok (2.804s)
2023-01-11T21:05:10.3317471Z   test_avg_pool2d4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3317648Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3318027Z [2023-01-11 20:45:23,826] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 30
2023-01-11T21:05:10.3318413Z [2023-01-11 20:45:26,709] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 30
2023-01-11T21:05:10.3318422Z 
2023-01-11T21:05:10.3318558Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3318654Z import torch
2023-01-11T21:05:10.3318746Z import random
2023-01-11T21:05:10.3318912Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3319094Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3319101Z 
2023-01-11T21:05:10.3319208Z aten = torch.ops.aten
2023-01-11T21:05:10.3319377Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3319508Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3319516Z 
2023-01-11T21:05:10.3319610Z 
2023-01-11T21:05:10.3319817Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3320109Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3320287Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3320439Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3320534Z {
2023-01-11T21:05:10.3320807Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3320904Z     {
2023-01-11T21:05:10.3321032Z         #pragma omp for 
2023-01-11T21:05:10.3321153Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.3321244Z         {
2023-01-11T21:05:10.3321355Z             #pragma GCC ivdep
2023-01-11T21:05:10.3321478Z             for(long i1=0; i1<55; i1+=1)
2023-01-11T21:05:10.3321560Z             {
2023-01-11T21:05:10.3321681Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3321819Z                 for(long i2=0; i2<55; i2+=1)
2023-01-11T21:05:10.3321922Z                 {
2023-01-11T21:05:10.3322020Z                     {
2023-01-11T21:05:10.3322116Z                         {
2023-01-11T21:05:10.3322279Z                             auto tmp0 = in_ptr0[(2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3322570Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3322733Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3322895Z                             auto tmp5 = in_ptr0[111 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3323052Z                             auto tmp7 = in_ptr0[112 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3323203Z                             auto tmp9 = in_ptr0[113 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3323363Z                             auto tmp11 = in_ptr0[222 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3323527Z                             auto tmp13 = in_ptr0[223 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3323682Z                             auto tmp15 = in_ptr0[224 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.3323801Z                             auto tmp2 = tmp1 + tmp0;
2023-01-11T21:05:10.3323939Z                             auto tmp4 = tmp3 + tmp2;
2023-01-11T21:05:10.3324075Z                             auto tmp6 = tmp5 + tmp4;
2023-01-11T21:05:10.3324209Z                             auto tmp8 = tmp7 + tmp6;
2023-01-11T21:05:10.3324344Z                             auto tmp10 = tmp9 + tmp8;
2023-01-11T21:05:10.3324482Z                             auto tmp12 = tmp11 + tmp10;
2023-01-11T21:05:10.3324614Z                             auto tmp14 = tmp13 + tmp12;
2023-01-11T21:05:10.3324754Z                             auto tmp16 = tmp15 + tmp14;
2023-01-11T21:05:10.3324900Z                             auto tmp17 = static_cast<float>(0.1111111111111111);
2023-01-11T21:05:10.3325032Z                             auto tmp18 = tmp16 * tmp17;
2023-01-11T21:05:10.3325175Z                             out_ptr0[i2 + (55*i1) + (3025*i0)] = tmp18;
2023-01-11T21:05:10.3325270Z                         }
2023-01-11T21:05:10.3325358Z                     }
2023-01-11T21:05:10.3325447Z                 }
2023-01-11T21:05:10.3325538Z             }
2023-01-11T21:05:10.3325615Z         }
2023-01-11T21:05:10.3325696Z     }
2023-01-11T21:05:10.3325781Z }
2023-01-11T21:05:10.3325921Z ''')
2023-01-11T21:05:10.3325930Z 
2023-01-11T21:05:10.3325935Z 
2023-01-11T21:05:10.3326071Z async_compile.wait(globals())
2023-01-11T21:05:10.3326176Z del async_compile
2023-01-11T21:05:10.3326184Z 
2023-01-11T21:05:10.3326287Z def call(args):
2023-01-11T21:05:10.3326374Z     arg0_1, = args
2023-01-11T21:05:10.3326476Z     args.clear()
2023-01-11T21:05:10.3326814Z     buf0 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3327005Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3327098Z     del arg0_1
2023-01-11T21:05:10.3327317Z     return (buf0, )
2023-01-11T21:05:10.3327324Z 
2023-01-11T21:05:10.3327330Z 
2023-01-11T21:05:10.3327439Z if __name__ == "__main__":
2023-01-11T21:05:10.3327601Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3327771Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3328108Z     arg0_1 = rand_strided((2, 8, 111, 111), (98568, 12321, 111, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3328259Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3328267Z 
2023-01-11T21:05:10.3328372Z ok (3.023s)
2023-01-11T21:05:10.3329037Z   test_avg_pool2d5_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3329211Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3329579Z [2023-01-11 20:45:26,841] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 31
2023-01-11T21:05:10.3329587Z 
2023-01-11T21:05:10.3329794Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3329902Z import torch
2023-01-11T21:05:10.3329996Z import random
2023-01-11T21:05:10.3330173Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3330357Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3330364Z 
2023-01-11T21:05:10.3330480Z aten = torch.ops.aten
2023-01-11T21:05:10.3330687Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3330826Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3330833Z 
2023-01-11T21:05:10.3330839Z 
2023-01-11T21:05:10.3331051Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3331352Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3331524Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3331675Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3331769Z {
2023-01-11T21:05:10.3331918Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3332007Z     {
2023-01-11T21:05:10.3332117Z         #pragma omp for 
2023-01-11T21:05:10.3332237Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3332315Z         {
2023-01-11T21:05:10.3332428Z             #pragma GCC ivdep
2023-01-11T21:05:10.3332545Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.3332644Z             {
2023-01-11T21:05:10.3332730Z                 {
2023-01-11T21:05:10.3332823Z                     {
2023-01-11T21:05:10.3333075Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:05:10.3333227Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3333366Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3333513Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:05:10.3333656Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3333813Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:05:10.3334125Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:05:10.3334283Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:05:10.3334429Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:05:10.3334582Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:05:10.3334742Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:05:10.3334871Z                         float tmp11 = 0.0;
2023-01-11T21:05:10.3334975Z                         if(tmp10)
2023-01-11T21:05:10.3335072Z                         {
2023-01-11T21:05:10.3335338Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3335446Z                             tmp11 = tmp12;
2023-01-11T21:05:10.3335638Z                         }
2023-01-11T21:05:10.3335793Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:05:10.3335927Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:05:10.3336057Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:05:10.3336175Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:05:10.3336294Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:05:10.3336390Z                         float tmp18 = 0.0;
2023-01-11T21:05:10.3336495Z                         if(tmp17)
2023-01-11T21:05:10.3336595Z                         {
2023-01-11T21:05:10.3336866Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3336986Z                             tmp18 = tmp19;
2023-01-11T21:05:10.3337083Z                         }
2023-01-11T21:05:10.3337224Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:05:10.3337384Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:05:10.3337533Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:05:10.3337671Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:05:10.3337884Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:05:10.3338034Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:05:10.3338164Z                         float tmp26 = 0.0;
2023-01-11T21:05:10.3338279Z                         if(tmp25)
2023-01-11T21:05:10.3338370Z                         {
2023-01-11T21:05:10.3338757Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3338883Z                             tmp26 = tmp27;
2023-01-11T21:05:10.3338986Z                         }
2023-01-11T21:05:10.3339127Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:05:10.3339279Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:05:10.3339411Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:05:10.3339550Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:05:10.3339669Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:05:10.3339810Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:05:10.3339942Z                         float tmp34 = 0.0;
2023-01-11T21:05:10.3340050Z                         if(tmp33)
2023-01-11T21:05:10.3340150Z                         {
2023-01-11T21:05:10.3340418Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3340542Z                             tmp34 = tmp35;
2023-01-11T21:05:10.3340627Z                         }
2023-01-11T21:05:10.3340771Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:05:10.3340910Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:05:10.3341037Z                         float tmp38 = 0.0;
2023-01-11T21:05:10.3341145Z                         if(tmp37)
2023-01-11T21:05:10.3341243Z                         {
2023-01-11T21:05:10.3341401Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:05:10.3341509Z                             tmp38 = tmp39;
2023-01-11T21:05:10.3341609Z                         }
2023-01-11T21:05:10.3341739Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:05:10.3341882Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:05:10.3342009Z                         float tmp42 = 0.0;
2023-01-11T21:05:10.3342112Z                         if(tmp41)
2023-01-11T21:05:10.3342215Z                         {
2023-01-11T21:05:10.3342357Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3342477Z                             tmp42 = tmp43;
2023-01-11T21:05:10.3342577Z                         }
2023-01-11T21:05:10.3342714Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:05:10.3342872Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:05:10.3343008Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:05:10.3343246Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:05:10.3343365Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:05:10.3343495Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:05:10.3343623Z                         float tmp50 = 0.0;
2023-01-11T21:05:10.3343732Z                         if(tmp49)
2023-01-11T21:05:10.3343831Z                         {
2023-01-11T21:05:10.3343986Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3344100Z                             tmp50 = tmp51;
2023-01-11T21:05:10.3344185Z                         }
2023-01-11T21:05:10.3344312Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:05:10.3344445Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:05:10.3344565Z                         float tmp54 = 0.0;
2023-01-11T21:05:10.3344671Z                         if(tmp53)
2023-01-11T21:05:10.3344765Z                         {
2023-01-11T21:05:10.3344932Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3345051Z                             tmp54 = tmp55;
2023-01-11T21:05:10.3345140Z                         }
2023-01-11T21:05:10.3345337Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:05:10.3345477Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:05:10.3345592Z                         float tmp58 = 0.0;
2023-01-11T21:05:10.3345697Z                         if(tmp57)
2023-01-11T21:05:10.3345784Z                         {
2023-01-11T21:05:10.3345956Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3346056Z                             tmp58 = tmp59;
2023-01-11T21:05:10.3346152Z                         }
2023-01-11T21:05:10.3346289Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:05:10.3346421Z                         float tmp61 = 0.0;
2023-01-11T21:05:10.3346533Z                         if(tmp10)
2023-01-11T21:05:10.3346636Z                         {
2023-01-11T21:05:10.3346794Z                             auto tmp62 = static_cast<float>(1);
2023-01-11T21:05:10.3346896Z                             tmp61 = tmp62;
2023-01-11T21:05:10.3346989Z                         }
2023-01-11T21:05:10.3347113Z                         float tmp63 = 0.0;
2023-01-11T21:05:10.3347222Z                         if(tmp17)
2023-01-11T21:05:10.3347316Z                         {
2023-01-11T21:05:10.3347466Z                             auto tmp64 = static_cast<float>(1);
2023-01-11T21:05:10.3347587Z                             tmp63 = tmp64;
2023-01-11T21:05:10.3347667Z                         }
2023-01-11T21:05:10.3347800Z                         auto tmp65 = tmp63 + tmp61;
2023-01-11T21:05:10.3347924Z                         float tmp66 = 0.0;
2023-01-11T21:05:10.3348031Z                         if(tmp25)
2023-01-11T21:05:10.3348129Z                         {
2023-01-11T21:05:10.3348280Z                             auto tmp67 = static_cast<float>(1);
2023-01-11T21:05:10.3348401Z                             tmp66 = tmp67;
2023-01-11T21:05:10.3348481Z                         }
2023-01-11T21:05:10.3348622Z                         auto tmp68 = tmp66 + tmp65;
2023-01-11T21:05:10.3348750Z                         float tmp69 = 0.0;
2023-01-11T21:05:10.3348858Z                         if(tmp33)
2023-01-11T21:05:10.3348959Z                         {
2023-01-11T21:05:10.3349113Z                             auto tmp70 = static_cast<float>(1);
2023-01-11T21:05:10.3349213Z                             tmp69 = tmp70;
2023-01-11T21:05:10.3349295Z                         }
2023-01-11T21:05:10.3349425Z                         auto tmp71 = tmp69 + tmp68;
2023-01-11T21:05:10.3349552Z                         float tmp72 = 0.0;
2023-01-11T21:05:10.3349657Z                         if(tmp37)
2023-01-11T21:05:10.3349761Z                         {
2023-01-11T21:05:10.3349912Z                             auto tmp73 = static_cast<float>(1);
2023-01-11T21:05:10.3350112Z                             tmp72 = tmp73;
2023-01-11T21:05:10.3350197Z                         }
2023-01-11T21:05:10.3350328Z                         auto tmp74 = tmp72 + tmp71;
2023-01-11T21:05:10.3350455Z                         float tmp75 = 0.0;
2023-01-11T21:05:10.3350562Z                         if(tmp41)
2023-01-11T21:05:10.3350660Z                         {
2023-01-11T21:05:10.3350812Z                             auto tmp76 = static_cast<float>(1);
2023-01-11T21:05:10.3350915Z                             tmp75 = tmp76;
2023-01-11T21:05:10.3350990Z                         }
2023-01-11T21:05:10.3351112Z                         auto tmp77 = tmp75 + tmp74;
2023-01-11T21:05:10.3351221Z                         float tmp78 = 0.0;
2023-01-11T21:05:10.3351325Z                         if(tmp49)
2023-01-11T21:05:10.3351424Z                         {
2023-01-11T21:05:10.3351558Z                             auto tmp79 = static_cast<float>(1);
2023-01-11T21:05:10.3351668Z                             tmp78 = tmp79;
2023-01-11T21:05:10.3351758Z                         }
2023-01-11T21:05:10.3351892Z                         auto tmp80 = tmp78 + tmp77;
2023-01-11T21:05:10.3352023Z                         float tmp81 = 0.0;
2023-01-11T21:05:10.3352198Z                         if(tmp53)
2023-01-11T21:05:10.3352308Z                         {
2023-01-11T21:05:10.3352462Z                             auto tmp82 = static_cast<float>(1);
2023-01-11T21:05:10.3352582Z                             tmp81 = tmp82;
2023-01-11T21:05:10.3352667Z                         }
2023-01-11T21:05:10.3352791Z                         auto tmp83 = tmp81 + tmp80;
2023-01-11T21:05:10.3352911Z                         float tmp84 = 0.0;
2023-01-11T21:05:10.3353023Z                         if(tmp57)
2023-01-11T21:05:10.3353124Z                         {
2023-01-11T21:05:10.3353281Z                             auto tmp85 = static_cast<float>(1);
2023-01-11T21:05:10.3353402Z                             tmp84 = tmp85;
2023-01-11T21:05:10.3353495Z                         }
2023-01-11T21:05:10.3353637Z                         auto tmp86 = tmp84 + tmp83;
2023-01-11T21:05:10.3353765Z                         auto tmp87 = tmp60 / tmp86;
2023-01-11T21:05:10.3353906Z                         out_ptr0[i1 + (4*i0)] = tmp87;
2023-01-11T21:05:10.3354002Z                     }
2023-01-11T21:05:10.3354096Z                 }
2023-01-11T21:05:10.3354186Z             }
2023-01-11T21:05:10.3354256Z         }
2023-01-11T21:05:10.3354343Z     }
2023-01-11T21:05:10.3354435Z }
2023-01-11T21:05:10.3354570Z ''')
2023-01-11T21:05:10.3354579Z 
2023-01-11T21:05:10.3354584Z 
2023-01-11T21:05:10.3354715Z async_compile.wait(globals())
2023-01-11T21:05:10.3354829Z del async_compile
2023-01-11T21:05:10.3354836Z 
2023-01-11T21:05:10.3354939Z def call(args):
2023-01-11T21:05:10.3355019Z     arg0_1, = args
2023-01-11T21:05:10.3355119Z     args.clear()
2023-01-11T21:05:10.3355454Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3355641Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3355739Z     del arg0_1
2023-01-11T21:05:10.3355845Z     return (buf0, )
2023-01-11T21:05:10.3355852Z 
2023-01-11T21:05:10.3355860Z 
2023-01-11T21:05:10.3355973Z if __name__ == "__main__":
2023-01-11T21:05:10.3356143Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3356313Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3356629Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3356797Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3357199Z [2023-01-11 20:45:29,667] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 31
2023-01-11T21:05:10.3357206Z 
2023-01-11T21:05:10.3357298Z ok (2.877s)
2023-01-11T21:05:10.3357928Z   test_avg_pool2d6_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3358199Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3358568Z [2023-01-11 20:45:29,715] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 32
2023-01-11T21:05:10.3358973Z [2023-01-11 20:45:32,570] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 32
2023-01-11T21:05:10.3358981Z 
2023-01-11T21:05:10.3359113Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3359206Z import torch
2023-01-11T21:05:10.3359308Z import random
2023-01-11T21:05:10.3359473Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3359648Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3359655Z 
2023-01-11T21:05:10.3359768Z aten = torch.ops.aten
2023-01-11T21:05:10.3359959Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3360083Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3360089Z 
2023-01-11T21:05:10.3360096Z 
2023-01-11T21:05:10.3360348Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3360774Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3360949Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3361081Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3361162Z {
2023-01-11T21:05:10.3361305Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3361389Z     {
2023-01-11T21:05:10.3361478Z         #pragma omp for 
2023-01-11T21:05:10.3361588Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3361673Z         {
2023-01-11T21:05:10.3361783Z             #pragma GCC ivdep
2023-01-11T21:05:10.3361890Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.3361982Z             {
2023-01-11T21:05:10.3362071Z                 {
2023-01-11T21:05:10.3362150Z                     {
2023-01-11T21:05:10.3362399Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:05:10.3362547Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3362675Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3362815Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:05:10.3362950Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3363078Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:05:10.3363312Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:05:10.3363448Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:05:10.3363577Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:05:10.3363710Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:05:10.3363844Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:05:10.3363978Z                         float tmp11 = 0.0;
2023-01-11T21:05:10.3364091Z                         if(tmp10)
2023-01-11T21:05:10.3364185Z                         {
2023-01-11T21:05:10.3364439Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3364560Z                             tmp11 = tmp12;
2023-01-11T21:05:10.3364662Z                         }
2023-01-11T21:05:10.3364817Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:05:10.3364959Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:05:10.3365102Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:05:10.3365224Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:05:10.3365328Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:05:10.3365448Z                         float tmp18 = 0.0;
2023-01-11T21:05:10.3365555Z                         if(tmp17)
2023-01-11T21:05:10.3365778Z                         {
2023-01-11T21:05:10.3366036Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3366156Z                             tmp18 = tmp19;
2023-01-11T21:05:10.3366266Z                         }
2023-01-11T21:05:10.3366385Z                         auto tmp20 = tmp18 + tmp11;
2023-01-11T21:05:10.3366548Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:05:10.3366677Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:05:10.3366811Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:05:10.3366939Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:05:10.3367066Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:05:10.3367195Z                         float tmp26 = 0.0;
2023-01-11T21:05:10.3367294Z                         if(tmp25)
2023-01-11T21:05:10.3367390Z                         {
2023-01-11T21:05:10.3367650Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3367776Z                             tmp26 = tmp27;
2023-01-11T21:05:10.3367875Z                         }
2023-01-11T21:05:10.3368007Z                         auto tmp28 = tmp26 + tmp20;
2023-01-11T21:05:10.3368250Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:05:10.3368373Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:05:10.3368513Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:05:10.3368652Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:05:10.3368785Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:05:10.3368909Z                         float tmp34 = 0.0;
2023-01-11T21:05:10.3369015Z                         if(tmp33)
2023-01-11T21:05:10.3369121Z                         {
2023-01-11T21:05:10.3369397Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3369508Z                             tmp34 = tmp35;
2023-01-11T21:05:10.3369616Z                         }
2023-01-11T21:05:10.3369746Z                         auto tmp36 = tmp34 + tmp28;
2023-01-11T21:05:10.3369881Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:05:10.3370005Z                         float tmp38 = 0.0;
2023-01-11T21:05:10.3370113Z                         if(tmp37)
2023-01-11T21:05:10.3370209Z                         {
2023-01-11T21:05:10.3370357Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:05:10.3370471Z                             tmp38 = tmp39;
2023-01-11T21:05:10.3370572Z                         }
2023-01-11T21:05:10.3370714Z                         auto tmp40 = tmp38 + tmp36;
2023-01-11T21:05:10.3370848Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:05:10.3370970Z                         float tmp42 = 0.0;
2023-01-11T21:05:10.3371079Z                         if(tmp41)
2023-01-11T21:05:10.3371166Z                         {
2023-01-11T21:05:10.3371325Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3371447Z                             tmp42 = tmp43;
2023-01-11T21:05:10.3371550Z                         }
2023-01-11T21:05:10.3371692Z                         auto tmp44 = tmp42 + tmp40;
2023-01-11T21:05:10.3371853Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:05:10.3371989Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:05:10.3372115Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:05:10.3372254Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:05:10.3372389Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:05:10.3372511Z                         float tmp50 = 0.0;
2023-01-11T21:05:10.3372621Z                         if(tmp49)
2023-01-11T21:05:10.3372720Z                         {
2023-01-11T21:05:10.3372881Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3372983Z                             tmp50 = tmp51;
2023-01-11T21:05:10.3373166Z                         }
2023-01-11T21:05:10.3373304Z                         auto tmp52 = tmp50 + tmp44;
2023-01-11T21:05:10.3373435Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:05:10.3373564Z                         float tmp54 = 0.0;
2023-01-11T21:05:10.3373673Z                         if(tmp53)
2023-01-11T21:05:10.3373772Z                         {
2023-01-11T21:05:10.3373915Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3374037Z                             tmp54 = tmp55;
2023-01-11T21:05:10.3374135Z                         }
2023-01-11T21:05:10.3374267Z                         auto tmp56 = tmp54 + tmp52;
2023-01-11T21:05:10.3374401Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:05:10.3374522Z                         float tmp58 = 0.0;
2023-01-11T21:05:10.3374636Z                         if(tmp57)
2023-01-11T21:05:10.3374734Z                         {
2023-01-11T21:05:10.3374896Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.3375021Z                             tmp58 = tmp59;
2023-01-11T21:05:10.3375125Z                         }
2023-01-11T21:05:10.3375354Z                         auto tmp60 = tmp58 + tmp56;
2023-01-11T21:05:10.3375522Z                         auto tmp61 = static_cast<float>(0.3333333333333333);
2023-01-11T21:05:10.3375659Z                         auto tmp62 = tmp60 * tmp61;
2023-01-11T21:05:10.3375797Z                         out_ptr0[i1 + (4*i0)] = tmp62;
2023-01-11T21:05:10.3375879Z                     }
2023-01-11T21:05:10.3375980Z                 }
2023-01-11T21:05:10.3376074Z             }
2023-01-11T21:05:10.3376159Z         }
2023-01-11T21:05:10.3376255Z     }
2023-01-11T21:05:10.3376339Z }
2023-01-11T21:05:10.3376462Z ''')
2023-01-11T21:05:10.3376472Z 
2023-01-11T21:05:10.3376493Z 
2023-01-11T21:05:10.3376616Z async_compile.wait(globals())
2023-01-11T21:05:10.3376732Z del async_compile
2023-01-11T21:05:10.3376743Z 
2023-01-11T21:05:10.3376844Z def call(args):
2023-01-11T21:05:10.3376942Z     arg0_1, = args
2023-01-11T21:05:10.3377051Z     args.clear()
2023-01-11T21:05:10.3377380Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3377573Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3377656Z     del arg0_1
2023-01-11T21:05:10.3377759Z     return (buf0, )
2023-01-11T21:05:10.3377766Z 
2023-01-11T21:05:10.3377773Z 
2023-01-11T21:05:10.3377889Z if __name__ == "__main__":
2023-01-11T21:05:10.3378057Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3378256Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3378676Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3378842Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3378849Z 
2023-01-11T21:05:10.3378944Z ok (2.903s)
2023-01-11T21:05:10.3379611Z   test_avg_pool2d7_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3379797Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3380203Z [2023-01-11 20:45:32,618] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 33
2023-01-11T21:05:10.3380553Z [2023-01-11 20:45:32,631] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.avg_pool2d
2023-01-11T21:05:10.3380947Z [2023-01-11 20:45:32,636] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 33
2023-01-11T21:05:10.3380955Z 
2023-01-11T21:05:10.3381090Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3381282Z import torch
2023-01-11T21:05:10.3381388Z import random
2023-01-11T21:05:10.3381556Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3381729Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3381739Z 
2023-01-11T21:05:10.3381843Z aten = torch.ops.aten
2023-01-11T21:05:10.3382024Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3382169Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3382175Z 
2023-01-11T21:05:10.3382182Z 
2023-01-11T21:05:10.3382315Z async_compile.wait(globals())
2023-01-11T21:05:10.3382422Z del async_compile
2023-01-11T21:05:10.3382430Z 
2023-01-11T21:05:10.3382528Z def call(args):
2023-01-11T21:05:10.3382633Z     arg0_1, = args
2023-01-11T21:05:10.3382726Z     args.clear()
2023-01-11T21:05:10.3382906Z     buf0 = aten.avg_pool2d(arg0_1, [13, 13], [1, 1], [0, 0], False, True, None)
2023-01-11T21:05:10.3383010Z     del arg0_1
2023-01-11T21:05:10.3383108Z     buf1 = buf0
2023-01-11T21:05:10.3383266Z     assert_size_stride(buf1, (1, 1, 12, 12), (144, 144, 12, 1))
2023-01-11T21:05:10.3383367Z     del buf0
2023-01-11T21:05:10.3383466Z     return (buf1, )
2023-01-11T21:05:10.3383474Z 
2023-01-11T21:05:10.3383479Z 
2023-01-11T21:05:10.3383638Z if __name__ == "__main__":
2023-01-11T21:05:10.3383803Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3383972Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3384309Z     arg0_1 = rand_strided((1, 1, 24, 24), (576, 576, 24, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3384464Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3384472Z 
2023-01-11T21:05:10.3384568Z ok (0.064s)
2023-01-11T21:05:10.3385241Z   test_avg_pool2d_backward2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3385433Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3385837Z [2023-01-11 20:45:32,685] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 34
2023-01-11T21:05:10.3385845Z 
2023-01-11T21:05:10.3385973Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3386079Z import torch
2023-01-11T21:05:10.3386174Z import random
2023-01-11T21:05:10.3386354Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3386524Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3386532Z 
2023-01-11T21:05:10.3386635Z aten = torch.ops.aten
2023-01-11T21:05:10.3386820Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3386953Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3386959Z 
2023-01-11T21:05:10.3386965Z 
2023-01-11T21:05:10.3387144Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3387432Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3387602Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3387749Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3387838Z {
2023-01-11T21:05:10.3387977Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3388061Z     {
2023-01-11T21:05:10.3388155Z         #pragma omp for 
2023-01-11T21:05:10.3388268Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:05:10.3388365Z         {
2023-01-11T21:05:10.3388477Z             #pragma GCC ivdep
2023-01-11T21:05:10.3388605Z             for(long i1=0; i1<15; i1+=1)
2023-01-11T21:05:10.3388696Z             {
2023-01-11T21:05:10.3388790Z                 {
2023-01-11T21:05:10.3388873Z                     {
2023-01-11T21:05:10.3389120Z                         auto tmp0 = static_cast<int>((-1) + i0);
2023-01-11T21:05:10.3389362Z                         auto tmp1 = static_cast<int>((-1) + i1);
2023-01-11T21:05:10.3389623Z                         auto tmp2 = static_cast<int>(2 + i0);
2023-01-11T21:05:10.3389777Z                         auto tmp3 = static_cast<int>(2 + i1);
2023-01-11T21:05:10.3389929Z                         auto tmp4 = static_cast<int>(0);
2023-01-11T21:05:10.3390128Z                         auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:05:10.3390315Z                         auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:05:10.3390444Z                         auto tmp7 = static_cast<int>(20);
2023-01-11T21:05:10.3390618Z                         auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:05:10.3390766Z                         auto tmp9 = static_cast<int>(15);
2023-01-11T21:05:10.3390938Z                         auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp3, tmp9);
2023-01-11T21:05:10.3391077Z                         auto tmp11 = tmp5 + tmp4;
2023-01-11T21:05:10.3391218Z                         auto tmp12 = tmp6 + tmp4;
2023-01-11T21:05:10.3391365Z                         auto tmp13 = static_cast<int>(1);
2023-01-11T21:05:10.3391510Z                         auto tmp14 = static_cast<int>(3);
2023-01-11T21:05:10.3391699Z                         auto tmp15 = tmp11 * tmp13;
2023-01-11T21:05:10.3391924Z                         auto tmp16 = tmp15 - tmp13;
2023-01-11T21:05:10.3392056Z                         auto tmp17 = tmp12 * tmp13;
2023-01-11T21:05:10.3392262Z                         auto tmp18 = tmp17 - tmp13;
2023-01-11T21:05:10.3392396Z                         auto tmp19 = tmp16 + tmp14;
2023-01-11T21:05:10.3392519Z                         auto tmp20 = tmp7 + tmp13;
2023-01-11T21:05:10.3392696Z                         auto tmp21 = (tmp20 != tmp20) ? tmp20 : std::min(tmp19, tmp20);
2023-01-11T21:05:10.3392810Z                         auto tmp22 = tmp18 + tmp14;
2023-01-11T21:05:10.3392941Z                         auto tmp23 = tmp9 + tmp13;
2023-01-11T21:05:10.3393130Z                         auto tmp24 = (tmp23 != tmp23) ? tmp23 : std::min(tmp22, tmp23);
2023-01-11T21:05:10.3393326Z                         auto tmp25 = (tmp4 != tmp4) ? tmp4 : std::max(tmp16, tmp4);
2023-01-11T21:05:10.3393513Z                         auto tmp26 = (tmp4 != tmp4) ? tmp4 : std::max(tmp18, tmp4);
2023-01-11T21:05:10.3393696Z                         auto tmp27 = (tmp7 != tmp7) ? tmp7 : std::min(tmp21, tmp7);
2023-01-11T21:05:10.3393873Z                         auto tmp28 = (tmp9 != tmp9) ? tmp9 : std::min(tmp24, tmp9);
2023-01-11T21:05:10.3394082Z                         auto tmp29 = tmp27 - tmp25;
2023-01-11T21:05:10.3394285Z                         auto tmp30 = tmp28 - tmp26;
2023-01-11T21:05:10.3394402Z                         auto tmp31 = tmp29 * tmp30;
2023-01-11T21:05:10.3394604Z                         auto tmp32 = tmp8 - tmp13;
2023-01-11T21:05:10.3394782Z                         auto tmp33 = (tmp32 != tmp32) ? tmp32 : std::min(tmp11, tmp32);
2023-01-11T21:05:10.3394998Z                         auto tmp34 = tmp10 - tmp13;
2023-01-11T21:05:10.3395186Z                         auto tmp35 = (tmp34 != tmp34) ? tmp34 : std::min(tmp12, tmp34);
2023-01-11T21:05:10.3395340Z                         auto tmp36 = in_ptr0[tmp35 + (15*tmp33)];
2023-01-11T21:05:10.3395464Z                         auto tmp37 = tmp36 / tmp31;
2023-01-11T21:05:10.3395595Z                         auto tmp38 = tmp11 < tmp8;
2023-01-11T21:05:10.3395711Z                         auto tmp39 = tmp12 < tmp10;
2023-01-11T21:05:10.3395847Z                         auto tmp40 = tmp38 & tmp39;
2023-01-11T21:05:10.3396003Z                         auto tmp41 = static_cast<float>(0.0);
2023-01-11T21:05:10.3396147Z                         auto tmp42 = tmp40 ? tmp37 : tmp41;
2023-01-11T21:05:10.3396282Z                         auto tmp43 = tmp6 + tmp13;
2023-01-11T21:05:10.3396417Z                         auto tmp44 = tmp43 * tmp13;
2023-01-11T21:05:10.3396624Z                         auto tmp45 = tmp44 - tmp13;
2023-01-11T21:05:10.3396740Z                         auto tmp46 = tmp45 + tmp14;
2023-01-11T21:05:10.3397026Z                         auto tmp47 = (tmp23 != tmp23) ? tmp23 : std::min(tmp46, tmp23);
2023-01-11T21:05:10.3397212Z                         auto tmp48 = (tmp4 != tmp4) ? tmp4 : std::max(tmp45, tmp4);
2023-01-11T21:05:10.3397382Z                         auto tmp49 = (tmp9 != tmp9) ? tmp9 : std::min(tmp47, tmp9);
2023-01-11T21:05:10.3397601Z                         auto tmp50 = tmp49 - tmp48;
2023-01-11T21:05:10.3397725Z                         auto tmp51 = tmp29 * tmp50;
2023-01-11T21:05:10.3397912Z                         auto tmp52 = (tmp34 != tmp34) ? tmp34 : std::min(tmp43, tmp34);
2023-01-11T21:05:10.3398071Z                         auto tmp53 = in_ptr0[tmp52 + (15*tmp33)];
2023-01-11T21:05:10.3398195Z                         auto tmp54 = tmp53 / tmp51;
2023-01-11T21:05:10.3398318Z                         auto tmp55 = tmp43 < tmp10;
2023-01-11T21:05:10.3398449Z                         auto tmp56 = tmp38 & tmp55;
2023-01-11T21:05:10.3398574Z                         auto tmp57 = tmp42 + tmp54;
2023-01-11T21:05:10.3398718Z                         auto tmp58 = tmp56 ? tmp57 : tmp42;
2023-01-11T21:05:10.3398871Z                         auto tmp59 = static_cast<int>(2);
2023-01-11T21:05:10.3399063Z                         auto tmp60 = tmp6 + tmp59;
2023-01-11T21:05:10.3399200Z                         auto tmp61 = tmp60 * tmp13;
2023-01-11T21:05:10.3399400Z                         auto tmp62 = tmp61 - tmp13;
2023-01-11T21:05:10.3399532Z                         auto tmp63 = tmp62 + tmp14;
2023-01-11T21:05:10.3399722Z                         auto tmp64 = (tmp23 != tmp23) ? tmp23 : std::min(tmp63, tmp23);
2023-01-11T21:05:10.3399909Z                         auto tmp65 = (tmp4 != tmp4) ? tmp4 : std::max(tmp62, tmp4);
2023-01-11T21:05:10.3400083Z                         auto tmp66 = (tmp9 != tmp9) ? tmp9 : std::min(tmp64, tmp9);
2023-01-11T21:05:10.3400283Z                         auto tmp67 = tmp66 - tmp65;
2023-01-11T21:05:10.3400408Z                         auto tmp68 = tmp29 * tmp67;
2023-01-11T21:05:10.3400749Z                         auto tmp69 = (tmp34 != tmp34) ? tmp34 : std::min(tmp60, tmp34);
2023-01-11T21:05:10.3400895Z                         auto tmp70 = in_ptr0[tmp69 + (15*tmp33)];
2023-01-11T21:05:10.3401036Z                         auto tmp71 = tmp70 / tmp68;
2023-01-11T21:05:10.3401172Z                         auto tmp72 = tmp60 < tmp10;
2023-01-11T21:05:10.3401299Z                         auto tmp73 = tmp38 & tmp72;
2023-01-11T21:05:10.3401415Z                         auto tmp74 = tmp58 + tmp71;
2023-01-11T21:05:10.3401558Z                         auto tmp75 = tmp73 ? tmp74 : tmp58;
2023-01-11T21:05:10.3401688Z                         auto tmp76 = tmp5 + tmp13;
2023-01-11T21:05:10.3401812Z                         auto tmp77 = tmp76 * tmp13;
2023-01-11T21:05:10.3402008Z                         auto tmp78 = tmp77 - tmp13;
2023-01-11T21:05:10.3402127Z                         auto tmp79 = tmp78 + tmp14;
2023-01-11T21:05:10.3402320Z                         auto tmp80 = (tmp20 != tmp20) ? tmp20 : std::min(tmp79, tmp20);
2023-01-11T21:05:10.3402503Z                         auto tmp81 = (tmp4 != tmp4) ? tmp4 : std::max(tmp78, tmp4);
2023-01-11T21:05:10.3402695Z                         auto tmp82 = (tmp7 != tmp7) ? tmp7 : std::min(tmp80, tmp7);
2023-01-11T21:05:10.3402904Z                         auto tmp83 = tmp82 - tmp81;
2023-01-11T21:05:10.3403024Z                         auto tmp84 = tmp83 * tmp30;
2023-01-11T21:05:10.3403194Z                         auto tmp85 = (tmp32 != tmp32) ? tmp32 : std::min(tmp76, tmp32);
2023-01-11T21:05:10.3403326Z                         auto tmp86 = in_ptr0[tmp35 + (15*tmp85)];
2023-01-11T21:05:10.3403462Z                         auto tmp87 = tmp86 / tmp84;
2023-01-11T21:05:10.3403604Z                         auto tmp88 = tmp76 < tmp8;
2023-01-11T21:05:10.3403736Z                         auto tmp89 = tmp88 & tmp39;
2023-01-11T21:05:10.3403870Z                         auto tmp90 = tmp75 + tmp87;
2023-01-11T21:05:10.3404143Z                         auto tmp91 = tmp89 ? tmp90 : tmp75;
2023-01-11T21:05:10.3404278Z                         auto tmp92 = tmp83 * tmp50;
2023-01-11T21:05:10.3404412Z                         auto tmp93 = in_ptr0[tmp52 + (15*tmp85)];
2023-01-11T21:05:10.3404542Z                         auto tmp94 = tmp93 / tmp92;
2023-01-11T21:05:10.3404684Z                         auto tmp95 = tmp88 & tmp55;
2023-01-11T21:05:10.3404810Z                         auto tmp96 = tmp91 + tmp94;
2023-01-11T21:05:10.3404960Z                         auto tmp97 = tmp95 ? tmp96 : tmp91;
2023-01-11T21:05:10.3405098Z                         auto tmp98 = tmp83 * tmp67;
2023-01-11T21:05:10.3405243Z                         auto tmp99 = in_ptr0[tmp69 + (15*tmp85)];
2023-01-11T21:05:10.3405368Z                         auto tmp100 = tmp99 / tmp98;
2023-01-11T21:05:10.3405498Z                         auto tmp101 = tmp88 & tmp72;
2023-01-11T21:05:10.3405638Z                         auto tmp102 = tmp97 + tmp100;
2023-01-11T21:05:10.3405781Z                         auto tmp103 = tmp101 ? tmp102 : tmp97;
2023-01-11T21:05:10.3405921Z                         auto tmp104 = tmp5 + tmp59;
2023-01-11T21:05:10.3406055Z                         auto tmp105 = tmp104 * tmp13;
2023-01-11T21:05:10.3406367Z                         auto tmp106 = tmp105 - tmp13;
2023-01-11T21:05:10.3406507Z                         auto tmp107 = tmp106 + tmp14;
2023-01-11T21:05:10.3406678Z                         auto tmp108 = (tmp20 != tmp20) ? tmp20 : std::min(tmp107, tmp20);
2023-01-11T21:05:10.3406869Z                         auto tmp109 = (tmp4 != tmp4) ? tmp4 : std::max(tmp106, tmp4);
2023-01-11T21:05:10.3407049Z                         auto tmp110 = (tmp7 != tmp7) ? tmp7 : std::min(tmp108, tmp7);
2023-01-11T21:05:10.3407265Z                         auto tmp111 = tmp110 - tmp109;
2023-01-11T21:05:10.3407406Z                         auto tmp112 = tmp111 * tmp30;
2023-01-11T21:05:10.3407589Z                         auto tmp113 = (tmp32 != tmp32) ? tmp32 : std::min(tmp104, tmp32);
2023-01-11T21:05:10.3407754Z                         auto tmp114 = in_ptr0[tmp35 + (15*tmp113)];
2023-01-11T21:05:10.3407889Z                         auto tmp115 = tmp114 / tmp112;
2023-01-11T21:05:10.3408007Z                         auto tmp116 = tmp104 < tmp8;
2023-01-11T21:05:10.3408139Z                         auto tmp117 = tmp116 & tmp39;
2023-01-11T21:05:10.3408281Z                         auto tmp118 = tmp103 + tmp115;
2023-01-11T21:05:10.3408425Z                         auto tmp119 = tmp117 ? tmp118 : tmp103;
2023-01-11T21:05:10.3408573Z                         auto tmp120 = tmp111 * tmp50;
2023-01-11T21:05:10.3408725Z                         auto tmp121 = in_ptr0[tmp52 + (15*tmp113)];
2023-01-11T21:05:10.3408854Z                         auto tmp122 = tmp121 / tmp120;
2023-01-11T21:05:10.3408986Z                         auto tmp123 = tmp116 & tmp55;
2023-01-11T21:05:10.3409110Z                         auto tmp124 = tmp119 + tmp122;
2023-01-11T21:05:10.3409260Z                         auto tmp125 = tmp123 ? tmp124 : tmp119;
2023-01-11T21:05:10.3409404Z                         auto tmp126 = tmp111 * tmp67;
2023-01-11T21:05:10.3409551Z                         auto tmp127 = in_ptr0[tmp69 + (15*tmp113)];
2023-01-11T21:05:10.3409682Z                         auto tmp128 = tmp127 / tmp126;
2023-01-11T21:05:10.3409816Z                         auto tmp129 = tmp116 & tmp72;
2023-01-11T21:05:10.3409945Z                         auto tmp130 = tmp125 + tmp128;
2023-01-11T21:05:10.3410077Z                         auto tmp131 = tmp129 ? tmp130 : tmp125;
2023-01-11T21:05:10.3410201Z                         out_ptr0[i1 + (15*i0)] = tmp131;
2023-01-11T21:05:10.3410289Z                     }
2023-01-11T21:05:10.3410378Z                 }
2023-01-11T21:05:10.3410469Z             }
2023-01-11T21:05:10.3410562Z         }
2023-01-11T21:05:10.3410652Z     }
2023-01-11T21:05:10.3410714Z }
2023-01-11T21:05:10.3410841Z ''')
2023-01-11T21:05:10.3410851Z 
2023-01-11T21:05:10.3410855Z 
2023-01-11T21:05:10.3410987Z async_compile.wait(globals())
2023-01-11T21:05:10.3411167Z del async_compile
2023-01-11T21:05:10.3411174Z 
2023-01-11T21:05:10.3411274Z def call(args):
2023-01-11T21:05:10.3411378Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3411485Z     args.clear()
2023-01-11T21:05:10.3411804Z     buf0 = empty_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3411998Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3412091Z     del arg0_1
2023-01-11T21:05:10.3412188Z     return (buf0, )
2023-01-11T21:05:10.3412194Z 
2023-01-11T21:05:10.3412199Z 
2023-01-11T21:05:10.3412299Z if __name__ == "__main__":
2023-01-11T21:05:10.3412466Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3412636Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3412948Z     arg0_1 = rand_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3413235Z     arg1_1 = rand_strided((1, 1, 20, 15), (300, 300, 15, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3413396Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3413882Z [2023-01-11 20:45:35,519] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 34
2023-01-11T21:05:10.3413893Z 
2023-01-11T21:05:10.3413993Z ok (2.887s)
2023-01-11T21:05:10.3414648Z   test_avg_pool2d_backward3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3414832Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3415223Z [2023-01-11 20:45:35,630] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 35
2023-01-11T21:05:10.3415607Z [2023-01-11 20:45:38,419] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 35
2023-01-11T21:05:10.3415614Z 
2023-01-11T21:05:10.3415759Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3415868Z import torch
2023-01-11T21:05:10.3415956Z import random
2023-01-11T21:05:10.3416127Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3416308Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3416315Z 
2023-01-11T21:05:10.3416426Z aten = torch.ops.aten
2023-01-11T21:05:10.3416637Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3416778Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3416784Z 
2023-01-11T21:05:10.3416790Z 
2023-01-11T21:05:10.3416982Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3417290Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3417440Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3417579Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3417669Z {
2023-01-11T21:05:10.3417815Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3417908Z     {
2023-01-11T21:05:10.3418019Z         #pragma omp for 
2023-01-11T21:05:10.3418134Z         for(long i0=0; i0<2016; i0+=1)
2023-01-11T21:05:10.3418218Z         {
2023-01-11T21:05:10.3418324Z             #pragma GCC ivdep
2023-01-11T21:05:10.3418437Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:05:10.3418622Z             {
2023-01-11T21:05:10.3418745Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3418878Z                 for(long i2=0; i2<21; i2+=1)
2023-01-11T21:05:10.3418952Z                 {
2023-01-11T21:05:10.3419043Z                     {
2023-01-11T21:05:10.3419137Z                         {
2023-01-11T21:05:10.3419306Z                             auto tmp0 = static_cast<int>(((1 + i1) / 2));
2023-01-11T21:05:10.3419476Z                             auto tmp1 = static_cast<int>(((1 + i2) / 2));
2023-01-11T21:05:10.3419736Z                             auto tmp2 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:05:10.3419895Z                             auto tmp3 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:05:10.3420059Z                             auto tmp4 = static_cast<int>(0);
2023-01-11T21:05:10.3420241Z                             auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:05:10.3420424Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:05:10.3420578Z                             auto tmp7 = static_cast<int>(11);
2023-01-11T21:05:10.3420756Z                             auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:05:10.3420935Z                             auto tmp9 = (tmp7 != tmp7) ? tmp7 : std::min(tmp3, tmp7);
2023-01-11T21:05:10.3421071Z                             auto tmp10 = tmp5 + tmp4;
2023-01-11T21:05:10.3421209Z                             auto tmp11 = tmp6 + tmp4;
2023-01-11T21:05:10.3421366Z                             auto tmp12 = static_cast<int>(1);
2023-01-11T21:05:10.3421590Z                             auto tmp13 = tmp8 - tmp12;
2023-01-11T21:05:10.3421847Z                             auto tmp14 = (tmp13 != tmp13) ? tmp13 : std::min(tmp10, tmp13);
2023-01-11T21:05:10.3422072Z                             auto tmp15 = tmp9 - tmp12;
2023-01-11T21:05:10.3422257Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp11, tmp15);
2023-01-11T21:05:10.3422429Z                             auto tmp17 = in_ptr0[tmp16 + (11*tmp14) + (121*i0)];
2023-01-11T21:05:10.3422566Z                             auto tmp18 = tmp17 / 1;
2023-01-11T21:05:10.3422699Z                             auto tmp19 = tmp10 < tmp8;
2023-01-11T21:05:10.3422835Z                             auto tmp20 = tmp11 < tmp9;
2023-01-11T21:05:10.3422959Z                             auto tmp21 = tmp19 & tmp20;
2023-01-11T21:05:10.3423122Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:05:10.3423275Z                             auto tmp23 = tmp21 ? tmp18 : tmp22;
2023-01-11T21:05:10.3423425Z                             out_ptr0[i2 + (21*i1) + (441*i0)] = tmp23;
2023-01-11T21:05:10.3423527Z                         }
2023-01-11T21:05:10.3423624Z                     }
2023-01-11T21:05:10.3423721Z                 }
2023-01-11T21:05:10.3423798Z             }
2023-01-11T21:05:10.3423886Z         }
2023-01-11T21:05:10.3423981Z     }
2023-01-11T21:05:10.3424065Z }
2023-01-11T21:05:10.3424182Z ''')
2023-01-11T21:05:10.3424191Z 
2023-01-11T21:05:10.3424197Z 
2023-01-11T21:05:10.3424332Z async_compile.wait(globals())
2023-01-11T21:05:10.3424428Z del async_compile
2023-01-11T21:05:10.3424434Z 
2023-01-11T21:05:10.3424530Z def call(args):
2023-01-11T21:05:10.3424629Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3424731Z     args.clear()
2023-01-11T21:05:10.3425065Z     buf0 = empty_strided((1, 2016, 21, 21), (889056, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3425265Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3425359Z     del arg0_1
2023-01-11T21:05:10.3425455Z     return (buf0, )
2023-01-11T21:05:10.3425462Z 
2023-01-11T21:05:10.3425471Z 
2023-01-11T21:05:10.3425582Z if __name__ == "__main__":
2023-01-11T21:05:10.3425736Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3425902Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3426241Z     arg0_1 = rand_strided((1, 2016, 11, 11), (243936, 121, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3426561Z     arg1_1 = rand_strided((1, 2016, 21, 21), (889056, 441, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3426722Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3426729Z 
2023-01-11T21:05:10.3426835Z ok (3.386s)
2023-01-11T21:05:10.3427520Z   test_avg_pool2d_backward4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3427802Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3428213Z [2023-01-11 20:45:38,958] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 36
2023-01-11T21:05:10.3428537Z [2023-01-11 20:45:38,977] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.avg_pool2d_backward
2023-01-11T21:05:10.3428909Z [2023-01-11 20:45:38,981] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 36
2023-01-11T21:05:10.3428917Z 
2023-01-11T21:05:10.3429061Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3429172Z import torch
2023-01-11T21:05:10.3429273Z import random
2023-01-11T21:05:10.3429453Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3429640Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3429648Z 
2023-01-11T21:05:10.3429759Z aten = torch.ops.aten
2023-01-11T21:05:10.3430036Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3430188Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3430195Z 
2023-01-11T21:05:10.3430202Z 
2023-01-11T21:05:10.3430343Z async_compile.wait(globals())
2023-01-11T21:05:10.3430446Z del async_compile
2023-01-11T21:05:10.3430452Z 
2023-01-11T21:05:10.3430556Z def call(args):
2023-01-11T21:05:10.3430676Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3430791Z     args.clear()
2023-01-11T21:05:10.3431007Z     buf0 = aten.avg_pool2d_backward(arg0_1, arg1_1, [13, 13], [1, 1], [0, 0], True, False, None)
2023-01-11T21:05:10.3431104Z     del arg0_1
2023-01-11T21:05:10.3431206Z     del arg1_1
2023-01-11T21:05:10.3431300Z     buf1 = buf0
2023-01-11T21:05:10.3431470Z     assert_size_stride(buf1, (1, 16, 24, 24), (9216, 576, 24, 1))
2023-01-11T21:05:10.3431569Z     del buf0
2023-01-11T21:05:10.3431668Z     return (buf1, )
2023-01-11T21:05:10.3431676Z 
2023-01-11T21:05:10.3431682Z 
2023-01-11T21:05:10.3431791Z if __name__ == "__main__":
2023-01-11T21:05:10.3431943Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3432120Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3432440Z     arg0_1 = rand_strided((1, 16, 12, 12), (2304, 144, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3432763Z     arg1_1 = rand_strided((1, 16, 24, 24), (9216, 576, 24, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3432939Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3432947Z 
2023-01-11T21:05:10.3433047Z ok (0.086s)
2023-01-11T21:05:10.3433750Z   test_avg_pool2d_backward_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3433942Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3434352Z [2023-01-11 20:45:39,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 37
2023-01-11T21:05:10.3434744Z [2023-01-11 20:45:41,782] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 37
2023-01-11T21:05:10.3434751Z 
2023-01-11T21:05:10.3434880Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3434983Z import torch
2023-01-11T21:05:10.3435089Z import random
2023-01-11T21:05:10.3435259Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3435439Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3435446Z 
2023-01-11T21:05:10.3435559Z aten = torch.ops.aten
2023-01-11T21:05:10.3435880Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3435999Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3436019Z 
2023-01-11T21:05:10.3436025Z 
2023-01-11T21:05:10.3436212Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3436521Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3436704Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3436852Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3436944Z {
2023-01-11T21:05:10.3437090Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3437186Z     {
2023-01-11T21:05:10.3437292Z         #pragma omp for 
2023-01-11T21:05:10.3437414Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3437506Z         {
2023-01-11T21:05:10.3437630Z             #pragma GCC ivdep
2023-01-11T21:05:10.3437754Z             for(long i1=0; i1<14; i1+=1)
2023-01-11T21:05:10.3437853Z             {
2023-01-11T21:05:10.3437983Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3438099Z                 for(long i2=0; i2<14; i2+=1)
2023-01-11T21:05:10.3438193Z                 {
2023-01-11T21:05:10.3438294Z                     {
2023-01-11T21:05:10.3438543Z                         {
2023-01-11T21:05:10.3438759Z                             auto tmp0 = static_cast<int>((i1 / 2));
2023-01-11T21:05:10.3438964Z                             auto tmp1 = static_cast<int>((i2 / 2));
2023-01-11T21:05:10.3439121Z                             auto tmp2 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:05:10.3439501Z                             auto tmp3 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:05:10.3439697Z                             auto tmp4 = static_cast<int>(0);
2023-01-11T21:05:10.3439961Z                             auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp0, tmp4);
2023-01-11T21:05:10.3440183Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp1, tmp4);
2023-01-11T21:05:10.3440380Z                             auto tmp7 = static_cast<int>(7);
2023-01-11T21:05:10.3440745Z                             auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp2, tmp7);
2023-01-11T21:05:10.3440924Z                             auto tmp9 = (tmp7 != tmp7) ? tmp7 : std::min(tmp3, tmp7);
2023-01-11T21:05:10.3441099Z                             auto tmp10 = tmp5 + tmp4;
2023-01-11T21:05:10.3441280Z                             auto tmp11 = tmp6 + tmp4;
2023-01-11T21:05:10.3441538Z                             auto tmp12 = static_cast<int>(1);
2023-01-11T21:05:10.3441839Z                             auto tmp13 = tmp8 - tmp12;
2023-01-11T21:05:10.3442070Z                             auto tmp14 = (tmp13 != tmp13) ? tmp13 : std::min(tmp10, tmp13);
2023-01-11T21:05:10.3442374Z                             auto tmp15 = tmp9 - tmp12;
2023-01-11T21:05:10.3442603Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp11, tmp15);
2023-01-11T21:05:10.3442763Z                             auto tmp17 = in_ptr0[tmp16 + (7*tmp14) + (49*i0)];
2023-01-11T21:05:10.3442942Z                             auto tmp18 = tmp17 / 4;
2023-01-11T21:05:10.3443127Z                             auto tmp19 = tmp10 < tmp8;
2023-01-11T21:05:10.3443307Z                             auto tmp20 = tmp11 < tmp9;
2023-01-11T21:05:10.3443550Z                             auto tmp21 = tmp19 & tmp20;
2023-01-11T21:05:10.3443744Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:05:10.3443918Z                             auto tmp23 = tmp21 ? tmp18 : tmp22;
2023-01-11T21:05:10.3444105Z                             out_ptr0[i2 + (14*i1) + (196*i0)] = tmp23;
2023-01-11T21:05:10.3444192Z                         }
2023-01-11T21:05:10.3444329Z                     }
2023-01-11T21:05:10.3444461Z                 }
2023-01-11T21:05:10.3444590Z             }
2023-01-11T21:05:10.3444730Z         }
2023-01-11T21:05:10.3444904Z     }
2023-01-11T21:05:10.3444987Z }
2023-01-11T21:05:10.3445163Z ''')
2023-01-11T21:05:10.3445275Z 
2023-01-11T21:05:10.3445281Z 
2023-01-11T21:05:10.3445512Z async_compile.wait(globals())
2023-01-11T21:05:10.3445663Z del async_compile
2023-01-11T21:05:10.3445672Z 
2023-01-11T21:05:10.3445817Z def call(args):
2023-01-11T21:05:10.3445972Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3446126Z     args.clear()
2023-01-11T21:05:10.3446493Z     buf0 = empty_strided((2, 4, 14, 14), (784, 196, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3446670Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3446848Z     del arg0_1
2023-01-11T21:05:10.3446996Z     return (buf0, )
2023-01-11T21:05:10.3447002Z 
2023-01-11T21:05:10.3447008Z 
2023-01-11T21:05:10.3447154Z if __name__ == "__main__":
2023-01-11T21:05:10.3447352Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3447565Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3447927Z     arg0_1 = rand_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3448271Z     arg1_1 = rand_strided((2, 4, 14, 14), (784, 196, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3448416Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3448424Z 
2023-01-11T21:05:10.3448641Z ok (2.793s)
2023-01-11T21:05:10.3449379Z   test_baddbmm_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3449647Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3450072Z [2023-01-11 20:45:41,855] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 38
2023-01-11T21:05:10.3450514Z [2023-01-11 20:45:44,573] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 38
2023-01-11T21:05:10.3450528Z 
2023-01-11T21:05:10.3450702Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3450844Z import torch
2023-01-11T21:05:10.3450985Z import random
2023-01-11T21:05:10.3451139Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3451356Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3451363Z 
2023-01-11T21:05:10.3451560Z aten = torch.ops.aten
2023-01-11T21:05:10.3451791Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3451982Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3451989Z 
2023-01-11T21:05:10.3451996Z 
2023-01-11T21:05:10.3452237Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3452567Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3452776Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.3452911Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:05:10.3453038Z {
2023-01-11T21:05:10.3453217Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3453402Z     {
2023-01-11T21:05:10.3453625Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3453786Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.3453906Z         {
2023-01-11T21:05:10.3454013Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:05:10.3454134Z             {
2023-01-11T21:05:10.3454310Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:05:10.3454439Z                 {
2023-01-11T21:05:10.3454686Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i2) + (100*i0));
2023-01-11T21:05:10.3454997Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + (16*i2) + (100*i1) + (12800*i0));
2023-01-11T21:05:10.3455163Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3455310Z                     tmp2.store(in_out_ptr0 + (16*i2) + (100*i1) + (12800*i0));
2023-01-11T21:05:10.3455520Z                 }
2023-01-11T21:05:10.3455699Z                 #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.3455862Z                 for(long i2=96; i2<100; i2+=1)
2023-01-11T21:05:10.3455994Z                 {
2023-01-11T21:05:10.3456175Z                     auto tmp0 = in_ptr0[i2 + (100*i0)];
2023-01-11T21:05:10.3456389Z                     auto tmp1 = in_out_ptr0[i2 + (100*i1) + (12800*i0)];
2023-01-11T21:05:10.3456648Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3456784Z                     in_out_ptr0[i2 + (100*i1) + (12800*i0)] = tmp2;
2023-01-11T21:05:10.3456916Z                 }
2023-01-11T21:05:10.3457041Z             }
2023-01-11T21:05:10.3457172Z         }
2023-01-11T21:05:10.3457295Z     }
2023-01-11T21:05:10.3457416Z }
2023-01-11T21:05:10.3457533Z ''')
2023-01-11T21:05:10.3457541Z 
2023-01-11T21:05:10.3457611Z 
2023-01-11T21:05:10.3457724Z async_compile.wait(globals())
2023-01-11T21:05:10.3457866Z del async_compile
2023-01-11T21:05:10.3457873Z 
2023-01-11T21:05:10.3458068Z def call(args):
2023-01-11T21:05:10.3458220Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.3458363Z     args.clear()
2023-01-11T21:05:10.3458899Z     buf0 = empty_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3459093Z     aten.bmm.out(arg1_1, arg2_1, out=buf0)
2023-01-11T21:05:10.3459171Z     del arg1_1
2023-01-11T21:05:10.3459300Z     del arg2_1
2023-01-11T21:05:10.3459456Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.3459729Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:05:10.3459936Z     del arg0_1
2023-01-11T21:05:10.3460071Z     return (buf1, )
2023-01-11T21:05:10.3460078Z 
2023-01-11T21:05:10.3460084Z 
2023-01-11T21:05:10.3460224Z if __name__ == "__main__":
2023-01-11T21:05:10.3460373Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3460604Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3460962Z     arg0_1 = rand_strided((6, 1, 100), (100, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3461310Z     arg1_1 = rand_strided((6, 128, 64), (8192, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3461668Z     arg2_1 = rand_strided((6, 64, 100), (6400, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3461891Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.3461898Z 
2023-01-11T21:05:10.3462086Z ok (2.872s)
2023-01-11T21:05:10.3462756Z   test_batch_norm_2d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3462986Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3463366Z [2023-01-11 20:45:45,245] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 39
2023-01-11T21:05:10.3463804Z [2023-01-11 20:45:48,212] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 39
2023-01-11T21:05:10.3464433Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3464652Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3465111Z [2023-01-11 20:45:48,794] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 40
2023-01-11T21:05:10.3465119Z 
2023-01-11T21:05:10.3465300Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3465498Z import torch
2023-01-11T21:05:10.3465641Z import random
2023-01-11T21:05:10.3465840Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3466078Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3466137Z 
2023-01-11T21:05:10.3466237Z aten = torch.ops.aten
2023-01-11T21:05:10.3466471Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3466639Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3466646Z 
2023-01-11T21:05:10.3466652Z 
2023-01-11T21:05:10.3466901Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3467198Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3467454Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3467641Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3467823Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.3467954Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:05:10.3468137Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:05:10.3468317Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3468538Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.3468773Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.3468961Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:05:10.3469131Z {
2023-01-11T21:05:10.3469262Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3469386Z     {
2023-01-11T21:05:10.3469534Z         #pragma omp for 
2023-01-11T21:05:10.3469691Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.3469820Z         {
2023-01-11T21:05:10.3469963Z             {
2023-01-11T21:05:10.3470045Z                 {
2023-01-11T21:05:10.3470209Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3470368Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.3470536Z                 }
2023-01-11T21:05:10.3470672Z             }
2023-01-11T21:05:10.3470795Z         }
2023-01-11T21:05:10.3470956Z         #pragma omp for 
2023-01-11T21:05:10.3471062Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.3471241Z         {
2023-01-11T21:05:10.3471371Z             {
2023-01-11T21:05:10.3471501Z                 {
2023-01-11T21:05:10.3471675Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.3471863Z                     out_ptr1[i0] = tmp0;
2023-01-11T21:05:10.3471991Z                 }
2023-01-11T21:05:10.3472070Z             }
2023-01-11T21:05:10.3472201Z         }
2023-01-11T21:05:10.3472392Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3472553Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3472678Z         {
2023-01-11T21:05:10.3472839Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.3472924Z             {
2023-01-11T21:05:10.3473093Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:05:10.3473254Z                 {
2023-01-11T21:05:10.3473526Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + (16*i2) + (64*i1) + (640*i0));
2023-01-11T21:05:10.3473772Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i1]);
2023-01-11T21:05:10.3473999Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr1[i1]);
2023-01-11T21:05:10.3474269Z                     auto tmp11 = at::vec::Vectorized<float>(in_ptr3[i1]);
2023-01-11T21:05:10.3474492Z                     auto tmp13 = at::vec::Vectorized<float>(in_ptr4[i1]);
2023-01-11T21:05:10.3474757Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.3475067Z                     auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:05:10.3475235Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.3475441Z                     auto tmp6 = tmp5.sqrt();
2023-01-11T21:05:10.3475644Z                     auto tmp7 = tmp6.reciprocal();
2023-01-11T21:05:10.3475879Z                     auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3476040Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.3476282Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:05:10.3476405Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:05:10.3476569Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.3476814Z                     auto tmp15 = at::vec::clamp_min(tmp14, decltype(tmp14)(0));
2023-01-11T21:05:10.3477018Z                     tmp15.store(out_ptr2 + (16*i2) + (64*i1) + (640*i0));
2023-01-11T21:05:10.3477201Z                 }
2023-01-11T21:05:10.3477370Z                 #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.3477533Z                 for(long i2=64; i2<64; i2+=1)
2023-01-11T21:05:10.3477701Z                 {
2023-01-11T21:05:10.3477844Z                     auto tmp0 = in_ptr2[i2 + (64*i1) + (640*i0)];
2023-01-11T21:05:10.3478014Z                     auto tmp1 = out_ptr0[i1];
2023-01-11T21:05:10.3478179Z                     auto tmp3 = out_ptr1[i1];
2023-01-11T21:05:10.3478340Z                     auto tmp11 = in_ptr3[i1];
2023-01-11T21:05:10.3478516Z                     auto tmp13 = in_ptr4[i1];
2023-01-11T21:05:10.3478813Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.3479083Z                     auto tmp4 = static_cast<float>(1e-05);
2023-01-11T21:05:10.3479250Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.3479416Z                     auto tmp6 = std::sqrt(tmp5);
2023-01-11T21:05:10.3479558Z                     auto tmp7 = 1 / tmp6;
2023-01-11T21:05:10.3479727Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:05:10.3479891Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.3480045Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:05:10.3480218Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:05:10.3480340Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.3480545Z                     auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:05:10.3481132Z                     out_ptr2[i2 + (64*i1) + (640*i0)] = tmp15;
2023-01-11T21:05:10.3481393Z                 }
2023-01-11T21:05:10.3481488Z             }
2023-01-11T21:05:10.3481584Z         }
2023-01-11T21:05:10.3481730Z         #pragma omp for 
2023-01-11T21:05:10.3481804Z         for(long i0=0; i0<1280; i0+=1)
2023-01-11T21:05:10.3481898Z         {
2023-01-11T21:05:10.3481990Z             {
2023-01-11T21:05:10.3482170Z                 {
2023-01-11T21:05:10.3482310Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:05:10.3482447Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:05:10.3482568Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:05:10.3482640Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.3482736Z                 }
2023-01-11T21:05:10.3482824Z             }
2023-01-11T21:05:10.3482917Z         }
2023-01-11T21:05:10.3483006Z     }
2023-01-11T21:05:10.3483126Z }
2023-01-11T21:05:10.3483225Z ''')
2023-01-11T21:05:10.3483275Z 
2023-01-11T21:05:10.3483279Z 
2023-01-11T21:05:10.3483358Z async_compile.wait(globals())
2023-01-11T21:05:10.3483465Z del async_compile
2023-01-11T21:05:10.3483471Z 
2023-01-11T21:05:10.3483568Z def call(args):
2023-01-11T21:05:10.3483759Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
2023-01-11T21:05:10.3483860Z     args.clear()
2023-01-11T21:05:10.3484088Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3484318Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3484519Z     buf2 = empty_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3484777Z     buf3 = empty_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3485136Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(primals_6.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.3485355Z     del primals_1
2023-01-11T21:05:10.3485454Z     del primals_2
2023-01-11T21:05:10.3485550Z     del primals_3
2023-01-11T21:05:10.3485656Z     del primals_4
2023-01-11T21:05:10.3485803Z     return (buf0, buf1, buf2, primals_6, buf0, buf1, buf3, )
2023-01-11T21:05:10.3485809Z 
2023-01-11T21:05:10.3485814Z 
2023-01-11T21:05:10.3485917Z if __name__ == "__main__":
2023-01-11T21:05:10.3486019Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3486196Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3486434Z     primals_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3486663Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3486914Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3487142Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3487360Z     primals_5 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3487608Z     primals_6 = rand_strided((2, 10, 8, 8), (640, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3487815Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]))
2023-01-11T21:05:10.3487822Z 
2023-01-11T21:05:10.3487872Z 
2023-01-11T21:05:10.3487956Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3488073Z import torch
2023-01-11T21:05:10.3488171Z import random
2023-01-11T21:05:10.3488312Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3488470Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3488475Z 
2023-01-11T21:05:10.3488579Z aten = torch.ops.aten
2023-01-11T21:05:10.3488743Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3488822Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3488827Z 
2023-01-11T21:05:10.3488831Z 
2023-01-11T21:05:10.3488994Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3489232Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3489405Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3489540Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3489706Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.3489839Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:05:10.3489969Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:05:10.3490054Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3490178Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.3490305Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.3490432Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:05:10.3490541Z {
2023-01-11T21:05:10.3490679Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3490771Z     {
2023-01-11T21:05:10.3490835Z         #pragma omp for 
2023-01-11T21:05:10.3490947Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.3491038Z         {
2023-01-11T21:05:10.3491131Z             {
2023-01-11T21:05:10.3491226Z                 {
2023-01-11T21:05:10.3491346Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3491421Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.3491544Z                 }
2023-01-11T21:05:10.3491632Z             }
2023-01-11T21:05:10.3491748Z         }
2023-01-11T21:05:10.3491853Z         #pragma omp for 
2023-01-11T21:05:10.3491963Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.3492053Z         {
2023-01-11T21:05:10.3492102Z             {
2023-01-11T21:05:10.3492196Z                 {
2023-01-11T21:05:10.3492324Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.3492454Z                     out_ptr1[i0] = tmp0;
2023-01-11T21:05:10.3492547Z                 }
2023-01-11T21:05:10.3492636Z             }
2023-01-11T21:05:10.3492760Z         }
2023-01-11T21:05:10.3492835Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3492941Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.3493038Z         {
2023-01-11T21:05:10.3493152Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.3493245Z             {
2023-01-11T21:05:10.3493381Z                 for(long i2=0; i2<16; i2+=1)
2023-01-11T21:05:10.3493431Z                 {
2023-01-11T21:05:10.3493657Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + (16*i2) + (256*i1) + (2560*i0));
2023-01-11T21:05:10.3493819Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i1]);
2023-01-11T21:05:10.3493971Z                     auto tmp3 = at::vec::Vectorized<float>(out_ptr1[i1]);
2023-01-11T21:05:10.3494132Z                     auto tmp11 = at::vec::Vectorized<float>(in_ptr3[i1]);
2023-01-11T21:05:10.3494282Z                     auto tmp13 = at::vec::Vectorized<float>(in_ptr4[i1]);
2023-01-11T21:05:10.3494456Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.3494694Z                     auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:05:10.3494833Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.3494942Z                     auto tmp6 = tmp5.sqrt();
2023-01-11T21:05:10.3495072Z                     auto tmp7 = tmp6.reciprocal();
2023-01-11T21:05:10.3495239Z                     auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3495364Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.3495485Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:05:10.3495605Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:05:10.3495732Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.3495892Z                     auto tmp15 = at::vec::clamp_min(tmp14, decltype(tmp14)(0));
2023-01-11T21:05:10.3495994Z                     tmp15.store(out_ptr2 + (16*i2) + (256*i1) + (2560*i0));
2023-01-11T21:05:10.3496137Z                 }
2023-01-11T21:05:10.3496263Z                 #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.3496396Z                 for(long i2=256; i2<256; i2+=1)
2023-01-11T21:05:10.3496486Z                 {
2023-01-11T21:05:10.3496621Z                     auto tmp0 = in_ptr2[i2 + (256*i1) + (2560*i0)];
2023-01-11T21:05:10.3496741Z                     auto tmp1 = out_ptr0[i1];
2023-01-11T21:05:10.3496820Z                     auto tmp3 = out_ptr1[i1];
2023-01-11T21:05:10.3496938Z                     auto tmp11 = in_ptr3[i1];
2023-01-11T21:05:10.3497059Z                     auto tmp13 = in_ptr4[i1];
2023-01-11T21:05:10.3497249Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.3497451Z                     auto tmp4 = static_cast<float>(1e-05);
2023-01-11T21:05:10.3497569Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.3497697Z                     auto tmp6 = std::sqrt(tmp5);
2023-01-11T21:05:10.3497769Z                     auto tmp7 = 1 / tmp6;
2023-01-11T21:05:10.3497901Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:05:10.3498017Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.3498135Z                     auto tmp10 = tmp2 * tmp9;
2023-01-11T21:05:10.3498296Z                     auto tmp12 = tmp10 * tmp11;
2023-01-11T21:05:10.3498444Z                     auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.3498676Z                     auto tmp15 = tmp14 * (tmp14>0);
2023-01-11T21:05:10.3498769Z                     out_ptr2[i2 + (256*i1) + (2560*i0)] = tmp15;
2023-01-11T21:05:10.3498864Z                 }
2023-01-11T21:05:10.3498955Z             }
2023-01-11T21:05:10.3499044Z         }
2023-01-11T21:05:10.3499148Z         #pragma omp for 
2023-01-11T21:05:10.3499259Z         for(long i0=0; i0<7680; i0+=1)
2023-01-11T21:05:10.3499356Z         {
2023-01-11T21:05:10.3499406Z             {
2023-01-11T21:05:10.3499521Z                 {
2023-01-11T21:05:10.3499642Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:05:10.3499816Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:05:10.3499934Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:05:10.3500046Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.3500150Z                 }
2023-01-11T21:05:10.3500199Z             }
2023-01-11T21:05:10.3500322Z         }
2023-01-11T21:05:10.3500413Z     }
2023-01-11T21:05:10.3500518Z }
2023-01-11T21:05:10.3500631Z ''')
2023-01-11T21:05:10.3500639Z 
2023-01-11T21:05:10.3500643Z 
2023-01-11T21:05:10.3500762Z async_compile.wait(globals())
2023-01-11T21:05:10.3500862Z del async_compile
2023-01-11T21:05:10.3500868Z 
2023-01-11T21:05:10.3500924Z def call(args):
2023-01-11T21:05:10.3501099Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
2023-01-11T21:05:10.3501200Z     args.clear()
2023-01-11T21:05:10.3501422Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3501644Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3501915Z     buf2 = empty_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3502188Z     buf3 = empty_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3502543Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(primals_4.data_ptr()), c_void_p(primals_6.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.3502603Z     del primals_1
2023-01-11T21:05:10.3502710Z     del primals_2
2023-01-11T21:05:10.3502805Z     del primals_3
2023-01-11T21:05:10.3502933Z     del primals_4
2023-01-11T21:05:10.3503078Z     return (buf0, buf1, buf2, primals_6, buf0, buf1, buf3, )
2023-01-11T21:05:10.3503084Z 
2023-01-11T21:05:10.3503088Z 
2023-01-11T21:05:10.3503192Z if __name__ == "__main__":
2023-01-11T21:05:10.3503351Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3503503Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3503692Z     primals_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3503927Z     primals_2 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3504150Z     primals_3 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3504373Z     primals_4 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3504586Z     primals_5 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3504842Z     primals_6 = rand_strided((3, 10, 16, 16), (2560, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3505042Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]))
2023-01-11T21:05:10.3505361Z [2023-01-11 20:45:51,622] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 40
2023-01-11T21:05:10.3505370Z 
2023-01-11T21:05:10.3505475Z ok (6.977s)
2023-01-11T21:05:10.3505909Z   test_bernoulli1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3506063Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3506346Z [2023-01-11 20:45:51,735] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 41
2023-01-11T21:05:10.3506636Z [2023-01-11 20:45:54,458] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 41
2023-01-11T21:05:10.3506641Z 
2023-01-11T21:05:10.3506790Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3506887Z import torch
2023-01-11T21:05:10.3507029Z import random
2023-01-11T21:05:10.3507195Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3507303Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3507353Z 
2023-01-11T21:05:10.3507421Z aten = torch.ops.aten
2023-01-11T21:05:10.3507583Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3507705Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3507710Z 
2023-01-11T21:05:10.3507714Z 
2023-01-11T21:05:10.3507875Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3508106Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3508259Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3508351Z {
2023-01-11T21:05:10.3508436Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3518541Z     {
2023-01-11T21:05:10.3518699Z         #pragma omp for 
2023-01-11T21:05:10.3518787Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.3518862Z         {
2023-01-11T21:05:10.3519014Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:05:10.3519110Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3519275Z         }
2023-01-11T21:05:10.3519373Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3519443Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.3519507Z         {
2023-01-11T21:05:10.3519607Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:05:10.3519689Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.3519753Z         }
2023-01-11T21:05:10.3519814Z     }
2023-01-11T21:05:10.3519875Z }
2023-01-11T21:05:10.3519968Z ''')
2023-01-11T21:05:10.3519975Z 
2023-01-11T21:05:10.3519979Z 
2023-01-11T21:05:10.3520067Z async_compile.wait(globals())
2023-01-11T21:05:10.3520143Z del async_compile
2023-01-11T21:05:10.3520148Z 
2023-01-11T21:05:10.3520220Z def call(args):
2023-01-11T21:05:10.3520291Z     arg0_1, = args
2023-01-11T21:05:10.3520366Z     args.clear()
2023-01-11T21:05:10.3520571Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3520888Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3520976Z     aten.bernoulli_(buf0, )
2023-01-11T21:05:10.3521055Z     return (buf0, buf0, )
2023-01-11T21:05:10.3521060Z 
2023-01-11T21:05:10.3521065Z 
2023-01-11T21:05:10.3521140Z if __name__ == "__main__":
2023-01-11T21:05:10.3521255Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3521378Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3521581Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3521689Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3521694Z 
2023-01-11T21:05:10.3521747Z ok (2.823s)
2023-01-11T21:05:10.3522188Z   test_bernoulli2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3522321Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3522584Z [2023-01-11 20:45:54,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 42
2023-01-11T21:05:10.3522840Z [2023-01-11 20:45:54,511] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.3523102Z [2023-01-11 20:45:57,232] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 42
2023-01-11T21:05:10.3523107Z 
2023-01-11T21:05:10.3523202Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3523270Z import torch
2023-01-11T21:05:10.3523337Z import random
2023-01-11T21:05:10.3523439Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3523631Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3523636Z 
2023-01-11T21:05:10.3523712Z aten = torch.ops.aten
2023-01-11T21:05:10.3523847Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3523942Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3524102Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.3524108Z 
2023-01-11T21:05:10.3524112Z 
2023-01-11T21:05:10.3524245Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3524449Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3524562Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:05:10.3524653Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3524747Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:05:10.3524809Z {
2023-01-11T21:05:10.3524905Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3524969Z     {
2023-01-11T21:05:10.3525045Z         #pragma omp for 
2023-01-11T21:05:10.3525126Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3525175Z         {
2023-01-11T21:05:10.3525236Z             {
2023-01-11T21:05:10.3525342Z                 {
2023-01-11T21:05:10.3525428Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.3525517Z                     auto tmp5 = in_ptr1[i0];
2023-01-11T21:05:10.3525617Z                     auto tmp1 = static_cast<int>(65535);
2023-01-11T21:05:10.3525695Z                     auto tmp2 = tmp0 ^ tmp1;
2023-01-11T21:05:10.3525794Z                     auto tmp3 = static_cast<int>(i0);
2023-01-11T21:05:10.3525927Z                     auto tmp4 = static_cast<float>(normalized_rand_cpu(tmp2, tmp3));;
2023-01-11T21:05:10.3526014Z                     auto tmp6 = tmp4 < tmp5;
2023-01-11T21:05:10.3526096Z                     out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.3526157Z                 }
2023-01-11T21:05:10.3526219Z             }
2023-01-11T21:05:10.3526270Z         }
2023-01-11T21:05:10.3526329Z     }
2023-01-11T21:05:10.3526388Z }
2023-01-11T21:05:10.3526465Z ''')
2023-01-11T21:05:10.3526470Z 
2023-01-11T21:05:10.3526475Z 
2023-01-11T21:05:10.3526568Z async_compile.wait(globals())
2023-01-11T21:05:10.3526639Z del async_compile
2023-01-11T21:05:10.3526644Z 
2023-01-11T21:05:10.3526712Z def call(args):
2023-01-11T21:05:10.3526778Z     arg0_1, = args
2023-01-11T21:05:10.3526835Z     args.clear()
2023-01-11T21:05:10.3526965Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.3527150Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3527321Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3527388Z     del arg0_1
2023-01-11T21:05:10.3527456Z     return (buf0, )
2023-01-11T21:05:10.3527461Z 
2023-01-11T21:05:10.3527466Z 
2023-01-11T21:05:10.3527540Z if __name__ == "__main__":
2023-01-11T21:05:10.3527641Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3527760Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3527954Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3528144Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3528251Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3528255Z 
2023-01-11T21:05:10.3528320Z ok (2.774s)
2023-01-11T21:05:10.3528757Z   test_bitwise2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3528883Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3529167Z [2023-01-11 20:45:57,307] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 43
2023-01-11T21:05:10.3529429Z [2023-01-11 20:46:00,066] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 43
2023-01-11T21:05:10.3529434Z 
2023-01-11T21:05:10.3529516Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3529583Z import torch
2023-01-11T21:05:10.3529652Z import random
2023-01-11T21:05:10.3529764Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3529885Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3529889Z 
2023-01-11T21:05:10.3529965Z aten = torch.ops.aten
2023-01-11T21:05:10.3530097Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3530175Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3530195Z 
2023-01-11T21:05:10.3530199Z 
2023-01-11T21:05:10.3530321Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3530523Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3530639Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.3530740Z                        const bool* __restrict__ in_ptr1,
2023-01-11T21:05:10.3530864Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:05:10.3530963Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:05:10.3531052Z                        bool* __restrict__ out_ptr2,
2023-01-11T21:05:10.3531129Z                        bool* __restrict__ out_ptr3)
2023-01-11T21:05:10.3531187Z {
2023-01-11T21:05:10.3531283Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3531342Z     {
2023-01-11T21:05:10.3531417Z         #pragma omp for 
2023-01-11T21:05:10.3531497Z         for(long i0=0; i0<40; i0+=1)
2023-01-11T21:05:10.3531558Z         {
2023-01-11T21:05:10.3531607Z             {
2023-01-11T21:05:10.3531668Z                 {
2023-01-11T21:05:10.3531758Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3531850Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.3531937Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.3532023Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:05:10.3532112Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:05:10.3532184Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:05:10.3532267Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.3532347Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.3532428Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.3532506Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:05:10.3532568Z                 }
2023-01-11T21:05:10.3532630Z             }
2023-01-11T21:05:10.3532679Z         }
2023-01-11T21:05:10.3532737Z     }
2023-01-11T21:05:10.3532795Z }
2023-01-11T21:05:10.3532872Z ''')
2023-01-11T21:05:10.3532877Z 
2023-01-11T21:05:10.3532881Z 
2023-01-11T21:05:10.3532969Z async_compile.wait(globals())
2023-01-11T21:05:10.3533041Z del async_compile
2023-01-11T21:05:10.3533046Z 
2023-01-11T21:05:10.3533114Z def call(args):
2023-01-11T21:05:10.3533176Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3533245Z     args.clear()
2023-01-11T21:05:10.3533440Z     buf0 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3533628Z     buf1 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3533813Z     buf2 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3533995Z     buf3 = empty_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3534229Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.3534296Z     del arg0_1
2023-01-11T21:05:10.3534348Z     del arg1_1
2023-01-11T21:05:10.3534433Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:05:10.3534439Z 
2023-01-11T21:05:10.3534472Z 
2023-01-11T21:05:10.3534546Z if __name__ == "__main__":
2023-01-11T21:05:10.3534659Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3534779Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3534970Z     arg0_1 = rand_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3535157Z     arg1_1 = rand_strided((2, 20), (20, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3535257Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3535273Z 
2023-01-11T21:05:10.3535326Z ok (2.833s)
2023-01-11T21:05:10.3535761Z   test_bitwise_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3535887Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3536144Z [2023-01-11 20:46:00,127] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 44
2023-01-11T21:05:10.3536484Z [2023-01-11 20:46:02,877] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 44
2023-01-11T21:05:10.3536490Z 
2023-01-11T21:05:10.3536583Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3536652Z import torch
2023-01-11T21:05:10.3536720Z import random
2023-01-11T21:05:10.3536833Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3536938Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3536945Z 
2023-01-11T21:05:10.3537019Z aten = torch.ops.aten
2023-01-11T21:05:10.3537151Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3537242Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3537247Z 
2023-01-11T21:05:10.3537251Z 
2023-01-11T21:05:10.3537382Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3537584Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3537697Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.3537799Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.3537882Z                        int* __restrict__ out_ptr0,
2023-01-11T21:05:10.3537971Z                        int* __restrict__ out_ptr1,
2023-01-11T21:05:10.3538060Z                        int* __restrict__ out_ptr2,
2023-01-11T21:05:10.3538148Z                        int* __restrict__ out_ptr3)
2023-01-11T21:05:10.3538206Z {
2023-01-11T21:05:10.3538302Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3538363Z     {
2023-01-11T21:05:10.3538426Z         #pragma omp for 
2023-01-11T21:05:10.3538607Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.3538675Z         {
2023-01-11T21:05:10.3538740Z             {
2023-01-11T21:05:10.3538804Z                 {
2023-01-11T21:05:10.3538897Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3538973Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.3539054Z                     auto tmp1 = ~tmp0;
2023-01-11T21:05:10.3539146Z                     auto tmp3 = tmp0 | tmp2;
2023-01-11T21:05:10.3539232Z                     auto tmp4 = tmp0 ^ tmp2;
2023-01-11T21:05:10.3539322Z                     auto tmp5 = tmp0 & tmp2;
2023-01-11T21:05:10.3539404Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.3539483Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.3539553Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.3539634Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:05:10.3539699Z                 }
2023-01-11T21:05:10.3539763Z             }
2023-01-11T21:05:10.3539822Z         }
2023-01-11T21:05:10.3539880Z     }
2023-01-11T21:05:10.3539938Z }
2023-01-11T21:05:10.3540002Z ''')
2023-01-11T21:05:10.3540008Z 
2023-01-11T21:05:10.3540012Z 
2023-01-11T21:05:10.3540137Z async_compile.wait(globals())
2023-01-11T21:05:10.3540206Z del async_compile
2023-01-11T21:05:10.3540211Z 
2023-01-11T21:05:10.3540278Z def call(args):
2023-01-11T21:05:10.3540350Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3540422Z     args.clear()
2023-01-11T21:05:10.3540614Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.3540795Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.3540969Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.3541152Z     buf3 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.3541383Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.3541452Z     del arg0_1
2023-01-11T21:05:10.3541517Z     del arg1_1
2023-01-11T21:05:10.3541603Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:05:10.3541610Z 
2023-01-11T21:05:10.3541615Z 
2023-01-11T21:05:10.3541689Z if __name__ == "__main__":
2023-01-11T21:05:10.3541800Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3541938Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3542129Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.3542312Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.3542427Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3542432Z 
2023-01-11T21:05:10.3542496Z ok (2.814s)
2023-01-11T21:05:10.3542931Z   test_bmm1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3543057Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3543311Z [2023-01-11 20:46:02,954] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 45
2023-01-11T21:05:10.3543575Z [2023-01-11 20:46:05,767] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 45
2023-01-11T21:05:10.3543959Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3544085Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3544336Z [2023-01-11 20:46:05,828] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 46
2023-01-11T21:05:10.3544597Z [2023-01-11 20:46:08,701] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 46
2023-01-11T21:05:10.3544602Z 
2023-01-11T21:05:10.3544694Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3544763Z import torch
2023-01-11T21:05:10.3544830Z import random
2023-01-11T21:05:10.3544944Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3545061Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3545066Z 
2023-01-11T21:05:10.3545130Z aten = torch.ops.aten
2023-01-11T21:05:10.3545261Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3545353Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3545358Z 
2023-01-11T21:05:10.3545362Z 
2023-01-11T21:05:10.3545494Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3545699Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3545816Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3545970Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3546070Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3546154Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3546216Z {
2023-01-11T21:05:10.3546310Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3546368Z     {
2023-01-11T21:05:10.3546443Z         #pragma omp for 
2023-01-11T21:05:10.3546524Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3546584Z         {
2023-01-11T21:05:10.3546710Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3546841Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3546924Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3547014Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3547079Z         }
2023-01-11T21:05:10.3547172Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3547254Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:05:10.3547303Z         {
2023-01-11T21:05:10.3547386Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3547483Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.3547592Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3547673Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3547733Z         }
2023-01-11T21:05:10.3547806Z         #pragma omp for 
2023-01-11T21:05:10.3547872Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3547931Z         {
2023-01-11T21:05:10.3548059Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.3548188Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.3548269Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3548358Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.3548418Z         }
2023-01-11T21:05:10.3548498Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3548580Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:05:10.3548639Z         {
2023-01-11T21:05:10.3548719Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.3548815Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3548899Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3548976Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.3549023Z         }
2023-01-11T21:05:10.3549082Z     }
2023-01-11T21:05:10.3549140Z }
2023-01-11T21:05:10.3549217Z ''')
2023-01-11T21:05:10.3549221Z 
2023-01-11T21:05:10.3549225Z 
2023-01-11T21:05:10.3549362Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:05:10.3549567Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3549683Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.3549729Z {
2023-01-11T21:05:10.3549825Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3549886Z     {
2023-01-11T21:05:10.3549963Z         #pragma omp for 
2023-01-11T21:05:10.3550046Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3550108Z         {
2023-01-11T21:05:10.3550245Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.3550365Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.3550449Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3550545Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.3550608Z         }
2023-01-11T21:05:10.3550703Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3550785Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:05:10.3550846Z         {
2023-01-11T21:05:10.3550923Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.3551021Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.3551103Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3551185Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3551246Z         }
2023-01-11T21:05:10.3551339Z     }
2023-01-11T21:05:10.3551397Z }
2023-01-11T21:05:10.3551461Z ''')
2023-01-11T21:05:10.3551467Z 
2023-01-11T21:05:10.3551471Z 
2023-01-11T21:05:10.3551560Z async_compile.wait(globals())
2023-01-11T21:05:10.3551633Z del async_compile
2023-01-11T21:05:10.3551640Z 
2023-01-11T21:05:10.3551711Z def call(args):
2023-01-11T21:05:10.3551790Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3551860Z     args.clear()
2023-01-11T21:05:10.3552064Z     buf0 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3552147Z     aten.bmm.out(arg0_1, arg1_1, out=buf0)
2023-01-11T21:05:10.3552351Z     buf1 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3552546Z     buf2 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3552736Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.3552806Z     del arg0_1
2023-01-11T21:05:10.3552875Z     del arg1_1
2023-01-11T21:05:10.3553071Z     buf3 = empty_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3553163Z     aten.bmm.out(buf1, buf2, out=buf3)
2023-01-11T21:05:10.3553244Z     del buf1
2023-01-11T21:05:10.3553312Z     del buf2
2023-01-11T21:05:10.3553397Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:05:10.3553499Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.3553576Z     return (buf0, buf4, )
2023-01-11T21:05:10.3553582Z 
2023-01-11T21:05:10.3553586Z 
2023-01-11T21:05:10.3553662Z if __name__ == "__main__":
2023-01-11T21:05:10.3553774Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3553884Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3554087Z     arg0_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3554286Z     arg1_1 = rand_strided((2, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3554403Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3554408Z 
2023-01-11T21:05:10.3554413Z 
2023-01-11T21:05:10.3554505Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3554576Z import torch
2023-01-11T21:05:10.3554647Z import random
2023-01-11T21:05:10.3554761Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3554870Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3554886Z 
2023-01-11T21:05:10.3554950Z aten = torch.ops.aten
2023-01-11T21:05:10.3555083Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3555171Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3555176Z 
2023-01-11T21:05:10.3555180Z 
2023-01-11T21:05:10.3555310Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3555514Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3555632Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3555737Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3555823Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3555915Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3555974Z {
2023-01-11T21:05:10.3556068Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3556127Z     {
2023-01-11T21:05:10.3556201Z         #pragma omp for 
2023-01-11T21:05:10.3556281Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3556329Z         {
2023-01-11T21:05:10.3556460Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3556590Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3556673Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3556763Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3556826Z         }
2023-01-11T21:05:10.3556918Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3557001Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:05:10.3557077Z         {
2023-01-11T21:05:10.3557159Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3557255Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.3557339Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3557416Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3557476Z         }
2023-01-11T21:05:10.3557537Z         #pragma omp for 
2023-01-11T21:05:10.3557615Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:05:10.3557673Z         {
2023-01-11T21:05:10.3557799Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.3557927Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.3558007Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3558097Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.3558157Z         }
2023-01-11T21:05:10.3558238Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3558322Z         for(long i0=80; i0<80; i0+=1)
2023-01-11T21:05:10.3558381Z         {
2023-01-11T21:05:10.3558461Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.3558557Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3558673Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3558741Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.3558799Z         }
2023-01-11T21:05:10.3558860Z     }
2023-01-11T21:05:10.3558918Z }
2023-01-11T21:05:10.3558996Z ''')
2023-01-11T21:05:10.3559001Z 
2023-01-11T21:05:10.3559005Z 
2023-01-11T21:05:10.3559137Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:05:10.3559339Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3559452Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.3559499Z {
2023-01-11T21:05:10.3559593Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3559652Z     {
2023-01-11T21:05:10.3559726Z         #pragma omp for 
2023-01-11T21:05:10.3559805Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.3559864Z         {
2023-01-11T21:05:10.3560001Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.3560123Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.3560203Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3560296Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.3560357Z         }
2023-01-11T21:05:10.3560450Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3560528Z         for(long i0=160; i0<160; i0+=1)
2023-01-11T21:05:10.3560588Z         {
2023-01-11T21:05:10.3560825Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.3560925Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.3561007Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3561092Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3561154Z         }
2023-01-11T21:05:10.3561213Z     }
2023-01-11T21:05:10.3561261Z }
2023-01-11T21:05:10.3561343Z ''')
2023-01-11T21:05:10.3561348Z 
2023-01-11T21:05:10.3561353Z 
2023-01-11T21:05:10.3561441Z async_compile.wait(globals())
2023-01-11T21:05:10.3561514Z del async_compile
2023-01-11T21:05:10.3561520Z 
2023-01-11T21:05:10.3561591Z def call(args):
2023-01-11T21:05:10.3561666Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3561736Z     args.clear()
2023-01-11T21:05:10.3561944Z     buf0 = empty_strided((1, 16, 10), (160, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3562096Z     aten.mm.out(as_strided(arg0_1, (16, 8), (8, 1)), as_strided(arg1_1, (8, 10), (10, 1)), out=as_strided(buf0, (16, 10), (10, 1)))
2023-01-11T21:05:10.3562298Z     buf1 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3562494Z     buf2 = empty_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3562681Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.3562815Z     del arg0_1
2023-01-11T21:05:10.3562881Z     del arg1_1
2023-01-11T21:05:10.3563087Z     buf3 = empty_strided((1, 16, 10), (160, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3563251Z     aten.mm.out(as_strided(buf1, (16, 8), (8, 1)), as_strided(buf2, (8, 10), (10, 1)), out=as_strided(buf3, (16, 10), (10, 1)))
2023-01-11T21:05:10.3563304Z     del buf1
2023-01-11T21:05:10.3563367Z     del buf2
2023-01-11T21:05:10.3563450Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:05:10.3563553Z     kernel_cpp_1(c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.3563629Z     return (buf0, buf4, )
2023-01-11T21:05:10.3563634Z 
2023-01-11T21:05:10.3563638Z 
2023-01-11T21:05:10.3563713Z if __name__ == "__main__":
2023-01-11T21:05:10.3563826Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3563950Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3564142Z     arg0_1 = rand_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3564340Z     arg1_1 = rand_strided((1, 8, 10), (80, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3564456Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3564503Z 
2023-01-11T21:05:10.3564573Z ok (5.822s)
2023-01-11T21:05:10.3565009Z   test_bmm2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3565133Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3565392Z [2023-01-11 20:46:08,742] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 47
2023-01-11T21:05:10.3565655Z [2023-01-11 20:46:08,748] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 47
2023-01-11T21:05:10.3565664Z 
2023-01-11T21:05:10.3565755Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3565812Z import torch
2023-01-11T21:05:10.3565880Z import random
2023-01-11T21:05:10.3565995Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3566113Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3566118Z 
2023-01-11T21:05:10.3566193Z aten = torch.ops.aten
2023-01-11T21:05:10.3566325Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3566414Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3566419Z 
2023-01-11T21:05:10.3566423Z 
2023-01-11T21:05:10.3566509Z async_compile.wait(globals())
2023-01-11T21:05:10.3566568Z del async_compile
2023-01-11T21:05:10.3566573Z 
2023-01-11T21:05:10.3566641Z def call(args):
2023-01-11T21:05:10.3566718Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3566786Z     args.clear()
2023-01-11T21:05:10.3566987Z     buf0 = empty_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3567150Z     aten.mm.out(as_strided(arg0_1, (8, 8), (1, 8)), as_strided(arg1_1, (8, 8), (8, 1)), out=as_strided(buf0, (8, 8), (8, 1)))
2023-01-11T21:05:10.3567217Z     del arg0_1
2023-01-11T21:05:10.3567270Z     del arg1_1
2023-01-11T21:05:10.3567338Z     return (buf0, )
2023-01-11T21:05:10.3567343Z 
2023-01-11T21:05:10.3567348Z 
2023-01-11T21:05:10.3567419Z if __name__ == "__main__":
2023-01-11T21:05:10.3567530Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3567649Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3567852Z     arg0_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3568049Z     arg1_1 = rand_strided((1, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3568162Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3568167Z 
2023-01-11T21:05:10.3568220Z ok (0.043s)
2023-01-11T21:05:10.3568689Z   test_bool_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3568817Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3569072Z [2023-01-11 20:46:08,842] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 48
2023-01-11T21:05:10.3569330Z [2023-01-11 20:46:11,671] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 48
2023-01-11T21:05:10.3569335Z 
2023-01-11T21:05:10.3569427Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3569495Z import torch
2023-01-11T21:05:10.3569566Z import random
2023-01-11T21:05:10.3569678Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3569787Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3569803Z 
2023-01-11T21:05:10.3569867Z aten = torch.ops.aten
2023-01-11T21:05:10.3570026Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3570116Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3570121Z 
2023-01-11T21:05:10.3570126Z 
2023-01-11T21:05:10.3570260Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3570462Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3570578Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.3570680Z                        const bool* __restrict__ in_ptr1,
2023-01-11T21:05:10.3570763Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:05:10.3570855Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:05:10.3570945Z                        bool* __restrict__ out_ptr2,
2023-01-11T21:05:10.3571036Z                        bool* __restrict__ out_ptr3,
2023-01-11T21:05:10.3571129Z                        bool* __restrict__ out_ptr4,
2023-01-11T21:05:10.3571218Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:05:10.3571310Z                        bool* __restrict__ out_ptr6,
2023-01-11T21:05:10.3571398Z                        bool* __restrict__ out_ptr7,
2023-01-11T21:05:10.3571474Z                        bool* __restrict__ out_ptr8)
2023-01-11T21:05:10.3571532Z {
2023-01-11T21:05:10.3571627Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3571686Z     {
2023-01-11T21:05:10.3571760Z         #pragma omp for 
2023-01-11T21:05:10.3571839Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3571887Z         {
2023-01-11T21:05:10.3571948Z             {
2023-01-11T21:05:10.3572010Z                 {
2023-01-11T21:05:10.3572100Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3572194Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.3572284Z                     auto tmp2 = tmp0 || tmp1;
2023-01-11T21:05:10.3572375Z                     auto tmp3 = tmp0 && tmp1;
2023-01-11T21:05:10.3572449Z                     auto tmp4 = tmp0 & tmp1;
2023-01-11T21:05:10.3572538Z                     auto tmp5 = tmp0 | tmp1;
2023-01-11T21:05:10.3572623Z                     auto tmp6 = tmp0 ^ tmp1;
2023-01-11T21:05:10.3572709Z                     auto tmp7 = tmp0 == 0;
2023-01-11T21:05:10.3572793Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3572872Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.3572951Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.3573020Z                     out_ptr3[i0] = tmp5;
2023-01-11T21:05:10.3573099Z                     out_ptr4[i0] = tmp6;
2023-01-11T21:05:10.3573177Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:05:10.3573254Z                     out_ptr6[i0] = tmp2;
2023-01-11T21:05:10.3573333Z                     out_ptr7[i0] = tmp7;
2023-01-11T21:05:10.3573410Z                     out_ptr8[i0] = tmp1;
2023-01-11T21:05:10.3573505Z                 }
2023-01-11T21:05:10.3573555Z             }
2023-01-11T21:05:10.3573616Z         }
2023-01-11T21:05:10.3573675Z     }
2023-01-11T21:05:10.3573732Z }
2023-01-11T21:05:10.3573808Z ''')
2023-01-11T21:05:10.3573815Z 
2023-01-11T21:05:10.3573820Z 
2023-01-11T21:05:10.3573906Z async_compile.wait(globals())
2023-01-11T21:05:10.3573976Z del async_compile
2023-01-11T21:05:10.3573981Z 
2023-01-11T21:05:10.3574036Z def call(args):
2023-01-11T21:05:10.3574110Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3574178Z     args.clear()
2023-01-11T21:05:10.3574368Z     buf0 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3574551Z     buf1 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3574726Z     buf2 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3574902Z     buf3 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3575077Z     buf4 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3575245Z     buf5 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3575451Z     buf6 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3575624Z     buf7 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3575800Z     buf8 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3576151Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:05:10.3576218Z     del arg0_1
2023-01-11T21:05:10.3576282Z     del arg1_1
2023-01-11T21:05:10.3576399Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, )
2023-01-11T21:05:10.3576404Z 
2023-01-11T21:05:10.3576410Z 
2023-01-11T21:05:10.3576484Z if __name__ == "__main__":
2023-01-11T21:05:10.3576585Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3576706Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3576892Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3577075Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.3577188Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3577193Z 
2023-01-11T21:05:10.3577257Z ok (2.927s)
2023-01-11T21:05:10.3577587Z   test_both_scalars_cpu (__main__.CpuTests) ... [2023-01-11 20:46:11,824] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 49
2023-01-11T21:05:10.3577847Z [2023-01-11 20:46:14,623] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 49
2023-01-11T21:05:10.3577852Z 
2023-01-11T21:05:10.3577932Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3578002Z import torch
2023-01-11T21:05:10.3578070Z import random
2023-01-11T21:05:10.3578183Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3578302Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3578309Z 
2023-01-11T21:05:10.3578385Z aten = torch.ops.aten
2023-01-11T21:05:10.3578612Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3578693Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3578711Z 
2023-01-11T21:05:10.3578716Z 
2023-01-11T21:05:10.3578837Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3579037Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3579150Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3579249Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.3579342Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.3579435Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.3579564Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.3579645Z                        float* __restrict__ out_ptr5)
2023-01-11T21:05:10.3579706Z {
2023-01-11T21:05:10.3579768Z     {
2023-01-11T21:05:10.3579832Z         {
2023-01-11T21:05:10.3579930Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:05:10.3580030Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:05:10.3580112Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3580178Z             out_ptr0[0] = tmp2;
2023-01-11T21:05:10.3580237Z         }
2023-01-11T21:05:10.3580297Z     }
2023-01-11T21:05:10.3580357Z     {
2023-01-11T21:05:10.3580417Z         {
2023-01-11T21:05:10.3580516Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:05:10.3580613Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:05:10.3580682Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3580757Z             out_ptr1[0] = tmp2;
2023-01-11T21:05:10.3580821Z         }
2023-01-11T21:05:10.3580883Z     }
2023-01-11T21:05:10.3580943Z     {
2023-01-11T21:05:10.3581004Z         {
2023-01-11T21:05:10.3581089Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:05:10.3581219Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:05:10.3581350Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.3581425Z             out_ptr2[0] = tmp2;
2023-01-11T21:05:10.3581487Z         }
2023-01-11T21:05:10.3581546Z     }
2023-01-11T21:05:10.3581608Z     {
2023-01-11T21:05:10.3581655Z         {
2023-01-11T21:05:10.3581756Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:05:10.3581854Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:05:10.3581980Z             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.3582059Z             out_ptr3[0] = tmp2;
2023-01-11T21:05:10.3582124Z         }
2023-01-11T21:05:10.3582183Z     }
2023-01-11T21:05:10.3582232Z     {
2023-01-11T21:05:10.3582293Z         {
2023-01-11T21:05:10.3582388Z             auto tmp0 = static_cast<float>(4);
2023-01-11T21:05:10.3582488Z             auto tmp1 = static_cast<float>(3.3);
2023-01-11T21:05:10.3582570Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3582646Z             out_ptr4[0] = tmp2;
2023-01-11T21:05:10.3582694Z         }
2023-01-11T21:05:10.3582755Z     }
2023-01-11T21:05:10.3582815Z     {
2023-01-11T21:05:10.3582878Z         {
2023-01-11T21:05:10.3582978Z             auto tmp0 = static_cast<float>(3.3);
2023-01-11T21:05:10.3583074Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:05:10.3583157Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3583220Z             out_ptr5[0] = tmp2;
2023-01-11T21:05:10.3583281Z         }
2023-01-11T21:05:10.3583342Z     }
2023-01-11T21:05:10.3583401Z }
2023-01-11T21:05:10.3583486Z ''')
2023-01-11T21:05:10.3583490Z 
2023-01-11T21:05:10.3583494Z 
2023-01-11T21:05:10.3583584Z async_compile.wait(globals())
2023-01-11T21:05:10.3583656Z del async_compile
2023-01-11T21:05:10.3583661Z 
2023-01-11T21:05:10.3583719Z def call(args):
2023-01-11T21:05:10.3583905Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3584083Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3584264Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3584440Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3584613Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3584785Z     buf5 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3585018Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:05:10.3585109Z     return (buf0, buf1, buf2, buf3, buf4, buf5, )
2023-01-11T21:05:10.3585128Z 
2023-01-11T21:05:10.3585133Z 
2023-01-11T21:05:10.3585196Z if __name__ == "__main__":
2023-01-11T21:05:10.3585361Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3585483Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3585586Z     print_performance(lambda: call([]))
2023-01-11T21:05:10.3585593Z 
2023-01-11T21:05:10.3585659Z ok (2.957s)
2023-01-11T21:05:10.3586095Z   test_cat_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3586223Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3586485Z [2023-01-11 20:46:14,740] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 50
2023-01-11T21:05:10.3586746Z [2023-01-11 20:46:17,627] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 50
2023-01-11T21:05:10.3587187Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3587316Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3587568Z [2023-01-11 20:46:17,776] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 51
2023-01-11T21:05:10.3587573Z 
2023-01-11T21:05:10.3587664Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3587735Z import torch
2023-01-11T21:05:10.3587803Z import random
2023-01-11T21:05:10.3587918Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3588037Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3588042Z 
2023-01-11T21:05:10.3588120Z aten = torch.ops.aten
2023-01-11T21:05:10.3588239Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3588329Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3588333Z 
2023-01-11T21:05:10.3588340Z 
2023-01-11T21:05:10.3588475Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3588677Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3588794Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3588892Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3588988Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.3589080Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.3589162Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.3589255Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.3589352Z                        double* __restrict__ out_ptr5,
2023-01-11T21:05:10.3589451Z                        double* __restrict__ out_ptr6)
2023-01-11T21:05:10.3589511Z {
2023-01-11T21:05:10.3589608Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3589669Z     {
2023-01-11T21:05:10.3589735Z         #pragma omp for 
2023-01-11T21:05:10.3589814Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3589878Z         {
2023-01-11T21:05:10.3589957Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.3590017Z             {
2023-01-11T21:05:10.3590163Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i0) + (16*i1));
2023-01-11T21:05:10.3590296Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.3590371Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3590470Z                 tmp0.store(out_ptr0 + (16*i1) + (36*i0));
2023-01-11T21:05:10.3590570Z                 tmp2.store(out_ptr1 + (16*i1) + (36*i0));
2023-01-11T21:05:10.3590633Z             }
2023-01-11T21:05:10.3590759Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.3590842Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:05:10.3590906Z             {
2023-01-11T21:05:10.3590989Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:05:10.3591086Z                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3591171Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3591261Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:05:10.3591349Z                 out_ptr1[i1 + (36*i0)] = tmp2;
2023-01-11T21:05:10.3591410Z             }
2023-01-11T21:05:10.3591471Z         }
2023-01-11T21:05:10.3591534Z         #pragma omp for 
2023-01-11T21:05:10.3591614Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3591673Z         {
2023-01-11T21:05:10.3591751Z             #pragma GCC ivdep
2023-01-11T21:05:10.3591831Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.3591891Z             {
2023-01-11T21:05:10.3591953Z                 {
2023-01-11T21:05:10.3592008Z                     {
2023-01-11T21:05:10.3592108Z                         auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:05:10.3592212Z                         auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.3592332Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3592426Z                         out_ptr2[i1 + (36*i0)] = tmp2;
2023-01-11T21:05:10.3592488Z                     }
2023-01-11T21:05:10.3592549Z                 }
2023-01-11T21:05:10.3592598Z             }
2023-01-11T21:05:10.3592656Z         }
2023-01-11T21:05:10.3592731Z         #pragma omp for 
2023-01-11T21:05:10.3592809Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.3592869Z         {
2023-01-11T21:05:10.3592928Z             {
2023-01-11T21:05:10.3592977Z                 {
2023-01-11T21:05:10.3593065Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3593164Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3593253Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3593361Z                     auto tmp3 = static_cast<double>(tmp2);
2023-01-11T21:05:10.3593442Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.3593526Z                     out_ptr4[i0] = tmp2;
2023-01-11T21:05:10.3593597Z                     out_ptr5[i0] = tmp3;
2023-01-11T21:05:10.3593677Z                     out_ptr6[i0] = tmp3;
2023-01-11T21:05:10.3593743Z                 }
2023-01-11T21:05:10.3593803Z             }
2023-01-11T21:05:10.3593865Z         }
2023-01-11T21:05:10.3593924Z     }
2023-01-11T21:05:10.3593980Z }
2023-01-11T21:05:10.3594047Z ''')
2023-01-11T21:05:10.3594052Z 
2023-01-11T21:05:10.3594056Z 
2023-01-11T21:05:10.3594144Z async_compile.wait(globals())
2023-01-11T21:05:10.3594214Z del async_compile
2023-01-11T21:05:10.3594218Z 
2023-01-11T21:05:10.3594287Z def call(args):
2023-01-11T21:05:10.3594355Z     arg0_1, = args
2023-01-11T21:05:10.3594426Z     args.clear()
2023-01-11T21:05:10.3594625Z     buf3 = empty_strided((8, 36), (36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3594715Z     buf0 = as_strided(buf3, (8, 16), (36, 1))  # alias
2023-01-11T21:05:10.3594814Z     buf2 = as_strided(buf3, (8, 16), (36, 1), 20)  # alias
2023-01-11T21:05:10.3594913Z     buf1 = as_strided(buf3, (8, 4), (36, 1), 16)  # alias
2023-01-11T21:05:10.3595108Z     buf6 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3595206Z     buf4 = as_strided(buf6, (8, 16), (16, 1))  # alias
2023-01-11T21:05:10.3595307Z     buf5 = as_strided(buf6, (8, 16), (16, 1), 128)  # alias
2023-01-11T21:05:10.3595499Z     buf9 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.3595596Z     buf7 = as_strided(buf9, (8, 16), (16, 1))  # alias
2023-01-11T21:05:10.3595683Z     buf8 = as_strided(buf9, (8, 16), (16, 1), 128)  # alias
2023-01-11T21:05:10.3595959Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:05:10.3596066Z     del arg0_1
2023-01-11T21:05:10.3596146Z     return (buf3, buf6, buf9, )
2023-01-11T21:05:10.3596152Z 
2023-01-11T21:05:10.3596158Z 
2023-01-11T21:05:10.3596231Z if __name__ == "__main__":
2023-01-11T21:05:10.3596344Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3596466Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3596662Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3596756Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3596772Z 
2023-01-11T21:05:10.3596776Z 
2023-01-11T21:05:10.3596856Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3596922Z import torch
2023-01-11T21:05:10.3596991Z import random
2023-01-11T21:05:10.3597102Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3597220Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3597227Z 
2023-01-11T21:05:10.3597303Z aten = torch.ops.aten
2023-01-11T21:05:10.3597436Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3597514Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3597566Z 
2023-01-11T21:05:10.3597571Z 
2023-01-11T21:05:10.3597691Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3597891Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3598006Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3598108Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3598208Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.3598312Z                        const double* __restrict__ in_ptr3,
2023-01-11T21:05:10.3598409Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3598490Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.3598583Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.3598675Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.3598766Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.3598857Z                        float* __restrict__ out_ptr5,
2023-01-11T21:05:10.3598952Z                        double* __restrict__ out_ptr6,
2023-01-11T21:05:10.3599047Z                        double* __restrict__ out_ptr7,
2023-01-11T21:05:10.3599139Z                        float* __restrict__ out_ptr8,
2023-01-11T21:05:10.3599222Z                        double* __restrict__ out_ptr9)
2023-01-11T21:05:10.3599280Z {
2023-01-11T21:05:10.3599375Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3599434Z     {
2023-01-11T21:05:10.3599523Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3599601Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.3599650Z         {
2023-01-11T21:05:10.3599729Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.3599793Z             {
2023-01-11T21:05:10.3599873Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3599960Z                 for(long i2=0; i2<16; i2+=1)
2023-01-11T21:05:10.3600024Z                 {
2023-01-11T21:05:10.3600091Z                     {
2023-01-11T21:05:10.3600146Z                         {
2023-01-11T21:05:10.3600254Z                             auto tmp0 = in_ptr0[i0 + (3*i2) + (48*i1)];
2023-01-11T21:05:10.3600362Z                             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.3600454Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3600557Z                             auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.3600802Z                             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.3600946Z                             out_ptr0[i2 + (48*i1) + (144*i0)] = tmp0;
2023-01-11T21:05:10.3601052Z                             out_ptr1[i2 + (48*i1) + (144*i0)] = tmp2;
2023-01-11T21:05:10.3601138Z                             out_ptr2[i2 + (48*i1) + (144*i0)] = tmp4;
2023-01-11T21:05:10.3601274Z                         }
2023-01-11T21:05:10.3601340Z                     }
2023-01-11T21:05:10.3601403Z                 }
2023-01-11T21:05:10.3601465Z             }
2023-01-11T21:05:10.3601527Z         }
2023-01-11T21:05:10.3601605Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3601683Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.3601745Z         {
2023-01-11T21:05:10.3601832Z             for(long i1=0; i1<144; i1+=1)
2023-01-11T21:05:10.3601897Z             {
2023-01-11T21:05:10.3601961Z                 {
2023-01-11T21:05:10.3602026Z                     {
2023-01-11T21:05:10.3602114Z                         auto tmp0 = in_ptr1[i1 + (144*i0)];
2023-01-11T21:05:10.3602210Z                         out_ptr3[i0 + (3*i1)] = tmp0;
2023-01-11T21:05:10.3602274Z                     }
2023-01-11T21:05:10.3602340Z                 }
2023-01-11T21:05:10.3602405Z             }
2023-01-11T21:05:10.3602464Z         }
2023-01-11T21:05:10.3602556Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3602623Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.3602682Z         {
2023-01-11T21:05:10.3602764Z             for(long i1=0; i1<48; i1+=1)
2023-01-11T21:05:10.3602868Z             {
2023-01-11T21:05:10.3602931Z                 {
2023-01-11T21:05:10.3602996Z                     {
2023-01-11T21:05:10.3603093Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.3603186Z                         auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3603280Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.3603374Z                         out_ptr4[i1 + (48*i0)] = tmp2;
2023-01-11T21:05:10.3603437Z                     }
2023-01-11T21:05:10.3603499Z                 }
2023-01-11T21:05:10.3603559Z             }
2023-01-11T21:05:10.3603606Z         }
2023-01-11T21:05:10.3603686Z         #pragma omp for 
2023-01-11T21:05:10.3603765Z         for(long i0=0; i0<144; i0+=1)
2023-01-11T21:05:10.3603828Z         {
2023-01-11T21:05:10.3603889Z             {
2023-01-11T21:05:10.3603950Z                 {
2023-01-11T21:05:10.3604041Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:05:10.3604136Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.3604219Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:05:10.3604303Z                     out_ptr6[i0] = tmp1;
2023-01-11T21:05:10.3604384Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:05:10.3604447Z                 }
2023-01-11T21:05:10.3604508Z             }
2023-01-11T21:05:10.3604568Z         }
2023-01-11T21:05:10.3604644Z         #pragma omp for  collapse(3)
2023-01-11T21:05:10.3604728Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3604787Z         {
2023-01-11T21:05:10.3604869Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.3604930Z             {
2023-01-11T21:05:10.3605016Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:05:10.3605077Z                 {
2023-01-11T21:05:10.3605133Z                     {
2023-01-11T21:05:10.3605198Z                         {
2023-01-11T21:05:10.3605305Z                             auto tmp0 = in_ptr2[i2 + (48*i1) + (144*i0)];
2023-01-11T21:05:10.3605411Z                             out_ptr8[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:05:10.3605478Z                         }
2023-01-11T21:05:10.3605540Z                     }
2023-01-11T21:05:10.3605602Z                 }
2023-01-11T21:05:10.3605649Z             }
2023-01-11T21:05:10.3605709Z         }
2023-01-11T21:05:10.3605796Z         #pragma omp for  collapse(3)
2023-01-11T21:05:10.3605873Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3605932Z         {
2023-01-11T21:05:10.3606012Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.3606060Z             {
2023-01-11T21:05:10.3606145Z                 for(long i2=0; i2<48; i2+=1)
2023-01-11T21:05:10.3606207Z                 {
2023-01-11T21:05:10.3606273Z                     {
2023-01-11T21:05:10.3606336Z                         {
2023-01-11T21:05:10.3606484Z                             auto tmp0 = in_ptr3[i2 + (48*i1) + (144*i0)];
2023-01-11T21:05:10.3606584Z                             out_ptr9[i1 + (3*i2) + (144*i0)] = tmp0;
2023-01-11T21:05:10.3606641Z                         }
2023-01-11T21:05:10.3606704Z                     }
2023-01-11T21:05:10.3606764Z                 }
2023-01-11T21:05:10.3606824Z             }
2023-01-11T21:05:10.3606882Z         }
2023-01-11T21:05:10.3606942Z     }
2023-01-11T21:05:10.3607000Z }
2023-01-11T21:05:10.3607086Z ''')
2023-01-11T21:05:10.3607092Z 
2023-01-11T21:05:10.3607096Z 
2023-01-11T21:05:10.3607186Z async_compile.wait(globals())
2023-01-11T21:05:10.3607257Z del async_compile
2023-01-11T21:05:10.3607262Z 
2023-01-11T21:05:10.3607335Z def call(args):
2023-01-11T21:05:10.3607401Z     arg0_1, = args
2023-01-11T21:05:10.3607470Z     args.clear()
2023-01-11T21:05:10.3607691Z     buf3 = empty_strided((1, 3, 3, 48), (432, 144, 48, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3607822Z     buf0 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1))  # alias
2023-01-11T21:05:10.3607973Z     buf1 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1), 16)  # alias
2023-01-11T21:05:10.3608219Z     buf2 = as_strided(buf3, (1, 3, 3, 16), (432, 144, 48, 1), 32)  # alias
2023-01-11T21:05:10.3608854Z     buf4 = empty_strided((1, 3, 3, 48), (432, 1, 144, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3609237Z     buf7 = empty_strided((2, 3, 3, 16), (144, 48, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3609486Z     buf5 = as_strided(buf7, (1, 3, 3, 16), (144, 48, 16, 1))  # alias
2023-01-11T21:05:10.3609730Z     buf6 = as_strided(buf7, (1, 3, 3, 16), (144, 48, 16, 1), 144)  # alias
2023-01-11T21:05:10.3610079Z     buf11 = empty_strided((2, 3, 3, 16), (144, 48, 16, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.3610347Z     buf9 = as_strided(buf11, (1, 3, 3, 16), (144, 48, 16, 1))  # alias
2023-01-11T21:05:10.3610575Z     buf10 = as_strided(buf11, (1, 3, 3, 16), (144, 48, 16, 1), 144)  # alias
2023-01-11T21:05:10.3610922Z     buf8 = empty_strided((2, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3611290Z     buf12 = empty_strided((2, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.3611867Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf12.data_ptr()))
2023-01-11T21:05:10.3612343Z     del arg0_1
2023-01-11T21:05:10.3612508Z     del buf0
2023-01-11T21:05:10.3612669Z     del buf1
2023-01-11T21:05:10.3612813Z     del buf10
2023-01-11T21:05:10.3612970Z     del buf11
2023-01-11T21:05:10.3613129Z     del buf2
2023-01-11T21:05:10.3613271Z     del buf3
2023-01-11T21:05:10.3613432Z     del buf5
2023-01-11T21:05:10.3613584Z     del buf6
2023-01-11T21:05:10.3613724Z     del buf7
2023-01-11T21:05:10.3613882Z     del buf9
2023-01-11T21:05:10.3614057Z     return (buf4, buf8, buf12, )
2023-01-11T21:05:10.3614179Z 
2023-01-11T21:05:10.3614184Z 
2023-01-11T21:05:10.3614245Z if __name__ == "__main__":
2023-01-11T21:05:10.3614462Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3614722Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3615089Z     arg0_1 = rand_strided((1, 3, 3, 16), (144, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3615345Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3615745Z [2023-01-11 20:46:20,793] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 51
2023-01-11T21:05:10.3615948Z 
2023-01-11T21:05:10.3616013Z ok (6.165s)
2023-01-11T21:05:10.3616547Z   test_cat_extern_kernel_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3617165Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3617577Z [2023-01-11 20:46:20,958] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 52
2023-01-11T21:05:10.3618030Z [2023-01-11 20:46:23,782] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 52
2023-01-11T21:05:10.3618226Z 
2023-01-11T21:05:10.3618318Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3618565Z import torch
2023-01-11T21:05:10.3618735Z import random
2023-01-11T21:05:10.3618945Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3619192Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3619344Z 
2023-01-11T21:05:10.3619420Z aten = torch.ops.aten
2023-01-11T21:05:10.3619663Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3619951Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3621627Z 
2023-01-11T21:05:10.3621632Z 
2023-01-11T21:05:10.3621799Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3622133Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3622479Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3622703Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3622888Z {
2023-01-11T21:05:10.3623073Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3623248Z     {
2023-01-11T21:05:10.3623410Z         #pragma omp for 
2023-01-11T21:05:10.3623601Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.3623761Z         {
2023-01-11T21:05:10.3623941Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.3624133Z             {
2023-01-11T21:05:10.3624383Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (256*i0));
2023-01-11T21:05:10.3624637Z                 tmp0.store(out_ptr0 + (16*i1) + (512*i0));
2023-01-11T21:05:10.3624838Z             }
2023-01-11T21:05:10.3625020Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.3625227Z             for(long i1=256; i1<256; i1+=1)
2023-01-11T21:05:10.3625392Z             {
2023-01-11T21:05:10.3625580Z                 auto tmp0 = in_ptr0[i1 + (256*i0)];
2023-01-11T21:05:10.3625791Z                 out_ptr0[i1 + (512*i0)] = tmp0;
2023-01-11T21:05:10.3625957Z             }
2023-01-11T21:05:10.3626109Z         }
2023-01-11T21:05:10.3626258Z     }
2023-01-11T21:05:10.3626391Z }
2023-01-11T21:05:10.3626554Z ''')
2023-01-11T21:05:10.3626644Z 
2023-01-11T21:05:10.3626648Z 
2023-01-11T21:05:10.3626735Z async_compile.wait(globals())
2023-01-11T21:05:10.3626910Z del async_compile
2023-01-11T21:05:10.3627016Z 
2023-01-11T21:05:10.3627088Z def call(args):
2023-01-11T21:05:10.3627278Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.3627452Z     args.clear()
2023-01-11T21:05:10.3627765Z     buf0 = empty_strided((256, 1600), (1600, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3628018Z     aten.mm.out(arg1_1, arg2_1, out=buf0)
2023-01-11T21:05:10.3628195Z     del arg1_1
2023-01-11T21:05:10.3628358Z     del arg2_1
2023-01-11T21:05:10.3628654Z     buf3 = empty_strided((256, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3628913Z     buf1 = as_strided(buf3, (256, 256), (512, 1))  # alias
2023-01-11T21:05:10.3629145Z     aten.mm.out(as_strided(buf0, (256, 100), (1600, 1)), arg3_1, out=buf1)
2023-01-11T21:05:10.3629356Z     del arg3_1
2023-01-11T21:05:10.3629516Z     del buf0
2023-01-11T21:05:10.3629702Z     buf2 = as_strided(buf3, (256, 256), (512, 1), 256)  # alias
2023-01-11T21:05:10.3629964Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.3630297Z     del arg0_1
2023-01-11T21:05:10.3630450Z     return (buf3, )
2023-01-11T21:05:10.3630556Z 
2023-01-11T21:05:10.3630561Z 
2023-01-11T21:05:10.3630634Z if __name__ == "__main__":
2023-01-11T21:05:10.3630855Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3631102Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3631454Z     arg0_1 = rand_strided((256, 256), (256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3631816Z     arg1_1 = rand_strided((256, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3632178Z     arg2_1 = rand_strided((1024, 1600), (1600, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3632532Z     arg3_1 = rand_strided((100, 256), (256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3632816Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.3632970Z 
2023-01-11T21:05:10.3633037Z ok (3.559s)
2023-01-11T21:05:10.3633618Z   test_cat_upcasting_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3634192Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3634605Z [2023-01-11 20:46:24,397] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 53
2023-01-11T21:05:10.3635064Z [2023-01-11 20:46:27,258] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 53
2023-01-11T21:05:10.3635266Z 
2023-01-11T21:05:10.3635360Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3635543Z import torch
2023-01-11T21:05:10.3635734Z import random
2023-01-11T21:05:10.3635949Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3636203Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3636353Z 
2023-01-11T21:05:10.3636429Z aten = torch.ops.aten
2023-01-11T21:05:10.3636672Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3636905Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3637030Z 
2023-01-11T21:05:10.3637035Z 
2023-01-11T21:05:10.3637169Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3637498Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3637837Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3638076Z                        const half* __restrict__ in_ptr1,
2023-01-11T21:05:10.3638311Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3638535Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3638725Z {
2023-01-11T21:05:10.3638902Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3639091Z     {
2023-01-11T21:05:10.3639253Z         #pragma omp for 
2023-01-11T21:05:10.3639431Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3639602Z         {
2023-01-11T21:05:10.3639770Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.3639932Z             {
2023-01-11T21:05:10.3640172Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i0) + (16*i1));
2023-01-11T21:05:10.3640434Z                 tmp0.store(out_ptr0 + (16*i1) + (36*i0));
2023-01-11T21:05:10.3640799Z             }
2023-01-11T21:05:10.3640992Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.3641197Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:05:10.3641373Z             {
2023-01-11T21:05:10.3641546Z                 auto tmp0 = in_ptr0[i1 + (16*i0)];
2023-01-11T21:05:10.3641756Z                 out_ptr0[i1 + (36*i0)] = tmp0;
2023-01-11T21:05:10.3641934Z             }
2023-01-11T21:05:10.3642073Z         }
2023-01-11T21:05:10.3642239Z         #pragma omp for 
2023-01-11T21:05:10.3642424Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3642658Z         {
2023-01-11T21:05:10.3642828Z             #pragma GCC ivdep
2023-01-11T21:05:10.3643021Z             for(long i1=0; i1<20; i1+=1)
2023-01-11T21:05:10.3643181Z             {
2023-01-11T21:05:10.3643337Z                 {
2023-01-11T21:05:10.3643496Z                     {
2023-01-11T21:05:10.3643702Z                         auto tmp0 = static_cast<float>(in_ptr1[i1 + (20*i0)]);
2023-01-11T21:05:10.3643957Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.3644181Z                         out_ptr1[i1 + (36*i0)] = tmp1;
2023-01-11T21:05:10.3644351Z                     }
2023-01-11T21:05:10.3644509Z                 }
2023-01-11T21:05:10.3644664Z             }
2023-01-11T21:05:10.3644803Z         }
2023-01-11T21:05:10.3644951Z     }
2023-01-11T21:05:10.3645095Z }
2023-01-11T21:05:10.3645250Z ''')
2023-01-11T21:05:10.3645340Z 
2023-01-11T21:05:10.3645345Z 
2023-01-11T21:05:10.3645430Z async_compile.wait(globals())
2023-01-11T21:05:10.3645623Z del async_compile
2023-01-11T21:05:10.3645729Z 
2023-01-11T21:05:10.3645785Z def call(args):
2023-01-11T21:05:10.3645960Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3646133Z     args.clear()
2023-01-11T21:05:10.3646476Z     buf2 = empty_strided((8, 36), (36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3646717Z     buf0 = as_strided(buf2, (8, 16), (36, 1))  # alias
2023-01-11T21:05:10.3646940Z     buf1 = as_strided(buf2, (8, 20), (36, 1), 16)  # alias
2023-01-11T21:05:10.3647251Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3647503Z     del arg0_1
2023-01-11T21:05:10.3647665Z     del arg1_1
2023-01-11T21:05:10.3647830Z     return (buf2, )
2023-01-11T21:05:10.3647936Z 
2023-01-11T21:05:10.3647940Z 
2023-01-11T21:05:10.3648003Z if __name__ == "__main__":
2023-01-11T21:05:10.3648238Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3648514Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3648878Z     arg0_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3649235Z     arg1_1 = rand_strided((8, 20), (20, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.3649488Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3649628Z 
2023-01-11T21:05:10.3649692Z ok (2.905s)
2023-01-11T21:05:10.3650222Z   test_cauchy_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3650781Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3651175Z [2023-01-11 20:46:27,313] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 54
2023-01-11T21:05:10.3651627Z [2023-01-11 20:46:30,098] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 54
2023-01-11T21:05:10.3651827Z 
2023-01-11T21:05:10.3651924Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3652112Z import torch
2023-01-11T21:05:10.3652268Z import random
2023-01-11T21:05:10.3652479Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3652737Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3652877Z 
2023-01-11T21:05:10.3652953Z aten = torch.ops.aten
2023-01-11T21:05:10.3653191Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3653435Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3653561Z 
2023-01-11T21:05:10.3653566Z 
2023-01-11T21:05:10.3653687Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3654015Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3654388Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3654627Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3654838Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3655019Z {
2023-01-11T21:05:10.3655162Z     {
2023-01-11T21:05:10.3655297Z         {
2023-01-11T21:05:10.3655459Z             float tmp6 = 0;
2023-01-11T21:05:10.3655668Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3655847Z             {
2023-01-11T21:05:10.3656043Z                 #pragma omp for reduction(+:tmp6) 
2023-01-11T21:05:10.3656259Z                 for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.3656424Z                 {
2023-01-11T21:05:10.3656605Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:05:10.3656785Z                     {
2023-01-11T21:05:10.3656935Z                         {
2023-01-11T21:05:10.3657128Z                             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3657338Z                             auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.3657749Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.3657955Z                             auto tmp3 = 1 / tmp2;
2023-01-11T21:05:10.3658239Z                             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.3658532Z                             auto tmp5 = tmp3 * tmp4;
2023-01-11T21:05:10.3658734Z                             tmp6 += tmp5;
2023-01-11T21:05:10.3658912Z                         }
2023-01-11T21:05:10.3659077Z                     }
2023-01-11T21:05:10.3659223Z                 }
2023-01-11T21:05:10.3659379Z             }
2023-01-11T21:05:10.3659557Z             out_ptr0[0] = tmp6;
2023-01-11T21:05:10.3659741Z         }
2023-01-11T21:05:10.3659876Z     }
2023-01-11T21:05:10.3660075Z }
2023-01-11T21:05:10.3660290Z ''')
2023-01-11T21:05:10.3660370Z 
2023-01-11T21:05:10.3660375Z 
2023-01-11T21:05:10.3660461Z async_compile.wait(globals())
2023-01-11T21:05:10.3660650Z del async_compile
2023-01-11T21:05:10.3660760Z 
2023-01-11T21:05:10.3660829Z def call(args):
2023-01-11T21:05:10.3660991Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3661163Z     args.clear()
2023-01-11T21:05:10.3661449Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3661781Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3662016Z     del arg0_1
2023-01-11T21:05:10.3662177Z     del arg1_1
2023-01-11T21:05:10.3662344Z     return (buf0, )
2023-01-11T21:05:10.3662439Z 
2023-01-11T21:05:10.3662444Z 
2023-01-11T21:05:10.3662520Z if __name__ == "__main__":
2023-01-11T21:05:10.3662738Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3662994Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3663317Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3663662Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3663935Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3664078Z 
2023-01-11T21:05:10.3664141Z ok (2.837s)
2023-01-11T21:05:10.3664702Z   test_clamp_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3665475Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3666181Z [2023-01-11 20:46:30,169] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 55
2023-01-11T21:05:10.3666777Z [2023-01-11 20:46:32,988] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 55
2023-01-11T21:05:10.3666975Z 
2023-01-11T21:05:10.3667057Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3667318Z import torch
2023-01-11T21:05:10.3667485Z import random
2023-01-11T21:05:10.3667683Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3667942Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3668096Z 
2023-01-11T21:05:10.3668172Z aten = torch.ops.aten
2023-01-11T21:05:10.3668400Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3668646Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3668771Z 
2023-01-11T21:05:10.3668775Z 
2023-01-11T21:05:10.3668910Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3669243Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3669569Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3669810Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3670031Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3670238Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.3670455Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.3670641Z {
2023-01-11T21:05:10.3670808Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3671029Z     {
2023-01-11T21:05:10.3671195Z         #pragma omp for 
2023-01-11T21:05:10.3671371Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.3671545Z         {
2023-01-11T21:05:10.3671773Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3672053Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.3672416Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-0.10000000149011612));
2023-01-11T21:05:10.3672680Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:05:10.3672946Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.10000000149011612));
2023-01-11T21:05:10.3673205Z             auto tmp4 = at::vec::minimum(tmp2, tmp3);
2023-01-11T21:05:10.3673453Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:05:10.3673709Z             auto tmp7 = at::vec::maximum(tmp5, tmp6);
2023-01-11T21:05:10.3673922Z             auto tmp8 = tmp0 + tmp5;
2023-01-11T21:05:10.3674131Z             auto tmp9 = at::vec::minimum(tmp8, tmp6);
2023-01-11T21:05:10.3674346Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3674554Z             tmp7.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.3674748Z             tmp9.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.3674924Z         }
2023-01-11T21:05:10.3675108Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3675299Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.3675472Z         {
2023-01-11T21:05:10.3675646Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3675833Z             auto tmp5 = in_ptr1[i0];
2023-01-11T21:05:10.3676115Z             auto tmp1 = static_cast<float>(-0.10000000149011612);
2023-01-11T21:05:10.3676372Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:05:10.3676622Z             auto tmp3 = static_cast<float>(0.10000000149011612);
2023-01-11T21:05:10.3676863Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:05:10.3677105Z             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:05:10.3677349Z             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:05:10.3677558Z             auto tmp8 = tmp0 + tmp5;
2023-01-11T21:05:10.3677787Z             auto tmp9 = (tmp6 != tmp6) ? tmp6 : std::min(tmp8, tmp6);
2023-01-11T21:05:10.3677999Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.3678173Z             out_ptr1[i0] = tmp7;
2023-01-11T21:05:10.3678356Z             out_ptr2[i0] = tmp9;
2023-01-11T21:05:10.3678525Z         }
2023-01-11T21:05:10.3678660Z     }
2023-01-11T21:05:10.3693366Z }
2023-01-11T21:05:10.3693579Z ''')
2023-01-11T21:05:10.3693680Z 
2023-01-11T21:05:10.3693687Z 
2023-01-11T21:05:10.3693957Z async_compile.wait(globals())
2023-01-11T21:05:10.3694287Z del async_compile
2023-01-11T21:05:10.3694487Z 
2023-01-11T21:05:10.3694614Z def call(args):
2023-01-11T21:05:10.3694879Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.3695046Z     args.clear()
2023-01-11T21:05:10.3695357Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3695705Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3696027Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3696391Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.3696680Z     del arg0_1
2023-01-11T21:05:10.3696845Z     del arg1_1
2023-01-11T21:05:10.3697009Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.3697127Z 
2023-01-11T21:05:10.3697131Z 
2023-01-11T21:05:10.3697205Z if __name__ == "__main__":
2023-01-11T21:05:10.3697425Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3697673Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3698084Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3698432Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3698782Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.3698928Z 
2023-01-11T21:05:10.3698992Z ok (2.894s)
2023-01-11T21:05:10.3699521Z   test_clone_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3700080Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3700491Z [2023-01-11 20:46:33,109] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 56
2023-01-11T21:05:10.3700932Z [2023-01-11 20:46:35,871] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 56
2023-01-11T21:05:10.3701130Z 
2023-01-11T21:05:10.3701223Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3701412Z import torch
2023-01-11T21:05:10.3701567Z import random
2023-01-11T21:05:10.3701779Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3702036Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3702185Z 
2023-01-11T21:05:10.3702248Z aten = torch.ops.aten
2023-01-11T21:05:10.3702491Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3702733Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3702859Z 
2023-01-11T21:05:10.3702864Z 
2023-01-11T21:05:10.3702996Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3703310Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3703649Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3703887Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3704095Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3704277Z {
2023-01-11T21:05:10.3704452Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3704625Z     {
2023-01-11T21:05:10.3704785Z         #pragma omp for 
2023-01-11T21:05:10.3704971Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.3705130Z         {
2023-01-11T21:05:10.3705353Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.3705635Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.3705868Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3706096Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.3706373Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.3706575Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.3706770Z             tmp4.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.3706948Z         }
2023-01-11T21:05:10.3707132Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.3707323Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.3707497Z         {
2023-01-11T21:05:10.3707667Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3707863Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.3708068Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.3708278Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:05:10.3708469Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.3708658Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.3708844Z             out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.3708998Z         }
2023-01-11T21:05:10.3709147Z     }
2023-01-11T21:05:10.3709291Z }
2023-01-11T21:05:10.3709437Z ''')
2023-01-11T21:05:10.3709531Z 
2023-01-11T21:05:10.3709535Z 
2023-01-11T21:05:10.3709621Z async_compile.wait(globals())
2023-01-11T21:05:10.3709809Z del async_compile
2023-01-11T21:05:10.3709914Z 
2023-01-11T21:05:10.3709982Z def call(args):
2023-01-11T21:05:10.3710169Z     arg0_1, = args
2023-01-11T21:05:10.3710341Z     args.clear()
2023-01-11T21:05:10.3710639Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3710972Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3711279Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3711521Z     del arg0_1
2023-01-11T21:05:10.3711680Z     return (buf0, buf1, )
2023-01-11T21:05:10.3711795Z 
2023-01-11T21:05:10.3711800Z 
2023-01-11T21:05:10.3711873Z if __name__ == "__main__":
2023-01-11T21:05:10.3712090Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3712347Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3712677Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3712935Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3713070Z 
2023-01-11T21:05:10.3713135Z ok (2.880s)
2023-01-11T21:05:10.3713664Z   test_constant_pad_1d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3714226Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3714630Z [2023-01-11 20:46:35,932] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 57
2023-01-11T21:05:10.3715080Z [2023-01-11 20:46:38,736] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 57
2023-01-11T21:05:10.3715278Z 
2023-01-11T21:05:10.3715357Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3715547Z import torch
2023-01-11T21:05:10.3715720Z import random
2023-01-11T21:05:10.3715922Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3716178Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3716329Z 
2023-01-11T21:05:10.3716403Z aten = torch.ops.aten
2023-01-11T21:05:10.3716641Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3716973Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3717203Z 
2023-01-11T21:05:10.3717210Z 
2023-01-11T21:05:10.3717465Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3717939Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3718266Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3718507Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3718768Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3718950Z {
2023-01-11T21:05:10.3719116Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3719305Z     {
2023-01-11T21:05:10.3719465Z         #pragma omp for 
2023-01-11T21:05:10.3719639Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.3719812Z         {
2023-01-11T21:05:10.3719978Z             #pragma GCC ivdep
2023-01-11T21:05:10.3720155Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:05:10.3720333Z             {
2023-01-11T21:05:10.3720487Z                 {
2023-01-11T21:05:10.3720766Z                     {
2023-01-11T21:05:10.3720972Z                         auto tmp0 = static_cast<long>(i1);
2023-01-11T21:05:10.3721203Z                         auto tmp1 = static_cast<long>(31);
2023-01-11T21:05:10.3721411Z                         auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.3721612Z                         float tmp3 = 6.0;
2023-01-11T21:05:10.3721801Z                         if(tmp2)
2023-01-11T21:05:10.3721961Z                         {
2023-01-11T21:05:10.3722164Z                             auto tmp4 = in_ptr0[i1 + (31*i0)];
2023-01-11T21:05:10.3722373Z                             tmp3 = tmp4;
2023-01-11T21:05:10.3722629Z                         }
2023-01-11T21:05:10.3722826Z                         out_ptr0[i1 + (32*i0)] = tmp3;
2023-01-11T21:05:10.3723010Z                     }
2023-01-11T21:05:10.3723155Z                 }
2023-01-11T21:05:10.3723313Z             }
2023-01-11T21:05:10.3723465Z         }
2023-01-11T21:05:10.3723616Z         #pragma omp for 
2023-01-11T21:05:10.3723803Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.3723974Z         {
2023-01-11T21:05:10.3724129Z             #pragma GCC ivdep
2023-01-11T21:05:10.3724320Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:05:10.3724494Z             {
2023-01-11T21:05:10.3724635Z                 {
2023-01-11T21:05:10.3724792Z                     {
2023-01-11T21:05:10.3725068Z                         auto tmp0 = static_cast<long>((-2) + i1);
2023-01-11T21:05:10.3725305Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3725511Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3725737Z                         auto tmp3 = static_cast<long>(31);
2023-01-11T21:05:10.3725956Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3726150Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:05:10.3726350Z                         float tmp6 = 99.0;
2023-01-11T21:05:10.3726536Z                         if(tmp5)
2023-01-11T21:05:10.3726694Z                         {
2023-01-11T21:05:10.3726959Z                             auto tmp7 = in_ptr0[(-2) + i1 + (31*i0)];
2023-01-11T21:05:10.3727170Z                             tmp6 = tmp7;
2023-01-11T21:05:10.3727331Z                         }
2023-01-11T21:05:10.3727525Z                         out_ptr1[i1 + (36*i0)] = tmp6;
2023-01-11T21:05:10.3727714Z                     }
2023-01-11T21:05:10.3727862Z                 }
2023-01-11T21:05:10.3728019Z             }
2023-01-11T21:05:10.3728173Z         }
2023-01-11T21:05:10.3728309Z     }
2023-01-11T21:05:10.3728457Z }
2023-01-11T21:05:10.3728615Z ''')
2023-01-11T21:05:10.3728709Z 
2023-01-11T21:05:10.3728714Z 
2023-01-11T21:05:10.3728803Z async_compile.wait(globals())
2023-01-11T21:05:10.3728980Z del async_compile
2023-01-11T21:05:10.3729086Z 
2023-01-11T21:05:10.3729155Z def call(args):
2023-01-11T21:05:10.3729322Z     arg0_1, = args
2023-01-11T21:05:10.3729476Z     args.clear()
2023-01-11T21:05:10.3729783Z     buf0 = empty_strided((2, 16, 32), (512, 32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3730144Z     buf1 = empty_strided((2, 16, 36), (576, 36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3730447Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3730691Z     del arg0_1
2023-01-11T21:05:10.3730863Z     return (buf0, buf1, )
2023-01-11T21:05:10.3731026Z 
2023-01-11T21:05:10.3731031Z 
2023-01-11T21:05:10.3731104Z if __name__ == "__main__":
2023-01-11T21:05:10.3731307Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3731573Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3731923Z     arg0_1 = rand_strided((2, 16, 31), (496, 31, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3732174Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3732308Z 
2023-01-11T21:05:10.3732373Z ok (2.869s)
2023-01-11T21:05:10.3732912Z   test_constant_pad_2d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3733487Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3733887Z [2023-01-11 20:46:38,816] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 58
2023-01-11T21:05:10.3734366Z [2023-01-11 20:46:41,678] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 58
2023-01-11T21:05:10.3734565Z 
2023-01-11T21:05:10.3734659Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3734851Z import torch
2023-01-11T21:05:10.3735005Z import random
2023-01-11T21:05:10.3735220Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3735476Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3735615Z 
2023-01-11T21:05:10.3735690Z aten = torch.ops.aten
2023-01-11T21:05:10.3735930Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3736173Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3736300Z 
2023-01-11T21:05:10.3736305Z 
2023-01-11T21:05:10.3736426Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3736752Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3737092Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3737331Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3737539Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3737721Z {
2023-01-11T21:05:10.3737897Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3738070Z     {
2023-01-11T21:05:10.3738236Z         #pragma omp for 
2023-01-11T21:05:10.3738421Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.3738676Z         {
2023-01-11T21:05:10.3738843Z             #pragma GCC ivdep
2023-01-11T21:05:10.3739035Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.3739196Z             {
2023-01-11T21:05:10.3739350Z                 {
2023-01-11T21:05:10.3739508Z                     {
2023-01-11T21:05:10.3739761Z                         auto tmp0 = static_cast<long>((-1) + i0);
2023-01-11T21:05:10.3739998Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3740216Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3740422Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:05:10.3740641Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3740923Z                         auto tmp5 = static_cast<long>((-1) + i1);
2023-01-11T21:05:10.3741132Z                         auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:05:10.3741345Z                         auto tmp7 = tmp5 < tmp3;
2023-01-11T21:05:10.3741552Z                         auto tmp8 = tmp2 & tmp4;
2023-01-11T21:05:10.3741761Z                         auto tmp9 = tmp8 & tmp6;
2023-01-11T21:05:10.3741955Z                         auto tmp10 = tmp9 & tmp7;
2023-01-11T21:05:10.3742160Z                         float tmp11 = 6.0;
2023-01-11T21:05:10.3742345Z                         if(tmp10)
2023-01-11T21:05:10.3742504Z                         {
2023-01-11T21:05:10.3742808Z                             auto tmp12 = in_ptr0[(-9) + i1 + (8*i0)];
2023-01-11T21:05:10.3743020Z                             tmp11 = tmp12;
2023-01-11T21:05:10.3743188Z                         }
2023-01-11T21:05:10.3743382Z                         out_ptr0[i1 + (10*i0)] = tmp11;
2023-01-11T21:05:10.3743566Z                     }
2023-01-11T21:05:10.3743712Z                 }
2023-01-11T21:05:10.3743773Z             }
2023-01-11T21:05:10.3743841Z         }
2023-01-11T21:05:10.3743916Z         #pragma omp for 
2023-01-11T21:05:10.3743996Z         for(long i0=0; i0<15; i0+=1)
2023-01-11T21:05:10.3744057Z         {
2023-01-11T21:05:10.3744123Z             #pragma GCC ivdep
2023-01-11T21:05:10.3744208Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:05:10.3744269Z             {
2023-01-11T21:05:10.3744331Z                 {
2023-01-11T21:05:10.3744395Z                     {
2023-01-11T21:05:10.3744560Z                         auto tmp0 = static_cast<long>((-3) + i0);
2023-01-11T21:05:10.3744662Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3744744Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3744844Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:05:10.3744969Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3745132Z                         auto tmp5 = static_cast<long>((-1) + i1);
2023-01-11T21:05:10.3745223Z                         auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:05:10.3745313Z                         auto tmp7 = tmp5 < tmp3;
2023-01-11T21:05:10.3745403Z                         auto tmp8 = tmp2 & tmp4;
2023-01-11T21:05:10.3745480Z                         auto tmp9 = tmp8 & tmp6;
2023-01-11T21:05:10.3745570Z                         auto tmp10 = tmp9 & tmp7;
2023-01-11T21:05:10.3745657Z                         float tmp11 = 99.0;
2023-01-11T21:05:10.3745734Z                         if(tmp10)
2023-01-11T21:05:10.3745801Z                         {
2023-01-11T21:05:10.3745968Z                             auto tmp12 = in_ptr0[(-25) + i1 + (8*i0)];
2023-01-11T21:05:10.3746053Z                             tmp11 = tmp12;
2023-01-11T21:05:10.3746120Z                         }
2023-01-11T21:05:10.3746204Z                         out_ptr1[i1 + (11*i0)] = tmp11;
2023-01-11T21:05:10.3746267Z                     }
2023-01-11T21:05:10.3746330Z                 }
2023-01-11T21:05:10.3746391Z             }
2023-01-11T21:05:10.3746450Z         }
2023-01-11T21:05:10.3746512Z     }
2023-01-11T21:05:10.3746560Z }
2023-01-11T21:05:10.3746637Z ''')
2023-01-11T21:05:10.3746641Z 
2023-01-11T21:05:10.3746646Z 
2023-01-11T21:05:10.3746734Z async_compile.wait(globals())
2023-01-11T21:05:10.3746806Z del async_compile
2023-01-11T21:05:10.3746811Z 
2023-01-11T21:05:10.3746879Z def call(args):
2023-01-11T21:05:10.3746946Z     arg0_1, = args
2023-01-11T21:05:10.3747014Z     args.clear()
2023-01-11T21:05:10.3747229Z     buf0 = empty_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3747434Z     buf1 = empty_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3747595Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3747667Z     del arg0_1
2023-01-11T21:05:10.3747744Z     return (buf0, buf1, )
2023-01-11T21:05:10.3747749Z 
2023-01-11T21:05:10.3747753Z 
2023-01-11T21:05:10.3747828Z if __name__ == "__main__":
2023-01-11T21:05:10.3747942Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3748065Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3748275Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3748370Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3748375Z 
2023-01-11T21:05:10.3748440Z ok (2.939s)
2023-01-11T21:05:10.3748895Z   test_constant_pad_3d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3749065Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3749323Z [2023-01-11 20:46:41,755] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 59
2023-01-11T21:05:10.3749585Z [2023-01-11 20:46:44,611] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 59
2023-01-11T21:05:10.3749590Z 
2023-01-11T21:05:10.3749686Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3749754Z import torch
2023-01-11T21:05:10.3749822Z import random
2023-01-11T21:05:10.3749924Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3750044Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3750049Z 
2023-01-11T21:05:10.3750128Z aten = torch.ops.aten
2023-01-11T21:05:10.3750261Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3750352Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3750357Z 
2023-01-11T21:05:10.3750361Z 
2023-01-11T21:05:10.3750522Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3750726Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3750844Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3750931Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3751029Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3751089Z {
2023-01-11T21:05:10.3751187Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3751246Z     {
2023-01-11T21:05:10.3751336Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.3751416Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3751465Z         {
2023-01-11T21:05:10.3751551Z             for(long i1=0; i1<15; i1+=1)
2023-01-11T21:05:10.3751614Z             {
2023-01-11T21:05:10.3751695Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3751784Z                 for(long i2=0; i2<11; i2+=1)
2023-01-11T21:05:10.3751849Z                 {
2023-01-11T21:05:10.3751931Z                     #pragma GCC ivdep
2023-01-11T21:05:10.3752009Z                     for(long i3=0; i3<7; i3+=1)
2023-01-11T21:05:10.3752072Z                     {
2023-01-11T21:05:10.3752139Z                         {
2023-01-11T21:05:10.3752208Z                             {
2023-01-11T21:05:10.3752383Z                                 auto tmp0 = static_cast<long>((-5) + i1);
2023-01-11T21:05:10.3752492Z                                 auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3752589Z                                 auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3752683Z                                 auto tmp3 = static_cast<long>(4);
2023-01-11T21:05:10.3752781Z                                 auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3752956Z                                 auto tmp5 = static_cast<long>((-3) + i2);
2023-01-11T21:05:10.3753053Z                                 auto tmp6 = tmp5 >= tmp1;
2023-01-11T21:05:10.3753151Z                                 auto tmp7 = tmp5 < tmp3;
2023-01-11T21:05:10.3753324Z                                 auto tmp8 = static_cast<long>((-1) + i3);
2023-01-11T21:05:10.3753419Z                                 auto tmp9 = tmp8 >= tmp1;
2023-01-11T21:05:10.3753501Z                                 auto tmp10 = tmp8 < tmp3;
2023-01-11T21:05:10.3753598Z                                 auto tmp11 = tmp2 & tmp4;
2023-01-11T21:05:10.3753694Z                                 auto tmp12 = tmp11 & tmp6;
2023-01-11T21:05:10.3753793Z                                 auto tmp13 = tmp12 & tmp7;
2023-01-11T21:05:10.3753887Z                                 auto tmp14 = tmp13 & tmp9;
2023-01-11T21:05:10.3753987Z                                 auto tmp15 = tmp14 & tmp10;
2023-01-11T21:05:10.3754109Z                                 float tmp16 = 6.0;
2023-01-11T21:05:10.3754188Z                                 if(tmp15)
2023-01-11T21:05:10.3754247Z                                 {
2023-01-11T21:05:10.3754454Z                                     auto tmp17 = in_ptr0[(-93) + i3 + (4*i2) + (16*i1) + (64*i0)];
2023-01-11T21:05:10.3754542Z                                     tmp16 = tmp17;
2023-01-11T21:05:10.3754612Z                                 }
2023-01-11T21:05:10.3754724Z                                 out_ptr0[i3 + (7*i2) + (77*i1) + (1155*i0)] = tmp16;
2023-01-11T21:05:10.3754795Z                             }
2023-01-11T21:05:10.3754862Z                         }
2023-01-11T21:05:10.3754914Z                     }
2023-01-11T21:05:10.3754977Z                 }
2023-01-11T21:05:10.3755038Z             }
2023-01-11T21:05:10.3755102Z         }
2023-01-11T21:05:10.3755177Z         #pragma omp for 
2023-01-11T21:05:10.3755257Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.3755320Z         {
2023-01-11T21:05:10.3755386Z             #pragma GCC ivdep
2023-01-11T21:05:10.3755472Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:05:10.3755534Z             {
2023-01-11T21:05:10.3755646Z                 #pragma GCC ivdep
2023-01-11T21:05:10.3755737Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:05:10.3755800Z                 {
2023-01-11T21:05:10.3755851Z                     {
2023-01-11T21:05:10.3755917Z                         {
2023-01-11T21:05:10.3756088Z                             auto tmp0 = static_cast<long>((-3) + i1);
2023-01-11T21:05:10.3756194Z                             auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.3756289Z                             auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.3756394Z                             auto tmp3 = static_cast<long>(4);
2023-01-11T21:05:10.3756489Z                             auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.3756582Z                             auto tmp5 = tmp2 & tmp4;
2023-01-11T21:05:10.3756659Z                             float tmp6 = 6.0;
2023-01-11T21:05:10.3756734Z                             if(tmp5)
2023-01-11T21:05:10.3756801Z                             {
2023-01-11T21:05:10.3756987Z                                 auto tmp7 = in_ptr0[(-12) + i2 + (4*i1) + (16*i0)];
2023-01-11T21:05:10.3757071Z                                 tmp6 = tmp7;
2023-01-11T21:05:10.3757138Z                             }
2023-01-11T21:05:10.3757242Z                             out_ptr1[i2 + (4*i1) + (44*i0)] = tmp6;
2023-01-11T21:05:10.3757295Z                         }
2023-01-11T21:05:10.3757359Z                     }
2023-01-11T21:05:10.3757421Z                 }
2023-01-11T21:05:10.3757484Z             }
2023-01-11T21:05:10.3757544Z         }
2023-01-11T21:05:10.3757605Z     }
2023-01-11T21:05:10.3757663Z }
2023-01-11T21:05:10.3757727Z ''')
2023-01-11T21:05:10.3757732Z 
2023-01-11T21:05:10.3757736Z 
2023-01-11T21:05:10.3757825Z async_compile.wait(globals())
2023-01-11T21:05:10.3757897Z del async_compile
2023-01-11T21:05:10.3757904Z 
2023-01-11T21:05:10.3757974Z def call(args):
2023-01-11T21:05:10.3758045Z     arg0_1, = args
2023-01-11T21:05:10.3758116Z     args.clear()
2023-01-11T21:05:10.3758338Z     buf0 = empty_strided((2, 15, 11, 7), (1155, 77, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3758539Z     buf1 = empty_strided((2, 4, 11, 4), (176, 44, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3758703Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3758771Z     del arg0_1
2023-01-11T21:05:10.3758849Z     return (buf0, buf1, )
2023-01-11T21:05:10.3758854Z 
2023-01-11T21:05:10.3758858Z 
2023-01-11T21:05:10.3758932Z if __name__ == "__main__":
2023-01-11T21:05:10.3759048Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3759171Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3759385Z     arg0_1 = rand_strided((2, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3759514Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.3759532Z 
2023-01-11T21:05:10.3759586Z ok (2.935s)
2023-01-11T21:05:10.3760060Z   test_conv2d_backward_channels_last_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3760188Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3760446Z [2023-01-11 20:46:44,797] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 60
2023-01-11T21:05:10.3760890Z [2023-01-11 20:46:44,835] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 60
2023-01-11T21:05:10.3760897Z 
2023-01-11T21:05:10.3760995Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3761064Z import torch
2023-01-11T21:05:10.3761134Z import random
2023-01-11T21:05:10.3761235Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3761431Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3761437Z 
2023-01-11T21:05:10.3761515Z aten = torch.ops.aten
2023-01-11T21:05:10.3761648Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3761739Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3761744Z 
2023-01-11T21:05:10.3761747Z 
2023-01-11T21:05:10.3761834Z async_compile.wait(globals())
2023-01-11T21:05:10.3761905Z del async_compile
2023-01-11T21:05:10.3761910Z 
2023-01-11T21:05:10.3761978Z def call(args):
2023-01-11T21:05:10.3762045Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.3762115Z     args.clear()
2023-01-11T21:05:10.3762279Z     buf0 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [320], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, True, True])
2023-01-11T21:05:10.3762351Z     del arg0_1
2023-01-11T21:05:10.3762418Z     del arg1_1
2023-01-11T21:05:10.3762482Z     del arg2_1
2023-01-11T21:05:10.3762549Z     buf1 = buf0[0]
2023-01-11T21:05:10.3762651Z     assert_size_stride(buf1, (2, 2048, 8, 8), (131072, 1, 16384, 2048))
2023-01-11T21:05:10.3762719Z     buf2 = buf0[1]
2023-01-11T21:05:10.3762828Z     assert_size_stride(buf2, (320, 2048, 1, 1), (2048, 1, 2048, 2048))
2023-01-11T21:05:10.3762894Z     buf3 = buf0[2]
2023-01-11T21:05:10.3762989Z     assert_size_stride(buf3, (320, ), (1, ))
2023-01-11T21:05:10.3763052Z     del buf0
2023-01-11T21:05:10.3763132Z     return (buf1, buf2, buf3, )
2023-01-11T21:05:10.3763137Z 
2023-01-11T21:05:10.3763141Z 
2023-01-11T21:05:10.3763215Z if __name__ == "__main__":
2023-01-11T21:05:10.3763314Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3763434Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3763656Z     arg0_1 = rand_strided((2, 320, 8, 8), (20480, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3763877Z     arg1_1 = rand_strided((2, 2048, 8, 8), (131072, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3764102Z     arg2_1 = rand_strided((320, 2048, 1, 1), (2048, 1, 2048, 2048), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3764222Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.3764226Z 
2023-01-11T21:05:10.3764291Z ok (0.704s)
2023-01-11T21:05:10.3764477Z   test_conv2d_binary_cpu (__main__.CpuTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:05:10.3764928Z   test_conv2d_channels_last_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3765081Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3765338Z [2023-01-11 20:46:45,469] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 61
2023-01-11T21:05:10.3765600Z [2023-01-11 20:46:48,196] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 61
2023-01-11T21:05:10.3766000Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3766125Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3766378Z [2023-01-11 20:46:48,390] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 62
2023-01-11T21:05:10.3766638Z [2023-01-11 20:46:48,436] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 62
2023-01-11T21:05:10.3767064Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3767190Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3767441Z [2023-01-11 20:46:48,576] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 63
2023-01-11T21:05:10.3767695Z [2023-01-11 20:46:48,618] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 63
2023-01-11T21:05:10.3767702Z 
2023-01-11T21:05:10.3767781Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3767850Z import torch
2023-01-11T21:05:10.3767919Z import random
2023-01-11T21:05:10.3768033Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3768152Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3768157Z 
2023-01-11T21:05:10.3768232Z aten = torch.ops.aten
2023-01-11T21:05:10.3768365Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3768442Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3768459Z 
2023-01-11T21:05:10.3768463Z 
2023-01-11T21:05:10.3768583Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3768783Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3768901Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3769001Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3769060Z {
2023-01-11T21:05:10.3769155Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3769217Z     {
2023-01-11T21:05:10.3769294Z         #pragma omp for  collapse(3)
2023-01-11T21:05:10.3769376Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3769438Z         {
2023-01-11T21:05:10.3769517Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.3769579Z             {
2023-01-11T21:05:10.3769670Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:05:10.3769732Z                 {
2023-01-11T21:05:10.3769786Z                     {
2023-01-11T21:05:10.3769853Z                         {
2023-01-11T21:05:10.3769961Z                             auto tmp0 = in_ptr0[i2 + (256*i1) + (768*i0)];
2023-01-11T21:05:10.3770066Z                             out_ptr0[i1 + (3*i2) + (768*i0)] = tmp0;
2023-01-11T21:05:10.3770131Z                         }
2023-01-11T21:05:10.3770195Z                     }
2023-01-11T21:05:10.3770255Z                 }
2023-01-11T21:05:10.3770304Z             }
2023-01-11T21:05:10.3770363Z         }
2023-01-11T21:05:10.3770423Z     }
2023-01-11T21:05:10.3770482Z }
2023-01-11T21:05:10.3770558Z ''')
2023-01-11T21:05:10.3770563Z 
2023-01-11T21:05:10.3770567Z 
2023-01-11T21:05:10.3770700Z async_compile.wait(globals())
2023-01-11T21:05:10.3770770Z del async_compile
2023-01-11T21:05:10.3770778Z 
2023-01-11T21:05:10.3770834Z def call(args):
2023-01-11T21:05:10.3770934Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.3771006Z     args.clear()
2023-01-11T21:05:10.3771223Z     buf0 = empty_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3771363Z     kernel_cpp_0(c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3771505Z     buf1 = aten.convolution(buf0, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.3771610Z     assert_size_stride(buf1, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:05:10.3771662Z     del buf0
2023-01-11T21:05:10.3771735Z     del primals_2
2023-01-11T21:05:10.3771831Z     return (buf1, primals_1, primals_3, )
2023-01-11T21:05:10.3771836Z 
2023-01-11T21:05:10.3771840Z 
2023-01-11T21:05:10.3771914Z if __name__ == "__main__":
2023-01-11T21:05:10.3772028Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3772147Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3772389Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3772587Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3772795Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3772932Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.3772937Z 
2023-01-11T21:05:10.3772942Z 
2023-01-11T21:05:10.3773034Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3773101Z import torch
2023-01-11T21:05:10.3773170Z import random
2023-01-11T21:05:10.3773284Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3773404Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3773409Z 
2023-01-11T21:05:10.3773487Z aten = torch.ops.aten
2023-01-11T21:05:10.3773606Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3773695Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3773700Z 
2023-01-11T21:05:10.3773704Z 
2023-01-11T21:05:10.3773792Z async_compile.wait(globals())
2023-01-11T21:05:10.3773862Z del async_compile
2023-01-11T21:05:10.3773867Z 
2023-01-11T21:05:10.3773936Z def call(args):
2023-01-11T21:05:10.3774034Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.3774103Z     args.clear()
2023-01-11T21:05:10.3774255Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.3774351Z     assert_size_stride(buf0, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:05:10.3774422Z     del primals_2
2023-01-11T21:05:10.3774518Z     return (buf0, primals_1, primals_3, )
2023-01-11T21:05:10.3774523Z 
2023-01-11T21:05:10.3774526Z 
2023-01-11T21:05:10.3774599Z if __name__ == "__main__":
2023-01-11T21:05:10.3774713Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3774832Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3775047Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3775243Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3775450Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3775586Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.3775591Z 
2023-01-11T21:05:10.3775595Z 
2023-01-11T21:05:10.3775688Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3775755Z import torch
2023-01-11T21:05:10.3775823Z import random
2023-01-11T21:05:10.3775935Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3776053Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3776058Z 
2023-01-11T21:05:10.3776168Z aten = torch.ops.aten
2023-01-11T21:05:10.3776287Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3776378Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3776383Z 
2023-01-11T21:05:10.3776388Z 
2023-01-11T21:05:10.3776474Z async_compile.wait(globals())
2023-01-11T21:05:10.3776546Z del async_compile
2023-01-11T21:05:10.3776551Z 
2023-01-11T21:05:10.3776619Z def call(args):
2023-01-11T21:05:10.3776718Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.3776788Z     args.clear()
2023-01-11T21:05:10.3776939Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.3777032Z     assert_size_stride(buf0, (2, 3, 16, 16), (768, 1, 48, 3))
2023-01-11T21:05:10.3777101Z     del primals_2
2023-01-11T21:05:10.3777198Z     return (buf0, primals_1, primals_3, )
2023-01-11T21:05:10.3777203Z 
2023-01-11T21:05:10.3777207Z 
2023-01-11T21:05:10.3777283Z if __name__ == "__main__":
2023-01-11T21:05:10.3777396Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3777514Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3777756Z     primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3777953Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3778159Z     primals_3 = rand_strided((2, 3, 16, 16), (768, 1, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3778293Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.3778298Z 
2023-01-11T21:05:10.3778363Z ok (3.296s)
2023-01-11T21:05:10.3778892Z   test_conv2d_packed_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3779021Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3779283Z [2023-01-11 20:46:48,711] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 64
2023-01-11T21:05:10.3779546Z [2023-01-11 20:46:48,753] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 64
2023-01-11T21:05:10.3779551Z 
2023-01-11T21:05:10.3779645Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3779715Z import torch
2023-01-11T21:05:10.3779771Z import random
2023-01-11T21:05:10.3779884Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3780001Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3780006Z 
2023-01-11T21:05:10.3780081Z aten = torch.ops.aten
2023-01-11T21:05:10.3780214Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3780305Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3780312Z 
2023-01-11T21:05:10.3780316Z 
2023-01-11T21:05:10.3780402Z async_compile.wait(globals())
2023-01-11T21:05:10.3780473Z del async_compile
2023-01-11T21:05:10.3780478Z 
2023-01-11T21:05:10.3780534Z def call(args):
2023-01-11T21:05:10.3780617Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.3780687Z     args.clear()
2023-01-11T21:05:10.3780825Z     buf0 = aten.convolution(arg2_1, arg0_1, arg1_1, (3, 3), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.3780934Z     assert_size_stride(buf0, (1, 64, 18, 18), (20736, 324, 18, 1))
2023-01-11T21:05:10.3781000Z     del arg0_1
2023-01-11T21:05:10.3781065Z     del arg1_1
2023-01-11T21:05:10.3781117Z     del arg2_1
2023-01-11T21:05:10.3781185Z     return (buf0, )
2023-01-11T21:05:10.3781191Z 
2023-01-11T21:05:10.3781195Z 
2023-01-11T21:05:10.3781268Z if __name__ == "__main__":
2023-01-11T21:05:10.3781380Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3781499Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3781748Z     arg0_1 = rand_strided((64, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3781941Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3782164Z     arg2_1 = rand_strided((1, 3, 56, 56), (9408, 3136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3782282Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.3782287Z 
2023-01-11T21:05:10.3782340Z ok (0.146s)
2023-01-11T21:05:10.3782524Z   test_conv2d_unary_cpu (__main__.CpuTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:05:10.3782973Z   test_conv3d_channels_last_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3783102Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3783389Z [2023-01-11 20:46:48,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 65
2023-01-11T21:05:10.3783650Z [2023-01-11 20:46:51,614] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 65
2023-01-11T21:05:10.3784048Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3784171Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3784424Z [2023-01-11 20:46:51,835] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 66
2023-01-11T21:05:10.3784684Z [2023-01-11 20:46:54,598] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 66
2023-01-11T21:05:10.3785081Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3785191Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3785441Z [2023-01-11 20:46:54,845] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 67
2023-01-11T21:05:10.3785698Z [2023-01-11 20:46:54,904] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 67
2023-01-11T21:05:10.3785704Z 
2023-01-11T21:05:10.3785796Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3785863Z import torch
2023-01-11T21:05:10.3785934Z import random
2023-01-11T21:05:10.3786096Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3786292Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3786300Z 
2023-01-11T21:05:10.3786431Z aten = torch.ops.aten
2023-01-11T21:05:10.3786683Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3786854Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3786862Z 
2023-01-11T21:05:10.3786869Z 
2023-01-11T21:05:10.3787055Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3787259Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3787380Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3787479Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.3787540Z {
2023-01-11T21:05:10.3787624Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3787684Z     {
2023-01-11T21:05:10.3787760Z         #pragma omp for 
2023-01-11T21:05:10.3787879Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.3787940Z         {
2023-01-11T21:05:10.3788003Z             {
2023-01-11T21:05:10.3788065Z                 {
2023-01-11T21:05:10.3788149Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3788233Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.3788295Z                 }
2023-01-11T21:05:10.3788356Z             }
2023-01-11T21:05:10.3788415Z         }
2023-01-11T21:05:10.3788474Z     }
2023-01-11T21:05:10.3788520Z }
2023-01-11T21:05:10.3788600Z ''')
2023-01-11T21:05:10.3788605Z 
2023-01-11T21:05:10.3788609Z 
2023-01-11T21:05:10.3788697Z async_compile.wait(globals())
2023-01-11T21:05:10.3788768Z del async_compile
2023-01-11T21:05:10.3788772Z 
2023-01-11T21:05:10.3788841Z def call(args):
2023-01-11T21:05:10.3788941Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.3789010Z     args.clear()
2023-01-11T21:05:10.3789222Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3789349Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.3789527Z     buf1 = aten.convolution(primals_3, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.3789639Z     assert_size_stride(buf1, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:05:10.3789703Z     del buf0
2023-01-11T21:05:10.3789773Z     del primals_2
2023-01-11T21:05:10.3789870Z     return (buf1, primals_1, primals_3, )
2023-01-11T21:05:10.3789875Z 
2023-01-11T21:05:10.3789879Z 
2023-01-11T21:05:10.3789953Z if __name__ == "__main__":
2023-01-11T21:05:10.3790065Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3790173Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3790392Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3790590Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3790826Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3790967Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.3790972Z 
2023-01-11T21:05:10.3790976Z 
2023-01-11T21:05:10.3791071Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3791140Z import torch
2023-01-11T21:05:10.3791208Z import random
2023-01-11T21:05:10.3791308Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3791428Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3791433Z 
2023-01-11T21:05:10.3791508Z aten = torch.ops.aten
2023-01-11T21:05:10.3791639Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3791730Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3791735Z 
2023-01-11T21:05:10.3791739Z 
2023-01-11T21:05:10.3791870Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3792073Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3792193Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3792285Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3792385Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3792480Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3792542Z {
2023-01-11T21:05:10.3792636Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3792696Z     {
2023-01-11T21:05:10.3792771Z         #pragma omp for 
2023-01-11T21:05:10.3792837Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.3792897Z         {
2023-01-11T21:05:10.3792958Z             {
2023-01-11T21:05:10.3793020Z                 {
2023-01-11T21:05:10.3793110Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3793193Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.3793255Z                 }
2023-01-11T21:05:10.3793336Z             }
2023-01-11T21:05:10.3793396Z         }
2023-01-11T21:05:10.3793487Z         #pragma omp for  collapse(3)
2023-01-11T21:05:10.3793566Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3793626Z         {
2023-01-11T21:05:10.3793709Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.3793758Z             {
2023-01-11T21:05:10.3793846Z                 for(long i2=0; i2<4096; i2+=1)
2023-01-11T21:05:10.3793908Z                 {
2023-01-11T21:05:10.3793975Z                     {
2023-01-11T21:05:10.3794042Z                         {
2023-01-11T21:05:10.3794152Z                             auto tmp0 = in_ptr1[i1 + (3*i2) + (12288*i0)];
2023-01-11T21:05:10.3794258Z                             out_ptr1[i2 + (4096*i1) + (12288*i0)] = tmp0;
2023-01-11T21:05:10.3794312Z                         }
2023-01-11T21:05:10.3794376Z                     }
2023-01-11T21:05:10.3794439Z                 }
2023-01-11T21:05:10.3794502Z             }
2023-01-11T21:05:10.3794563Z         }
2023-01-11T21:05:10.3794627Z     }
2023-01-11T21:05:10.3794685Z }
2023-01-11T21:05:10.3794751Z ''')
2023-01-11T21:05:10.3794756Z 
2023-01-11T21:05:10.3794760Z 
2023-01-11T21:05:10.3794849Z async_compile.wait(globals())
2023-01-11T21:05:10.3794949Z del async_compile
2023-01-11T21:05:10.3794954Z 
2023-01-11T21:05:10.3795027Z def call(args):
2023-01-11T21:05:10.3795126Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.3795196Z     args.clear()
2023-01-11T21:05:10.3795412Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3795642Z     buf1 = empty_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3795824Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3795970Z     buf2 = aten.convolution(buf1, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.3796085Z     assert_size_stride(buf2, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:05:10.3796148Z     del buf0
2023-01-11T21:05:10.3796212Z     del buf1
2023-01-11T21:05:10.3796281Z     del primals_2
2023-01-11T21:05:10.3796381Z     return (buf2, primals_1, primals_3, )
2023-01-11T21:05:10.3796386Z 
2023-01-11T21:05:10.3796390Z 
2023-01-11T21:05:10.3796464Z if __name__ == "__main__":
2023-01-11T21:05:10.3796565Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3796684Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3796904Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3797101Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3797333Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 1, 768, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3797470Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.3797478Z 
2023-01-11T21:05:10.3797482Z 
2023-01-11T21:05:10.3797575Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3797642Z import torch
2023-01-11T21:05:10.3797698Z import random
2023-01-11T21:05:10.3797814Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3797934Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3797938Z 
2023-01-11T21:05:10.3798017Z aten = torch.ops.aten
2023-01-11T21:05:10.3798149Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3798239Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3798245Z 
2023-01-11T21:05:10.3798249Z 
2023-01-11T21:05:10.3798382Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.3798583Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.3798689Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.3798789Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.3798921Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.3799016Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.3799075Z {
2023-01-11T21:05:10.3799172Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.3799233Z     {
2023-01-11T21:05:10.3799296Z         #pragma omp for 
2023-01-11T21:05:10.3799376Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.3799436Z         {
2023-01-11T21:05:10.3799498Z             {
2023-01-11T21:05:10.3799560Z                 {
2023-01-11T21:05:10.3799650Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.3799733Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.3799785Z                 }
2023-01-11T21:05:10.3799846Z             }
2023-01-11T21:05:10.3799908Z         }
2023-01-11T21:05:10.3799996Z         #pragma omp for  collapse(3)
2023-01-11T21:05:10.3800074Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.3800134Z         {
2023-01-11T21:05:10.3800204Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.3800265Z             {
2023-01-11T21:05:10.3800352Z                 for(long i2=0; i2<4096; i2+=1)
2023-01-11T21:05:10.3800416Z                 {
2023-01-11T21:05:10.3800522Z                     {
2023-01-11T21:05:10.3800734Z                         {
2023-01-11T21:05:10.3800848Z                             auto tmp0 = in_ptr1[i1 + (3*i2) + (12288*i0)];
2023-01-11T21:05:10.3800941Z                             out_ptr1[i2 + (4096*i1) + (12288*i0)] = tmp0;
2023-01-11T21:05:10.3801008Z                         }
2023-01-11T21:05:10.3801071Z                     }
2023-01-11T21:05:10.3801135Z                 }
2023-01-11T21:05:10.3801196Z             }
2023-01-11T21:05:10.3801254Z         }
2023-01-11T21:05:10.3801314Z     }
2023-01-11T21:05:10.3801360Z }
2023-01-11T21:05:10.3801441Z ''')
2023-01-11T21:05:10.3801446Z 
2023-01-11T21:05:10.3801450Z 
2023-01-11T21:05:10.3801539Z async_compile.wait(globals())
2023-01-11T21:05:10.3801614Z del async_compile
2023-01-11T21:05:10.3801619Z 
2023-01-11T21:05:10.3801688Z def call(args):
2023-01-11T21:05:10.3801788Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.3801857Z     args.clear()
2023-01-11T21:05:10.3802059Z     buf0 = empty_strided((3, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3802289Z     buf1 = empty_strided((2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3802489Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.3802633Z     buf2 = aten.convolution(buf1, buf0, primals_2, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.3802744Z     assert_size_stride(buf2, (2, 3, 16, 16, 16), (12288, 4096, 256, 16, 1))
2023-01-11T21:05:10.3802809Z     del buf0
2023-01-11T21:05:10.3802872Z     del buf1
2023-01-11T21:05:10.3802942Z     del primals_2
2023-01-11T21:05:10.3803029Z     return (buf2, primals_1, primals_3, )
2023-01-11T21:05:10.3803047Z 
2023-01-11T21:05:10.3803051Z 
2023-01-11T21:05:10.3803112Z if __name__ == "__main__":
2023-01-11T21:05:10.3803225Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3803349Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3803565Z     primals_1 = rand_strided((3, 3, 1, 1, 1), (3, 1, 3, 3, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3803759Z     primals_2 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3803992Z     primals_3 = rand_strided((2, 3, 16, 16, 16), (12288, 1, 768, 48, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3804128Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.3804134Z 
2023-01-11T21:05:10.3804200Z ok (6.176s)
2023-01-11T21:05:10.3804326Z   test_conv_autotune_cpu (__main__.CpuTests) ... skip: requires cuda (0.002s)
2023-01-11T21:05:10.3804656Z   test_conv_backward_cpu (__main__.CpuTests) ... [2023-01-11 20:46:55,131] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 68
2023-01-11T21:05:10.3804988Z [2023-01-11 20:46:55,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 68
2023-01-11T21:05:10.3804994Z 
2023-01-11T21:05:10.3805089Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3805157Z import torch
2023-01-11T21:05:10.3805225Z import random
2023-01-11T21:05:10.3805338Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3805458Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3805463Z 
2023-01-11T21:05:10.3805527Z aten = torch.ops.aten
2023-01-11T21:05:10.3805658Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3805749Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3805754Z 
2023-01-11T21:05:10.3805759Z 
2023-01-11T21:05:10.3805844Z async_compile.wait(globals())
2023-01-11T21:05:10.3805914Z del async_compile
2023-01-11T21:05:10.3805922Z 
2023-01-11T21:05:10.3805994Z def call(args):
2023-01-11T21:05:10.3806120Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1 = args
2023-01-11T21:05:10.3806189Z     args.clear()
2023-01-11T21:05:10.3806376Z     buf0 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [4], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, True, True])
2023-01-11T21:05:10.3806447Z     buf1 = buf0[0]
2023-01-11T21:05:10.3806552Z     assert_size_stride(buf1, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:05:10.3806619Z     buf2 = buf0[1]
2023-01-11T21:05:10.3806726Z     assert_size_stride(buf2, (4, 4, 3, 3), (36, 9, 3, 1))
2023-01-11T21:05:10.3806793Z     buf3 = buf0[2]
2023-01-11T21:05:10.3806885Z     assert_size_stride(buf3, (4, ), (1, ))
2023-01-11T21:05:10.3806937Z     del buf0
2023-01-11T21:05:10.3807105Z     buf4 = aten.convolution_backward(arg0_1, arg1_1, arg2_1, [4], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, False, False])
2023-01-11T21:05:10.3807171Z     del arg0_1
2023-01-11T21:05:10.3807240Z     del arg1_1
2023-01-11T21:05:10.3807304Z     del arg2_1
2023-01-11T21:05:10.3807369Z     buf5 = buf4[0]
2023-01-11T21:05:10.3807474Z     assert_size_stride(buf5, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:05:10.3807526Z     del buf4
2023-01-11T21:05:10.3807683Z     buf6 = aten.convolution_backward(arg3_1, arg4_1, arg5_1, [4], [1], [0], [1], False, [0], 1, [True, True, True])
2023-01-11T21:05:10.3807748Z     del arg3_1
2023-01-11T21:05:10.3807810Z     del arg4_1
2023-01-11T21:05:10.3807873Z     del arg5_1
2023-01-11T21:05:10.3807938Z     buf7 = buf6[0]
2023-01-11T21:05:10.3808041Z     assert_size_stride(buf7, (3, 4, 5, 5), (100, 25, 5, 1))
2023-01-11T21:05:10.3808094Z     buf8 = buf6[1]
2023-01-11T21:05:10.3808193Z     assert_size_stride(buf8, (4, 4, 3, 3), (36, 9, 3, 1))
2023-01-11T21:05:10.3808261Z     buf9 = buf6[2]
2023-01-11T21:05:10.3808353Z     assert_size_stride(buf9, (4, ), (1, ))
2023-01-11T21:05:10.3808416Z     del buf6
2023-01-11T21:05:10.3808584Z     buf10 = aten.convolution_backward(arg6_1, arg7_1, arg8_1, [4], [1, 1, 1], [0, 0, 0], [1, 1, 1], False, [0, 0, 0], 1, [True, True, True])
2023-01-11T21:05:10.3808652Z     del arg6_1
2023-01-11T21:05:10.3808704Z     del arg7_1
2023-01-11T21:05:10.3808770Z     del arg8_1
2023-01-11T21:05:10.3808837Z     buf11 = buf10[0]
2023-01-11T21:05:10.3808949Z     assert_size_stride(buf11, (3, 4, 5, 5, 5), (500, 125, 25, 5, 1))
2023-01-11T21:05:10.3809017Z     buf12 = buf10[1]
2023-01-11T21:05:10.3809124Z     assert_size_stride(buf12, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
2023-01-11T21:05:10.3809190Z     buf13 = buf10[2]
2023-01-11T21:05:10.3809271Z     assert_size_stride(buf13, (4, ), (1, ))
2023-01-11T21:05:10.3809339Z     del buf10
2023-01-11T21:05:10.3809465Z     return (buf1, buf2, buf3, buf5, buf7, buf8, buf9, buf11, buf12, buf13, )
2023-01-11T21:05:10.3809471Z 
2023-01-11T21:05:10.3809475Z 
2023-01-11T21:05:10.3809552Z if __name__ == "__main__":
2023-01-11T21:05:10.3809663Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3809817Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3810159Z     arg0_1 = rand_strided((3, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3810377Z     arg1_1 = rand_strided((3, 4, 5, 5), (100, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3810570Z     arg2_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3810773Z     arg3_1 = rand_strided((3, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3810983Z     arg4_1 = rand_strided((3, 4, 5, 5), (100, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3811184Z     arg5_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3811397Z     arg6_1 = rand_strided((3, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3811618Z     arg7_1 = rand_strided((3, 4, 5, 5, 5), (500, 125, 25, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3811838Z     arg8_1 = rand_strided((4, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3812031Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1]))
2023-01-11T21:05:10.3812037Z 
2023-01-11T21:05:10.3812103Z ok (0.334s)
2023-01-11T21:05:10.3812535Z   test_conv_bn_fuse_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3812661Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3812921Z [2023-01-11 20:46:55,484] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 69
2023-01-11T21:05:10.3813183Z [2023-01-11 20:46:55,527] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 69
2023-01-11T21:05:10.3813584Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3813707Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3813959Z [2023-01-11 20:46:55,726] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 70
2023-01-11T21:05:10.3814219Z [2023-01-11 20:46:55,770] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 70
2023-01-11T21:05:10.3814615Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3814743Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3814993Z [2023-01-11 20:46:55,992] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 71
2023-01-11T21:05:10.3815248Z [2023-01-11 20:46:56,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 71
2023-01-11T21:05:10.3815630Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3815760Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3816039Z [2023-01-11 20:46:56,232] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 72
2023-01-11T21:05:10.3816296Z [2023-01-11 20:46:56,275] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 72
2023-01-11T21:05:10.3816689Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3816808Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3817061Z [2023-01-11 20:46:56,480] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 73
2023-01-11T21:05:10.3817317Z [2023-01-11 20:46:56,522] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 73
2023-01-11T21:05:10.3817740Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3817863Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3818111Z [2023-01-11 20:46:56,722] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 74
2023-01-11T21:05:10.3818117Z 
2023-01-11T21:05:10.3818209Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3818264Z import torch
2023-01-11T21:05:10.3818332Z import random
2023-01-11T21:05:10.3818447Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3818661Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3818667Z 
2023-01-11T21:05:10.3818744Z aten = torch.ops.aten
2023-01-11T21:05:10.3818882Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3818970Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3818975Z 
2023-01-11T21:05:10.3818980Z 
2023-01-11T21:05:10.3819069Z async_compile.wait(globals())
2023-01-11T21:05:10.3819127Z del async_compile
2023-01-11T21:05:10.3819131Z 
2023-01-11T21:05:10.3819201Z def call(args):
2023-01-11T21:05:10.3819322Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3819392Z     args.clear()
2023-01-11T21:05:10.3819524Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:05:10.3819628Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:05:10.3819695Z     del arg0_1
2023-01-11T21:05:10.3819746Z     del arg1_1
2023-01-11T21:05:10.3819811Z     del arg7_1
2023-01-11T21:05:10.3819887Z     return (buf0, )
2023-01-11T21:05:10.3819892Z 
2023-01-11T21:05:10.3819896Z 
2023-01-11T21:05:10.3819969Z if __name__ == "__main__":
2023-01-11T21:05:10.3820084Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3820205Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3820414Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3820592Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3820782Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3820981Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3821265Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3821466Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3821647Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3821854Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3822088Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3822093Z 
2023-01-11T21:05:10.3822098Z 
2023-01-11T21:05:10.3822194Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3822249Z import torch
2023-01-11T21:05:10.3822319Z import random
2023-01-11T21:05:10.3822433Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3822556Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3822561Z 
2023-01-11T21:05:10.3822637Z aten = torch.ops.aten
2023-01-11T21:05:10.3862656Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3862954Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3862963Z 
2023-01-11T21:05:10.3862983Z 
2023-01-11T21:05:10.3863084Z async_compile.wait(globals())
2023-01-11T21:05:10.3863162Z del async_compile
2023-01-11T21:05:10.3863167Z 
2023-01-11T21:05:10.3863240Z def call(args):
2023-01-11T21:05:10.3863382Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3863470Z     args.clear()
2023-01-11T21:05:10.3863620Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:05:10.3863898Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:05:10.3863956Z     del arg0_1
2023-01-11T21:05:10.3864023Z     del arg1_1
2023-01-11T21:05:10.3864090Z     del arg7_1
2023-01-11T21:05:10.3864163Z     return (buf0, )
2023-01-11T21:05:10.3864169Z 
2023-01-11T21:05:10.3864173Z 
2023-01-11T21:05:10.3864252Z if __name__ == "__main__":
2023-01-11T21:05:10.3864369Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3864496Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3864751Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3864955Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3865153Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3865347Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3865540Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3865731Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3865911Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3866122Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3866267Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3866284Z 
2023-01-11T21:05:10.3866289Z 
2023-01-11T21:05:10.3866370Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3866441Z import torch
2023-01-11T21:05:10.3866513Z import random
2023-01-11T21:05:10.3866631Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3866760Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3866766Z 
2023-01-11T21:05:10.3866844Z aten = torch.ops.aten
2023-01-11T21:05:10.3866980Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3867059Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3867064Z 
2023-01-11T21:05:10.3867081Z 
2023-01-11T21:05:10.3867157Z async_compile.wait(globals())
2023-01-11T21:05:10.3867229Z del async_compile
2023-01-11T21:05:10.3867234Z 
2023-01-11T21:05:10.3867303Z def call(args):
2023-01-11T21:05:10.3867422Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3867497Z     args.clear()
2023-01-11T21:05:10.3867628Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:05:10.3867732Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:05:10.3867786Z     del arg0_1
2023-01-11T21:05:10.3867851Z     del arg1_1
2023-01-11T21:05:10.3867973Z     del arg7_1
2023-01-11T21:05:10.3868044Z     return (buf0, )
2023-01-11T21:05:10.3868049Z 
2023-01-11T21:05:10.3868053Z 
2023-01-11T21:05:10.3868128Z if __name__ == "__main__":
2023-01-11T21:05:10.3868243Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3868366Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3868561Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3868752Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3868945Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3869130Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3869320Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3869511Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3869697Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3869903Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3870109Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3870115Z 
2023-01-11T21:05:10.3870119Z 
2023-01-11T21:05:10.3870202Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3870270Z import torch
2023-01-11T21:05:10.3870341Z import random
2023-01-11T21:05:10.3870460Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3870580Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3870585Z 
2023-01-11T21:05:10.3870664Z aten = torch.ops.aten
2023-01-11T21:05:10.3870798Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3870876Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3870898Z 
2023-01-11T21:05:10.3870902Z 
2023-01-11T21:05:10.3870977Z async_compile.wait(globals())
2023-01-11T21:05:10.3871052Z del async_compile
2023-01-11T21:05:10.3871057Z 
2023-01-11T21:05:10.3871128Z def call(args):
2023-01-11T21:05:10.3871250Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3871324Z     args.clear()
2023-01-11T21:05:10.3871456Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:05:10.3871564Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:05:10.3871618Z     del arg0_1
2023-01-11T21:05:10.3871701Z     del arg1_1
2023-01-11T21:05:10.3871765Z     del arg7_1
2023-01-11T21:05:10.3871835Z     return (buf0, )
2023-01-11T21:05:10.3871840Z 
2023-01-11T21:05:10.3871844Z 
2023-01-11T21:05:10.3871920Z if __name__ == "__main__":
2023-01-11T21:05:10.3872051Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3872175Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3872387Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3872573Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3872765Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3872958Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3873149Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3873334Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3873532Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3873745Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3873900Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3873905Z 
2023-01-11T21:05:10.3873909Z 
2023-01-11T21:05:10.3874002Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3874090Z import torch
2023-01-11T21:05:10.3874160Z import random
2023-01-11T21:05:10.3874274Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3874398Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3874403Z 
2023-01-11T21:05:10.3874481Z aten = torch.ops.aten
2023-01-11T21:05:10.3874613Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3874703Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3874708Z 
2023-01-11T21:05:10.3874712Z 
2023-01-11T21:05:10.3874788Z async_compile.wait(globals())
2023-01-11T21:05:10.3874858Z del async_compile
2023-01-11T21:05:10.3874863Z 
2023-01-11T21:05:10.3874931Z def call(args):
2023-01-11T21:05:10.3875049Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3875123Z     args.clear()
2023-01-11T21:05:10.3875255Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:05:10.3875379Z     assert_size_stride(buf0, (1, 32, 110), (3520, 110, 1))
2023-01-11T21:05:10.3875445Z     del arg0_1
2023-01-11T21:05:10.3875500Z     del arg1_1
2023-01-11T21:05:10.3875568Z     del arg7_1
2023-01-11T21:05:10.3875638Z     return (buf0, )
2023-01-11T21:05:10.3875671Z 
2023-01-11T21:05:10.3875675Z 
2023-01-11T21:05:10.3875767Z if __name__ == "__main__":
2023-01-11T21:05:10.3875897Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3876022Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3876236Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3876415Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3876608Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3876794Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3876982Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3877172Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3877351Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3877560Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3877716Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3877721Z 
2023-01-11T21:05:10.3877725Z 
2023-01-11T21:05:10.3877821Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3877879Z import torch
2023-01-11T21:05:10.3877947Z import random
2023-01-11T21:05:10.3878062Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3878181Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3878187Z 
2023-01-11T21:05:10.3878263Z aten = torch.ops.aten
2023-01-11T21:05:10.3878397Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3878491Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3878496Z 
2023-01-11T21:05:10.3878500Z 
2023-01-11T21:05:10.3878592Z async_compile.wait(globals())
2023-01-11T21:05:10.3878651Z del async_compile
2023-01-11T21:05:10.3878658Z 
2023-01-11T21:05:10.3878731Z def call(args):
2023-01-11T21:05:10.3878848Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3878917Z     args.clear()
2023-01-11T21:05:10.3879047Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:05:10.3879155Z     assert_size_stride(buf0, (1, 128, 110), (14080, 110, 1))
2023-01-11T21:05:10.3879221Z     del arg0_1
2023-01-11T21:05:10.3879276Z     del arg1_1
2023-01-11T21:05:10.3879342Z     del arg7_1
2023-01-11T21:05:10.3879416Z     return (buf0, )
2023-01-11T21:05:10.3879421Z 
2023-01-11T21:05:10.3879425Z 
2023-01-11T21:05:10.3879500Z if __name__ == "__main__":
2023-01-11T21:05:10.3879615Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3879765Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3879975Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3880170Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3880350Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3880535Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3880927Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3881117Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3881294Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3881504Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3881659Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3881933Z [2023-01-11 20:46:56,764] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 74
2023-01-11T21:05:10.3882388Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3882506Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3882765Z [2023-01-11 20:46:56,970] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 75
2023-01-11T21:05:10.3883031Z [2023-01-11 20:46:57,012] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 75
2023-01-11T21:05:10.3883436Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3883565Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3883822Z [2023-01-11 20:46:57,212] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 76
2023-01-11T21:05:10.3884083Z [2023-01-11 20:46:57,255] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 76
2023-01-11T21:05:10.3884478Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3884607Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3884861Z [2023-01-11 20:46:57,456] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 77
2023-01-11T21:05:10.3885127Z [2023-01-11 20:46:57,497] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 77
2023-01-11T21:05:10.3885517Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3885643Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3885895Z [2023-01-11 20:46:57,697] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 78
2023-01-11T21:05:10.3886155Z [2023-01-11 20:46:57,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 78
2023-01-11T21:05:10.3886592Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3886718Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3886972Z [2023-01-11 20:46:57,940] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 79
2023-01-11T21:05:10.3887232Z [2023-01-11 20:46:57,981] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 79
2023-01-11T21:05:10.3887626Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3887754Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3888043Z [2023-01-11 20:46:58,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 80
2023-01-11T21:05:10.3888050Z 
2023-01-11T21:05:10.3888055Z 
2023-01-11T21:05:10.3888149Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3888207Z import torch
2023-01-11T21:05:10.3888276Z import random
2023-01-11T21:05:10.3888390Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3888511Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3888517Z 
2023-01-11T21:05:10.3888596Z aten = torch.ops.aten
2023-01-11T21:05:10.3888732Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3888824Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3888829Z 
2023-01-11T21:05:10.3888835Z 
2023-01-11T21:05:10.3888922Z async_compile.wait(globals())
2023-01-11T21:05:10.3888981Z del async_compile
2023-01-11T21:05:10.3888987Z 
2023-01-11T21:05:10.3889060Z def call(args):
2023-01-11T21:05:10.3889183Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3889255Z     args.clear()
2023-01-11T21:05:10.3889387Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:05:10.3889498Z     assert_size_stride(buf0, (1, 32, 108), (3456, 108, 1))
2023-01-11T21:05:10.3889569Z     del arg0_1
2023-01-11T21:05:10.3889623Z     del arg1_1
2023-01-11T21:05:10.3889692Z     del arg7_1
2023-01-11T21:05:10.3889763Z     return (buf0, )
2023-01-11T21:05:10.3889768Z 
2023-01-11T21:05:10.3889772Z 
2023-01-11T21:05:10.3889850Z if __name__ == "__main__":
2023-01-11T21:05:10.3889963Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3890086Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3890295Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3890490Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3890672Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3890860Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3891068Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3891256Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3891437Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3891643Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3891799Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3891804Z 
2023-01-11T21:05:10.3891841Z 
2023-01-11T21:05:10.3891935Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3891992Z import torch
2023-01-11T21:05:10.3892062Z import random
2023-01-11T21:05:10.3892177Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3892301Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3892306Z 
2023-01-11T21:05:10.3892384Z aten = torch.ops.aten
2023-01-11T21:05:10.3892520Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3892610Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3892615Z 
2023-01-11T21:05:10.3892619Z 
2023-01-11T21:05:10.3892706Z async_compile.wait(globals())
2023-01-11T21:05:10.3892765Z del async_compile
2023-01-11T21:05:10.3892769Z 
2023-01-11T21:05:10.3892838Z def call(args):
2023-01-11T21:05:10.3892959Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3893029Z     args.clear()
2023-01-11T21:05:10.3893159Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:05:10.3893268Z     assert_size_stride(buf0, (1, 128, 108), (13824, 108, 1))
2023-01-11T21:05:10.3893336Z     del arg0_1
2023-01-11T21:05:10.3893389Z     del arg1_1
2023-01-11T21:05:10.3893482Z     del arg7_1
2023-01-11T21:05:10.3893555Z     return (buf0, )
2023-01-11T21:05:10.3893559Z 
2023-01-11T21:05:10.3893563Z 
2023-01-11T21:05:10.3893639Z if __name__ == "__main__":
2023-01-11T21:05:10.3893752Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3893872Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3894079Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3894273Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3894470Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3894674Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3894865Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3895081Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3895264Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3895479Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3895634Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3895640Z 
2023-01-11T21:05:10.3895644Z 
2023-01-11T21:05:10.3895741Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3895798Z import torch
2023-01-11T21:05:10.3895869Z import random
2023-01-11T21:05:10.3895983Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3896106Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3896111Z 
2023-01-11T21:05:10.3896191Z aten = torch.ops.aten
2023-01-11T21:05:10.3896328Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3896423Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3896428Z 
2023-01-11T21:05:10.3896431Z 
2023-01-11T21:05:10.3896525Z async_compile.wait(globals())
2023-01-11T21:05:10.3896587Z del async_compile
2023-01-11T21:05:10.3896607Z 
2023-01-11T21:05:10.3896666Z def call(args):
2023-01-11T21:05:10.3896784Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3896860Z     args.clear()
2023-01-11T21:05:10.3897010Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:05:10.3897118Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:05:10.3897188Z     del arg0_1
2023-01-11T21:05:10.3897257Z     del arg1_1
2023-01-11T21:05:10.3897310Z     del arg7_1
2023-01-11T21:05:10.3897383Z     return (buf0, )
2023-01-11T21:05:10.3897388Z 
2023-01-11T21:05:10.3897392Z 
2023-01-11T21:05:10.3897470Z if __name__ == "__main__":
2023-01-11T21:05:10.3897621Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3897747Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3897974Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3898168Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3898346Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3898640Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3898834Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3899026Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3899206Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3899414Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3899574Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3899580Z 
2023-01-11T21:05:10.3899584Z 
2023-01-11T21:05:10.3899750Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3899822Z import torch
2023-01-11T21:05:10.3899880Z import random
2023-01-11T21:05:10.3899996Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3900121Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3900125Z 
2023-01-11T21:05:10.3900209Z aten = torch.ops.aten
2023-01-11T21:05:10.3900345Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3900438Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3900443Z 
2023-01-11T21:05:10.3900447Z 
2023-01-11T21:05:10.3900536Z async_compile.wait(globals())
2023-01-11T21:05:10.3900609Z del async_compile
2023-01-11T21:05:10.3900614Z 
2023-01-11T21:05:10.3900671Z def call(args):
2023-01-11T21:05:10.3900789Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3900864Z     args.clear()
2023-01-11T21:05:10.3900999Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:05:10.3901111Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:05:10.3901181Z     del arg0_1
2023-01-11T21:05:10.3901254Z     del arg1_1
2023-01-11T21:05:10.3901307Z     del arg7_1
2023-01-11T21:05:10.3901379Z     return (buf0, )
2023-01-11T21:05:10.3901384Z 
2023-01-11T21:05:10.3901388Z 
2023-01-11T21:05:10.3901463Z if __name__ == "__main__":
2023-01-11T21:05:10.3901581Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3901707Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3901918Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3902113Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3902305Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3902511Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3902706Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3902937Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3903131Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3910328Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3910540Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3910547Z 
2023-01-11T21:05:10.3910551Z 
2023-01-11T21:05:10.3910650Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3910722Z import torch
2023-01-11T21:05:10.3910778Z import random
2023-01-11T21:05:10.3910896Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3911069Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3911074Z 
2023-01-11T21:05:10.3911150Z aten = torch.ops.aten
2023-01-11T21:05:10.3911288Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3911378Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3911383Z 
2023-01-11T21:05:10.3911387Z 
2023-01-11T21:05:10.3911473Z async_compile.wait(globals())
2023-01-11T21:05:10.3911544Z del async_compile
2023-01-11T21:05:10.3911550Z 
2023-01-11T21:05:10.3911605Z def call(args):
2023-01-11T21:05:10.3911723Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3911793Z     args.clear()
2023-01-11T21:05:10.3911924Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:05:10.3912031Z     assert_size_stride(buf0, (1, 32, 112), (3584, 112, 1))
2023-01-11T21:05:10.3912098Z     del arg0_1
2023-01-11T21:05:10.3912163Z     del arg1_1
2023-01-11T21:05:10.3912218Z     del arg7_1
2023-01-11T21:05:10.3912289Z     return (buf0, )
2023-01-11T21:05:10.3912294Z 
2023-01-11T21:05:10.3912298Z 
2023-01-11T21:05:10.3912372Z if __name__ == "__main__":
2023-01-11T21:05:10.3912522Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3912648Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3912869Z     arg0_1 = rand_strided((32, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3913059Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3913249Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3913420Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3913606Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3913794Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3913970Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3914177Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3914332Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3914338Z 
2023-01-11T21:05:10.3914342Z 
2023-01-11T21:05:10.3914434Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3914506Z import torch
2023-01-11T21:05:10.3914561Z import random
2023-01-11T21:05:10.3914675Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3914793Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3914798Z 
2023-01-11T21:05:10.3914875Z aten = torch.ops.aten
2023-01-11T21:05:10.3915005Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3915093Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3915098Z 
2023-01-11T21:05:10.3915102Z 
2023-01-11T21:05:10.3915187Z async_compile.wait(globals())
2023-01-11T21:05:10.3915259Z del async_compile
2023-01-11T21:05:10.3915264Z 
2023-01-11T21:05:10.3915322Z def call(args):
2023-01-11T21:05:10.3915440Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3915515Z     args.clear()
2023-01-11T21:05:10.3915649Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:05:10.3915756Z     assert_size_stride(buf0, (1, 128, 112), (14336, 112, 1))
2023-01-11T21:05:10.3915823Z     del arg0_1
2023-01-11T21:05:10.3915889Z     del arg1_1
2023-01-11T21:05:10.3915942Z     del arg7_1
2023-01-11T21:05:10.3916011Z     return (buf0, )
2023-01-11T21:05:10.3916016Z 
2023-01-11T21:05:10.3916020Z 
2023-01-11T21:05:10.3916092Z if __name__ == "__main__":
2023-01-11T21:05:10.3916206Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3916325Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3916527Z     arg0_1 = rand_strided((128, 3, 1), (3, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3916762Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3916954Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3917133Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3917322Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3917508Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3917685Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3917891Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3918043Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3918309Z [2023-01-11 20:46:58,215] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 80
2023-01-11T21:05:10.3918758Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3918885Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3919140Z [2023-01-11 20:46:58,419] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 81
2023-01-11T21:05:10.3919388Z [2023-01-11 20:46:58,460] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 81
2023-01-11T21:05:10.3919785Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3919913Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3920167Z [2023-01-11 20:46:58,655] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 82
2023-01-11T21:05:10.3920433Z [2023-01-11 20:46:58,696] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 82
2023-01-11T21:05:10.3921007Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3921133Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3921387Z [2023-01-11 20:46:58,894] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 83
2023-01-11T21:05:10.3921649Z [2023-01-11 20:46:58,934] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 83
2023-01-11T21:05:10.3922046Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3922169Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3922407Z [2023-01-11 20:46:59,132] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 84
2023-01-11T21:05:10.3922664Z [2023-01-11 20:46:59,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 84
2023-01-11T21:05:10.3923103Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3923368Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3923758Z [2023-01-11 20:46:59,391] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 85
2023-01-11T21:05:10.3924214Z [2023-01-11 20:46:59,435] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 85
2023-01-11T21:05:10.3924888Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3925099Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3925556Z [2023-01-11 20:46:59,754] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 86
2023-01-11T21:05:10.3925565Z 
2023-01-11T21:05:10.3925648Z 
2023-01-11T21:05:10.3925816Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3925925Z import torch
2023-01-11T21:05:10.3926040Z import random
2023-01-11T21:05:10.3926222Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3926416Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3926425Z 
2023-01-11T21:05:10.3926551Z aten = torch.ops.aten
2023-01-11T21:05:10.3926782Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3926926Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3926934Z 
2023-01-11T21:05:10.3926941Z 
2023-01-11T21:05:10.3927092Z async_compile.wait(globals())
2023-01-11T21:05:10.3927209Z del async_compile
2023-01-11T21:05:10.3927217Z 
2023-01-11T21:05:10.3927336Z def call(args):
2023-01-11T21:05:10.3927532Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3927640Z     args.clear()
2023-01-11T21:05:10.3927879Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 1)
2023-01-11T21:05:10.3928058Z     assert_size_stride(buf0, (1, 32, 110), (3520, 110, 1))
2023-01-11T21:05:10.3928151Z     del arg0_1
2023-01-11T21:05:10.3928253Z     del arg1_1
2023-01-11T21:05:10.3928357Z     del arg7_1
2023-01-11T21:05:10.3928474Z     return (buf0, )
2023-01-11T21:05:10.3928483Z 
2023-01-11T21:05:10.3928490Z 
2023-01-11T21:05:10.3928614Z if __name__ == "__main__":
2023-01-11T21:05:10.3928795Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3929012Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3929378Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3929712Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3930056Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3930383Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3930731Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3931056Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3931383Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3931736Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3932004Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3932013Z 
2023-01-11T21:05:10.3932028Z 
2023-01-11T21:05:10.3932182Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3932297Z import torch
2023-01-11T21:05:10.3932404Z import random
2023-01-11T21:05:10.3932684Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3932894Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3932902Z 
2023-01-11T21:05:10.3933033Z aten = torch.ops.aten
2023-01-11T21:05:10.3933262Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3933405Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3933414Z 
2023-01-11T21:05:10.3933422Z 
2023-01-11T21:05:10.3933579Z async_compile.wait(globals())
2023-01-11T21:05:10.3933698Z del async_compile
2023-01-11T21:05:10.3933707Z 
2023-01-11T21:05:10.3954550Z def call(args):
2023-01-11T21:05:10.3954822Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3954943Z     args.clear()
2023-01-11T21:05:10.3955176Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (1,), False, (0,), 4)
2023-01-11T21:05:10.3955352Z     assert_size_stride(buf0, (1, 128, 110), (14080, 110, 1))
2023-01-11T21:05:10.3955459Z     del arg0_1
2023-01-11T21:05:10.3955578Z     del arg1_1
2023-01-11T21:05:10.3955677Z     del arg7_1
2023-01-11T21:05:10.3955791Z     return (buf0, )
2023-01-11T21:05:10.3955802Z 
2023-01-11T21:05:10.3955810Z 
2023-01-11T21:05:10.3955934Z if __name__ == "__main__":
2023-01-11T21:05:10.3956243Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3956452Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3956874Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3957231Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3957569Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3957916Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3958245Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3958577Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3958900Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3959271Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3959529Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3959539Z 
2023-01-11T21:05:10.3959557Z 
2023-01-11T21:05:10.3959708Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3959831Z import torch
2023-01-11T21:05:10.3959947Z import random
2023-01-11T21:05:10.3960146Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3960343Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3960352Z 
2023-01-11T21:05:10.3960478Z aten = torch.ops.aten
2023-01-11T21:05:10.3960932Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3961094Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3961103Z 
2023-01-11T21:05:10.3961115Z 
2023-01-11T21:05:10.3961258Z async_compile.wait(globals())
2023-01-11T21:05:10.3961376Z del async_compile
2023-01-11T21:05:10.3961384Z 
2023-01-11T21:05:10.3961486Z def call(args):
2023-01-11T21:05:10.3961694Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3961814Z     args.clear()
2023-01-11T21:05:10.3962039Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 1)
2023-01-11T21:05:10.3962205Z     assert_size_stride(buf0, (1, 32, 108), (3456, 108, 1))
2023-01-11T21:05:10.3962309Z     del arg0_1
2023-01-11T21:05:10.3962430Z     del arg1_1
2023-01-11T21:05:10.3962538Z     del arg7_1
2023-01-11T21:05:10.3962650Z     return (buf0, )
2023-01-11T21:05:10.3962658Z 
2023-01-11T21:05:10.3962665Z 
2023-01-11T21:05:10.3962786Z if __name__ == "__main__":
2023-01-11T21:05:10.3962969Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3963178Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3963686Z     arg0_1 = rand_strided((32, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3964022Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3964374Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3964707Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3965036Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3965382Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3965698Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3966067Z     arg7_1 = rand_strided((1, 3, 112), (336, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3966341Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3966350Z 
2023-01-11T21:05:10.3966361Z 
2023-01-11T21:05:10.3966513Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3966625Z import torch
2023-01-11T21:05:10.3966747Z import random
2023-01-11T21:05:10.3966937Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3967223Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3967233Z 
2023-01-11T21:05:10.3967361Z aten = torch.ops.aten
2023-01-11T21:05:10.3967597Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3967753Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3967761Z 
2023-01-11T21:05:10.3967768Z 
2023-01-11T21:05:10.3967906Z async_compile.wait(globals())
2023-01-11T21:05:10.3968015Z del async_compile
2023-01-11T21:05:10.3968021Z 
2023-01-11T21:05:10.3968121Z def call(args):
2023-01-11T21:05:10.3968308Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3968434Z     args.clear()
2023-01-11T21:05:10.3968657Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1,), (0,), (2,), False, (0,), 4)
2023-01-11T21:05:10.3968838Z     assert_size_stride(buf0, (1, 128, 108), (13824, 108, 1))
2023-01-11T21:05:10.3968943Z     del arg0_1
2023-01-11T21:05:10.3969051Z     del arg1_1
2023-01-11T21:05:10.3969168Z     del arg7_1
2023-01-11T21:05:10.3969275Z     return (buf0, )
2023-01-11T21:05:10.3969283Z 
2023-01-11T21:05:10.3969300Z 
2023-01-11T21:05:10.3969416Z if __name__ == "__main__":
2023-01-11T21:05:10.3969609Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3969817Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3970201Z     arg0_1 = rand_strided((128, 3, 3), (9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3970534Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3970884Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3971209Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3971550Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3971890Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3972184Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3972475Z     arg7_1 = rand_strided((1, 12, 112), (1344, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3972701Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3972708Z 
2023-01-11T21:05:10.3972716Z 
2023-01-11T21:05:10.3972848Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3972968Z import torch
2023-01-11T21:05:10.3973090Z import random
2023-01-11T21:05:10.3973272Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3973490Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3973499Z 
2023-01-11T21:05:10.3973623Z aten = torch.ops.aten
2023-01-11T21:05:10.3973914Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3974053Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3974060Z 
2023-01-11T21:05:10.3974066Z 
2023-01-11T21:05:10.3974199Z async_compile.wait(globals())
2023-01-11T21:05:10.3974330Z del async_compile
2023-01-11T21:05:10.3974338Z 
2023-01-11T21:05:10.3974459Z def call(args):
2023-01-11T21:05:10.3974667Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3974772Z     args.clear()
2023-01-11T21:05:10.3975003Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.3975188Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 12544, 112, 1))
2023-01-11T21:05:10.3975302Z     del arg0_1
2023-01-11T21:05:10.3975415Z     del arg1_1
2023-01-11T21:05:10.3975527Z     del arg7_1
2023-01-11T21:05:10.3975642Z     return (buf0, )
2023-01-11T21:05:10.3975652Z 
2023-01-11T21:05:10.3975660Z 
2023-01-11T21:05:10.3975769Z if __name__ == "__main__":
2023-01-11T21:05:10.3975960Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3976174Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3976630Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3976959Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3977291Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3977639Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3977979Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3978299Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3978725Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3979116Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3979399Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3979409Z 
2023-01-11T21:05:10.3979417Z 
2023-01-11T21:05:10.3979582Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3979699Z import torch
2023-01-11T21:05:10.3979811Z import random
2023-01-11T21:05:10.3980018Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3980221Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3980230Z 
2023-01-11T21:05:10.3980358Z aten = torch.ops.aten
2023-01-11T21:05:10.3980587Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3980747Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3980755Z 
2023-01-11T21:05:10.3980763Z 
2023-01-11T21:05:10.3980919Z async_compile.wait(globals())
2023-01-11T21:05:10.3981040Z del async_compile
2023-01-11T21:05:10.3981047Z 
2023-01-11T21:05:10.3981160Z def call(args):
2023-01-11T21:05:10.3981368Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3981476Z     args.clear()
2023-01-11T21:05:10.3981713Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.3981893Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:05:10.3982008Z     del arg0_1
2023-01-11T21:05:10.3982119Z     del arg1_1
2023-01-11T21:05:10.3982229Z     del arg7_1
2023-01-11T21:05:10.3982348Z     return (buf0, )
2023-01-11T21:05:10.3982357Z 
2023-01-11T21:05:10.3982364Z 
2023-01-11T21:05:10.3982489Z if __name__ == "__main__":
2023-01-11T21:05:10.3982665Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.3982878Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.3983261Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3983607Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3984007Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3984356Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3984689Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3985014Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3985347Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.3985713Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.3985987Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.3986465Z [2023-01-11 20:46:59,797] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 86
2023-01-11T21:05:10.3987275Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3987502Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3987980Z [2023-01-11 20:47:00,135] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 87
2023-01-11T21:05:10.3988464Z [2023-01-11 20:47:00,177] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 87
2023-01-11T21:05:10.3989188Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3989409Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3989853Z [2023-01-11 20:47:00,818] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 88
2023-01-11T21:05:10.3990350Z [2023-01-11 20:47:00,860] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 88
2023-01-11T21:05:10.3991060Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3991280Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3991734Z [2023-01-11 20:47:01,467] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 89
2023-01-11T21:05:10.3992216Z [2023-01-11 20:47:01,508] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 89
2023-01-11T21:05:10.3992958Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3993183Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3993637Z [2023-01-11 20:47:01,835] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 90
2023-01-11T21:05:10.3994119Z [2023-01-11 20:47:01,877] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 90
2023-01-11T21:05:10.3994855Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3995131Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3995605Z [2023-01-11 20:47:02,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 91
2023-01-11T21:05:10.3996093Z [2023-01-11 20:47:02,261] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 91
2023-01-11T21:05:10.3996824Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.3997049Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.3997505Z [2023-01-11 20:47:02,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 92
2023-01-11T21:05:10.3997519Z 
2023-01-11T21:05:10.3997528Z 
2023-01-11T21:05:10.3997702Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.3997879Z import torch
2023-01-11T21:05:10.3998007Z import random
2023-01-11T21:05:10.3998212Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.3998422Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.3998431Z 
2023-01-11T21:05:10.3998565Z aten = torch.ops.aten
2023-01-11T21:05:10.3998793Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.3998962Z async_compile = AsyncCompile()
2023-01-11T21:05:10.3998971Z 
2023-01-11T21:05:10.3998978Z 
2023-01-11T21:05:10.3999134Z async_compile.wait(globals())
2023-01-11T21:05:10.3999254Z del async_compile
2023-01-11T21:05:10.3999263Z 
2023-01-11T21:05:10.3999385Z def call(args):
2023-01-11T21:05:10.3999584Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.3999712Z     args.clear()
2023-01-11T21:05:10.3999936Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4000136Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 12544, 112, 1))
2023-01-11T21:05:10.4000253Z     del arg0_1
2023-01-11T21:05:10.4000361Z     del arg1_1
2023-01-11T21:05:10.4000475Z     del arg7_1
2023-01-11T21:05:10.4000738Z     return (buf0, )
2023-01-11T21:05:10.4000746Z 
2023-01-11T21:05:10.4000753Z 
2023-01-11T21:05:10.4000873Z if __name__ == "__main__":
2023-01-11T21:05:10.4001073Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4001267Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4001666Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4002021Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4002354Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4002706Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4003046Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4003388Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4003714Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4004085Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4004372Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4004382Z 
2023-01-11T21:05:10.4004390Z 
2023-01-11T21:05:10.4004557Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4004677Z import torch
2023-01-11T21:05:10.4004793Z import random
2023-01-11T21:05:10.4004989Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4005309Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4005319Z 
2023-01-11T21:05:10.4005452Z aten = torch.ops.aten
2023-01-11T21:05:10.4005668Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4005823Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4005831Z 
2023-01-11T21:05:10.4005838Z 
2023-01-11T21:05:10.4005994Z async_compile.wait(globals())
2023-01-11T21:05:10.4006121Z del async_compile
2023-01-11T21:05:10.4006129Z 
2023-01-11T21:05:10.4006249Z def call(args):
2023-01-11T21:05:10.4006446Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4006566Z     args.clear()
2023-01-11T21:05:10.4006806Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4006986Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:05:10.4007101Z     del arg0_1
2023-01-11T21:05:10.4007210Z     del arg1_1
2023-01-11T21:05:10.4007319Z     del arg7_1
2023-01-11T21:05:10.4007439Z     return (buf0, )
2023-01-11T21:05:10.4007448Z 
2023-01-11T21:05:10.4007456Z 
2023-01-11T21:05:10.4007581Z if __name__ == "__main__":
2023-01-11T21:05:10.4007871Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4008088Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4008454Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4008814Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4009162Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4009497Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4009841Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4010183Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4010502Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4010902Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4011171Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4011181Z 
2023-01-11T21:05:10.4011202Z 
2023-01-11T21:05:10.4011354Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4011465Z import torch
2023-01-11T21:05:10.4011580Z import random
2023-01-11T21:05:10.4011776Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4011980Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4011989Z 
2023-01-11T21:05:10.4012124Z aten = torch.ops.aten
2023-01-11T21:05:10.4012365Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4012504Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4012512Z 
2023-01-11T21:05:10.4012534Z 
2023-01-11T21:05:10.4012672Z async_compile.wait(globals())
2023-01-11T21:05:10.4012801Z del async_compile
2023-01-11T21:05:10.4012810Z 
2023-01-11T21:05:10.4012923Z def call(args):
2023-01-11T21:05:10.4013133Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4013254Z     args.clear()
2023-01-11T21:05:10.4013493Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4013682Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 12544, 112, 1))
2023-01-11T21:05:10.4013780Z     del arg0_1
2023-01-11T21:05:10.4013889Z     del arg1_1
2023-01-11T21:05:10.4014002Z     del arg7_1
2023-01-11T21:05:10.4014123Z     return (buf0, )
2023-01-11T21:05:10.4014132Z 
2023-01-11T21:05:10.4014139Z 
2023-01-11T21:05:10.4014268Z if __name__ == "__main__":
2023-01-11T21:05:10.4014470Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4014682Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4015126Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4015483Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4015821Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4016167Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4016512Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4016842Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4017171Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4017570Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4017835Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4017845Z 
2023-01-11T21:05:10.4017857Z 
2023-01-11T21:05:10.4018008Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4018125Z import torch
2023-01-11T21:05:10.4018243Z import random
2023-01-11T21:05:10.4018596Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4018824Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4018833Z 
2023-01-11T21:05:10.4018967Z aten = torch.ops.aten
2023-01-11T21:05:10.4019202Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4019357Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4019366Z 
2023-01-11T21:05:10.4019371Z 
2023-01-11T21:05:10.4019510Z async_compile.wait(globals())
2023-01-11T21:05:10.4019634Z del async_compile
2023-01-11T21:05:10.4019643Z 
2023-01-11T21:05:10.4019764Z def call(args):
2023-01-11T21:05:10.4019963Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4020083Z     args.clear()
2023-01-11T21:05:10.4020320Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4020517Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:05:10.4020624Z     del arg0_1
2023-01-11T21:05:10.4020717Z     del arg1_1
2023-01-11T21:05:10.4020836Z     del arg7_1
2023-01-11T21:05:10.4020961Z     return (buf0, )
2023-01-11T21:05:10.4020969Z 
2023-01-11T21:05:10.4020977Z 
2023-01-11T21:05:10.4021105Z if __name__ == "__main__":
2023-01-11T21:05:10.4021296Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4021510Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4021899Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4022229Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4022573Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4022915Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4023270Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4023608Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4023944Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4024332Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4024609Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4024618Z 
2023-01-11T21:05:10.4024626Z 
2023-01-11T21:05:10.4024793Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4024897Z import torch
2023-01-11T21:05:10.4025021Z import random
2023-01-11T21:05:10.4025220Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4025437Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4025446Z 
2023-01-11T21:05:10.4025652Z aten = torch.ops.aten
2023-01-11T21:05:10.4025877Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4026034Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4026043Z 
2023-01-11T21:05:10.4026050Z 
2023-01-11T21:05:10.4026207Z async_compile.wait(globals())
2023-01-11T21:05:10.4026321Z del async_compile
2023-01-11T21:05:10.4026330Z 
2023-01-11T21:05:10.4026448Z def call(args):
2023-01-11T21:05:10.4026647Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4026766Z     args.clear()
2023-01-11T21:05:10.4026998Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4027191Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 12544, 112, 1))
2023-01-11T21:05:10.4027311Z     del arg0_1
2023-01-11T21:05:10.4027408Z     del arg1_1
2023-01-11T21:05:10.4027511Z     del arg7_1
2023-01-11T21:05:10.4027627Z     return (buf0, )
2023-01-11T21:05:10.4027635Z 
2023-01-11T21:05:10.4027646Z 
2023-01-11T21:05:10.4027785Z if __name__ == "__main__":
2023-01-11T21:05:10.4027986Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4028205Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4028638Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4029001Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4029335Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4029678Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4030029Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4030374Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4030692Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4031102Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4031372Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4031382Z 
2023-01-11T21:05:10.4031394Z 
2023-01-11T21:05:10.4031564Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4031682Z import torch
2023-01-11T21:05:10.4031791Z import random
2023-01-11T21:05:10.4031996Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4032199Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4032208Z 
2023-01-11T21:05:10.4032337Z aten = torch.ops.aten
2023-01-11T21:05:10.4032575Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4032736Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4032745Z 
2023-01-11T21:05:10.4032753Z 
2023-01-11T21:05:10.4032907Z async_compile.wait(globals())
2023-01-11T21:05:10.4033011Z del async_compile
2023-01-11T21:05:10.4033035Z 
2023-01-11T21:05:10.4033141Z def call(args):
2023-01-11T21:05:10.4033324Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4033450Z     args.clear()
2023-01-11T21:05:10.4033695Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4033883Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:05:10.4033995Z     del arg0_1
2023-01-11T21:05:10.4034108Z     del arg1_1
2023-01-11T21:05:10.4034209Z     del arg7_1
2023-01-11T21:05:10.4034327Z     return (buf0, )
2023-01-11T21:05:10.4034336Z 
2023-01-11T21:05:10.4034343Z 
2023-01-11T21:05:10.4034475Z if __name__ == "__main__":
2023-01-11T21:05:10.4034668Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4034878Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4035270Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4035679Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4036028Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4036370Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4036703Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4037047Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4037372Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4037764Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4038046Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4038522Z [2023-01-11 20:47:03,008] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 92
2023-01-11T21:05:10.4039321Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4039550Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4040020Z [2023-01-11 20:47:03,658] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 93
2023-01-11T21:05:10.4040480Z [2023-01-11 20:47:03,700] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 93
2023-01-11T21:05:10.4041350Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4041579Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4042070Z [2023-01-11 20:47:04,039] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 94
2023-01-11T21:05:10.4042540Z [2023-01-11 20:47:06,805] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 94
2023-01-11T21:05:10.4043273Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4043497Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4043959Z [2023-01-11 20:47:07,222] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 95
2023-01-11T21:05:10.4044447Z [2023-01-11 20:47:07,265] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 95
2023-01-11T21:05:10.4045179Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4045401Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4045849Z [2023-01-11 20:47:07,942] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 96
2023-01-11T21:05:10.4045860Z 
2023-01-11T21:05:10.4045886Z 
2023-01-11T21:05:10.4046036Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4046158Z import torch
2023-01-11T21:05:10.4046276Z import random
2023-01-11T21:05:10.4046478Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4046800Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4046809Z 
2023-01-11T21:05:10.4046946Z aten = torch.ops.aten
2023-01-11T21:05:10.4047191Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4047336Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4047344Z 
2023-01-11T21:05:10.4047365Z 
2023-01-11T21:05:10.4047498Z async_compile.wait(globals())
2023-01-11T21:05:10.4047622Z del async_compile
2023-01-11T21:05:10.4047630Z 
2023-01-11T21:05:10.4047751Z def call(args):
2023-01-11T21:05:10.4047960Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4048081Z     args.clear()
2023-01-11T21:05:10.4048304Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4048491Z     assert_size_stride(buf0, (1, 32, 110, 110), (387200, 12100, 110, 1))
2023-01-11T21:05:10.4048593Z     del arg0_1
2023-01-11T21:05:10.4048706Z     del arg1_1
2023-01-11T21:05:10.4048821Z     del arg7_1
2023-01-11T21:05:10.4048944Z     return (buf0, )
2023-01-11T21:05:10.4048953Z 
2023-01-11T21:05:10.4048961Z 
2023-01-11T21:05:10.4049092Z if __name__ == "__main__":
2023-01-11T21:05:10.4049364Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4049579Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4049963Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4050294Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4050638Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4050990Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4051324Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4051664Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4051995Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4052397Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4052678Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4052687Z 
2023-01-11T21:05:10.4052695Z 
2023-01-11T21:05:10.4052839Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4052959Z import torch
2023-01-11T21:05:10.4053078Z import random
2023-01-11T21:05:10.4053278Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4053493Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4053501Z 
2023-01-11T21:05:10.4053629Z aten = torch.ops.aten
2023-01-11T21:05:10.4053852Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4053998Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4054020Z 
2023-01-11T21:05:10.4054027Z 
2023-01-11T21:05:10.4054265Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4054622Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4054833Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4055009Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4055112Z {
2023-01-11T21:05:10.4055277Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4055376Z     {
2023-01-11T21:05:10.4055492Z         #pragma omp for 
2023-01-11T21:05:10.4055633Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.4055743Z         {
2023-01-11T21:05:10.4055879Z             #pragma GCC ivdep
2023-01-11T21:05:10.4056015Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4056122Z             {
2023-01-11T21:05:10.4056264Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4056400Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4056508Z                 {
2023-01-11T21:05:10.4056688Z                     {
2023-01-11T21:05:10.4056795Z                         {
2023-01-11T21:05:10.4056984Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4057166Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4057281Z                         }
2023-01-11T21:05:10.4057379Z                     }
2023-01-11T21:05:10.4057486Z                 }
2023-01-11T21:05:10.4057590Z             }
2023-01-11T21:05:10.4057689Z         }
2023-01-11T21:05:10.4057787Z     }
2023-01-11T21:05:10.4057888Z }
2023-01-11T21:05:10.4058023Z ''')
2023-01-11T21:05:10.4058050Z 
2023-01-11T21:05:10.4058057Z 
2023-01-11T21:05:10.4058203Z async_compile.wait(globals())
2023-01-11T21:05:10.4058328Z del async_compile
2023-01-11T21:05:10.4058337Z 
2023-01-11T21:05:10.4058455Z def call(args):
2023-01-11T21:05:10.4058743Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4058857Z     args.clear()
2023-01-11T21:05:10.4059255Z     buf0 = empty_strided((32, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4059489Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4059585Z     del arg0_1
2023-01-11T21:05:10.4059872Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4060073Z     assert_size_stride(buf1, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:05:10.4060186Z     del arg1_1
2023-01-11T21:05:10.4060298Z     del arg7_1
2023-01-11T21:05:10.4060417Z     return (buf1, )
2023-01-11T21:05:10.4060427Z 
2023-01-11T21:05:10.4060434Z 
2023-01-11T21:05:10.4060562Z if __name__ == "__main__":
2023-01-11T21:05:10.4060755Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4060966Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4061355Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4061693Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4062047Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4062387Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4062730Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4063073Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4063391Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4063781Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4064054Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4064063Z 
2023-01-11T21:05:10.4064069Z 
2023-01-11T21:05:10.4064230Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4064350Z import torch
2023-01-11T21:05:10.4064470Z import random
2023-01-11T21:05:10.4064673Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4064888Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4064896Z 
2023-01-11T21:05:10.4065033Z aten = torch.ops.aten
2023-01-11T21:05:10.4065243Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4065402Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4065411Z 
2023-01-11T21:05:10.4065419Z 
2023-01-11T21:05:10.4065572Z async_compile.wait(globals())
2023-01-11T21:05:10.4065699Z del async_compile
2023-01-11T21:05:10.4065707Z 
2023-01-11T21:05:10.4065827Z def call(args):
2023-01-11T21:05:10.4066028Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4066146Z     args.clear()
2023-01-11T21:05:10.4066380Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4066561Z     assert_size_stride(buf0, (1, 128, 110, 110), (1548800, 12100, 110, 1))
2023-01-11T21:05:10.4066747Z     del arg0_1
2023-01-11T21:05:10.4066855Z     del arg1_1
2023-01-11T21:05:10.4066970Z     del arg7_1
2023-01-11T21:05:10.4067089Z     return (buf0, )
2023-01-11T21:05:10.4067097Z 
2023-01-11T21:05:10.4067109Z 
2023-01-11T21:05:10.4067239Z if __name__ == "__main__":
2023-01-11T21:05:10.4067441Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4067636Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4068027Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4068380Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4068721Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4069073Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4069427Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4069777Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4070088Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4070538Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4070817Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4070825Z 
2023-01-11T21:05:10.4070832Z 
2023-01-11T21:05:10.4070984Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4071107Z import torch
2023-01-11T21:05:10.4071226Z import random
2023-01-11T21:05:10.4071433Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4071649Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4071658Z 
2023-01-11T21:05:10.4071788Z aten = torch.ops.aten
2023-01-11T21:05:10.4072011Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4072164Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4072173Z 
2023-01-11T21:05:10.4072180Z 
2023-01-11T21:05:10.4072427Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4072793Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4073003Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4073177Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4073280Z {
2023-01-11T21:05:10.4073449Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4073535Z     {
2023-01-11T21:05:10.4073667Z         #pragma omp for 
2023-01-11T21:05:10.4073810Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.4073920Z         {
2023-01-11T21:05:10.4074056Z             #pragma GCC ivdep
2023-01-11T21:05:10.4074200Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4074303Z             {
2023-01-11T21:05:10.4074424Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4074579Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4074688Z                 {
2023-01-11T21:05:10.4074802Z                     {
2023-01-11T21:05:10.4074918Z                         {
2023-01-11T21:05:10.4075105Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4075275Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4075378Z                         }
2023-01-11T21:05:10.4075491Z                     }
2023-01-11T21:05:10.4075603Z                 }
2023-01-11T21:05:10.4075710Z             }
2023-01-11T21:05:10.4075817Z         }
2023-01-11T21:05:10.4075920Z     }
2023-01-11T21:05:10.4076019Z }
2023-01-11T21:05:10.4076144Z ''')
2023-01-11T21:05:10.4076154Z 
2023-01-11T21:05:10.4076160Z 
2023-01-11T21:05:10.4076314Z async_compile.wait(globals())
2023-01-11T21:05:10.4076439Z del async_compile
2023-01-11T21:05:10.4076447Z 
2023-01-11T21:05:10.4076569Z def call(args):
2023-01-11T21:05:10.4076780Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4077014Z     args.clear()
2023-01-11T21:05:10.4077394Z     buf0 = empty_strided((128, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4077621Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4077737Z     del arg0_1
2023-01-11T21:05:10.4077960Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4078152Z     assert_size_stride(buf1, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:05:10.4078268Z     del arg1_1
2023-01-11T21:05:10.4078381Z     del arg7_1
2023-01-11T21:05:10.4078502Z     return (buf1, )
2023-01-11T21:05:10.4078511Z 
2023-01-11T21:05:10.4078518Z 
2023-01-11T21:05:10.4078643Z if __name__ == "__main__":
2023-01-11T21:05:10.4078827Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4079050Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4079439Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4079763Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4080190Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4080533Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4081059Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4081405Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4081705Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4082111Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4082380Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4082879Z [2023-01-11 20:47:10,749] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 96
2023-01-11T21:05:10.4083630Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4083846Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4084261Z [2023-01-11 20:47:11,446] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 97
2023-01-11T21:05:10.4084681Z [2023-01-11 20:47:11,489] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 97
2023-01-11T21:05:10.4085443Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4085673Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4086145Z [2023-01-11 20:47:11,810] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 98
2023-01-11T21:05:10.4086617Z [2023-01-11 20:47:11,863] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 98
2023-01-11T21:05:10.4087366Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4087586Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4088175Z [2023-01-11 20:47:12,253] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 99
2023-01-11T21:05:10.4088662Z [2023-01-11 20:47:12,297] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 99
2023-01-11T21:05:10.4089414Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4089636Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4090119Z [2023-01-11 20:47:12,976] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 100
2023-01-11T21:05:10.4090129Z 
2023-01-11T21:05:10.4090136Z 
2023-01-11T21:05:10.4090306Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4090428Z import torch
2023-01-11T21:05:10.4090535Z import random
2023-01-11T21:05:10.4090744Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4090964Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4091035Z 
2023-01-11T21:05:10.4091175Z aten = torch.ops.aten
2023-01-11T21:05:10.4091415Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4091576Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4091583Z 
2023-01-11T21:05:10.4091593Z 
2023-01-11T21:05:10.4091753Z async_compile.wait(globals())
2023-01-11T21:05:10.4091876Z del async_compile
2023-01-11T21:05:10.4091885Z 
2023-01-11T21:05:10.4091995Z def call(args):
2023-01-11T21:05:10.4092207Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4092331Z     args.clear()
2023-01-11T21:05:10.4092574Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4092775Z     assert_size_stride(buf0, (1, 32, 108, 108), (373248, 11664, 108, 1))
2023-01-11T21:05:10.4092895Z     del arg0_1
2023-01-11T21:05:10.4093013Z     del arg1_1
2023-01-11T21:05:10.4093114Z     del arg7_1
2023-01-11T21:05:10.4093236Z     return (buf0, )
2023-01-11T21:05:10.4093249Z 
2023-01-11T21:05:10.4093257Z 
2023-01-11T21:05:10.4093384Z if __name__ == "__main__":
2023-01-11T21:05:10.4093590Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4093809Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4094199Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4094560Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4094909Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4095238Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4095582Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4095930Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4096263Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4096670Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4096949Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4096959Z 
2023-01-11T21:05:10.4096966Z 
2023-01-11T21:05:10.4097132Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4097255Z import torch
2023-01-11T21:05:10.4097380Z import random
2023-01-11T21:05:10.4097572Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4097787Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4097796Z 
2023-01-11T21:05:10.4097932Z aten = torch.ops.aten
2023-01-11T21:05:10.4098171Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4098373Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4098381Z 
2023-01-11T21:05:10.4098387Z 
2023-01-11T21:05:10.4098735Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4099116Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4099326Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4099488Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4099589Z {
2023-01-11T21:05:10.4099764Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4099871Z     {
2023-01-11T21:05:10.4100005Z         #pragma omp for 
2023-01-11T21:05:10.4100149Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.4100257Z         {
2023-01-11T21:05:10.4100382Z             #pragma GCC ivdep
2023-01-11T21:05:10.4100525Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4100633Z             {
2023-01-11T21:05:10.4100773Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4100930Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4101043Z                 {
2023-01-11T21:05:10.4101143Z                     {
2023-01-11T21:05:10.4101257Z                         {
2023-01-11T21:05:10.4101492Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4101670Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4101787Z                         }
2023-01-11T21:05:10.4101903Z                     }
2023-01-11T21:05:10.4102009Z                 }
2023-01-11T21:05:10.4102101Z             }
2023-01-11T21:05:10.4102208Z         }
2023-01-11T21:05:10.4102313Z     }
2023-01-11T21:05:10.4102417Z }
2023-01-11T21:05:10.4102555Z ''')
2023-01-11T21:05:10.4102564Z 
2023-01-11T21:05:10.4102571Z 
2023-01-11T21:05:10.4102729Z async_compile.wait(globals())
2023-01-11T21:05:10.4102856Z del async_compile
2023-01-11T21:05:10.4102864Z 
2023-01-11T21:05:10.4102985Z def call(args):
2023-01-11T21:05:10.4103184Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4103308Z     args.clear()
2023-01-11T21:05:10.4103689Z     buf0 = empty_strided((32, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4103924Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4104041Z     del arg0_1
2023-01-11T21:05:10.4104278Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4104469Z     assert_size_stride(buf1, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:05:10.4104569Z     del arg1_1
2023-01-11T21:05:10.4104683Z     del arg7_1
2023-01-11T21:05:10.4104807Z     return (buf1, )
2023-01-11T21:05:10.4104815Z 
2023-01-11T21:05:10.4104823Z 
2023-01-11T21:05:10.4104954Z if __name__ == "__main__":
2023-01-11T21:05:10.4105148Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4105318Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4105688Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4106013Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4106269Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4106523Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4106789Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4107072Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4107376Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4107761Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4108028Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4108112Z 
2023-01-11T21:05:10.4108120Z 
2023-01-11T21:05:10.4108282Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4108383Z import torch
2023-01-11T21:05:10.4108501Z import random
2023-01-11T21:05:10.4108707Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4108921Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4108929Z 
2023-01-11T21:05:10.4109061Z aten = torch.ops.aten
2023-01-11T21:05:10.4109296Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4109453Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4109461Z 
2023-01-11T21:05:10.4109468Z 
2023-01-11T21:05:10.4109614Z async_compile.wait(globals())
2023-01-11T21:05:10.4109723Z del async_compile
2023-01-11T21:05:10.4109731Z 
2023-01-11T21:05:10.4109847Z def call(args):
2023-01-11T21:05:10.4110050Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4110169Z     args.clear()
2023-01-11T21:05:10.4110401Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4110593Z     assert_size_stride(buf0, (1, 128, 108, 108), (1492992, 11664, 108, 1))
2023-01-11T21:05:10.4110710Z     del arg0_1
2023-01-11T21:05:10.4110851Z     del arg1_1
2023-01-11T21:05:10.4110964Z     del arg7_1
2023-01-11T21:05:10.4111085Z     return (buf0, )
2023-01-11T21:05:10.4111093Z 
2023-01-11T21:05:10.4111101Z 
2023-01-11T21:05:10.4111223Z if __name__ == "__main__":
2023-01-11T21:05:10.4111422Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4111635Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4111992Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4112281Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4112547Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4112874Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4113198Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4113531Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4113834Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4114233Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4114450Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4114459Z 
2023-01-11T21:05:10.4114467Z 
2023-01-11T21:05:10.4114611Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4114721Z import torch
2023-01-11T21:05:10.4114813Z import random
2023-01-11T21:05:10.4114989Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4115146Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4115157Z 
2023-01-11T21:05:10.4115264Z aten = torch.ops.aten
2023-01-11T21:05:10.4115439Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4115558Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4115564Z 
2023-01-11T21:05:10.4115573Z 
2023-01-11T21:05:10.4115772Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4116055Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4116199Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4116342Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4116438Z {
2023-01-11T21:05:10.4116567Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4116656Z     {
2023-01-11T21:05:10.4116771Z         #pragma omp for 
2023-01-11T21:05:10.4116889Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.4116961Z         {
2023-01-11T21:05:10.4117062Z             #pragma GCC ivdep
2023-01-11T21:05:10.4117174Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4117372Z             {
2023-01-11T21:05:10.4117493Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4117615Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4117714Z                 {
2023-01-11T21:05:10.4117837Z                     {
2023-01-11T21:05:10.4117965Z                         {
2023-01-11T21:05:10.4118168Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4118352Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4118449Z                         }
2023-01-11T21:05:10.4118513Z                     }
2023-01-11T21:05:10.4118563Z                 }
2023-01-11T21:05:10.4118623Z             }
2023-01-11T21:05:10.4118682Z         }
2023-01-11T21:05:10.4118741Z     }
2023-01-11T21:05:10.4118800Z }
2023-01-11T21:05:10.4118895Z ''')
2023-01-11T21:05:10.4118901Z 
2023-01-11T21:05:10.4118905Z 
2023-01-11T21:05:10.4118993Z async_compile.wait(globals())
2023-01-11T21:05:10.4119054Z del async_compile
2023-01-11T21:05:10.4119071Z 
2023-01-11T21:05:10.4119127Z def call(args):
2023-01-11T21:05:10.4119245Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4119367Z     args.clear()
2023-01-11T21:05:10.4119585Z     buf0 = empty_strided((128, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4119717Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4119784Z     del arg0_1
2023-01-11T21:05:10.4119917Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4120016Z     assert_size_stride(buf1, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:05:10.4120081Z     del arg1_1
2023-01-11T21:05:10.4120145Z     del arg7_1
2023-01-11T21:05:10.4120214Z     return (buf1, )
2023-01-11T21:05:10.4120220Z 
2023-01-11T21:05:10.4120224Z 
2023-01-11T21:05:10.4120298Z if __name__ == "__main__":
2023-01-11T21:05:10.4120413Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4120534Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4120957Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4121142Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4121333Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4121520Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4121705Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4121897Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4122074Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4122298Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4122458Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4122725Z [2023-01-11 20:47:13,030] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 100
2023-01-11T21:05:10.4123127Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4123252Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4123509Z [2023-01-11 20:47:13,698] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 101
2023-01-11T21:05:10.4123770Z [2023-01-11 20:47:13,762] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 101
2023-01-11T21:05:10.4124248Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4124373Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4124629Z [2023-01-11 20:47:14,105] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 102
2023-01-11T21:05:10.4124892Z [2023-01-11 20:47:14,147] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 102
2023-01-11T21:05:10.4125286Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4125411Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4125691Z [2023-01-11 20:47:14,485] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 103
2023-01-11T21:05:10.4125955Z [2023-01-11 20:47:14,528] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 103
2023-01-11T21:05:10.4126348Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4126472Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4126721Z [2023-01-11 20:47:15,197] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 104
2023-01-11T21:05:10.4126982Z [2023-01-11 20:47:15,240] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 104
2023-01-11T21:05:10.4127376Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4127499Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4127752Z [2023-01-11 20:47:15,911] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 105
2023-01-11T21:05:10.4128009Z [2023-01-11 20:47:15,951] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 105
2023-01-11T21:05:10.4128398Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4128525Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4128764Z [2023-01-11 20:47:16,251] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 106
2023-01-11T21:05:10.4128783Z 
2023-01-11T21:05:10.4128787Z 
2023-01-11T21:05:10.4128868Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4128937Z import torch
2023-01-11T21:05:10.4129005Z import random
2023-01-11T21:05:10.4129124Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4129241Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4129246Z 
2023-01-11T21:05:10.4129323Z aten = torch.ops.aten
2023-01-11T21:05:10.4129457Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4129566Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4129571Z 
2023-01-11T21:05:10.4129587Z 
2023-01-11T21:05:10.4129662Z async_compile.wait(globals())
2023-01-11T21:05:10.4129732Z del async_compile
2023-01-11T21:05:10.4129737Z 
2023-01-11T21:05:10.4129806Z def call(args):
2023-01-11T21:05:10.4129926Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4129998Z     args.clear()
2023-01-11T21:05:10.4130132Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4130243Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 12544, 112, 1))
2023-01-11T21:05:10.4130297Z     del arg0_1
2023-01-11T21:05:10.4130362Z     del arg1_1
2023-01-11T21:05:10.4130425Z     del arg7_1
2023-01-11T21:05:10.4130493Z     return (buf0, )
2023-01-11T21:05:10.4130498Z 
2023-01-11T21:05:10.4130502Z 
2023-01-11T21:05:10.4130575Z if __name__ == "__main__":
2023-01-11T21:05:10.4130686Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4130811Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4131012Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4131231Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4131422Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4131609Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4131797Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4131981Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4132158Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4132384Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4132538Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4132546Z 
2023-01-11T21:05:10.4132551Z 
2023-01-11T21:05:10.4132633Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4132702Z import torch
2023-01-11T21:05:10.4132771Z import random
2023-01-11T21:05:10.4132886Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4133006Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4133011Z 
2023-01-11T21:05:10.4133086Z aten = torch.ops.aten
2023-01-11T21:05:10.4133218Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4133295Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4133312Z 
2023-01-11T21:05:10.4133317Z 
2023-01-11T21:05:10.4133391Z async_compile.wait(globals())
2023-01-11T21:05:10.4133461Z del async_compile
2023-01-11T21:05:10.4133466Z 
2023-01-11T21:05:10.4133533Z def call(args):
2023-01-11T21:05:10.4133650Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4133721Z     args.clear()
2023-01-11T21:05:10.4133855Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4133967Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:05:10.4134021Z     del arg0_1
2023-01-11T21:05:10.4134086Z     del arg1_1
2023-01-11T21:05:10.4134148Z     del arg7_1
2023-01-11T21:05:10.4134217Z     return (buf0, )
2023-01-11T21:05:10.4134223Z 
2023-01-11T21:05:10.4134227Z 
2023-01-11T21:05:10.4134299Z if __name__ == "__main__":
2023-01-11T21:05:10.4134411Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4134531Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4134740Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4134918Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4135106Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4135321Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4135504Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4135688Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4135866Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4136085Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4136239Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4136244Z 
2023-01-11T21:05:10.4136249Z 
2023-01-11T21:05:10.4136342Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4136398Z import torch
2023-01-11T21:05:10.4136465Z import random
2023-01-11T21:05:10.4136579Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4136697Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4136704Z 
2023-01-11T21:05:10.4136779Z aten = torch.ops.aten
2023-01-11T21:05:10.4136910Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4137045Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4137051Z 
2023-01-11T21:05:10.4137055Z 
2023-01-11T21:05:10.4137130Z async_compile.wait(globals())
2023-01-11T21:05:10.4137200Z del async_compile
2023-01-11T21:05:10.4137205Z 
2023-01-11T21:05:10.4137272Z def call(args):
2023-01-11T21:05:10.4137387Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4137459Z     args.clear()
2023-01-11T21:05:10.4137590Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4137701Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 12544, 112, 1))
2023-01-11T21:05:10.4137767Z     del arg0_1
2023-01-11T21:05:10.4137818Z     del arg1_1
2023-01-11T21:05:10.4137881Z     del arg7_1
2023-01-11T21:05:10.4137951Z     return (buf0, )
2023-01-11T21:05:10.4137956Z 
2023-01-11T21:05:10.4137960Z 
2023-01-11T21:05:10.4138034Z if __name__ == "__main__":
2023-01-11T21:05:10.4138144Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4138267Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4138569Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4138753Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4138945Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4139135Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4139323Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4139509Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4139687Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4139912Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4140067Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4140072Z 
2023-01-11T21:05:10.4140076Z 
2023-01-11T21:05:10.4140168Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4140224Z import torch
2023-01-11T21:05:10.4140293Z import random
2023-01-11T21:05:10.4140405Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4140523Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4140528Z 
2023-01-11T21:05:10.4140602Z aten = torch.ops.aten
2023-01-11T21:05:10.4140733Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4140822Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4140827Z 
2023-01-11T21:05:10.4140830Z 
2023-01-11T21:05:10.4140916Z async_compile.wait(globals())
2023-01-11T21:05:10.4141010Z del async_compile
2023-01-11T21:05:10.4141015Z 
2023-01-11T21:05:10.4141084Z def call(args):
2023-01-11T21:05:10.4141200Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4141272Z     args.clear()
2023-01-11T21:05:10.4141404Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4141514Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:05:10.4141579Z     del arg0_1
2023-01-11T21:05:10.4141633Z     del arg1_1
2023-01-11T21:05:10.4141696Z     del arg7_1
2023-01-11T21:05:10.4141765Z     return (buf0, )
2023-01-11T21:05:10.4141770Z 
2023-01-11T21:05:10.4141774Z 
2023-01-11T21:05:10.4141846Z if __name__ == "__main__":
2023-01-11T21:05:10.4141957Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4142076Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4142285Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4142478Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4142685Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4142874Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4143062Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4143250Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4143427Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4143647Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4143801Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4143806Z 
2023-01-11T21:05:10.4143810Z 
2023-01-11T21:05:10.4143905Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4143973Z import torch
2023-01-11T21:05:10.4144028Z import random
2023-01-11T21:05:10.4144141Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4144262Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4144267Z 
2023-01-11T21:05:10.4144342Z aten = torch.ops.aten
2023-01-11T21:05:10.4144473Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4144562Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4144567Z 
2023-01-11T21:05:10.4144571Z 
2023-01-11T21:05:10.4144656Z async_compile.wait(globals())
2023-01-11T21:05:10.4144713Z del async_compile
2023-01-11T21:05:10.4144730Z 
2023-01-11T21:05:10.4144787Z def call(args):
2023-01-11T21:05:10.4144902Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4144972Z     args.clear()
2023-01-11T21:05:10.4145107Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4145220Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 12544, 112, 1))
2023-01-11T21:05:10.4145285Z     del arg0_1
2023-01-11T21:05:10.4145349Z     del arg1_1
2023-01-11T21:05:10.4145402Z     del arg7_1
2023-01-11T21:05:10.4145471Z     return (buf0, )
2023-01-11T21:05:10.4145476Z 
2023-01-11T21:05:10.4145480Z 
2023-01-11T21:05:10.4145553Z if __name__ == "__main__":
2023-01-11T21:05:10.4145665Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4145783Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4145994Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4146183Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4146368Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4146539Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4146757Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4146939Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4147117Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4147339Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4147492Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4147497Z 
2023-01-11T21:05:10.4147501Z 
2023-01-11T21:05:10.4147593Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4147661Z import torch
2023-01-11T21:05:10.4147716Z import random
2023-01-11T21:05:10.4147829Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4147948Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4147952Z 
2023-01-11T21:05:10.4148028Z aten = torch.ops.aten
2023-01-11T21:05:10.4148161Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4148250Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4148255Z 
2023-01-11T21:05:10.4148259Z 
2023-01-11T21:05:10.4148375Z async_compile.wait(globals())
2023-01-11T21:05:10.4148447Z del async_compile
2023-01-11T21:05:10.4148452Z 
2023-01-11T21:05:10.4148507Z def call(args):
2023-01-11T21:05:10.4148624Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4148692Z     args.clear()
2023-01-11T21:05:10.4148825Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4148935Z     assert_size_stride(buf0, (1, 32, 112, 112), (401408, 1, 3584, 32))
2023-01-11T21:05:10.4148999Z     del arg0_1
2023-01-11T21:05:10.4149064Z     del arg1_1
2023-01-11T21:05:10.4149115Z     del arg7_1
2023-01-11T21:05:10.4149183Z     return (buf0, )
2023-01-11T21:05:10.4149188Z 
2023-01-11T21:05:10.4149192Z 
2023-01-11T21:05:10.4149269Z if __name__ == "__main__":
2023-01-11T21:05:10.4149383Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4149504Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4149713Z     arg0_1 = rand_strided((32, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4149902Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4150087Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4150260Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4150448Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4150632Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4150808Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4151025Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4151179Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4151450Z [2023-01-11 20:47:16,293] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 106
2023-01-11T21:05:10.4151851Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4151976Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4152218Z [2023-01-11 20:47:16,625] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 107
2023-01-11T21:05:10.4152474Z [2023-01-11 20:47:16,665] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 107
2023-01-11T21:05:10.4152898Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4153023Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4153277Z [2023-01-11 20:47:17,342] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 108
2023-01-11T21:05:10.4153536Z [2023-01-11 20:47:17,383] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 108
2023-01-11T21:05:10.4153929Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4154054Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4154333Z [2023-01-11 20:47:18,001] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 109
2023-01-11T21:05:10.4154590Z [2023-01-11 20:47:18,042] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 109
2023-01-11T21:05:10.4154981Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4155102Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4155342Z [2023-01-11 20:47:18,379] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 110
2023-01-11T21:05:10.4155601Z [2023-01-11 20:47:18,428] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 110
2023-01-11T21:05:10.4155993Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4156115Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4156370Z [2023-01-11 20:47:18,773] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 111
2023-01-11T21:05:10.4156375Z 
2023-01-11T21:05:10.4156379Z 
2023-01-11T21:05:10.4156472Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4156539Z import torch
2023-01-11T21:05:10.4156606Z import random
2023-01-11T21:05:10.4156721Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4156827Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4156844Z 
2023-01-11T21:05:10.4156908Z aten = torch.ops.aten
2023-01-11T21:05:10.4157041Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4157131Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4157136Z 
2023-01-11T21:05:10.4157141Z 
2023-01-11T21:05:10.4157226Z async_compile.wait(globals())
2023-01-11T21:05:10.4157295Z del async_compile
2023-01-11T21:05:10.4157300Z 
2023-01-11T21:05:10.4157368Z def call(args):
2023-01-11T21:05:10.4157484Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4157542Z     args.clear()
2023-01-11T21:05:10.4157674Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4157786Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 12544, 112, 1))
2023-01-11T21:05:10.4157887Z     del arg0_1
2023-01-11T21:05:10.4157951Z     del arg1_1
2023-01-11T21:05:10.4158014Z     del arg7_1
2023-01-11T21:05:10.4158082Z     return (buf0, )
2023-01-11T21:05:10.4158088Z 
2023-01-11T21:05:10.4158092Z 
2023-01-11T21:05:10.4158166Z if __name__ == "__main__":
2023-01-11T21:05:10.4158265Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4158385Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4158595Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4158788Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4158979Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4159169Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4159357Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4159544Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4159710Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4159965Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4160120Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4160126Z 
2023-01-11T21:05:10.4160130Z 
2023-01-11T21:05:10.4160223Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4160290Z import torch
2023-01-11T21:05:10.4160358Z import random
2023-01-11T21:05:10.4160471Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4160723Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4160731Z 
2023-01-11T21:05:10.4160825Z aten = torch.ops.aten
2023-01-11T21:05:10.4160961Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4161049Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4161057Z 
2023-01-11T21:05:10.4161061Z 
2023-01-11T21:05:10.4161148Z async_compile.wait(globals())
2023-01-11T21:05:10.4161218Z del async_compile
2023-01-11T21:05:10.4161223Z 
2023-01-11T21:05:10.4161291Z def call(args):
2023-01-11T21:05:10.4161410Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4161483Z     args.clear()
2023-01-11T21:05:10.4161604Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4161715Z     assert_size_stride(buf0, (1, 128, 112, 112), (1605632, 1, 14336, 128))
2023-01-11T21:05:10.4161781Z     del arg0_1
2023-01-11T21:05:10.4161845Z     del arg1_1
2023-01-11T21:05:10.4161908Z     del arg7_1
2023-01-11T21:05:10.4161976Z     return (buf0, )
2023-01-11T21:05:10.4161981Z 
2023-01-11T21:05:10.4161985Z 
2023-01-11T21:05:10.4162059Z if __name__ == "__main__":
2023-01-11T21:05:10.4162157Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4162279Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4162494Z     arg0_1 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4162686Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4162875Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4163060Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4163251Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4163439Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4163603Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4163826Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4163981Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4164045Z 
2023-01-11T21:05:10.4164049Z 
2023-01-11T21:05:10.4164144Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4164212Z import torch
2023-01-11T21:05:10.4164279Z import random
2023-01-11T21:05:10.4164395Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4164515Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4164520Z 
2023-01-11T21:05:10.4164583Z aten = torch.ops.aten
2023-01-11T21:05:10.4164714Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4164803Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4164808Z 
2023-01-11T21:05:10.4164812Z 
2023-01-11T21:05:10.4164899Z async_compile.wait(globals())
2023-01-11T21:05:10.4164969Z del async_compile
2023-01-11T21:05:10.4164974Z 
2023-01-11T21:05:10.4165042Z def call(args):
2023-01-11T21:05:10.4165156Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4165224Z     args.clear()
2023-01-11T21:05:10.4165348Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4165459Z     assert_size_stride(buf0, (1, 32, 110, 110), (387200, 12100, 110, 1))
2023-01-11T21:05:10.4165562Z     del arg0_1
2023-01-11T21:05:10.4165628Z     del arg1_1
2023-01-11T21:05:10.4165692Z     del arg7_1
2023-01-11T21:05:10.4165761Z     return (buf0, )
2023-01-11T21:05:10.4165766Z 
2023-01-11T21:05:10.4165770Z 
2023-01-11T21:05:10.4165843Z if __name__ == "__main__":
2023-01-11T21:05:10.4165955Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4166063Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4166273Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4166461Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4166650Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4166837Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4167019Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4167204Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4167381Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4167589Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4167743Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4167748Z 
2023-01-11T21:05:10.4167752Z 
2023-01-11T21:05:10.4167844Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4167912Z import torch
2023-01-11T21:05:10.4167980Z import random
2023-01-11T21:05:10.4168092Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4168211Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4168218Z 
2023-01-11T21:05:10.4168293Z aten = torch.ops.aten
2023-01-11T21:05:10.4168413Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4168504Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4168509Z 
2023-01-11T21:05:10.4168513Z 
2023-01-11T21:05:10.4168645Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4168845Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4168962Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4169061Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4169120Z {
2023-01-11T21:05:10.4169214Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4169263Z     {
2023-01-11T21:05:10.4169337Z         #pragma omp for 
2023-01-11T21:05:10.4169417Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.4169479Z         {
2023-01-11T21:05:10.4169557Z             #pragma GCC ivdep
2023-01-11T21:05:10.4169667Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4169717Z             {
2023-01-11T21:05:10.4169796Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4169887Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4169949Z                 {
2023-01-11T21:05:10.4170013Z                     {
2023-01-11T21:05:10.4170078Z                         {
2023-01-11T21:05:10.4170188Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4170279Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4170344Z                         }
2023-01-11T21:05:10.4170407Z                     }
2023-01-11T21:05:10.4170469Z                 }
2023-01-11T21:05:10.4170529Z             }
2023-01-11T21:05:10.4170588Z         }
2023-01-11T21:05:10.4170647Z     }
2023-01-11T21:05:10.4170693Z }
2023-01-11T21:05:10.4170772Z ''')
2023-01-11T21:05:10.4170777Z 
2023-01-11T21:05:10.4170781Z 
2023-01-11T21:05:10.4170872Z async_compile.wait(globals())
2023-01-11T21:05:10.4170941Z del async_compile
2023-01-11T21:05:10.4170946Z 
2023-01-11T21:05:10.4171014Z def call(args):
2023-01-11T21:05:10.4171159Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4171229Z     args.clear()
2023-01-11T21:05:10.4171427Z     buf0 = empty_strided((32, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4171558Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4171623Z     del arg0_1
2023-01-11T21:05:10.4171755Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4171865Z     assert_size_stride(buf1, (1, 32, 110, 110), (387200, 1, 3520, 32))
2023-01-11T21:05:10.4171930Z     del arg1_1
2023-01-11T21:05:10.4171995Z     del arg7_1
2023-01-11T21:05:10.4172063Z     return (buf1, )
2023-01-11T21:05:10.4172068Z 
2023-01-11T21:05:10.4172072Z 
2023-01-11T21:05:10.4172133Z if __name__ == "__main__":
2023-01-11T21:05:10.4172246Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4172367Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4172578Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4172817Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4173086Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4173273Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4173461Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4173637Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4173813Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4174030Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4174186Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4174191Z 
2023-01-11T21:05:10.4174195Z 
2023-01-11T21:05:10.4174291Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4174358Z import torch
2023-01-11T21:05:10.4174425Z import random
2023-01-11T21:05:10.4174539Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4174646Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4174651Z 
2023-01-11T21:05:10.4174727Z aten = torch.ops.aten
2023-01-11T21:05:10.4174857Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4174948Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4174953Z 
2023-01-11T21:05:10.4174957Z 
2023-01-11T21:05:10.4175043Z async_compile.wait(globals())
2023-01-11T21:05:10.4175112Z del async_compile
2023-01-11T21:05:10.4175117Z 
2023-01-11T21:05:10.4175189Z def call(args):
2023-01-11T21:05:10.4175345Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4175402Z     args.clear()
2023-01-11T21:05:10.4175537Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4175648Z     assert_size_stride(buf0, (1, 128, 110, 110), (1548800, 12100, 110, 1))
2023-01-11T21:05:10.4175715Z     del arg0_1
2023-01-11T21:05:10.4175781Z     del arg1_1
2023-01-11T21:05:10.4175844Z     del arg7_1
2023-01-11T21:05:10.4175914Z     return (buf0, )
2023-01-11T21:05:10.4175919Z 
2023-01-11T21:05:10.4175923Z 
2023-01-11T21:05:10.4175996Z if __name__ == "__main__":
2023-01-11T21:05:10.4176095Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4176215Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4176426Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4176619Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4176811Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4176999Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4177244Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4177419Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4177595Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4177817Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4177971Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4178240Z [2023-01-11 20:47:18,815] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 111
2023-01-11T21:05:10.4178729Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4178859Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4179117Z [2023-01-11 20:47:19,497] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 112
2023-01-11T21:05:10.4179379Z [2023-01-11 20:47:19,547] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 112
2023-01-11T21:05:10.4179777Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4179904Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4180148Z [2023-01-11 20:47:20,159] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 113
2023-01-11T21:05:10.4180410Z [2023-01-11 20:47:20,199] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 113
2023-01-11T21:05:10.4180804Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4180930Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4181185Z [2023-01-11 20:47:20,521] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 114
2023-01-11T21:05:10.4181445Z [2023-01-11 20:47:20,576] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 114
2023-01-11T21:05:10.4181871Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4181994Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4182247Z [2023-01-11 20:47:20,926] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 115
2023-01-11T21:05:10.4182252Z 
2023-01-11T21:05:10.4182256Z 
2023-01-11T21:05:10.4182348Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4182416Z import torch
2023-01-11T21:05:10.4182471Z import random
2023-01-11T21:05:10.4182585Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4182706Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4182713Z 
2023-01-11T21:05:10.4182793Z aten = torch.ops.aten
2023-01-11T21:05:10.4182925Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4183081Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4183086Z 
2023-01-11T21:05:10.4183091Z 
2023-01-11T21:05:10.4183226Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4183429Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4183536Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4183634Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4183694Z {
2023-01-11T21:05:10.4183821Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4183916Z     {
2023-01-11T21:05:10.4184025Z         #pragma omp for 
2023-01-11T21:05:10.4184106Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.4184155Z         {
2023-01-11T21:05:10.4184232Z             #pragma GCC ivdep
2023-01-11T21:05:10.4184316Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4184376Z             {
2023-01-11T21:05:10.4184457Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4184546Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4184596Z                 {
2023-01-11T21:05:10.4184661Z                     {
2023-01-11T21:05:10.4184726Z                         {
2023-01-11T21:05:10.4184837Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4184938Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4185004Z                         }
2023-01-11T21:05:10.4185068Z                     }
2023-01-11T21:05:10.4185117Z                 }
2023-01-11T21:05:10.4185177Z             }
2023-01-11T21:05:10.4185235Z         }
2023-01-11T21:05:10.4185295Z     }
2023-01-11T21:05:10.4185353Z }
2023-01-11T21:05:10.4185430Z ''')
2023-01-11T21:05:10.4185435Z 
2023-01-11T21:05:10.4185439Z 
2023-01-11T21:05:10.4185529Z async_compile.wait(globals())
2023-01-11T21:05:10.4185587Z del async_compile
2023-01-11T21:05:10.4185604Z 
2023-01-11T21:05:10.4185659Z def call(args):
2023-01-11T21:05:10.4185780Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4185851Z     args.clear()
2023-01-11T21:05:10.4186063Z     buf0 = empty_strided((128, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4186195Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4186259Z     del arg0_1
2023-01-11T21:05:10.4186391Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 4)
2023-01-11T21:05:10.4186488Z     assert_size_stride(buf1, (1, 128, 110, 110), (1548800, 1, 14080, 128))
2023-01-11T21:05:10.4186555Z     del arg1_1
2023-01-11T21:05:10.4186621Z     del arg7_1
2023-01-11T21:05:10.4186690Z     return (buf1, )
2023-01-11T21:05:10.4186696Z 
2023-01-11T21:05:10.4186700Z 
2023-01-11T21:05:10.4186812Z if __name__ == "__main__":
2023-01-11T21:05:10.4186924Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4187044Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4187257Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4187437Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4187628Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4187816Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4188006Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4188195Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4188373Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4188596Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4188752Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4188758Z 
2023-01-11T21:05:10.4188763Z 
2023-01-11T21:05:10.4188872Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4188942Z import torch
2023-01-11T21:05:10.4189009Z import random
2023-01-11T21:05:10.4189121Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4189240Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4189245Z 
2023-01-11T21:05:10.4189320Z aten = torch.ops.aten
2023-01-11T21:05:10.4189453Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4189542Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4189547Z 
2023-01-11T21:05:10.4189551Z 
2023-01-11T21:05:10.4189624Z async_compile.wait(globals())
2023-01-11T21:05:10.4189695Z del async_compile
2023-01-11T21:05:10.4189700Z 
2023-01-11T21:05:10.4189769Z def call(args):
2023-01-11T21:05:10.4189888Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4189957Z     args.clear()
2023-01-11T21:05:10.4190091Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4190200Z     assert_size_stride(buf0, (1, 32, 108, 108), (373248, 11664, 108, 1))
2023-01-11T21:05:10.4190268Z     del arg0_1
2023-01-11T21:05:10.4190320Z     del arg1_1
2023-01-11T21:05:10.4190384Z     del arg7_1
2023-01-11T21:05:10.4190452Z     return (buf0, )
2023-01-11T21:05:10.4190457Z 
2023-01-11T21:05:10.4190462Z 
2023-01-11T21:05:10.4190535Z if __name__ == "__main__":
2023-01-11T21:05:10.4190649Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4190769Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4190978Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4191156Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4191349Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4191534Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4191716Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4191896Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4192072Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4192293Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4192446Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4192451Z 
2023-01-11T21:05:10.4192457Z 
2023-01-11T21:05:10.4192549Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4192604Z import torch
2023-01-11T21:05:10.4192706Z import random
2023-01-11T21:05:10.4192818Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4192938Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4192943Z 
2023-01-11T21:05:10.4193021Z aten = torch.ops.aten
2023-01-11T21:05:10.4193152Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4193241Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4193246Z 
2023-01-11T21:05:10.4193251Z 
2023-01-11T21:05:10.4193383Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4193572Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4193690Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4193788Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4193849Z {
2023-01-11T21:05:10.4193945Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4194004Z     {
2023-01-11T21:05:10.4194078Z         #pragma omp for 
2023-01-11T21:05:10.4194149Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.4194210Z         {
2023-01-11T21:05:10.4194289Z             #pragma GCC ivdep
2023-01-11T21:05:10.4194371Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4194470Z             {
2023-01-11T21:05:10.4194553Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4194630Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4194693Z                 {
2023-01-11T21:05:10.4194757Z                     {
2023-01-11T21:05:10.4194823Z                         {
2023-01-11T21:05:10.4194932Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4195033Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4195099Z                         }
2023-01-11T21:05:10.4195151Z                     }
2023-01-11T21:05:10.4195212Z                 }
2023-01-11T21:05:10.4195272Z             }
2023-01-11T21:05:10.4195335Z         }
2023-01-11T21:05:10.4195395Z     }
2023-01-11T21:05:10.4195456Z }
2023-01-11T21:05:10.4195531Z ''')
2023-01-11T21:05:10.4195536Z 
2023-01-11T21:05:10.4195540Z 
2023-01-11T21:05:10.4195615Z async_compile.wait(globals())
2023-01-11T21:05:10.4195684Z del async_compile
2023-01-11T21:05:10.4195691Z 
2023-01-11T21:05:10.4195759Z def call(args):
2023-01-11T21:05:10.4195876Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4195946Z     args.clear()
2023-01-11T21:05:10.4196154Z     buf0 = empty_strided((32, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4196285Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4196350Z     del arg0_1
2023-01-11T21:05:10.4196470Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 1)
2023-01-11T21:05:10.4196578Z     assert_size_stride(buf1, (1, 32, 108, 108), (373248, 1, 3456, 32))
2023-01-11T21:05:10.4196644Z     del arg1_1
2023-01-11T21:05:10.4196708Z     del arg7_1
2023-01-11T21:05:10.4196778Z     return (buf1, )
2023-01-11T21:05:10.4196783Z 
2023-01-11T21:05:10.4196788Z 
2023-01-11T21:05:10.4196860Z if __name__ == "__main__":
2023-01-11T21:05:10.4196973Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4197081Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4197288Z     arg0_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4197477Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4197664Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4197849Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4198032Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4198219Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4198395Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4198633Z     arg7_1 = rand_strided((1, 3, 112, 112), (37632, 1, 336, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4198787Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4198792Z 
2023-01-11T21:05:10.4198796Z 
2023-01-11T21:05:10.4198889Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4198957Z import torch
2023-01-11T21:05:10.4199026Z import random
2023-01-11T21:05:10.4199137Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4199255Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4199259Z 
2023-01-11T21:05:10.4199334Z aten = torch.ops.aten
2023-01-11T21:05:10.4199452Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4199542Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4199547Z 
2023-01-11T21:05:10.4199551Z 
2023-01-11T21:05:10.4199637Z async_compile.wait(globals())
2023-01-11T21:05:10.4199709Z del async_compile
2023-01-11T21:05:10.4199714Z 
2023-01-11T21:05:10.4199782Z def call(args):
2023-01-11T21:05:10.4199897Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4199998Z     args.clear()
2023-01-11T21:05:10.4200133Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4200231Z     assert_size_stride(buf0, (1, 128, 108, 108), (1492992, 11664, 108, 1))
2023-01-11T21:05:10.4200299Z     del arg0_1
2023-01-11T21:05:10.4200363Z     del arg1_1
2023-01-11T21:05:10.4200426Z     del arg7_1
2023-01-11T21:05:10.4200495Z     return (buf0, )
2023-01-11T21:05:10.4200500Z 
2023-01-11T21:05:10.4200504Z 
2023-01-11T21:05:10.4200578Z if __name__ == "__main__":
2023-01-11T21:05:10.4200855Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4200975Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4201179Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4201373Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4201567Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4201753Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4201941Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4202125Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4202301Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4202524Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 12544, 112, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4202666Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4202934Z [2023-01-11 20:47:20,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 115
2023-01-11T21:05:10.4203339Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4203465Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4203722Z [2023-01-11 20:47:21,607] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 116
2023-01-11T21:05:10.4203986Z [2023-01-11 20:47:21,665] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 116
2023-01-11T21:05:10.4204382Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4204570Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4204829Z [2023-01-11 20:47:22,370] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 117
2023-01-11T21:05:10.4205087Z [2023-01-11 20:47:22,414] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 117
2023-01-11T21:05:10.4205533Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4205655Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4205898Z [2023-01-11 20:47:23,986] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 118
2023-01-11T21:05:10.4206161Z [2023-01-11 20:47:26,775] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 118
2023-01-11T21:05:10.4206593Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4206716Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4206969Z [2023-01-11 20:47:28,536] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 119
2023-01-11T21:05:10.4207227Z [2023-01-11 20:47:28,580] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 119
2023-01-11T21:05:10.4207622Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4207744Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4207993Z [2023-01-11 20:47:33,630] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 120
2023-01-11T21:05:10.4207999Z 
2023-01-11T21:05:10.4208004Z 
2023-01-11T21:05:10.4208096Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4208152Z import torch
2023-01-11T21:05:10.4208219Z import random
2023-01-11T21:05:10.4208333Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4208451Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4208457Z 
2023-01-11T21:05:10.4208533Z aten = torch.ops.aten
2023-01-11T21:05:10.4208668Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4208756Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4208761Z 
2023-01-11T21:05:10.4208766Z 
2023-01-11T21:05:10.4208900Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4209091Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4209209Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4209308Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4209367Z {
2023-01-11T21:05:10.4209461Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4209521Z     {
2023-01-11T21:05:10.4209596Z         #pragma omp for 
2023-01-11T21:05:10.4209665Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.4209726Z         {
2023-01-11T21:05:10.4209803Z             #pragma GCC ivdep
2023-01-11T21:05:10.4209884Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4209945Z             {
2023-01-11T21:05:10.4210057Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4210145Z                 for(long i2=0; i2<9; i2+=1)
2023-01-11T21:05:10.4210196Z                 {
2023-01-11T21:05:10.4210260Z                     {
2023-01-11T21:05:10.4210327Z                         {
2023-01-11T21:05:10.4210435Z                             auto tmp0 = in_ptr0[i2 + (9*i1) + (27*i0)];
2023-01-11T21:05:10.4210537Z                             out_ptr0[i1 + (3*i2) + (27*i0)] = tmp0;
2023-01-11T21:05:10.4210603Z                         }
2023-01-11T21:05:10.4210668Z                     }
2023-01-11T21:05:10.4210717Z                 }
2023-01-11T21:05:10.4210777Z             }
2023-01-11T21:05:10.4210838Z         }
2023-01-11T21:05:10.4210896Z     }
2023-01-11T21:05:10.4210955Z }
2023-01-11T21:05:10.4211031Z ''')
2023-01-11T21:05:10.4211036Z 
2023-01-11T21:05:10.4211039Z 
2023-01-11T21:05:10.4211127Z async_compile.wait(globals())
2023-01-11T21:05:10.4211186Z del async_compile
2023-01-11T21:05:10.4211191Z 
2023-01-11T21:05:10.4211261Z def call(args):
2023-01-11T21:05:10.4211379Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4211448Z     args.clear()
2023-01-11T21:05:10.4211687Z     buf0 = empty_strided((128, 3, 3, 3), (27, 1, 9, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4211820Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4211886Z     del arg0_1
2023-01-11T21:05:10.4212006Z     buf1 = aten.convolution(arg7_1, buf0, arg1_1, (1, 1), (0, 0), (2, 2), False, (0, 0), 4)
2023-01-11T21:05:10.4212116Z     assert_size_stride(buf1, (1, 128, 108, 108), (1492992, 1, 13824, 128))
2023-01-11T21:05:10.4212182Z     del arg1_1
2023-01-11T21:05:10.4212247Z     del arg7_1
2023-01-11T21:05:10.4212315Z     return (buf1, )
2023-01-11T21:05:10.4212320Z 
2023-01-11T21:05:10.4212324Z 
2023-01-11T21:05:10.4212397Z if __name__ == "__main__":
2023-01-11T21:05:10.4212508Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4212632Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4212831Z     arg0_1 = rand_strided((128, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4213028Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4213217Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4213403Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4213587Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4213776Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4213954Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4214177Z     arg7_1 = rand_strided((1, 12, 112, 112), (150528, 1, 1344, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4214318Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4214339Z 
2023-01-11T21:05:10.4214343Z 
2023-01-11T21:05:10.4214425Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4214492Z import torch
2023-01-11T21:05:10.4214563Z import random
2023-01-11T21:05:10.4214676Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4214794Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4214799Z 
2023-01-11T21:05:10.4214875Z aten = torch.ops.aten
2023-01-11T21:05:10.4215007Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4215084Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4215102Z 
2023-01-11T21:05:10.4215106Z 
2023-01-11T21:05:10.4215179Z async_compile.wait(globals())
2023-01-11T21:05:10.4215250Z del async_compile
2023-01-11T21:05:10.4215254Z 
2023-01-11T21:05:10.4215322Z def call(args):
2023-01-11T21:05:10.4215437Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4215539Z     args.clear()
2023-01-11T21:05:10.4215681Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4215798Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4215852Z     del arg0_1
2023-01-11T21:05:10.4215916Z     del arg1_1
2023-01-11T21:05:10.4215979Z     del arg7_1
2023-01-11T21:05:10.4216047Z     return (buf0, )
2023-01-11T21:05:10.4216052Z 
2023-01-11T21:05:10.4216058Z 
2023-01-11T21:05:10.4216131Z if __name__ == "__main__":
2023-01-11T21:05:10.4216242Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4216362Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4216578Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4216759Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4216947Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4217131Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4217360Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4217544Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4217721Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4217951Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4218105Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4218111Z 
2023-01-11T21:05:10.4218115Z 
2023-01-11T21:05:10.4218208Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4218263Z import torch
2023-01-11T21:05:10.4218330Z import random
2023-01-11T21:05:10.4218444Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4218651Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4218657Z 
2023-01-11T21:05:10.4218732Z aten = torch.ops.aten
2023-01-11T21:05:10.4218865Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4218959Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4218963Z 
2023-01-11T21:05:10.4218967Z 
2023-01-11T21:05:10.4219088Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4219288Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4219408Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4219507Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4219566Z {
2023-01-11T21:05:10.4219663Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4219722Z     {
2023-01-11T21:05:10.4219799Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4219878Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4219939Z         {
2023-01-11T21:05:10.4220028Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4220090Z             {
2023-01-11T21:05:10.4220153Z                 {
2023-01-11T21:05:10.4220218Z                     {
2023-01-11T21:05:10.4220307Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4220404Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4220469Z                     }
2023-01-11T21:05:10.4220530Z                 }
2023-01-11T21:05:10.4220590Z             }
2023-01-11T21:05:10.4220650Z         }
2023-01-11T21:05:10.4220709Z     }
2023-01-11T21:05:10.4220755Z }
2023-01-11T21:05:10.4220831Z ''')
2023-01-11T21:05:10.4220835Z 
2023-01-11T21:05:10.4220840Z 
2023-01-11T21:05:10.4220927Z async_compile.wait(globals())
2023-01-11T21:05:10.4220997Z del async_compile
2023-01-11T21:05:10.4221002Z 
2023-01-11T21:05:10.4221070Z def call(args):
2023-01-11T21:05:10.4221185Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4221295Z     args.clear()
2023-01-11T21:05:10.4221516Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4221651Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4221717Z     del arg7_1
2023-01-11T21:05:10.4221860Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4221974Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4222040Z     del arg0_1
2023-01-11T21:05:10.4222105Z     del arg1_1
2023-01-11T21:05:10.4222173Z     return (buf1, )
2023-01-11T21:05:10.4222178Z 
2023-01-11T21:05:10.4222182Z 
2023-01-11T21:05:10.4222244Z if __name__ == "__main__":
2023-01-11T21:05:10.4222355Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4222475Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4222689Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4222881Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4223096Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4223283Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4223467Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4223636Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4223812Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4224038Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4224191Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4224197Z 
2023-01-11T21:05:10.4224204Z 
2023-01-11T21:05:10.4224296Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4224362Z import torch
2023-01-11T21:05:10.4224430Z import random
2023-01-11T21:05:10.4224542Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4224652Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4224657Z 
2023-01-11T21:05:10.4224734Z aten = torch.ops.aten
2023-01-11T21:05:10.4224864Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4224953Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4224958Z 
2023-01-11T21:05:10.4224962Z 
2023-01-11T21:05:10.4225047Z async_compile.wait(globals())
2023-01-11T21:05:10.4225117Z del async_compile
2023-01-11T21:05:10.4225122Z 
2023-01-11T21:05:10.4225189Z def call(args):
2023-01-11T21:05:10.4225303Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4225361Z     args.clear()
2023-01-11T21:05:10.4225498Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4225618Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4225684Z     del arg0_1
2023-01-11T21:05:10.4225751Z     del arg1_1
2023-01-11T21:05:10.4225814Z     del arg7_1
2023-01-11T21:05:10.4225882Z     return (buf0, )
2023-01-11T21:05:10.4225887Z 
2023-01-11T21:05:10.4225891Z 
2023-01-11T21:05:10.4225964Z if __name__ == "__main__":
2023-01-11T21:05:10.4226063Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4226182Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4226400Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4226594Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4226785Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4226976Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4227198Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4227385Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4227550Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4227780Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4227933Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4227938Z 
2023-01-11T21:05:10.4227942Z 
2023-01-11T21:05:10.4228034Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4228105Z import torch
2023-01-11T21:05:10.4228173Z import random
2023-01-11T21:05:10.4228285Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4228404Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4228410Z 
2023-01-11T21:05:10.4228473Z aten = torch.ops.aten
2023-01-11T21:05:10.4228604Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4228693Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4228729Z 
2023-01-11T21:05:10.4228734Z 
2023-01-11T21:05:10.4228867Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4229069Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4229185Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4229283Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4229342Z {
2023-01-11T21:05:10.4229426Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4229487Z     {
2023-01-11T21:05:10.4229562Z         #pragma omp for 
2023-01-11T21:05:10.4229642Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4229704Z         {
2023-01-11T21:05:10.4229781Z             #pragma GCC ivdep
2023-01-11T21:05:10.4229860Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4229920Z             {
2023-01-11T21:05:10.4229983Z                 {
2023-01-11T21:05:10.4230047Z                     {
2023-01-11T21:05:10.4230150Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4230246Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4230310Z                     }
2023-01-11T21:05:10.4230358Z                 }
2023-01-11T21:05:10.4230418Z             }
2023-01-11T21:05:10.4230478Z         }
2023-01-11T21:05:10.4230537Z     }
2023-01-11T21:05:10.4230595Z }
2023-01-11T21:05:10.4230671Z ''')
2023-01-11T21:05:10.4230676Z 
2023-01-11T21:05:10.4230680Z 
2023-01-11T21:05:10.4230768Z async_compile.wait(globals())
2023-01-11T21:05:10.4230826Z del async_compile
2023-01-11T21:05:10.4230843Z 
2023-01-11T21:05:10.4230898Z def call(args):
2023-01-11T21:05:10.4231015Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4231084Z     args.clear()
2023-01-11T21:05:10.4231323Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4231455Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4231521Z     del arg7_1
2023-01-11T21:05:10.4231660Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4231763Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4231828Z     del arg0_1
2023-01-11T21:05:10.4231893Z     del arg1_1
2023-01-11T21:05:10.4231963Z     return (buf1, )
2023-01-11T21:05:10.4231968Z 
2023-01-11T21:05:10.4231972Z 
2023-01-11T21:05:10.4232044Z if __name__ == "__main__":
2023-01-11T21:05:10.4232157Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4232277Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4232493Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4232714Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4232909Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4233097Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4233284Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4233468Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4233644Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4233873Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4234026Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4234282Z [2023-01-11 20:47:36,390] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 120
2023-01-11T21:05:10.4234712Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4234840Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4235098Z [2023-01-11 20:47:41,346] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 121
2023-01-11T21:05:10.4235356Z [2023-01-11 20:47:41,389] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 121
2023-01-11T21:05:10.4235751Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4235876Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4236131Z [2023-01-11 20:47:42,951] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 122
2023-01-11T21:05:10.4236391Z [2023-01-11 20:47:43,003] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 122
2023-01-11T21:05:10.4236783Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4236907Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4237149Z [2023-01-11 20:47:44,842] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 123
2023-01-11T21:05:10.4237408Z [2023-01-11 20:47:44,884] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 123
2023-01-11T21:05:10.4237804Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4237926Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4238177Z [2023-01-11 20:47:50,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 124
2023-01-11T21:05:10.4238436Z [2023-01-11 20:47:50,175] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 124
2023-01-11T21:05:10.4238831Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4238991Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4239243Z [2023-01-11 20:47:55,237] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 125
2023-01-11T21:05:10.4239248Z 
2023-01-11T21:05:10.4239254Z 
2023-01-11T21:05:10.4239346Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4239415Z import torch
2023-01-11T21:05:10.4239471Z import random
2023-01-11T21:05:10.4239584Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4239702Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4239708Z 
2023-01-11T21:05:10.4239785Z aten = torch.ops.aten
2023-01-11T21:05:10.4239920Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4240010Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4240015Z 
2023-01-11T21:05:10.4240019Z 
2023-01-11T21:05:10.4240105Z async_compile.wait(globals())
2023-01-11T21:05:10.4240207Z del async_compile
2023-01-11T21:05:10.4240212Z 
2023-01-11T21:05:10.4240270Z def call(args):
2023-01-11T21:05:10.4240388Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4240458Z     args.clear()
2023-01-11T21:05:10.4240725Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4240859Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4240927Z     del arg0_1
2023-01-11T21:05:10.4240991Z     del arg1_1
2023-01-11T21:05:10.4241043Z     del arg7_1
2023-01-11T21:05:10.4241112Z     return (buf0, )
2023-01-11T21:05:10.4241117Z 
2023-01-11T21:05:10.4241122Z 
2023-01-11T21:05:10.4241198Z if __name__ == "__main__":
2023-01-11T21:05:10.4241312Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4241433Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4241658Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4241851Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4242102Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4242359Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4242603Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4242869Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4243103Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4243409Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4243644Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4243652Z 
2023-01-11T21:05:10.4243661Z 
2023-01-11T21:05:10.4243786Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4243880Z import torch
2023-01-11T21:05:10.4243972Z import random
2023-01-11T21:05:10.4244116Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4244323Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4244331Z 
2023-01-11T21:05:10.4244481Z aten = torch.ops.aten
2023-01-11T21:05:10.4244717Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4244878Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4244887Z 
2023-01-11T21:05:10.4244894Z 
2023-01-11T21:05:10.4245115Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4245383Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4245765Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4245943Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4246038Z {
2023-01-11T21:05:10.4246185Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4246246Z     {
2023-01-11T21:05:10.4246335Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4246414Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4246475Z         {
2023-01-11T21:05:10.4246564Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4246613Z             {
2023-01-11T21:05:10.4246675Z                 {
2023-01-11T21:05:10.4246739Z                     {
2023-01-11T21:05:10.4246839Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4246935Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4246998Z                     }
2023-01-11T21:05:10.4247047Z                 }
2023-01-11T21:05:10.4247108Z             }
2023-01-11T21:05:10.4247171Z         }
2023-01-11T21:05:10.4247229Z     }
2023-01-11T21:05:10.4247287Z }
2023-01-11T21:05:10.4247371Z ''')
2023-01-11T21:05:10.4247377Z 
2023-01-11T21:05:10.4247381Z 
2023-01-11T21:05:10.4247521Z async_compile.wait(globals())
2023-01-11T21:05:10.4247581Z del async_compile
2023-01-11T21:05:10.4247598Z 
2023-01-11T21:05:10.4247654Z def call(args):
2023-01-11T21:05:10.4247773Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4247842Z     args.clear()
2023-01-11T21:05:10.4248080Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4248211Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4248276Z     del arg7_1
2023-01-11T21:05:10.4248416Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4248517Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4248585Z     del arg0_1
2023-01-11T21:05:10.4248650Z     del arg1_1
2023-01-11T21:05:10.4248718Z     return (buf1, )
2023-01-11T21:05:10.4248723Z 
2023-01-11T21:05:10.4248727Z 
2023-01-11T21:05:10.4248802Z if __name__ == "__main__":
2023-01-11T21:05:10.4248915Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4249035Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4249250Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4249431Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4249621Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4249809Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4249994Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4250183Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4250359Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4250586Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4250739Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4250745Z 
2023-01-11T21:05:10.4250749Z 
2023-01-11T21:05:10.4250842Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4250898Z import torch
2023-01-11T21:05:10.4250966Z import random
2023-01-11T21:05:10.4251079Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4251197Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4251202Z 
2023-01-11T21:05:10.4251277Z aten = torch.ops.aten
2023-01-11T21:05:10.4251408Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4251535Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4251540Z 
2023-01-11T21:05:10.4251544Z 
2023-01-11T21:05:10.4251618Z async_compile.wait(globals())
2023-01-11T21:05:10.4251687Z del async_compile
2023-01-11T21:05:10.4251692Z 
2023-01-11T21:05:10.4251762Z def call(args):
2023-01-11T21:05:10.4251878Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4251951Z     args.clear()
2023-01-11T21:05:10.4252089Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4252204Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4252269Z     del arg0_1
2023-01-11T21:05:10.4252322Z     del arg1_1
2023-01-11T21:05:10.4252386Z     del arg7_1
2023-01-11T21:05:10.4252455Z     return (buf0, )
2023-01-11T21:05:10.4252460Z 
2023-01-11T21:05:10.4252464Z 
2023-01-11T21:05:10.4252537Z if __name__ == "__main__":
2023-01-11T21:05:10.4252649Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4252772Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4252993Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4253204Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4253397Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4253585Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4253773Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4253961Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4254138Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4254370Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4254526Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4254531Z 
2023-01-11T21:05:10.4254535Z 
2023-01-11T21:05:10.4254628Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4254685Z import torch
2023-01-11T21:05:10.4254753Z import random
2023-01-11T21:05:10.4254867Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4254985Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4254991Z 
2023-01-11T21:05:10.4255066Z aten = torch.ops.aten
2023-01-11T21:05:10.4255198Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4255290Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4255294Z 
2023-01-11T21:05:10.4255298Z 
2023-01-11T21:05:10.4255430Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4255622Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4255739Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4255838Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4255899Z {
2023-01-11T21:05:10.4255994Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4256053Z     {
2023-01-11T21:05:10.4256130Z         #pragma omp for 
2023-01-11T21:05:10.4256198Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4256259Z         {
2023-01-11T21:05:10.4256336Z             #pragma GCC ivdep
2023-01-11T21:05:10.4256424Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4256486Z             {
2023-01-11T21:05:10.4256550Z                 {
2023-01-11T21:05:10.4256613Z                     {
2023-01-11T21:05:10.4256702Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4256798Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4256862Z                     }
2023-01-11T21:05:10.4256922Z                 }
2023-01-11T21:05:10.4256983Z             }
2023-01-11T21:05:10.4257076Z         }
2023-01-11T21:05:10.4257124Z     }
2023-01-11T21:05:10.4257181Z }
2023-01-11T21:05:10.4257258Z ''')
2023-01-11T21:05:10.4257264Z 
2023-01-11T21:05:10.4257268Z 
2023-01-11T21:05:10.4257354Z async_compile.wait(globals())
2023-01-11T21:05:10.4257425Z del async_compile
2023-01-11T21:05:10.4257431Z 
2023-01-11T21:05:10.4257499Z def call(args):
2023-01-11T21:05:10.4257616Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4257685Z     args.clear()
2023-01-11T21:05:10.4257908Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4258039Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4258104Z     del arg7_1
2023-01-11T21:05:10.4258245Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4258360Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4258428Z     del arg0_1
2023-01-11T21:05:10.4258590Z     del arg1_1
2023-01-11T21:05:10.4258649Z     return (buf1, )
2023-01-11T21:05:10.4258665Z 
2023-01-11T21:05:10.4258669Z 
2023-01-11T21:05:10.4258781Z if __name__ == "__main__":
2023-01-11T21:05:10.4258896Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4259017Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4259235Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4259427Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4259617Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4259806Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4259980Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4260168Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4260345Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4260578Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4260730Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4260735Z 
2023-01-11T21:05:10.4260739Z 
2023-01-11T21:05:10.4260831Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4260898Z import torch
2023-01-11T21:05:10.4260967Z import random
2023-01-11T21:05:10.4261079Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4261187Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4261192Z 
2023-01-11T21:05:10.4261267Z aten = torch.ops.aten
2023-01-11T21:05:10.4261396Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4261486Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4261493Z 
2023-01-11T21:05:10.4261497Z 
2023-01-11T21:05:10.4261584Z async_compile.wait(globals())
2023-01-11T21:05:10.4261654Z del async_compile
2023-01-11T21:05:10.4261659Z 
2023-01-11T21:05:10.4261728Z def call(args):
2023-01-11T21:05:10.4261846Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4261903Z     args.clear()
2023-01-11T21:05:10.4262042Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4262156Z     assert_size_stride(buf0, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4262222Z     del arg0_1
2023-01-11T21:05:10.4262287Z     del arg1_1
2023-01-11T21:05:10.4262351Z     del arg7_1
2023-01-11T21:05:10.4262419Z     return (buf0, )
2023-01-11T21:05:10.4262425Z 
2023-01-11T21:05:10.4262429Z 
2023-01-11T21:05:10.4262490Z if __name__ == "__main__":
2023-01-11T21:05:10.4262603Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4262753Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4262969Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4263161Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4263348Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4263533Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4263715Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4263887Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4264063Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4264294Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4264446Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4264721Z [2023-01-11 20:47:55,280] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 125
2023-01-11T21:05:10.4265152Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4265279Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4265535Z [2023-01-11 20:47:56,695] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 126
2023-01-11T21:05:10.4265796Z [2023-01-11 20:47:56,746] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 126
2023-01-11T21:05:10.4266195Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4266321Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4266561Z [2023-01-11 20:47:58,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 127
2023-01-11T21:05:10.4266820Z [2023-01-11 20:47:58,475] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 127
2023-01-11T21:05:10.4267212Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4267337Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4267592Z [2023-01-11 20:48:03,314] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 128
2023-01-11T21:05:10.4267855Z [2023-01-11 20:48:03,366] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 128
2023-01-11T21:05:10.4268249Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4268369Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4268621Z [2023-01-11 20:48:08,168] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 129
2023-01-11T21:05:10.4268879Z [2023-01-11 20:48:08,211] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 129
2023-01-11T21:05:10.4269307Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4269416Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4269666Z [2023-01-11 20:48:09,815] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 130
2023-01-11T21:05:10.4269671Z 
2023-01-11T21:05:10.4269675Z 
2023-01-11T21:05:10.4269767Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4269836Z import torch
2023-01-11T21:05:10.4269903Z import random
2023-01-11T21:05:10.4270017Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4270139Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4270147Z 
2023-01-11T21:05:10.4270222Z aten = torch.ops.aten
2023-01-11T21:05:10.4270342Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4270461Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4270467Z 
2023-01-11T21:05:10.4270472Z 
2023-01-11T21:05:10.4270606Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4270808Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4270932Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4271031Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4271089Z {
2023-01-11T21:05:10.4271186Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4271233Z     {
2023-01-11T21:05:10.4271322Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4271401Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4271462Z         {
2023-01-11T21:05:10.4271552Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4271614Z             {
2023-01-11T21:05:10.4271676Z                 {
2023-01-11T21:05:10.4271728Z                     {
2023-01-11T21:05:10.4271831Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4271928Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4271992Z                     }
2023-01-11T21:05:10.4272053Z                 }
2023-01-11T21:05:10.4272113Z             }
2023-01-11T21:05:10.4272173Z         }
2023-01-11T21:05:10.4272219Z     }
2023-01-11T21:05:10.4272276Z }
2023-01-11T21:05:10.4272354Z ''')
2023-01-11T21:05:10.4272358Z 
2023-01-11T21:05:10.4272362Z 
2023-01-11T21:05:10.4272449Z async_compile.wait(globals())
2023-01-11T21:05:10.4272519Z del async_compile
2023-01-11T21:05:10.4272524Z 
2023-01-11T21:05:10.4272590Z def call(args):
2023-01-11T21:05:10.4272710Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4272768Z     args.clear()
2023-01-11T21:05:10.4273006Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4273138Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4273204Z     del arg7_1
2023-01-11T21:05:10.4273342Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4273456Z     assert_size_stride(buf1, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4273521Z     del arg0_1
2023-01-11T21:05:10.4273573Z     del arg1_1
2023-01-11T21:05:10.4273642Z     return (buf1, )
2023-01-11T21:05:10.4273648Z 
2023-01-11T21:05:10.4273652Z 
2023-01-11T21:05:10.4273726Z if __name__ == "__main__":
2023-01-11T21:05:10.4273837Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4273958Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4274175Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4274443Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4274635Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4274811Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4274995Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4275177Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4275354Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4275582Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4275736Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4275741Z 
2023-01-11T21:05:10.4275745Z 
2023-01-11T21:05:10.4275844Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4275914Z import torch
2023-01-11T21:05:10.4275982Z import random
2023-01-11T21:05:10.4276084Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4276233Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4276238Z 
2023-01-11T21:05:10.4276316Z aten = torch.ops.aten
2023-01-11T21:05:10.4276448Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4276537Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4276542Z 
2023-01-11T21:05:10.4276546Z 
2023-01-11T21:05:10.4276631Z async_compile.wait(globals())
2023-01-11T21:05:10.4276701Z del async_compile
2023-01-11T21:05:10.4276705Z 
2023-01-11T21:05:10.4276773Z def call(args):
2023-01-11T21:05:10.4276877Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4276946Z     args.clear()
2023-01-11T21:05:10.4277086Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4277204Z     assert_size_stride(buf0, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4277270Z     del arg0_1
2023-01-11T21:05:10.4277335Z     del arg1_1
2023-01-11T21:05:10.4277399Z     del arg7_1
2023-01-11T21:05:10.4277457Z     return (buf0, )
2023-01-11T21:05:10.4277462Z 
2023-01-11T21:05:10.4277478Z 
2023-01-11T21:05:10.4277539Z if __name__ == "__main__":
2023-01-11T21:05:10.4277651Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4277771Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4277991Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4278184Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4278375Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4278563Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4278738Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4278924Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4279102Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4279336Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4350904Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4350928Z 
2023-01-11T21:05:10.4350933Z 
2023-01-11T21:05:10.4351132Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4351197Z import torch
2023-01-11T21:05:10.4351261Z import random
2023-01-11T21:05:10.4351371Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4351489Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4351494Z 
2023-01-11T21:05:10.4351774Z aten = torch.ops.aten
2023-01-11T21:05:10.4351904Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4351988Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4351994Z 
2023-01-11T21:05:10.4352004Z 
2023-01-11T21:05:10.4352201Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4352398Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4352511Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4352600Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4352652Z {
2023-01-11T21:05:10.4352744Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4352797Z     {
2023-01-11T21:05:10.4352866Z         #pragma omp for 
2023-01-11T21:05:10.4352940Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4352995Z         {
2023-01-11T21:05:10.4353061Z             #pragma GCC ivdep
2023-01-11T21:05:10.4353142Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4353201Z             {
2023-01-11T21:05:10.4353258Z                 {
2023-01-11T21:05:10.4353314Z                     {
2023-01-11T21:05:10.4353410Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4353556Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4353610Z                     }
2023-01-11T21:05:10.4353664Z                 }
2023-01-11T21:05:10.4353719Z             }
2023-01-11T21:05:10.4353774Z         }
2023-01-11T21:05:10.4353829Z     }
2023-01-11T21:05:10.4353879Z }
2023-01-11T21:05:10.4353947Z ''')
2023-01-11T21:05:10.4353952Z 
2023-01-11T21:05:10.4353962Z 
2023-01-11T21:05:10.4354039Z async_compile.wait(globals())
2023-01-11T21:05:10.4354103Z del async_compile
2023-01-11T21:05:10.4354108Z 
2023-01-11T21:05:10.4354169Z def call(args):
2023-01-11T21:05:10.4354280Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4354344Z     args.clear()
2023-01-11T21:05:10.4354581Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4354707Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4354762Z     del arg7_1
2023-01-11T21:05:10.4354902Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4355010Z     assert_size_stride(buf1, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4355070Z     del arg0_1
2023-01-11T21:05:10.4355128Z     del arg1_1
2023-01-11T21:05:10.4355185Z     return (buf1, )
2023-01-11T21:05:10.4355196Z 
2023-01-11T21:05:10.4355200Z 
2023-01-11T21:05:10.4355261Z if __name__ == "__main__":
2023-01-11T21:05:10.4355366Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4355480Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4355694Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4355886Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4356073Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4356254Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4356434Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4356605Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4356776Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4357001Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4357148Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4357154Z 
2023-01-11T21:05:10.4357158Z 
2023-01-11T21:05:10.4357275Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4357336Z import torch
2023-01-11T21:05:10.4357398Z import random
2023-01-11T21:05:10.4357505Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4357614Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4357619Z 
2023-01-11T21:05:10.4357688Z aten = torch.ops.aten
2023-01-11T21:05:10.4357814Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4357897Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4357903Z 
2023-01-11T21:05:10.4357907Z 
2023-01-11T21:05:10.4357985Z async_compile.wait(globals())
2023-01-11T21:05:10.4358049Z del async_compile
2023-01-11T21:05:10.4358054Z 
2023-01-11T21:05:10.4358115Z def call(args):
2023-01-11T21:05:10.4358222Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4358278Z     args.clear()
2023-01-11T21:05:10.4358413Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4358523Z     assert_size_stride(buf0, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4358582Z     del arg0_1
2023-01-11T21:05:10.4358642Z     del arg1_1
2023-01-11T21:05:10.4358737Z     del arg7_1
2023-01-11T21:05:10.4358803Z     return (buf0, )
2023-01-11T21:05:10.4358808Z 
2023-01-11T21:05:10.4358812Z 
2023-01-11T21:05:10.4358873Z if __name__ == "__main__":
2023-01-11T21:05:10.4358978Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4359091Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4359303Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4359491Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4359672Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4359850Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4360027Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4360196Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4360370Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4360749Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4360913Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4360919Z 
2023-01-11T21:05:10.4360923Z 
2023-01-11T21:05:10.4361010Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4361072Z import torch
2023-01-11T21:05:10.4361134Z import random
2023-01-11T21:05:10.4361244Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4361350Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4361362Z 
2023-01-11T21:05:10.4361425Z aten = torch.ops.aten
2023-01-11T21:05:10.4361555Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4361639Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4361644Z 
2023-01-11T21:05:10.4361649Z 
2023-01-11T21:05:10.4361781Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4361978Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4362093Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4362184Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4362232Z {
2023-01-11T21:05:10.4362325Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4362379Z     {
2023-01-11T21:05:10.4362462Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4362534Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4362589Z         {
2023-01-11T21:05:10.4362672Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4362722Z             {
2023-01-11T21:05:10.4362831Z                 {
2023-01-11T21:05:10.4362890Z                     {
2023-01-11T21:05:10.4362984Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4363078Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4363138Z                     }
2023-01-11T21:05:10.4363197Z                 }
2023-01-11T21:05:10.4363245Z             }
2023-01-11T21:05:10.4363299Z         }
2023-01-11T21:05:10.4363352Z     }
2023-01-11T21:05:10.4363404Z }
2023-01-11T21:05:10.4363476Z ''')
2023-01-11T21:05:10.4363482Z 
2023-01-11T21:05:10.4363486Z 
2023-01-11T21:05:10.4363570Z async_compile.wait(globals())
2023-01-11T21:05:10.4363634Z del async_compile
2023-01-11T21:05:10.4363639Z 
2023-01-11T21:05:10.4363695Z def call(args):
2023-01-11T21:05:10.4363804Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4363866Z     args.clear()
2023-01-11T21:05:10.4364095Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4364225Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4364287Z     del arg7_1
2023-01-11T21:05:10.4364456Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4364566Z     assert_size_stride(buf1, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4364620Z     del arg0_1
2023-01-11T21:05:10.4364678Z     del arg1_1
2023-01-11T21:05:10.4364740Z     return (buf1, )
2023-01-11T21:05:10.4364745Z 
2023-01-11T21:05:10.4364749Z 
2023-01-11T21:05:10.4364816Z if __name__ == "__main__":
2023-01-11T21:05:10.4364923Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4365038Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4365249Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4365436Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4365614Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4365793Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4365971Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4366148Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4366319Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4366541Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4366688Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4366952Z [2023-01-11 20:48:09,868] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 130
2023-01-11T21:05:10.4367344Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4367464Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4367712Z [2023-01-11 20:48:12,070] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 131
2023-01-11T21:05:10.4367969Z [2023-01-11 20:48:12,112] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 131
2023-01-11T21:05:10.4368359Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4368510Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4368761Z [2023-01-11 20:48:17,628] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 132
2023-01-11T21:05:10.4369018Z [2023-01-11 20:48:17,680] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 132
2023-01-11T21:05:10.4369406Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4369522Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4369767Z [2023-01-11 20:48:22,115] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 133
2023-01-11T21:05:10.4370019Z [2023-01-11 20:48:22,156] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 133
2023-01-11T21:05:10.4370456Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4370577Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4370825Z [2023-01-11 20:48:23,720] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 134
2023-01-11T21:05:10.4371077Z [2023-01-11 20:48:23,769] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 134
2023-01-11T21:05:10.4371465Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4371585Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4371829Z [2023-01-11 20:48:25,490] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 135
2023-01-11T21:05:10.4371834Z 
2023-01-11T21:05:10.4371839Z 
2023-01-11T21:05:10.4371924Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4371985Z import torch
2023-01-11T21:05:10.4372046Z import random
2023-01-11T21:05:10.4372149Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4372261Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4372266Z 
2023-01-11T21:05:10.4372336Z aten = torch.ops.aten
2023-01-11T21:05:10.4372461Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4372547Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4372554Z 
2023-01-11T21:05:10.4372558Z 
2023-01-11T21:05:10.4372639Z async_compile.wait(globals())
2023-01-11T21:05:10.4372701Z del async_compile
2023-01-11T21:05:10.4372706Z 
2023-01-11T21:05:10.4372770Z def call(args):
2023-01-11T21:05:10.4372874Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4372937Z     args.clear()
2023-01-11T21:05:10.4373068Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4373177Z     assert_size_stride(buf0, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4373238Z     del arg0_1
2023-01-11T21:05:10.4373295Z     del arg1_1
2023-01-11T21:05:10.4373351Z     del arg7_1
2023-01-11T21:05:10.4373407Z     return (buf0, )
2023-01-11T21:05:10.4373412Z 
2023-01-11T21:05:10.4373425Z 
2023-01-11T21:05:10.4373486Z if __name__ == "__main__":
2023-01-11T21:05:10.4373592Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4373738Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4373956Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4374148Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4374334Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4374513Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4374688Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4374865Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4375035Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4375265Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4375411Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4375418Z 
2023-01-11T21:05:10.4375423Z 
2023-01-11T21:05:10.4375507Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4375599Z import torch
2023-01-11T21:05:10.4375662Z import random
2023-01-11T21:05:10.4375764Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4375876Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4375881Z 
2023-01-11T21:05:10.4375950Z aten = torch.ops.aten
2023-01-11T21:05:10.4376075Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4376159Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4376164Z 
2023-01-11T21:05:10.4376167Z 
2023-01-11T21:05:10.4376295Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4376490Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4376601Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4376695Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4376743Z {
2023-01-11T21:05:10.4376830Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4376884Z     {
2023-01-11T21:05:10.4376954Z         #pragma omp for 
2023-01-11T21:05:10.4377030Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4377085Z         {
2023-01-11T21:05:10.4377151Z             #pragma GCC ivdep
2023-01-11T21:05:10.4377234Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4377289Z             {
2023-01-11T21:05:10.4377345Z                 {
2023-01-11T21:05:10.4377402Z                     {
2023-01-11T21:05:10.4377497Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4377587Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4377640Z                     }
2023-01-11T21:05:10.4377695Z                 }
2023-01-11T21:05:10.4377749Z             }
2023-01-11T21:05:10.4377801Z         }
2023-01-11T21:05:10.4377857Z     }
2023-01-11T21:05:10.4377910Z }
2023-01-11T21:05:10.4377980Z ''')
2023-01-11T21:05:10.4377985Z 
2023-01-11T21:05:10.4377989Z 
2023-01-11T21:05:10.4378065Z async_compile.wait(globals())
2023-01-11T21:05:10.4378129Z del async_compile
2023-01-11T21:05:10.4378134Z 
2023-01-11T21:05:10.4378197Z def call(args):
2023-01-11T21:05:10.4378305Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4378368Z     args.clear()
2023-01-11T21:05:10.4378689Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4378817Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4378877Z     del arg7_1
2023-01-11T21:05:10.4379003Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4379111Z     assert_size_stride(buf1, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4379207Z     del arg0_1
2023-01-11T21:05:10.4379265Z     del arg1_1
2023-01-11T21:05:10.4379328Z     return (buf1, )
2023-01-11T21:05:10.4379333Z 
2023-01-11T21:05:10.4379337Z 
2023-01-11T21:05:10.4379406Z if __name__ == "__main__":
2023-01-11T21:05:10.4379513Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4379621Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4379836Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4380022Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4380205Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4380385Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4380563Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4380741Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4380912Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4381158Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4381307Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4381314Z 
2023-01-11T21:05:10.4381318Z 
2023-01-11T21:05:10.4381406Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4381468Z import torch
2023-01-11T21:05:10.4381529Z import random
2023-01-11T21:05:10.4381636Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4381748Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4381753Z 
2023-01-11T21:05:10.4381823Z aten = torch.ops.aten
2023-01-11T21:05:10.4381942Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4382024Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4382031Z 
2023-01-11T21:05:10.4382035Z 
2023-01-11T21:05:10.4382115Z async_compile.wait(globals())
2023-01-11T21:05:10.4382178Z del async_compile
2023-01-11T21:05:10.4382183Z 
2023-01-11T21:05:10.4382244Z def call(args):
2023-01-11T21:05:10.4382355Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4382418Z     args.clear()
2023-01-11T21:05:10.4382550Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4382658Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4382711Z     del arg0_1
2023-01-11T21:05:10.4382769Z     del arg1_1
2023-01-11T21:05:10.4382825Z     del arg7_1
2023-01-11T21:05:10.4382887Z     return (buf0, )
2023-01-11T21:05:10.4382892Z 
2023-01-11T21:05:10.4382897Z 
2023-01-11T21:05:10.4382964Z if __name__ == "__main__":
2023-01-11T21:05:10.4383068Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4383183Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4383387Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4383572Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4383755Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4383935Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4384111Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4384287Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4384457Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4384680Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4384821Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4384861Z 
2023-01-11T21:05:10.4384865Z 
2023-01-11T21:05:10.4384947Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4385009Z import torch
2023-01-11T21:05:10.4385073Z import random
2023-01-11T21:05:10.4385179Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4385292Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4385297Z 
2023-01-11T21:05:10.4385366Z aten = torch.ops.aten
2023-01-11T21:05:10.4385489Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4385566Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4385571Z 
2023-01-11T21:05:10.4385583Z 
2023-01-11T21:05:10.4385702Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4385899Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4386008Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4386103Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4386158Z {
2023-01-11T21:05:10.4386247Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4386299Z     {
2023-01-11T21:05:10.4386404Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4386478Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4386533Z         {
2023-01-11T21:05:10.4386615Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4386670Z             {
2023-01-11T21:05:10.4386726Z                 {
2023-01-11T21:05:10.4386778Z                     {
2023-01-11T21:05:10.4386871Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4386961Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4387019Z                     }
2023-01-11T21:05:10.4387074Z                 }
2023-01-11T21:05:10.4387129Z             }
2023-01-11T21:05:10.4387183Z         }
2023-01-11T21:05:10.4387230Z     }
2023-01-11T21:05:10.4387282Z }
2023-01-11T21:05:10.4387356Z ''')
2023-01-11T21:05:10.4387360Z 
2023-01-11T21:05:10.4387365Z 
2023-01-11T21:05:10.4387447Z async_compile.wait(globals())
2023-01-11T21:05:10.4387509Z del async_compile
2023-01-11T21:05:10.4387514Z 
2023-01-11T21:05:10.4387577Z def call(args):
2023-01-11T21:05:10.4387688Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4387751Z     args.clear()
2023-01-11T21:05:10.4387973Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4388096Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4388156Z     del arg7_1
2023-01-11T21:05:10.4388288Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4388396Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4388456Z     del arg0_1
2023-01-11T21:05:10.4388512Z     del arg1_1
2023-01-11T21:05:10.4388572Z     return (buf1, )
2023-01-11T21:05:10.4388577Z 
2023-01-11T21:05:10.4388586Z 
2023-01-11T21:05:10.4388647Z if __name__ == "__main__":
2023-01-11T21:05:10.4388755Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4388870Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4389080Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4389265Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4389448Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4389627Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4389801Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4389977Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4390149Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4390401Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4390550Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4390556Z 
2023-01-11T21:05:10.4390560Z 
2023-01-11T21:05:10.4390647Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4390709Z import torch
2023-01-11T21:05:10.4390771Z import random
2023-01-11T21:05:10.4390874Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4390987Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4390993Z 
2023-01-11T21:05:10.4391063Z aten = torch.ops.aten
2023-01-11T21:05:10.4391190Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4391273Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4391278Z 
2023-01-11T21:05:10.4391282Z 
2023-01-11T21:05:10.4391362Z async_compile.wait(globals())
2023-01-11T21:05:10.4391428Z del async_compile
2023-01-11T21:05:10.4391432Z 
2023-01-11T21:05:10.4391494Z def call(args):
2023-01-11T21:05:10.4391604Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4391688Z     args.clear()
2023-01-11T21:05:10.4391826Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4391936Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4391996Z     del arg0_1
2023-01-11T21:05:10.4392052Z     del arg1_1
2023-01-11T21:05:10.4392109Z     del arg7_1
2023-01-11T21:05:10.4392171Z     return (buf0, )
2023-01-11T21:05:10.4392175Z 
2023-01-11T21:05:10.4392179Z 
2023-01-11T21:05:10.4392240Z if __name__ == "__main__":
2023-01-11T21:05:10.4392346Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4392461Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4392677Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4392866Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4393052Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4393235Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4393415Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4393588Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4393761Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4393985Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4394134Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4394401Z [2023-01-11 20:48:25,532] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 135
2023-01-11T21:05:10.4394809Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4394929Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4395179Z [2023-01-11 20:48:30,546] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 136
2023-01-11T21:05:10.4395435Z [2023-01-11 20:48:30,595] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 136
2023-01-11T21:05:10.4395834Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4395981Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4396230Z [2023-01-11 20:48:35,369] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 137
2023-01-11T21:05:10.4396486Z [2023-01-11 20:48:35,410] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 137
2023-01-11T21:05:10.4396886Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4397005Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4397254Z [2023-01-11 20:48:37,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 138
2023-01-11T21:05:10.4397535Z [2023-01-11 20:48:37,073] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 138
2023-01-11T21:05:10.4397925Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4398042Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4398287Z [2023-01-11 20:48:38,925] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 139
2023-01-11T21:05:10.4398539Z [2023-01-11 20:48:38,966] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 139
2023-01-11T21:05:10.4398930Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4399042Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4399284Z [2023-01-11 20:48:44,203] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 140
2023-01-11T21:05:10.4399289Z 
2023-01-11T21:05:10.4399294Z 
2023-01-11T21:05:10.4399379Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4399441Z import torch
2023-01-11T21:05:10.4399502Z import random
2023-01-11T21:05:10.4399609Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4399719Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4399724Z 
2023-01-11T21:05:10.4399796Z aten = torch.ops.aten
2023-01-11T21:05:10.4399916Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4400000Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4400005Z 
2023-01-11T21:05:10.4400009Z 
2023-01-11T21:05:10.4400135Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4400332Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4400443Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4400534Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4400727Z {
2023-01-11T21:05:10.4400852Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4400900Z     {
2023-01-11T21:05:10.4400968Z         #pragma omp for 
2023-01-11T21:05:10.4401040Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4401096Z         {
2023-01-11T21:05:10.4401168Z             #pragma GCC ivdep
2023-01-11T21:05:10.4401250Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4401373Z             {
2023-01-11T21:05:10.4401423Z                 {
2023-01-11T21:05:10.4401482Z                     {
2023-01-11T21:05:10.4401580Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4401673Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4401734Z                     }
2023-01-11T21:05:10.4401790Z                 }
2023-01-11T21:05:10.4401837Z             }
2023-01-11T21:05:10.4401892Z         }
2023-01-11T21:05:10.4401944Z     }
2023-01-11T21:05:10.4401996Z }
2023-01-11T21:05:10.4402072Z ''')
2023-01-11T21:05:10.4402077Z 
2023-01-11T21:05:10.4402081Z 
2023-01-11T21:05:10.4402162Z async_compile.wait(globals())
2023-01-11T21:05:10.4402226Z del async_compile
2023-01-11T21:05:10.4402231Z 
2023-01-11T21:05:10.4402293Z def call(args):
2023-01-11T21:05:10.4402398Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4402465Z     args.clear()
2023-01-11T21:05:10.4402696Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4402824Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4402884Z     del arg7_1
2023-01-11T21:05:10.4403067Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4403180Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4403235Z     del arg0_1
2023-01-11T21:05:10.4403295Z     del arg1_1
2023-01-11T21:05:10.4403358Z     return (buf1, )
2023-01-11T21:05:10.4403363Z 
2023-01-11T21:05:10.4403367Z 
2023-01-11T21:05:10.4403434Z if __name__ == "__main__":
2023-01-11T21:05:10.4403539Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4403652Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4403867Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4404057Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4404238Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4404422Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4404604Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4404781Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4404949Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4405173Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4405323Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4405328Z 
2023-01-11T21:05:10.4405332Z 
2023-01-11T21:05:10.4405419Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4405482Z import torch
2023-01-11T21:05:10.4405538Z import random
2023-01-11T21:05:10.4405644Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4405762Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4405767Z 
2023-01-11T21:05:10.4405837Z aten = torch.ops.aten
2023-01-11T21:05:10.4405964Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4406048Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4406053Z 
2023-01-11T21:05:10.4406057Z 
2023-01-11T21:05:10.4406138Z async_compile.wait(globals())
2023-01-11T21:05:10.4406194Z del async_compile
2023-01-11T21:05:10.4406207Z 
2023-01-11T21:05:10.4406262Z def call(args):
2023-01-11T21:05:10.4406373Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4406439Z     args.clear()
2023-01-11T21:05:10.4406578Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4406717Z     assert_size_stride(buf0, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4406778Z     del arg0_1
2023-01-11T21:05:10.4406838Z     del arg1_1
2023-01-11T21:05:10.4406888Z     del arg7_1
2023-01-11T21:05:10.4406958Z     return (buf0, )
2023-01-11T21:05:10.4406963Z 
2023-01-11T21:05:10.4406967Z 
2023-01-11T21:05:10.4407041Z if __name__ == "__main__":
2023-01-11T21:05:10.4407155Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4407272Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4407486Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4407675Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4407860Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4408033Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4408221Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4408405Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4408627Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4408862Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4409016Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4409021Z 
2023-01-11T21:05:10.4409025Z 
2023-01-11T21:05:10.4409119Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4409189Z import torch
2023-01-11T21:05:10.4409245Z import random
2023-01-11T21:05:10.4409359Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4409479Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4409484Z 
2023-01-11T21:05:10.4409561Z aten = torch.ops.aten
2023-01-11T21:05:10.4409696Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4409786Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4409791Z 
2023-01-11T21:05:10.4409795Z 
2023-01-11T21:05:10.4409929Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4410135Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4410239Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4410338Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4410399Z {
2023-01-11T21:05:10.4410496Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4410557Z     {
2023-01-11T21:05:10.4410645Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4410725Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4410773Z         {
2023-01-11T21:05:10.4410862Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4410923Z             {
2023-01-11T21:05:10.4410986Z                 {
2023-01-11T21:05:10.4411053Z                     {
2023-01-11T21:05:10.4411157Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4411254Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4411309Z                     }
2023-01-11T21:05:10.4411371Z                 }
2023-01-11T21:05:10.4411432Z             }
2023-01-11T21:05:10.4411494Z         }
2023-01-11T21:05:10.4411553Z     }
2023-01-11T21:05:10.4411611Z }
2023-01-11T21:05:10.4411676Z ''')
2023-01-11T21:05:10.4411693Z 
2023-01-11T21:05:10.4411697Z 
2023-01-11T21:05:10.4411773Z async_compile.wait(globals())
2023-01-11T21:05:10.4411844Z del async_compile
2023-01-11T21:05:10.4411849Z 
2023-01-11T21:05:10.4411917Z def call(args):
2023-01-11T21:05:10.4412033Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4412103Z     args.clear()
2023-01-11T21:05:10.4412338Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4412500Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4412553Z     del arg7_1
2023-01-11T21:05:10.4412693Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4412806Z     assert_size_stride(buf1, (1, 32, 55, 55, 55), (5324000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4412872Z     del arg0_1
2023-01-11T21:05:10.4412936Z     del arg1_1
2023-01-11T21:05:10.4413006Z     return (buf1, )
2023-01-11T21:05:10.4413011Z 
2023-01-11T21:05:10.4413015Z 
2023-01-11T21:05:10.4413091Z if __name__ == "__main__":
2023-01-11T21:05:10.4413204Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4413315Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4413532Z     arg0_1 = rand_strided((32, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4413725Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4413916Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4414102Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4414322Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4414507Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4414685Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4414899Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4415053Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4415059Z 
2023-01-11T21:05:10.4415063Z 
2023-01-11T21:05:10.4415156Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4415225Z import torch
2023-01-11T21:05:10.4415292Z import random
2023-01-11T21:05:10.4415409Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4415529Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4415534Z 
2023-01-11T21:05:10.4415610Z aten = torch.ops.aten
2023-01-11T21:05:10.4415733Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4415824Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4415829Z 
2023-01-11T21:05:10.4415833Z 
2023-01-11T21:05:10.4415920Z async_compile.wait(globals())
2023-01-11T21:05:10.4415989Z del async_compile
2023-01-11T21:05:10.4415994Z 
2023-01-11T21:05:10.4416063Z def call(args):
2023-01-11T21:05:10.4416179Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4416247Z     args.clear()
2023-01-11T21:05:10.4416389Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4416493Z     assert_size_stride(buf0, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4416562Z     del arg0_1
2023-01-11T21:05:10.4416628Z     del arg1_1
2023-01-11T21:05:10.4416693Z     del arg7_1
2023-01-11T21:05:10.4416762Z     return (buf0, )
2023-01-11T21:05:10.4416767Z 
2023-01-11T21:05:10.4416774Z 
2023-01-11T21:05:10.4416847Z if __name__ == "__main__":
2023-01-11T21:05:10.4416960Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4417082Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4417288Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4417481Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4417671Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4417860Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4418049Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4418266Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4418443Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4418803Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4418947Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4418952Z 
2023-01-11T21:05:10.4418971Z 
2023-01-11T21:05:10.4419052Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4419121Z import torch
2023-01-11T21:05:10.4419191Z import random
2023-01-11T21:05:10.4419307Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4419430Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4419434Z 
2023-01-11T21:05:10.4419514Z aten = torch.ops.aten
2023-01-11T21:05:10.4419650Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4419731Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4419736Z 
2023-01-11T21:05:10.4419740Z 
2023-01-11T21:05:10.4419873Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4420114Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4420236Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4420336Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4420396Z {
2023-01-11T21:05:10.4420495Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4420542Z     {
2023-01-11T21:05:10.4420617Z         #pragma omp for 
2023-01-11T21:05:10.4420699Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4420760Z         {
2023-01-11T21:05:10.4420839Z             #pragma GCC ivdep
2023-01-11T21:05:10.4420929Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4420993Z             {
2023-01-11T21:05:10.4421042Z                 {
2023-01-11T21:05:10.4421109Z                     {
2023-01-11T21:05:10.4421212Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4421308Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4421373Z                     }
2023-01-11T21:05:10.4421438Z                 }
2023-01-11T21:05:10.4421500Z             }
2023-01-11T21:05:10.4421547Z         }
2023-01-11T21:05:10.4421607Z     }
2023-01-11T21:05:10.4421667Z }
2023-01-11T21:05:10.4421747Z ''')
2023-01-11T21:05:10.4421752Z 
2023-01-11T21:05:10.4421756Z 
2023-01-11T21:05:10.4421846Z async_compile.wait(globals())
2023-01-11T21:05:10.4421916Z del async_compile
2023-01-11T21:05:10.4421922Z 
2023-01-11T21:05:10.4421990Z def call(args):
2023-01-11T21:05:10.4422095Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4422165Z     args.clear()
2023-01-11T21:05:10.4422404Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4422540Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4422607Z     del arg7_1
2023-01-11T21:05:10.4422748Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4422866Z     assert_size_stride(buf1, (1, 128, 55, 55, 55), (21296000, 166375, 3025, 55, 1))
2023-01-11T21:05:10.4422932Z     del arg0_1
2023-01-11T21:05:10.4422983Z     del arg1_1
2023-01-11T21:05:10.4423053Z     return (buf1, )
2023-01-11T21:05:10.4423059Z 
2023-01-11T21:05:10.4423063Z 
2023-01-11T21:05:10.4423137Z if __name__ == "__main__":
2023-01-11T21:05:10.4423251Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4423373Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4423592Z     arg0_1 = rand_strided((128, 3, 1, 1, 1), (3, 1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4423787Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4424014Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4424192Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4424383Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4424570Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4424749Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4424981Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4425138Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4425411Z [2023-01-11 20:48:44,254] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 140
2023-01-11T21:05:10.4425852Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4425984Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4426231Z [2023-01-11 20:48:49,225] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 141
2023-01-11T21:05:10.4426496Z [2023-01-11 20:48:49,267] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 141
2023-01-11T21:05:10.4426902Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4427031Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4427285Z [2023-01-11 20:48:50,842] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 142
2023-01-11T21:05:10.4427550Z [2023-01-11 20:48:50,892] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 142
2023-01-11T21:05:10.4427957Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4428085Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4428339Z [2023-01-11 20:48:52,654] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 143
2023-01-11T21:05:10.4428602Z [2023-01-11 20:48:52,696] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 143
2023-01-11T21:05:10.4429010Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4429138Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4429379Z [2023-01-11 20:48:57,588] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 144
2023-01-11T21:05:10.4429639Z [2023-01-11 20:48:57,637] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 144
2023-01-11T21:05:10.4430044Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4430203Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4430460Z [2023-01-11 20:49:02,375] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 145
2023-01-11T21:05:10.4430465Z 
2023-01-11T21:05:10.4430469Z 
2023-01-11T21:05:10.4430564Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4430632Z import torch
2023-01-11T21:05:10.4430702Z import random
2023-01-11T21:05:10.4430817Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4430926Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4430931Z 
2023-01-11T21:05:10.4431008Z aten = torch.ops.aten
2023-01-11T21:05:10.4431145Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4431237Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4431242Z 
2023-01-11T21:05:10.4431248Z 
2023-01-11T21:05:10.4431336Z async_compile.wait(globals())
2023-01-11T21:05:10.4431408Z del async_compile
2023-01-11T21:05:10.4431413Z 
2023-01-11T21:05:10.4431482Z def call(args):
2023-01-11T21:05:10.4431665Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4431725Z     args.clear()
2023-01-11T21:05:10.4431867Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4431983Z     assert_size_stride(buf0, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4432050Z     del arg0_1
2023-01-11T21:05:10.4432115Z     del arg1_1
2023-01-11T21:05:10.4432179Z     del arg7_1
2023-01-11T21:05:10.4432249Z     return (buf0, )
2023-01-11T21:05:10.4432253Z 
2023-01-11T21:05:10.4432258Z 
2023-01-11T21:05:10.4432332Z if __name__ == "__main__":
2023-01-11T21:05:10.4432433Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4432556Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4432784Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4432983Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4433174Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4433363Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4433552Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4433724Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4433905Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4434138Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4434295Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4434303Z 
2023-01-11T21:05:10.4434307Z 
2023-01-11T21:05:10.4434402Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4434472Z import torch
2023-01-11T21:05:10.4434541Z import random
2023-01-11T21:05:10.4434659Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4434768Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4434785Z 
2023-01-11T21:05:10.4434849Z aten = torch.ops.aten
2023-01-11T21:05:10.4434985Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4435076Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4435081Z 
2023-01-11T21:05:10.4435085Z 
2023-01-11T21:05:10.4435218Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4435423Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4435541Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4435640Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4435727Z {
2023-01-11T21:05:10.4435812Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4435871Z     {
2023-01-11T21:05:10.4435960Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4436043Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4436105Z         {
2023-01-11T21:05:10.4436194Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4436243Z             {
2023-01-11T21:05:10.4436306Z                 {
2023-01-11T21:05:10.4436373Z                     {
2023-01-11T21:05:10.4436474Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4436573Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4436638Z                     }
2023-01-11T21:05:10.4436702Z                 }
2023-01-11T21:05:10.4436750Z             }
2023-01-11T21:05:10.4436810Z         }
2023-01-11T21:05:10.4436870Z     }
2023-01-11T21:05:10.4436929Z }
2023-01-11T21:05:10.4437007Z ''')
2023-01-11T21:05:10.4437015Z 
2023-01-11T21:05:10.4437019Z 
2023-01-11T21:05:10.4437107Z async_compile.wait(globals())
2023-01-11T21:05:10.4437177Z del async_compile
2023-01-11T21:05:10.4437182Z 
2023-01-11T21:05:10.4437238Z def call(args):
2023-01-11T21:05:10.4437389Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4437461Z     args.clear()
2023-01-11T21:05:10.4437699Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4437833Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4437903Z     del arg7_1
2023-01-11T21:05:10.4438042Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4438157Z     assert_size_stride(buf1, (1, 32, 53, 53, 53), (4764064, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4438210Z     del arg0_1
2023-01-11T21:05:10.4438274Z     del arg1_1
2023-01-11T21:05:10.4438347Z     return (buf1, )
2023-01-11T21:05:10.4438352Z 
2023-01-11T21:05:10.4438356Z 
2023-01-11T21:05:10.4438430Z if __name__ == "__main__":
2023-01-11T21:05:10.4438547Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4438670Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4438892Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4439086Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4439264Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4439449Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4439635Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4439819Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4439997Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4440227Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4440385Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4440391Z 
2023-01-11T21:05:10.4440395Z 
2023-01-11T21:05:10.4440489Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4440545Z import torch
2023-01-11T21:05:10.4440762Z import random
2023-01-11T21:05:10.4440881Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4441002Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4441007Z 
2023-01-11T21:05:10.4441085Z aten = torch.ops.aten
2023-01-11T21:05:10.4441222Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4441314Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4441319Z 
2023-01-11T21:05:10.4441323Z 
2023-01-11T21:05:10.4441411Z async_compile.wait(globals())
2023-01-11T21:05:10.4441529Z del async_compile
2023-01-11T21:05:10.4441534Z 
2023-01-11T21:05:10.4441605Z def call(args):
2023-01-11T21:05:10.4441722Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4441796Z     args.clear()
2023-01-11T21:05:10.4441937Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4442057Z     assert_size_stride(buf0, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4442124Z     del arg0_1
2023-01-11T21:05:10.4442176Z     del arg1_1
2023-01-11T21:05:10.4442241Z     del arg7_1
2023-01-11T21:05:10.4442315Z     return (buf0, )
2023-01-11T21:05:10.4442320Z 
2023-01-11T21:05:10.4442324Z 
2023-01-11T21:05:10.4442398Z if __name__ == "__main__":
2023-01-11T21:05:10.4442513Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4442636Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4442863Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4443060Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4443277Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4443470Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4443656Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4443842Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4444019Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4444252Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4444407Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4444413Z 
2023-01-11T21:05:10.4444420Z 
2023-01-11T21:05:10.4444512Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4444581Z import torch
2023-01-11T21:05:10.4444636Z import random
2023-01-11T21:05:10.4444753Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4444876Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4444881Z 
2023-01-11T21:05:10.4444959Z aten = torch.ops.aten
2023-01-11T21:05:10.4445091Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4445182Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4445187Z 
2023-01-11T21:05:10.4445191Z 
2023-01-11T21:05:10.4445325Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4445531Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4445637Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4445739Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4445800Z {
2023-01-11T21:05:10.4445902Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4445962Z     {
2023-01-11T21:05:10.4446040Z         #pragma omp for 
2023-01-11T21:05:10.4446123Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4446172Z         {
2023-01-11T21:05:10.4446254Z             #pragma GCC ivdep
2023-01-11T21:05:10.4446346Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4446409Z             {
2023-01-11T21:05:10.4446472Z                 {
2023-01-11T21:05:10.4446537Z                     {
2023-01-11T21:05:10.4446628Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4446726Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4446791Z                     }
2023-01-11T21:05:10.4446854Z                 }
2023-01-11T21:05:10.4446915Z             }
2023-01-11T21:05:10.4446974Z         }
2023-01-11T21:05:10.4447035Z     }
2023-01-11T21:05:10.4447082Z }
2023-01-11T21:05:10.4447158Z ''')
2023-01-11T21:05:10.4447164Z 
2023-01-11T21:05:10.4447195Z 
2023-01-11T21:05:10.4447285Z async_compile.wait(globals())
2023-01-11T21:05:10.4447357Z del async_compile
2023-01-11T21:05:10.4447362Z 
2023-01-11T21:05:10.4447430Z def call(args):
2023-01-11T21:05:10.4447550Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4447620Z     args.clear()
2023-01-11T21:05:10.4447859Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4447982Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4448051Z     del arg7_1
2023-01-11T21:05:10.4448192Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (1, 1, 1), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4448310Z     assert_size_stride(buf1, (1, 128, 53, 53, 53), (19056256, 148877, 2809, 53, 1))
2023-01-11T21:05:10.4448377Z     del arg0_1
2023-01-11T21:05:10.4448441Z     del arg1_1
2023-01-11T21:05:10.4448513Z     return (buf1, )
2023-01-11T21:05:10.4448520Z 
2023-01-11T21:05:10.4448524Z 
2023-01-11T21:05:10.4448599Z if __name__ == "__main__":
2023-01-11T21:05:10.4448699Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4448869Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4449095Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4449290Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4449482Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4449669Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4449859Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4450032Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4450214Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4450448Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4450606Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4450611Z 
2023-01-11T21:05:10.4450615Z 
2023-01-11T21:05:10.4450710Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4450778Z import torch
2023-01-11T21:05:10.4450847Z import random
2023-01-11T21:05:10.4450962Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4451071Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4451088Z 
2023-01-11T21:05:10.4451151Z aten = torch.ops.aten
2023-01-11T21:05:10.4451286Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4451378Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4451383Z 
2023-01-11T21:05:10.4451387Z 
2023-01-11T21:05:10.4451474Z async_compile.wait(globals())
2023-01-11T21:05:10.4451548Z del async_compile
2023-01-11T21:05:10.4451553Z 
2023-01-11T21:05:10.4451624Z def call(args):
2023-01-11T21:05:10.4451743Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4451812Z     args.clear()
2023-01-11T21:05:10.4451942Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4452058Z     assert_size_stride(buf0, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4452123Z     del arg0_1
2023-01-11T21:05:10.4452188Z     del arg1_1
2023-01-11T21:05:10.4452251Z     del arg7_1
2023-01-11T21:05:10.4452320Z     return (buf0, )
2023-01-11T21:05:10.4452325Z 
2023-01-11T21:05:10.4452329Z 
2023-01-11T21:05:10.4452406Z if __name__ == "__main__":
2023-01-11T21:05:10.4452506Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4452627Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4452846Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4453069Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4453262Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4453448Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4453634Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4453819Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4453985Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4454219Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4454374Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4454643Z [2023-01-11 20:49:02,416] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 145
2023-01-11T21:05:10.4455084Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4455213Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4455470Z [2023-01-11 20:49:04,059] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 146
2023-01-11T21:05:10.4455734Z [2023-01-11 20:49:04,108] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 146
2023-01-11T21:05:10.4456141Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4456268Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4456527Z [2023-01-11 20:49:06,305] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 147
2023-01-11T21:05:10.4456777Z [2023-01-11 20:49:06,347] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 147
2023-01-11T21:05:10.4457183Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4457309Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4457564Z [2023-01-11 20:49:11,734] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 148
2023-01-11T21:05:10.4457828Z [2023-01-11 20:49:11,789] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 148
2023-01-11T21:05:10.4457836Z 
2023-01-11T21:05:10.4457840Z 
2023-01-11T21:05:10.4457935Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4458004Z import torch
2023-01-11T21:05:10.4458072Z import random
2023-01-11T21:05:10.4458188Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4458295Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4458300Z 
2023-01-11T21:05:10.4458376Z aten = torch.ops.aten
2023-01-11T21:05:10.4458601Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4458696Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4458701Z 
2023-01-11T21:05:10.4458706Z 
2023-01-11T21:05:10.4458842Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4459049Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4459210Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4459312Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4459360Z {
2023-01-11T21:05:10.4459460Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4459523Z     {
2023-01-11T21:05:10.4459614Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4459697Z         for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.4459760Z         {
2023-01-11T21:05:10.4459852Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4459902Z             {
2023-01-11T21:05:10.4459967Z                 {
2023-01-11T21:05:10.4460034Z                     {
2023-01-11T21:05:10.4460137Z                         auto tmp0 = in_ptr0[i0 + (3*i1)];
2023-01-11T21:05:10.4460237Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4460303Z                     }
2023-01-11T21:05:10.4460369Z                 }
2023-01-11T21:05:10.4460421Z             }
2023-01-11T21:05:10.4460483Z         }
2023-01-11T21:05:10.4460544Z     }
2023-01-11T21:05:10.4460604Z }
2023-01-11T21:05:10.4460682Z ''')
2023-01-11T21:05:10.4460687Z 
2023-01-11T21:05:10.4460691Z 
2023-01-11T21:05:10.4460812Z async_compile.wait(globals())
2023-01-11T21:05:10.4460886Z del async_compile
2023-01-11T21:05:10.4460890Z 
2023-01-11T21:05:10.4460946Z def call(args):
2023-01-11T21:05:10.4461065Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4461135Z     args.clear()
2023-01-11T21:05:10.4461374Z     buf0 = empty_strided((1, 3, 55, 55, 55), (499125, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4461507Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4461574Z     del arg7_1
2023-01-11T21:05:10.4461715Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 1)
2023-01-11T21:05:10.4461820Z     assert_size_stride(buf1, (1, 32, 51, 51, 51), (4244832, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4461887Z     del arg0_1
2023-01-11T21:05:10.4461952Z     del arg1_1
2023-01-11T21:05:10.4462022Z     return (buf1, )
2023-01-11T21:05:10.4462027Z 
2023-01-11T21:05:10.4462033Z 
2023-01-11T21:05:10.4462107Z if __name__ == "__main__":
2023-01-11T21:05:10.4462220Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4462343Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4462562Z     arg0_1 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4462742Z     arg1_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4462932Z     arg2_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4463120Z     arg3_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4463307Z     arg4_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4463493Z     arg5_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4463672Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4463902Z     arg7_1 = rand_strided((1, 3, 55, 55, 55), (499125, 1, 9075, 165, 3), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4464058Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4464064Z 
2023-01-11T21:05:10.4464068Z 
2023-01-11T21:05:10.4464162Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4464217Z import torch
2023-01-11T21:05:10.4464288Z import random
2023-01-11T21:05:10.4464403Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4464523Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4464528Z 
2023-01-11T21:05:10.4464606Z aten = torch.ops.aten
2023-01-11T21:05:10.4464740Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4464876Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4464881Z 
2023-01-11T21:05:10.4464885Z 
2023-01-11T21:05:10.4464971Z async_compile.wait(globals())
2023-01-11T21:05:10.4465029Z del async_compile
2023-01-11T21:05:10.4465036Z 
2023-01-11T21:05:10.4465105Z def call(args):
2023-01-11T21:05:10.4465221Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4465291Z     args.clear()
2023-01-11T21:05:10.4465431Z     buf0 = aten.convolution(arg7_1, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4465549Z     assert_size_stride(buf0, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4465615Z     del arg0_1
2023-01-11T21:05:10.4465668Z     del arg1_1
2023-01-11T21:05:10.4465732Z     del arg7_1
2023-01-11T21:05:10.4465804Z     return (buf0, )
2023-01-11T21:05:10.4465809Z 
2023-01-11T21:05:10.4465813Z 
2023-01-11T21:05:10.4465888Z if __name__ == "__main__":
2023-01-11T21:05:10.4466007Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4466130Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4466383Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4466581Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4466763Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4466952Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4467140Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4467324Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4467551Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4467856Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4468013Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4468018Z 
2023-01-11T21:05:10.4468022Z 
2023-01-11T21:05:10.4468117Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4468172Z import torch
2023-01-11T21:05:10.4468240Z import random
2023-01-11T21:05:10.4468354Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4468471Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4468475Z 
2023-01-11T21:05:10.4468550Z aten = torch.ops.aten
2023-01-11T21:05:10.4468683Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4468772Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4468776Z 
2023-01-11T21:05:10.4468780Z 
2023-01-11T21:05:10.4468913Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4469103Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4469224Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4469322Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4469382Z {
2023-01-11T21:05:10.4469481Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4469541Z     {
2023-01-11T21:05:10.4469616Z         #pragma omp for 
2023-01-11T21:05:10.4469685Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4469746Z         {
2023-01-11T21:05:10.4469825Z             #pragma GCC ivdep
2023-01-11T21:05:10.4469914Z             for(long i1=0; i1<166375; i1+=1)
2023-01-11T21:05:10.4469977Z             {
2023-01-11T21:05:10.4470040Z                 {
2023-01-11T21:05:10.4470105Z                     {
2023-01-11T21:05:10.4470195Z                         auto tmp0 = in_ptr0[i0 + (12*i1)];
2023-01-11T21:05:10.4470291Z                         out_ptr0[i1 + (166375*i0)] = tmp0;
2023-01-11T21:05:10.4470357Z                     }
2023-01-11T21:05:10.4470420Z                 }
2023-01-11T21:05:10.4470520Z             }
2023-01-11T21:05:10.4470580Z         }
2023-01-11T21:05:10.4470640Z     }
2023-01-11T21:05:10.4470686Z }
2023-01-11T21:05:10.4470764Z ''')
2023-01-11T21:05:10.4470769Z 
2023-01-11T21:05:10.4470773Z 
2023-01-11T21:05:10.4470865Z async_compile.wait(globals())
2023-01-11T21:05:10.4470936Z del async_compile
2023-01-11T21:05:10.4470940Z 
2023-01-11T21:05:10.4471009Z def call(args):
2023-01-11T21:05:10.4471124Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1 = args
2023-01-11T21:05:10.4471194Z     args.clear()
2023-01-11T21:05:10.4471419Z     buf0 = empty_strided((1, 12, 55, 55, 55), (1996500, 166375, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4471552Z     kernel_cpp_0(c_void_p(arg7_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4471618Z     del arg7_1
2023-01-11T21:05:10.4471758Z     buf1 = aten.convolution(buf0, arg0_1, arg1_1, (1, 1, 1), (0, 0, 0), (2, 2, 2), False, (0, 0, 0), 4)
2023-01-11T21:05:10.4471873Z     assert_size_stride(buf1, (1, 128, 51, 51, 51), (16979328, 132651, 2601, 51, 1))
2023-01-11T21:05:10.4471941Z     del arg0_1
2023-01-11T21:05:10.4472007Z     del arg1_1
2023-01-11T21:05:10.4472063Z     return (buf1, )
2023-01-11T21:05:10.4472080Z 
2023-01-11T21:05:10.4472114Z 
2023-01-11T21:05:10.4472176Z if __name__ == "__main__":
2023-01-11T21:05:10.4472288Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4472407Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4472628Z     arg0_1 = rand_strided((128, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4472820Z     arg1_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4473011Z     arg2_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4473201Z     arg3_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4473387Z     arg4_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4473563Z     arg5_1 = rand_strided((128, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4473739Z     arg6_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4473973Z     arg7_1 = rand_strided((1, 12, 55, 55, 55), (1996500, 1, 36300, 660, 12), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4474126Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1]))
2023-01-11T21:05:10.4474131Z 
2023-01-11T21:05:10.4474196Z ok (141.108s)
2023-01-11T21:05:10.4474649Z   test_conv_functional_bn_fuse_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4474774Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4475032Z [2023-01-11 20:49:16,611] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 149
2023-01-11T21:05:10.4475295Z [2023-01-11 20:49:16,654] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 149
2023-01-11T21:05:10.4475301Z 
2023-01-11T21:05:10.4475394Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4475449Z import torch
2023-01-11T21:05:10.4475517Z import random
2023-01-11T21:05:10.4475631Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4475749Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4475754Z 
2023-01-11T21:05:10.4475833Z aten = torch.ops.aten
2023-01-11T21:05:10.4475964Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4476053Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4476058Z 
2023-01-11T21:05:10.4476062Z 
2023-01-11T21:05:10.4476148Z async_compile.wait(globals())
2023-01-11T21:05:10.4476241Z del async_compile
2023-01-11T21:05:10.4476246Z 
2023-01-11T21:05:10.4476314Z def call(args):
2023-01-11T21:05:10.4476425Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1 = args
2023-01-11T21:05:10.4476497Z     args.clear()
2023-01-11T21:05:10.4476632Z     buf0 = aten.convolution(arg6_1, arg2_1, arg3_1, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4476746Z     assert_size_stride(buf0, (1, 64, 554, 54), (1914624, 29916, 54, 1))
2023-01-11T21:05:10.4476814Z     del arg2_1
2023-01-11T21:05:10.4476866Z     del arg3_1
2023-01-11T21:05:10.4476930Z     del arg6_1
2023-01-11T21:05:10.4476999Z     return (buf0, )
2023-01-11T21:05:10.4477004Z 
2023-01-11T21:05:10.4477009Z 
2023-01-11T21:05:10.4477085Z if __name__ == "__main__":
2023-01-11T21:05:10.4477200Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4477322Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4477518Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4477700Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4477908Z     arg2_1 = rand_strided((64, 3, 3, 3), (27, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4478129Z     arg3_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4478413Z     arg4_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4478673Z     arg5_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4478899Z     arg6_1 = rand_strided((1, 3, 556, 56), (93408, 31136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4479051Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1]))
2023-01-11T21:05:10.4479056Z 
2023-01-11T21:05:10.4479123Z ok (0.758s)
2023-01-11T21:05:10.4479579Z   test_convolution1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4479710Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4479961Z [2023-01-11 20:49:17,317] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 150
2023-01-11T21:05:10.4480227Z [2023-01-11 20:49:20,062] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 150
2023-01-11T21:05:10.4480233Z 
2023-01-11T21:05:10.4480328Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4480397Z import torch
2023-01-11T21:05:10.4480467Z import random
2023-01-11T21:05:10.4480691Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4480816Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4480821Z 
2023-01-11T21:05:10.4480903Z aten = torch.ops.aten
2023-01-11T21:05:10.4481026Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4481119Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4481124Z 
2023-01-11T21:05:10.4481130Z 
2023-01-11T21:05:10.4481265Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4481472Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4481595Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.4481693Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:05:10.4481756Z {
2023-01-11T21:05:10.4481855Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4481903Z     {
2023-01-11T21:05:10.4481983Z         #pragma omp for 
2023-01-11T21:05:10.4482067Z         for(long i0=0; i0<2352; i0+=1)
2023-01-11T21:05:10.4482129Z         {
2023-01-11T21:05:10.4482194Z             {
2023-01-11T21:05:10.4482257Z                 {
2023-01-11T21:05:10.4482416Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.4482511Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.4482617Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:05:10.4482714Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:05:10.4482805Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4482892Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.4482957Z                 }
2023-01-11T21:05:10.4483006Z             }
2023-01-11T21:05:10.4483067Z         }
2023-01-11T21:05:10.4483128Z     }
2023-01-11T21:05:10.4483189Z }
2023-01-11T21:05:10.4483270Z ''')
2023-01-11T21:05:10.4483276Z 
2023-01-11T21:05:10.4483280Z 
2023-01-11T21:05:10.4483371Z async_compile.wait(globals())
2023-01-11T21:05:10.4483443Z del async_compile
2023-01-11T21:05:10.4483448Z 
2023-01-11T21:05:10.4483504Z def call(args):
2023-01-11T21:05:10.4483608Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.4483680Z     args.clear()
2023-01-11T21:05:10.4483836Z     buf0 = aten.convolution(primals_3, primals_1, primals_2, (1, 1), (0, 0), (1, 1), False, (0, 0), 1)
2023-01-11T21:05:10.4483948Z     assert_size_stride(buf0, (2, 6, 14, 14), (1176, 196, 14, 1))
2023-01-11T21:05:10.4484063Z     del primals_2
2023-01-11T21:05:10.4484189Z     buf1 = as_strided(buf0, (2, 6, 14, 14), (1176, 196, 14, 1)); del buf0  # reuse
2023-01-11T21:05:10.4484405Z     buf2 = empty_strided((2, 6, 14, 14), (1176, 196, 14, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.4484526Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4484630Z     return (buf1, primals_1, primals_3, buf2, )
2023-01-11T21:05:10.4484635Z 
2023-01-11T21:05:10.4484640Z 
2023-01-11T21:05:10.4484714Z if __name__ == "__main__":
2023-01-11T21:05:10.4484827Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4484950Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4485170Z     primals_1 = rand_strided((6, 5, 3, 3), (45, 9, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4485370Z     primals_2 = rand_strided((6, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4485595Z     primals_3 = rand_strided((2, 5, 16, 16), (1280, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4485720Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.4485738Z 
2023-01-11T21:05:10.4485792Z ok (2.936s)
2023-01-11T21:05:10.4486249Z   test_convolution2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4486377Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4486637Z [2023-01-11 20:49:20,206] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 151
2023-01-11T21:05:10.4486906Z [2023-01-11 20:49:20,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 151
2023-01-11T21:05:10.4486914Z 
2023-01-11T21:05:10.4487010Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4487081Z import torch
2023-01-11T21:05:10.4487152Z import random
2023-01-11T21:05:10.4487255Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4487376Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4487381Z 
2023-01-11T21:05:10.4487462Z aten = torch.ops.aten
2023-01-11T21:05:10.4487594Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4487686Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4487691Z 
2023-01-11T21:05:10.4487695Z 
2023-01-11T21:05:10.4487782Z async_compile.wait(globals())
2023-01-11T21:05:10.4487853Z del async_compile
2023-01-11T21:05:10.4487858Z 
2023-01-11T21:05:10.4487960Z def call(args):
2023-01-11T21:05:10.4488028Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4488097Z     args.clear()
2023-01-11T21:05:10.4488230Z     buf0 = aten.convolution(arg0_1, arg1_1, arg2_1, (4,), (0,), (1,), True, (0,), 1)
2023-01-11T21:05:10.4488337Z     assert_size_stride(buf0, (2, 16, 364), (5824, 364, 1))
2023-01-11T21:05:10.4488403Z     del arg0_1
2023-01-11T21:05:10.4488471Z     del arg1_1
2023-01-11T21:05:10.4488534Z     del arg2_1
2023-01-11T21:05:10.4488591Z     return (buf0, )
2023-01-11T21:05:10.4488608Z 
2023-01-11T21:05:10.4488612Z 
2023-01-11T21:05:10.4488674Z if __name__ == "__main__":
2023-01-11T21:05:10.4488787Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4488909Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4489121Z     arg0_1 = rand_strided((2, 32, 90), (2880, 90, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4489329Z     arg1_1 = rand_strided((32, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4489528Z     arg2_1 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4489649Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4489655Z 
2023-01-11T21:05:10.4489769Z ok (0.200s)
2023-01-11T21:05:10.4490203Z   test_cos_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4490333Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4490594Z [2023-01-11 20:49:20,341] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 152
2023-01-11T21:05:10.4490858Z [2023-01-11 20:49:23,075] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 152
2023-01-11T21:05:10.4490866Z 
2023-01-11T21:05:10.4490959Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4491029Z import torch
2023-01-11T21:05:10.4491098Z import random
2023-01-11T21:05:10.4491217Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4491325Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4491345Z 
2023-01-11T21:05:10.4491409Z aten = torch.ops.aten
2023-01-11T21:05:10.4491543Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4491635Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4491640Z 
2023-01-11T21:05:10.4491644Z 
2023-01-11T21:05:10.4491777Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4491984Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4492105Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4492207Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4492307Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4492355Z {
2023-01-11T21:05:10.4492453Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4492516Z     {
2023-01-11T21:05:10.4492594Z         #pragma omp for 
2023-01-11T21:05:10.4492675Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4492737Z         {
2023-01-11T21:05:10.4492880Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4492963Z             auto tmp1 = tmp0.cos();
2023-01-11T21:05:10.4493098Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.4493182Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.4493314Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4493398Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.4493480Z             auto tmp6 = tmp5.cos();
2023-01-11T21:05:10.4493573Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4493685Z             tmp6.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4493746Z         }
2023-01-11T21:05:10.4493841Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4493923Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.4493986Z         {
2023-01-11T21:05:10.4494068Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4494157Z             auto tmp1 = std::cos(tmp0);
2023-01-11T21:05:10.4494244Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.4494325Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.4494422Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.4494504Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.4494591Z             auto tmp6 = std::cos(tmp5);
2023-01-11T21:05:10.4494669Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.4494745Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.4494792Z         }
2023-01-11T21:05:10.4494851Z     }
2023-01-11T21:05:10.4494911Z }
2023-01-11T21:05:10.4494994Z ''')
2023-01-11T21:05:10.4494998Z 
2023-01-11T21:05:10.4495003Z 
2023-01-11T21:05:10.4495090Z async_compile.wait(globals())
2023-01-11T21:05:10.4495162Z del async_compile
2023-01-11T21:05:10.4495167Z 
2023-01-11T21:05:10.4495236Z def call(args):
2023-01-11T21:05:10.4495323Z     arg0_1, = args
2023-01-11T21:05:10.4495395Z     args.clear()
2023-01-11T21:05:10.4495598Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4495797Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4495962Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4496031Z     del arg0_1
2023-01-11T21:05:10.4496106Z     return (buf0, buf1, )
2023-01-11T21:05:10.4496111Z 
2023-01-11T21:05:10.4496116Z 
2023-01-11T21:05:10.4496191Z if __name__ == "__main__":
2023-01-11T21:05:10.4496292Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4496414Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4496615Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4496722Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4496729Z 
2023-01-11T21:05:10.4496793Z ok (2.799s)
2023-01-11T21:05:10.4496985Z   test_cpp_wrapper_cpu (__main__.CpuTests) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:05:10.4497120Z   test_cudnn_rnn_cpu (__main__.CpuTests) ... skip: requires CUDA (0.006s)
2023-01-11T21:05:10.4497332Z   test_dense_mask_index_cpu (__main__.CpuTests) ... skip: https://github.com/pytorch/torchdynamo/issues/1697 (0.002s)
2023-01-11T21:05:10.4497775Z   test_div1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4497893Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4498157Z [2023-01-11 20:49:23,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 153
2023-01-11T21:05:10.4498419Z [2023-01-11 20:49:25,981] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 153
2023-01-11T21:05:10.4498425Z 
2023-01-11T21:05:10.4498619Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4498693Z import torch
2023-01-11T21:05:10.4498765Z import random
2023-01-11T21:05:10.4498884Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4499005Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4499010Z 
2023-01-11T21:05:10.4499074Z aten = torch.ops.aten
2023-01-11T21:05:10.4499211Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4499304Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4499344Z 
2023-01-11T21:05:10.4499348Z 
2023-01-11T21:05:10.4499488Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4499696Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4499815Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4499919Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4500018Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4500102Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4500195Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.4500288Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4500381Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.4500441Z {
2023-01-11T21:05:10.4500539Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4500599Z     {
2023-01-11T21:05:10.4500661Z         #pragma omp for 
2023-01-11T21:05:10.4500744Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4500805Z         {
2023-01-11T21:05:10.4500940Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4501104Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.4501191Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4501280Z             auto tmp3 = tmp2.floor();
2023-01-11T21:05:10.4501351Z             auto tmp4 = tmp2.trunc();
2023-01-11T21:05:10.4501442Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4501532Z             tmp3.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4501621Z             tmp4.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.4501708Z             tmp2.store(out_ptr3 + 16*i0);
2023-01-11T21:05:10.4501794Z             tmp3.store(out_ptr4 + 16*i0);
2023-01-11T21:05:10.4501855Z         }
2023-01-11T21:05:10.4501938Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4502021Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.4502084Z         {
2023-01-11T21:05:10.4502168Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4502249Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4502333Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4502425Z             auto tmp3 = std::floor(tmp2);
2023-01-11T21:05:10.4502503Z             auto tmp4 = std::trunc(tmp2);
2023-01-11T21:05:10.4502583Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4502660Z             out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4502736Z             out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.4502812Z             out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.4502888Z             out_ptr4[i0] = tmp3;
2023-01-11T21:05:10.4502947Z         }
2023-01-11T21:05:10.4502994Z     }
2023-01-11T21:05:10.4503053Z }
2023-01-11T21:05:10.4503132Z ''')
2023-01-11T21:05:10.4503137Z 
2023-01-11T21:05:10.4503142Z 
2023-01-11T21:05:10.4503230Z async_compile.wait(globals())
2023-01-11T21:05:10.4503301Z del async_compile
2023-01-11T21:05:10.4503308Z 
2023-01-11T21:05:10.4503378Z def call(args):
2023-01-11T21:05:10.4503453Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4503509Z     args.clear()
2023-01-11T21:05:10.4503708Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4503900Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4504088Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4504276Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4504462Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4504728Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4504797Z     del arg0_1
2023-01-11T21:05:10.4504849Z     del arg1_1
2023-01-11T21:05:10.4504978Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4504983Z 
2023-01-11T21:05:10.4504987Z 
2023-01-11T21:05:10.4505062Z if __name__ == "__main__":
2023-01-11T21:05:10.4505179Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4505302Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4505498Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4505694Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4505810Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4505815Z 
2023-01-11T21:05:10.4505879Z ok (2.895s)
2023-01-11T21:05:10.4506310Z   test_div2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4506441Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4506730Z [2023-01-11 20:49:26,115] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 154
2023-01-11T21:05:10.4507000Z [2023-01-11 20:49:28,782] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 154
2023-01-11T21:05:10.4507005Z 
2023-01-11T21:05:10.4507099Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4507167Z import torch
2023-01-11T21:05:10.4507236Z import random
2023-01-11T21:05:10.4507353Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4507460Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4507477Z 
2023-01-11T21:05:10.4507541Z aten = torch.ops.aten
2023-01-11T21:05:10.4507674Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4507765Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4507773Z 
2023-01-11T21:05:10.4507777Z 
2023-01-11T21:05:10.4507912Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4508123Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4508242Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4508346Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4508444Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4508528Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4508621Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.4508714Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4508805Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.4508865Z {
2023-01-11T21:05:10.4508962Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4509023Z     {
2023-01-11T21:05:10.4509086Z         #pragma omp for 
2023-01-11T21:05:10.4509172Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4509233Z         {
2023-01-11T21:05:10.4509297Z             {
2023-01-11T21:05:10.4509364Z                 {
2023-01-11T21:05:10.4509458Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4509536Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.4509644Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4509735Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.4509836Z                     auto tmp4 = std::floor(tmp3);
2023-01-11T21:05:10.4509935Z                     auto tmp5 = std::trunc(tmp3);
2023-01-11T21:05:10.4510019Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.4510102Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.4510183Z                     out_ptr2[i0] = tmp5;
2023-01-11T21:05:10.4510253Z                     out_ptr3[i0] = tmp3;
2023-01-11T21:05:10.4510333Z                     out_ptr4[i0] = tmp4;
2023-01-11T21:05:10.4510434Z                 }
2023-01-11T21:05:10.4510499Z             }
2023-01-11T21:05:10.4510560Z         }
2023-01-11T21:05:10.4510621Z     }
2023-01-11T21:05:10.4510667Z }
2023-01-11T21:05:10.4510745Z ''')
2023-01-11T21:05:10.4510753Z 
2023-01-11T21:05:10.4510757Z 
2023-01-11T21:05:10.4510847Z async_compile.wait(globals())
2023-01-11T21:05:10.4510919Z del async_compile
2023-01-11T21:05:10.4510924Z 
2023-01-11T21:05:10.4510994Z def call(args):
2023-01-11T21:05:10.4511069Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4511140Z     args.clear()
2023-01-11T21:05:10.4511338Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4511518Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4511705Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4511891Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4512076Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4512366Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4512438Z     del arg0_1
2023-01-11T21:05:10.4512506Z     del arg1_1
2023-01-11T21:05:10.4512603Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4512609Z 
2023-01-11T21:05:10.4512613Z 
2023-01-11T21:05:10.4512675Z if __name__ == "__main__":
2023-01-11T21:05:10.4512790Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4512913Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4513108Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4513303Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4513420Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4513427Z 
2023-01-11T21:05:10.4513494Z ok (2.800s)
2023-01-11T21:05:10.4513940Z   test_div3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4514069Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4514317Z [2023-01-11 20:49:28,884] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 155
2023-01-11T21:05:10.4514582Z [2023-01-11 20:49:31,615] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 155
2023-01-11T21:05:10.4514588Z 
2023-01-11T21:05:10.4514683Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4514753Z import torch
2023-01-11T21:05:10.4514824Z import random
2023-01-11T21:05:10.4514940Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4515061Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4515067Z 
2023-01-11T21:05:10.4515146Z aten = torch.ops.aten
2023-01-11T21:05:10.4515267Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4515358Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4515362Z 
2023-01-11T21:05:10.4515366Z 
2023-01-11T21:05:10.4515501Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4515705Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4515823Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4515928Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4516025Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4516122Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4516233Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.4516328Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4516422Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.4516485Z {
2023-01-11T21:05:10.4516584Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4516645Z     {
2023-01-11T21:05:10.4516722Z         #pragma omp for 
2023-01-11T21:05:10.4516792Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4516853Z         {
2023-01-11T21:05:10.4516915Z             {
2023-01-11T21:05:10.4516980Z                 {
2023-01-11T21:05:10.4517073Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4517165Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.4517274Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4517367Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:05:10.4517455Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:05:10.4517713Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:05:10.4517805Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:05:10.4517917Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.4518005Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.4518086Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.4518156Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:05:10.4518237Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:05:10.4518300Z                 }
2023-01-11T21:05:10.4518363Z             }
2023-01-11T21:05:10.4518425Z         }
2023-01-11T21:05:10.4518486Z     }
2023-01-11T21:05:10.4518544Z }
2023-01-11T21:05:10.4518608Z ''')
2023-01-11T21:05:10.4518613Z 
2023-01-11T21:05:10.4518617Z 
2023-01-11T21:05:10.4518708Z async_compile.wait(globals())
2023-01-11T21:05:10.4518780Z del async_compile
2023-01-11T21:05:10.4518784Z 
2023-01-11T21:05:10.4518856Z def call(args):
2023-01-11T21:05:10.4518931Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4519000Z     args.clear()
2023-01-11T21:05:10.4519193Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4519373Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4519563Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4519751Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4519935Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4520197Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4520266Z     del arg0_1
2023-01-11T21:05:10.4520330Z     del arg1_1
2023-01-11T21:05:10.4520427Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4520434Z 
2023-01-11T21:05:10.4520438Z 
2023-01-11T21:05:10.4520512Z if __name__ == "__main__":
2023-01-11T21:05:10.4520752Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4520880Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4521077Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4521271Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4521387Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4521392Z 
2023-01-11T21:05:10.4521459Z ok (2.831s)
2023-01-11T21:05:10.4521903Z   test_div4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4522130Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4522383Z [2023-01-11 20:49:31,718] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 156
2023-01-11T21:05:10.4522652Z [2023-01-11 20:49:31,748] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 156
2023-01-11T21:05:10.4522657Z 
2023-01-11T21:05:10.4522752Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4522821Z import torch
2023-01-11T21:05:10.4522894Z import random
2023-01-11T21:05:10.4523011Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4523132Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4523138Z 
2023-01-11T21:05:10.4523216Z aten = torch.ops.aten
2023-01-11T21:05:10.4523336Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4523427Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4523432Z 
2023-01-11T21:05:10.4523438Z 
2023-01-11T21:05:10.4523573Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4523777Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4523934Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4524040Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4524139Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4524234Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4524314Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.4524409Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4524501Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.4524560Z {
2023-01-11T21:05:10.4524659Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4524719Z     {
2023-01-11T21:05:10.4524793Z         #pragma omp for 
2023-01-11T21:05:10.4524862Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4524926Z         {
2023-01-11T21:05:10.4524991Z             {
2023-01-11T21:05:10.4525054Z                 {
2023-01-11T21:05:10.4525146Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4525238Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.4525347Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4525441Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:05:10.4525529Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:05:10.4525782Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:05:10.4525872Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:05:10.4525955Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.4526041Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.4526124Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.4526193Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:05:10.4526276Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:05:10.4526339Z                 }
2023-01-11T21:05:10.4526401Z             }
2023-01-11T21:05:10.4526462Z         }
2023-01-11T21:05:10.4526524Z     }
2023-01-11T21:05:10.4526583Z }
2023-01-11T21:05:10.4526647Z ''')
2023-01-11T21:05:10.4526652Z 
2023-01-11T21:05:10.4526656Z 
2023-01-11T21:05:10.4526746Z async_compile.wait(globals())
2023-01-11T21:05:10.4526817Z del async_compile
2023-01-11T21:05:10.4526822Z 
2023-01-11T21:05:10.4526891Z def call(args):
2023-01-11T21:05:10.4526965Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4527034Z     args.clear()
2023-01-11T21:05:10.4527231Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4527409Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4527596Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4527784Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4527997Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4528261Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4528329Z     del arg0_1
2023-01-11T21:05:10.4528395Z     del arg1_1
2023-01-11T21:05:10.4528492Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4528497Z 
2023-01-11T21:05:10.4528501Z 
2023-01-11T21:05:10.4528576Z if __name__ == "__main__":
2023-01-11T21:05:10.4528678Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4528801Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4528991Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4529180Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4529297Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4529302Z 
2023-01-11T21:05:10.4529367Z ok (0.130s)
2023-01-11T21:05:10.4529857Z   test_div5_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4529987Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4530251Z [2023-01-11 20:49:31,860] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 157
2023-01-11T21:05:10.4530506Z [2023-01-11 20:49:34,574] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 157
2023-01-11T21:05:10.4530512Z 
2023-01-11T21:05:10.4530606Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4530675Z import torch
2023-01-11T21:05:10.4530743Z import random
2023-01-11T21:05:10.4530859Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4530982Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4530987Z 
2023-01-11T21:05:10.4531063Z aten = torch.ops.aten
2023-01-11T21:05:10.4531200Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4531277Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4531282Z 
2023-01-11T21:05:10.4531287Z 
2023-01-11T21:05:10.4531418Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4531626Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4531747Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4531847Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4531944Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4532041Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.4532139Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4532218Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.4532280Z {
2023-01-11T21:05:10.4532378Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4532438Z     {
2023-01-11T21:05:10.4532514Z         #pragma omp for 
2023-01-11T21:05:10.4532597Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4532645Z         {
2023-01-11T21:05:10.4532707Z             {
2023-01-11T21:05:10.4532769Z                 {
2023-01-11T21:05:10.4532862Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4532971Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4533075Z                     auto tmp2 = static_cast<float>(16);
2023-01-11T21:05:10.4533164Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.4533256Z                     auto tmp4 = static_cast<long>(16);
2023-01-11T21:05:10.4533540Z                     auto tmp5 = ((tmp0 < 0) != (tmp4 < 0) ? (tmp0 % tmp4 != 0 ? tmp0 / tmp4 - 1 : tmp0 / tmp4) : tmp0 / tmp4);
2023-01-11T21:05:10.4533631Z                     auto tmp6 = tmp0 / tmp4;
2023-01-11T21:05:10.4533716Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.4533799Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.4533880Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.4533961Z                     out_ptr3[i0] = tmp3;
2023-01-11T21:05:10.4534042Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:05:10.4534093Z                 }
2023-01-11T21:05:10.4534155Z             }
2023-01-11T21:05:10.4534215Z         }
2023-01-11T21:05:10.4534275Z     }
2023-01-11T21:05:10.4534333Z }
2023-01-11T21:05:10.4534409Z ''')
2023-01-11T21:05:10.4534414Z 
2023-01-11T21:05:10.4534419Z 
2023-01-11T21:05:10.4534509Z async_compile.wait(globals())
2023-01-11T21:05:10.4534567Z del async_compile
2023-01-11T21:05:10.4534572Z 
2023-01-11T21:05:10.4534644Z def call(args):
2023-01-11T21:05:10.4534712Z     arg0_1, = args
2023-01-11T21:05:10.4534781Z     args.clear()
2023-01-11T21:05:10.4534977Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4535195Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4535384Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4535562Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4535746Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4535984Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4536053Z     del arg0_1
2023-01-11T21:05:10.4536149Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4536154Z 
2023-01-11T21:05:10.4536161Z 
2023-01-11T21:05:10.4536238Z if __name__ == "__main__":
2023-01-11T21:05:10.4536352Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4536475Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4536654Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4536763Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4536768Z 
2023-01-11T21:05:10.4536834Z ok (2.830s)
2023-01-11T21:05:10.4537276Z   test_div6_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4537403Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4537664Z [2023-01-11 20:49:34,709] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 158
2023-01-11T21:05:10.4537931Z [2023-01-11 20:49:37,471] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 158
2023-01-11T21:05:10.4537939Z 
2023-01-11T21:05:10.4538032Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4538102Z import torch
2023-01-11T21:05:10.4538157Z import random
2023-01-11T21:05:10.4538272Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4538391Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4538396Z 
2023-01-11T21:05:10.4538542Z aten = torch.ops.aten
2023-01-11T21:05:10.4538681Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4538776Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4538782Z 
2023-01-11T21:05:10.4538786Z 
2023-01-11T21:05:10.4538924Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4539131Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4539278Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.4539384Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4539486Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4539584Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4539682Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.4539778Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4539871Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.4539932Z {
2023-01-11T21:05:10.4540016Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4540077Z     {
2023-01-11T21:05:10.4540155Z         #pragma omp for 
2023-01-11T21:05:10.4540238Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4540300Z         {
2023-01-11T21:05:10.4540362Z             {
2023-01-11T21:05:10.4540412Z                 {
2023-01-11T21:05:10.4540504Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4540600Z                     auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.4540705Z                     auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:05:10.4540854Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.4540961Z                     auto tmp4 = static_cast<float>(tmp3);
2023-01-11T21:05:10.4541054Z                     auto tmp5 = tmp2 / tmp4;
2023-01-11T21:05:10.4541307Z                     auto tmp6 = ((tmp1 < 0) != (tmp3 < 0) ? (tmp1 % tmp3 != 0 ? tmp1 / tmp3 - 1 : tmp1 / tmp3) : tmp1 / tmp3);
2023-01-11T21:05:10.4541384Z                     auto tmp7 = tmp1 / tmp3;
2023-01-11T21:05:10.4541488Z                     auto tmp8 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4541577Z                     auto tmp9 = tmp8 / tmp4;
2023-01-11T21:05:10.4541661Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.4541743Z                     out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.4541825Z                     out_ptr2[i0] = tmp7;
2023-01-11T21:05:10.4541909Z                     out_ptr3[i0] = tmp9;
2023-01-11T21:05:10.4541977Z                     out_ptr4[i0] = tmp6;
2023-01-11T21:05:10.4542040Z                 }
2023-01-11T21:05:10.4542104Z             }
2023-01-11T21:05:10.4542166Z         }
2023-01-11T21:05:10.4542226Z     }
2023-01-11T21:05:10.4542286Z }
2023-01-11T21:05:10.4542350Z ''')
2023-01-11T21:05:10.4542368Z 
2023-01-11T21:05:10.4542372Z 
2023-01-11T21:05:10.4542448Z async_compile.wait(globals())
2023-01-11T21:05:10.4542523Z del async_compile
2023-01-11T21:05:10.4542527Z 
2023-01-11T21:05:10.4542599Z def call(args):
2023-01-11T21:05:10.4542675Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4542747Z     args.clear()
2023-01-11T21:05:10.4542943Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4543132Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4543308Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4543500Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4543683Z     buf4 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4543949Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4544019Z     del arg0_1
2023-01-11T21:05:10.4544084Z     del arg1_1
2023-01-11T21:05:10.4544180Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4544185Z 
2023-01-11T21:05:10.4544189Z 
2023-01-11T21:05:10.4544264Z if __name__ == "__main__":
2023-01-11T21:05:10.4544366Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4544489Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4544677Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.4544903Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4545018Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4545023Z 
2023-01-11T21:05:10.4545088Z ok (2.896s)
2023-01-11T21:05:10.4545530Z   test_div7_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4545658Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4545920Z [2023-01-11 20:49:37,608] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 159
2023-01-11T21:05:10.4546188Z [2023-01-11 20:49:40,340] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 159
2023-01-11T21:05:10.4546195Z 
2023-01-11T21:05:10.4546276Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4546345Z import torch
2023-01-11T21:05:10.4546415Z import random
2023-01-11T21:05:10.4546559Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4546680Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4546685Z 
2023-01-11T21:05:10.4546762Z aten = torch.ops.aten
2023-01-11T21:05:10.4546900Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4546977Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4546996Z 
2023-01-11T21:05:10.4547000Z 
2023-01-11T21:05:10.4547121Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4547325Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4547445Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4547547Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4547649Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4547746Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4547840Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.4547924Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4548017Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.4548077Z {
2023-01-11T21:05:10.4548175Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4548236Z     {
2023-01-11T21:05:10.4548312Z         #pragma omp for 
2023-01-11T21:05:10.4548396Z         for(long i0=0; i0<10000; i0+=1)
2023-01-11T21:05:10.4548444Z         {
2023-01-11T21:05:10.4548506Z             {
2023-01-11T21:05:10.4548571Z                 {
2023-01-11T21:05:10.4548664Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4548757Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.4548866Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4548974Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:05:10.4549051Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:05:10.4549303Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:05:10.4549394Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:05:10.4549480Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.4549565Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.4549648Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.4549730Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:05:10.4549798Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:05:10.4549863Z                 }
2023-01-11T21:05:10.4549925Z             }
2023-01-11T21:05:10.4549988Z         }
2023-01-11T21:05:10.4550052Z     }
2023-01-11T21:05:10.4550112Z }
2023-01-11T21:05:10.4550189Z ''')
2023-01-11T21:05:10.4550194Z 
2023-01-11T21:05:10.4550198Z 
2023-01-11T21:05:10.4550303Z async_compile.wait(globals())
2023-01-11T21:05:10.4550377Z del async_compile
2023-01-11T21:05:10.4550382Z 
2023-01-11T21:05:10.4550452Z def call(args):
2023-01-11T21:05:10.4550526Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4550598Z     args.clear()
2023-01-11T21:05:10.4550806Z     buf0 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4551006Z     buf1 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4551204Z     buf2 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4551392Z     buf3 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4551588Z     buf4 = empty_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4551851Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4551922Z     del arg0_1
2023-01-11T21:05:10.4551987Z     del arg1_1
2023-01-11T21:05:10.4552083Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4552089Z 
2023-01-11T21:05:10.4552122Z 
2023-01-11T21:05:10.4552200Z if __name__ == "__main__":
2023-01-11T21:05:10.4552313Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4552425Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4552623Z     arg0_1 = rand_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4552824Z     arg1_1 = rand_strided((100, 100), (100, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4552939Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4552944Z 
2023-01-11T21:05:10.4553010Z ok (2.881s)
2023-01-11T21:05:10.4553329Z   test_div8_cpu (__main__.CpuTests) ... [2023-01-11 20:49:40,433] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 160
2023-01-11T21:05:10.4553599Z [2023-01-11 20:49:43,164] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 160
2023-01-11T21:05:10.4553604Z 
2023-01-11T21:05:10.4553699Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4553757Z import torch
2023-01-11T21:05:10.4553824Z import random
2023-01-11T21:05:10.4553940Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4554060Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4554064Z 
2023-01-11T21:05:10.4554141Z aten = torch.ops.aten
2023-01-11T21:05:10.4554275Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4554366Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4554370Z 
2023-01-11T21:05:10.4554374Z 
2023-01-11T21:05:10.4554511Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4554704Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4554816Z extern "C" void kernel(long* __restrict__ out_ptr0,
2023-01-11T21:05:10.4554911Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4555004Z                        long* __restrict__ out_ptr2)
2023-01-11T21:05:10.4555063Z {
2023-01-11T21:05:10.4555126Z     {
2023-01-11T21:05:10.4555188Z         {
2023-01-11T21:05:10.4555276Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:05:10.4555375Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:05:10.4555459Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4555538Z             out_ptr0[0] = tmp2;
2023-01-11T21:05:10.4555600Z         }
2023-01-11T21:05:10.4555659Z     }
2023-01-11T21:05:10.4555717Z     {
2023-01-11T21:05:10.4555765Z         {
2023-01-11T21:05:10.4555865Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:05:10.4555963Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:05:10.4556209Z             auto tmp2 = ((tmp0 < 0) != (tmp1 < 0) ? (tmp0 % tmp1 != 0 ? tmp0 / tmp1 - 1 : tmp0 / tmp1) : tmp0 / tmp1);
2023-01-11T21:05:10.4556319Z             out_ptr1[0] = tmp2;
2023-01-11T21:05:10.4556381Z         }
2023-01-11T21:05:10.4556440Z     }
2023-01-11T21:05:10.4556488Z     {
2023-01-11T21:05:10.4556547Z         {
2023-01-11T21:05:10.4556648Z             auto tmp0 = static_cast<long>(1024);
2023-01-11T21:05:10.4556745Z             auto tmp1 = static_cast<long>(100);
2023-01-11T21:05:10.4556828Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4556904Z             out_ptr2[0] = tmp2;
2023-01-11T21:05:10.4556964Z         }
2023-01-11T21:05:10.4557012Z     }
2023-01-11T21:05:10.4557072Z }
2023-01-11T21:05:10.4557149Z ''')
2023-01-11T21:05:10.4557154Z 
2023-01-11T21:05:10.4557158Z 
2023-01-11T21:05:10.4557247Z async_compile.wait(globals())
2023-01-11T21:05:10.4557319Z del async_compile
2023-01-11T21:05:10.4557325Z 
2023-01-11T21:05:10.4557393Z def call(args):
2023-01-11T21:05:10.4557574Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4557739Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4557917Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4558105Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4558190Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4558195Z 
2023-01-11T21:05:10.4558199Z 
2023-01-11T21:05:10.4558274Z if __name__ == "__main__":
2023-01-11T21:05:10.4558387Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4558510Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4558608Z     print_performance(lambda: call([]))
2023-01-11T21:05:10.4558613Z 
2023-01-11T21:05:10.4558665Z ok (2.809s)
2023-01-11T21:05:10.4559114Z   test_div_prim_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4559243Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4559507Z [2023-01-11 20:49:43,237] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 161
2023-01-11T21:05:10.4559772Z [2023-01-11 20:49:45,947] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 161
2023-01-11T21:05:10.4560180Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4560306Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4560564Z [2023-01-11 20:49:46,019] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 162
2023-01-11T21:05:10.4560953Z [2023-01-11 20:49:48,685] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 162
2023-01-11T21:05:10.4560960Z 
2023-01-11T21:05:10.4561055Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4561126Z import torch
2023-01-11T21:05:10.4561183Z import random
2023-01-11T21:05:10.4561298Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4561420Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4561425Z 
2023-01-11T21:05:10.4561503Z aten = torch.ops.aten
2023-01-11T21:05:10.4561643Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4561734Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4561739Z 
2023-01-11T21:05:10.4561743Z 
2023-01-11T21:05:10.4561878Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4562084Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4562251Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4562357Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4562460Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4562522Z {
2023-01-11T21:05:10.4562620Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4562681Z     {
2023-01-11T21:05:10.4562759Z         #pragma omp for 
2023-01-11T21:05:10.4562828Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.4562891Z         {
2023-01-11T21:05:10.4563041Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4563176Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.4563264Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4563359Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4563423Z         }
2023-01-11T21:05:10.4563504Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4563592Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.4563654Z         {
2023-01-11T21:05:10.4563740Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4563822Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4563939Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4564020Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4564068Z         }
2023-01-11T21:05:10.4564129Z     }
2023-01-11T21:05:10.4564189Z }
2023-01-11T21:05:10.4564267Z ''')
2023-01-11T21:05:10.4564272Z 
2023-01-11T21:05:10.4564276Z 
2023-01-11T21:05:10.4564365Z async_compile.wait(globals())
2023-01-11T21:05:10.4564436Z del async_compile
2023-01-11T21:05:10.4564441Z 
2023-01-11T21:05:10.4564511Z def call(args):
2023-01-11T21:05:10.4564572Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4564641Z     args.clear()
2023-01-11T21:05:10.4564836Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4565000Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4565070Z     del arg0_1
2023-01-11T21:05:10.4565135Z     del arg1_1
2023-01-11T21:05:10.4565204Z     return (buf0, )
2023-01-11T21:05:10.4565210Z 
2023-01-11T21:05:10.4565216Z 
2023-01-11T21:05:10.4565291Z if __name__ == "__main__":
2023-01-11T21:05:10.4565393Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4565515Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4565712Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4565906Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4566022Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4566027Z 
2023-01-11T21:05:10.4566031Z 
2023-01-11T21:05:10.4566124Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4566192Z import torch
2023-01-11T21:05:10.4566248Z import random
2023-01-11T21:05:10.4566363Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4566488Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4566493Z 
2023-01-11T21:05:10.4566570Z aten = torch.ops.aten
2023-01-11T21:05:10.4566706Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4566796Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4566801Z 
2023-01-11T21:05:10.4566805Z 
2023-01-11T21:05:10.4566936Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4567139Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4567246Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4567350Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4567446Z                        long* __restrict__ out_ptr0)
2023-01-11T21:05:10.4567506Z {
2023-01-11T21:05:10.4567603Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4567663Z     {
2023-01-11T21:05:10.4567771Z         #pragma omp for 
2023-01-11T21:05:10.4567841Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:05:10.4567902Z         {
2023-01-11T21:05:10.4567965Z             {
2023-01-11T21:05:10.4568031Z                 {
2023-01-11T21:05:10.4568126Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4568216Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4568305Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4568376Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4568439Z                 }
2023-01-11T21:05:10.4568503Z             }
2023-01-11T21:05:10.4568566Z         }
2023-01-11T21:05:10.4568627Z     }
2023-01-11T21:05:10.4568686Z }
2023-01-11T21:05:10.4568763Z ''')
2023-01-11T21:05:10.4568768Z 
2023-01-11T21:05:10.4568772Z 
2023-01-11T21:05:10.4568849Z async_compile.wait(globals())
2023-01-11T21:05:10.4568920Z del async_compile
2023-01-11T21:05:10.4568924Z 
2023-01-11T21:05:10.4568993Z def call(args):
2023-01-11T21:05:10.4569067Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4569139Z     args.clear()
2023-01-11T21:05:10.4569329Z     buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4569537Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4569593Z     del arg0_1
2023-01-11T21:05:10.4569658Z     del arg1_1
2023-01-11T21:05:10.4569728Z     return (buf0, )
2023-01-11T21:05:10.4569733Z 
2023-01-11T21:05:10.4569737Z 
2023-01-11T21:05:10.4569811Z if __name__ == "__main__":
2023-01-11T21:05:10.4569926Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4570048Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4570239Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4570430Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4570532Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4570539Z 
2023-01-11T21:05:10.4570604Z ok (5.521s)
2023-01-11T21:05:10.4571058Z   test_div_zero_dim_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4571186Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4571450Z [2023-01-11 20:49:48,839] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 163
2023-01-11T21:05:10.4571715Z [2023-01-11 20:49:51,587] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 163
2023-01-11T21:05:10.4572123Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4572254Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4572513Z [2023-01-11 20:49:51,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 164
2023-01-11T21:05:10.4572775Z [2023-01-11 20:49:54,442] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 164
2023-01-11T21:05:10.4573183Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4573311Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4573584Z [2023-01-11 20:49:54,572] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 165
2023-01-11T21:05:10.4573851Z [2023-01-11 20:49:57,317] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 165
2023-01-11T21:05:10.4574256Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4574382Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4574637Z [2023-01-11 20:49:57,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 166
2023-01-11T21:05:10.4574642Z 
2023-01-11T21:05:10.4574739Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4574807Z import torch
2023-01-11T21:05:10.4574879Z import random
2023-01-11T21:05:10.4574997Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4575105Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4575110Z 
2023-01-11T21:05:10.4575214Z aten = torch.ops.aten
2023-01-11T21:05:10.4575350Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4575442Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4575447Z 
2023-01-11T21:05:10.4575451Z 
2023-01-11T21:05:10.4575586Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4575791Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4575911Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4576015Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4576102Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4576198Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4576293Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.4576387Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4576479Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.4576540Z {
2023-01-11T21:05:10.4576640Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4576687Z     {
2023-01-11T21:05:10.4576762Z         #pragma omp for 
2023-01-11T21:05:10.4576845Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.4576907Z         {
2023-01-11T21:05:10.4576970Z             {
2023-01-11T21:05:10.4577032Z                 {
2023-01-11T21:05:10.4577112Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4577202Z                     auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.4577291Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4577393Z                     auto tmp3 = std::floor(tmp2);
2023-01-11T21:05:10.4577492Z                     auto tmp4 = std::trunc(tmp2);
2023-01-11T21:05:10.4577576Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4577662Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4577743Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.4577812Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.4577894Z                     out_ptr4[i0] = tmp3;
2023-01-11T21:05:10.4577957Z                 }
2023-01-11T21:05:10.4578023Z             }
2023-01-11T21:05:10.4578084Z         }
2023-01-11T21:05:10.4578144Z     }
2023-01-11T21:05:10.4578190Z }
2023-01-11T21:05:10.4578267Z ''')
2023-01-11T21:05:10.4578271Z 
2023-01-11T21:05:10.4578276Z 
2023-01-11T21:05:10.4578365Z async_compile.wait(globals())
2023-01-11T21:05:10.4578437Z del async_compile
2023-01-11T21:05:10.4578442Z 
2023-01-11T21:05:10.4578599Z def call(args):
2023-01-11T21:05:10.4578676Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4578747Z     args.clear()
2023-01-11T21:05:10.4578946Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4579124Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4579351Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4579541Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4579726Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4579990Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4580060Z     del arg0_1
2023-01-11T21:05:10.4580127Z     del arg1_1
2023-01-11T21:05:10.4580225Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4580230Z 
2023-01-11T21:05:10.4580234Z 
2023-01-11T21:05:10.4580296Z if __name__ == "__main__":
2023-01-11T21:05:10.4580411Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4580535Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4580732Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4580918Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4581068Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4581074Z 
2023-01-11T21:05:10.4581078Z 
2023-01-11T21:05:10.4581174Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4581244Z import torch
2023-01-11T21:05:10.4581300Z import random
2023-01-11T21:05:10.4581415Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4581540Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4581545Z 
2023-01-11T21:05:10.4581623Z aten = torch.ops.aten
2023-01-11T21:05:10.4581759Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4581851Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4581856Z 
2023-01-11T21:05:10.4581860Z 
2023-01-11T21:05:10.4581993Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4582202Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4582309Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4582416Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4582516Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4582614Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4582709Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.4582804Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4582897Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.4582944Z {
2023-01-11T21:05:10.4583041Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4583102Z     {
2023-01-11T21:05:10.4583180Z         #pragma omp for 
2023-01-11T21:05:10.4583263Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.4583325Z         {
2023-01-11T21:05:10.4583392Z             {
2023-01-11T21:05:10.4583442Z                 {
2023-01-11T21:05:10.4583532Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.4583624Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4583717Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4583821Z                     auto tmp3 = std::floor(tmp2);
2023-01-11T21:05:10.4583919Z                     auto tmp4 = std::trunc(tmp2);
2023-01-11T21:05:10.4584004Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4584074Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4584156Z                     out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.4584237Z                     out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.4584318Z                     out_ptr4[i0] = tmp3;
2023-01-11T21:05:10.4584381Z                 }
2023-01-11T21:05:10.4584443Z             }
2023-01-11T21:05:10.4584504Z         }
2023-01-11T21:05:10.4584551Z     }
2023-01-11T21:05:10.4584609Z }
2023-01-11T21:05:10.4584717Z ''')
2023-01-11T21:05:10.4584722Z 
2023-01-11T21:05:10.4584726Z 
2023-01-11T21:05:10.4584816Z async_compile.wait(globals())
2023-01-11T21:05:10.4584888Z del async_compile
2023-01-11T21:05:10.4584893Z 
2023-01-11T21:05:10.4584964Z def call(args):
2023-01-11T21:05:10.4585039Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4585096Z     args.clear()
2023-01-11T21:05:10.4585292Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4585481Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4585671Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4585857Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4586040Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4586302Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4586373Z     del arg0_1
2023-01-11T21:05:10.4586427Z     del arg1_1
2023-01-11T21:05:10.4586550Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4586555Z 
2023-01-11T21:05:10.4586559Z 
2023-01-11T21:05:10.4586637Z if __name__ == "__main__":
2023-01-11T21:05:10.4586754Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4586877Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4587061Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4587254Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4587369Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4587374Z 
2023-01-11T21:05:10.4587378Z 
2023-01-11T21:05:10.4587471Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4587527Z import torch
2023-01-11T21:05:10.4587597Z import random
2023-01-11T21:05:10.4587712Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4587830Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4587835Z 
2023-01-11T21:05:10.4587916Z aten = torch.ops.aten
2023-01-11T21:05:10.4588049Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4588140Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4588145Z 
2023-01-11T21:05:10.4588149Z 
2023-01-11T21:05:10.4588268Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4588472Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4588590Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4588692Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4588791Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4588885Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4588979Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.4589075Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4589155Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.4589213Z {
2023-01-11T21:05:10.4589310Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4589371Z     {
2023-01-11T21:05:10.4589447Z         #pragma omp for 
2023-01-11T21:05:10.4589529Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.4589590Z         {
2023-01-11T21:05:10.4589639Z             {
2023-01-11T21:05:10.4589701Z                 {
2023-01-11T21:05:10.4589793Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4589883Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:05:10.4589990Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4590096Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:05:10.4590186Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:05:10.4590455Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:05:10.4590545Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:05:10.4590631Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.4590714Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.4590795Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.4590876Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:05:10.4590956Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:05:10.4591006Z                 }
2023-01-11T21:05:10.4591068Z             }
2023-01-11T21:05:10.4591128Z         }
2023-01-11T21:05:10.4591189Z     }
2023-01-11T21:05:10.4591248Z }
2023-01-11T21:05:10.4591324Z ''')
2023-01-11T21:05:10.4591329Z 
2023-01-11T21:05:10.4591333Z 
2023-01-11T21:05:10.4591421Z async_compile.wait(globals())
2023-01-11T21:05:10.4591480Z del async_compile
2023-01-11T21:05:10.4591484Z 
2023-01-11T21:05:10.4591554Z def call(args):
2023-01-11T21:05:10.4591631Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4591700Z     args.clear()
2023-01-11T21:05:10.4591895Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4592110Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4592298Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4592474Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4592658Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4592920Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4592989Z     del arg0_1
2023-01-11T21:05:10.4593053Z     del arg1_1
2023-01-11T21:05:10.4593149Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4593156Z 
2023-01-11T21:05:10.4593160Z 
2023-01-11T21:05:10.4593235Z if __name__ == "__main__":
2023-01-11T21:05:10.4593351Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4593476Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4593653Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4593834Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4593949Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4593955Z 
2023-01-11T21:05:10.4593959Z 
2023-01-11T21:05:10.4594052Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4594121Z import torch
2023-01-11T21:05:10.4594189Z import random
2023-01-11T21:05:10.4594302Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4594408Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4594425Z 
2023-01-11T21:05:10.4594489Z aten = torch.ops.aten
2023-01-11T21:05:10.4594625Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4594714Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4594719Z 
2023-01-11T21:05:10.4594723Z 
2023-01-11T21:05:10.4594861Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4595066Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4595185Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4595289Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4595374Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4595470Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.4595562Z                        long* __restrict__ out_ptr2,
2023-01-11T21:05:10.4595658Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4595750Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.4595811Z {
2023-01-11T21:05:10.4595940Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4595988Z     {
2023-01-11T21:05:10.4596063Z         #pragma omp for 
2023-01-11T21:05:10.4596145Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.4596207Z         {
2023-01-11T21:05:10.4596274Z             {
2023-01-11T21:05:10.4596338Z                 {
2023-01-11T21:05:10.4596428Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.4596507Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.4596613Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4596718Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:05:10.4596811Z                     auto tmp4 = tmp1 / tmp3;
2023-01-11T21:05:10.4597063Z                     auto tmp5 = ((tmp0 < 0) != (tmp2 < 0) ? (tmp0 % tmp2 != 0 ? tmp0 / tmp2 - 1 : tmp0 / tmp2) : tmp0 / tmp2);
2023-01-11T21:05:10.4597152Z                     auto tmp6 = tmp0 / tmp2;
2023-01-11T21:05:10.4597236Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.4597320Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.4597389Z                     out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.4597471Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:05:10.4597579Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:05:10.4597644Z                 }
2023-01-11T21:05:10.4597708Z             }
2023-01-11T21:05:10.4597769Z         }
2023-01-11T21:05:10.4597818Z     }
2023-01-11T21:05:10.4597876Z }
2023-01-11T21:05:10.4597951Z ''')
2023-01-11T21:05:10.4597957Z 
2023-01-11T21:05:10.4597961Z 
2023-01-11T21:05:10.4598054Z async_compile.wait(globals())
2023-01-11T21:05:10.4598125Z del async_compile
2023-01-11T21:05:10.4598130Z 
2023-01-11T21:05:10.4598200Z def call(args):
2023-01-11T21:05:10.4598274Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4598343Z     args.clear()
2023-01-11T21:05:10.4598524Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4598711Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4598900Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4599089Z     buf3 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4599273Z     buf4 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4599535Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4599604Z     del arg0_1
2023-01-11T21:05:10.4599669Z     del arg1_1
2023-01-11T21:05:10.4599752Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4599757Z 
2023-01-11T21:05:10.4599762Z 
2023-01-11T21:05:10.4599837Z if __name__ == "__main__":
2023-01-11T21:05:10.4599953Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4600075Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4600257Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4600446Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4600563Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4600971Z [2023-01-11 20:50:00,149] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 166
2023-01-11T21:05:10.4600977Z 
2023-01-11T21:05:10.4601029Z ok (11.467s)
2023-01-11T21:05:10.4601354Z   test_dropout_cpu (__main__.CpuTests) ... [2023-01-11 20:50:00,336] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 167
2023-01-11T21:05:10.4601609Z [2023-01-11 20:50:00,337] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.4601876Z [2023-01-11 20:50:03,067] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 167
2023-01-11T21:05:10.4602132Z [2023-01-11 20:50:03,229] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 168
2023-01-11T21:05:10.4602452Z [2023-01-11 20:50:03,230] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.4602717Z [2023-01-11 20:50:03,249] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 168
2023-01-11T21:05:10.4602722Z 
2023-01-11T21:05:10.4602817Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4602886Z import torch
2023-01-11T21:05:10.4602942Z import random
2023-01-11T21:05:10.4603060Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4603183Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4603188Z 
2023-01-11T21:05:10.4603269Z aten = torch.ops.aten
2023-01-11T21:05:10.4603405Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4603499Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4603665Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.4603673Z 
2023-01-11T21:05:10.4603677Z 
2023-01-11T21:05:10.4603811Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4604039Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4604158Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:05:10.4604264Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4604364Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4604427Z {
2023-01-11T21:05:10.4604524Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4604585Z     {
2023-01-11T21:05:10.4604647Z         #pragma omp for 
2023-01-11T21:05:10.4604730Z         for(long i0=0; i0<1000; i0+=1)
2023-01-11T21:05:10.4604791Z         {
2023-01-11T21:05:10.4604855Z             {
2023-01-11T21:05:10.4604919Z                 {
2023-01-11T21:05:10.4605006Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.4605100Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:05:10.4605188Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.4605328Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.4605437Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:05:10.4605527Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.4605633Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.4605722Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.4605826Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:05:10.4605903Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.4605987Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.4606050Z                 }
2023-01-11T21:05:10.4606113Z             }
2023-01-11T21:05:10.4606177Z         }
2023-01-11T21:05:10.4606236Z     }
2023-01-11T21:05:10.4606294Z }
2023-01-11T21:05:10.4606360Z ''')
2023-01-11T21:05:10.4606366Z 
2023-01-11T21:05:10.4606371Z 
2023-01-11T21:05:10.4606458Z async_compile.wait(globals())
2023-01-11T21:05:10.4606528Z del async_compile
2023-01-11T21:05:10.4606533Z 
2023-01-11T21:05:10.4606603Z def call(args):
2023-01-11T21:05:10.4606673Z     arg0_1, = args
2023-01-11T21:05:10.4606742Z     args.clear()
2023-01-11T21:05:10.4606875Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.4607060Z     buf0 = empty_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4607234Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4607302Z     del arg0_1
2023-01-11T21:05:10.4607373Z     return (buf0, )
2023-01-11T21:05:10.4607378Z 
2023-01-11T21:05:10.4607382Z 
2023-01-11T21:05:10.4607456Z if __name__ == "__main__":
2023-01-11T21:05:10.4607569Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4607691Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4607918Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4608102Z     arg0_1 = rand_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4608211Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4608216Z 
2023-01-11T21:05:10.4608220Z 
2023-01-11T21:05:10.4608312Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4608379Z import torch
2023-01-11T21:05:10.4608447Z import random
2023-01-11T21:05:10.4608563Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4608682Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4608687Z 
2023-01-11T21:05:10.4608763Z aten = torch.ops.aten
2023-01-11T21:05:10.4608883Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4608972Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4609132Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.4609139Z 
2023-01-11T21:05:10.4609143Z 
2023-01-11T21:05:10.4609274Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4609560Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4609679Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:05:10.4609785Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4609889Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4609936Z {
2023-01-11T21:05:10.4610034Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4610098Z     {
2023-01-11T21:05:10.4610175Z         #pragma omp for 
2023-01-11T21:05:10.4610260Z         for(long i0=0; i0<1000; i0+=1)
2023-01-11T21:05:10.4610325Z         {
2023-01-11T21:05:10.4610389Z             {
2023-01-11T21:05:10.4610438Z                 {
2023-01-11T21:05:10.4610526Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.4610619Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:05:10.4610726Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.4610866Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.4610974Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:05:10.4611066Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.4611159Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.4611250Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.4611355Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:05:10.4611446Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.4611530Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.4611593Z                 }
2023-01-11T21:05:10.4611655Z             }
2023-01-11T21:05:10.4611702Z         }
2023-01-11T21:05:10.4611760Z     }
2023-01-11T21:05:10.4611819Z }
2023-01-11T21:05:10.4611898Z ''')
2023-01-11T21:05:10.4611906Z 
2023-01-11T21:05:10.4611910Z 
2023-01-11T21:05:10.4612000Z async_compile.wait(globals())
2023-01-11T21:05:10.4612071Z del async_compile
2023-01-11T21:05:10.4612076Z 
2023-01-11T21:05:10.4612146Z def call(args):
2023-01-11T21:05:10.4612203Z     arg0_1, = args
2023-01-11T21:05:10.4612273Z     args.clear()
2023-01-11T21:05:10.4612406Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.4612603Z     buf0 = empty_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4612777Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4612845Z     del arg0_1
2023-01-11T21:05:10.4612916Z     return (buf0, )
2023-01-11T21:05:10.4612921Z 
2023-01-11T21:05:10.4612925Z 
2023-01-11T21:05:10.4613001Z if __name__ == "__main__":
2023-01-11T21:05:10.4613102Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4613225Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4613450Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4613644Z     arg0_1 = rand_strided((1000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4613755Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4613761Z 
2023-01-11T21:05:10.4613828Z ok (3.093s)
2023-01-11T21:05:10.4614182Z   test_dropout_deterministic_cpu (__main__.CpuTests) ... [2023-01-11 20:50:03,394] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 169
2023-01-11T21:05:10.4614439Z [2023-01-11 20:50:03,395] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.4614691Z [2023-01-11 20:50:06,105] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 169
2023-01-11T21:05:10.4614945Z [2023-01-11 20:50:06,232] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 170
2023-01-11T21:05:10.4615199Z [2023-01-11 20:50:06,233] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.4615499Z [2023-01-11 20:50:06,247] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 170
2023-01-11T21:05:10.4615504Z 
2023-01-11T21:05:10.4615600Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4615669Z import torch
2023-01-11T21:05:10.4615740Z import random
2023-01-11T21:05:10.4615855Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4615962Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4615980Z 
2023-01-11T21:05:10.4616045Z aten = torch.ops.aten
2023-01-11T21:05:10.4616179Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4616272Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4616433Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.4616439Z 
2023-01-11T21:05:10.4616444Z 
2023-01-11T21:05:10.4616581Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4616786Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4616904Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:05:10.4617011Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4617099Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4617160Z {
2023-01-11T21:05:10.4617261Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4617323Z     {
2023-01-11T21:05:10.4617402Z         #pragma omp for 
2023-01-11T21:05:10.4617487Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.4617535Z         {
2023-01-11T21:05:10.4617600Z             {
2023-01-11T21:05:10.4617667Z                 {
2023-01-11T21:05:10.4617754Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.4617846Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:05:10.4617951Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.4618092Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.4618185Z                     auto tmp3 = static_cast<float>(0.55);
2023-01-11T21:05:10.4618279Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.4618386Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.4618566Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.4618683Z                     auto tmp8 = static_cast<float>(2.2222222222222223);
2023-01-11T21:05:10.4618775Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.4618863Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.4618929Z                 }
2023-01-11T21:05:10.4618978Z             }
2023-01-11T21:05:10.4619042Z         }
2023-01-11T21:05:10.4619103Z     }
2023-01-11T21:05:10.4619164Z }
2023-01-11T21:05:10.4619244Z ''')
2023-01-11T21:05:10.4619250Z 
2023-01-11T21:05:10.4619254Z 
2023-01-11T21:05:10.4619343Z async_compile.wait(globals())
2023-01-11T21:05:10.4619454Z del async_compile
2023-01-11T21:05:10.4619458Z 
2023-01-11T21:05:10.4619515Z def call(args):
2023-01-11T21:05:10.4619584Z     arg0_1, = args
2023-01-11T21:05:10.4619656Z     args.clear()
2023-01-11T21:05:10.4619789Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.4619988Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4620163Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4620230Z     del arg0_1
2023-01-11T21:05:10.4620288Z     return (buf0, )
2023-01-11T21:05:10.4620293Z 
2023-01-11T21:05:10.4620310Z 
2023-01-11T21:05:10.4620373Z if __name__ == "__main__":
2023-01-11T21:05:10.4620487Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4620610Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4620807Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4621004Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4621110Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4621145Z 
2023-01-11T21:05:10.4621149Z 
2023-01-11T21:05:10.4621243Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4621297Z import torch
2023-01-11T21:05:10.4621366Z import random
2023-01-11T21:05:10.4621482Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4621600Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4621605Z 
2023-01-11T21:05:10.4621685Z aten = torch.ops.aten
2023-01-11T21:05:10.4621819Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4621909Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4622072Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.4622077Z 
2023-01-11T21:05:10.4622081Z 
2023-01-11T21:05:10.4622204Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4622410Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4622528Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:05:10.4622633Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4622731Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4622791Z {
2023-01-11T21:05:10.4622887Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4622936Z     {
2023-01-11T21:05:10.4623010Z         #pragma omp for 
2023-01-11T21:05:10.4623092Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.4623154Z         {
2023-01-11T21:05:10.4623216Z             {
2023-01-11T21:05:10.4623279Z                 {
2023-01-11T21:05:10.4623365Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.4623442Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:05:10.4623543Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.4623684Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.4623789Z                     auto tmp3 = static_cast<float>(0.55);
2023-01-11T21:05:10.4623881Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.4623986Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.4624077Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.4624189Z                     auto tmp8 = static_cast<float>(2.2222222222222223);
2023-01-11T21:05:10.4624266Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.4624350Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.4624412Z                 }
2023-01-11T21:05:10.4624474Z             }
2023-01-11T21:05:10.4624536Z         }
2023-01-11T21:05:10.4624596Z     }
2023-01-11T21:05:10.4624642Z }
2023-01-11T21:05:10.4624718Z ''')
2023-01-11T21:05:10.4624723Z 
2023-01-11T21:05:10.4624727Z 
2023-01-11T21:05:10.4624815Z async_compile.wait(globals())
2023-01-11T21:05:10.4624914Z del async_compile
2023-01-11T21:05:10.4624919Z 
2023-01-11T21:05:10.4624988Z def call(args):
2023-01-11T21:05:10.4625056Z     arg0_1, = args
2023-01-11T21:05:10.4625125Z     args.clear()
2023-01-11T21:05:10.4625258Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.4625443Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4625616Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4625685Z     del arg0_1
2023-01-11T21:05:10.4625755Z     return (buf0, )
2023-01-11T21:05:10.4625760Z 
2023-01-11T21:05:10.4625764Z 
2023-01-11T21:05:10.4625838Z if __name__ == "__main__":
2023-01-11T21:05:10.4625953Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4626075Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4626267Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4626454Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4626559Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4626564Z 
2023-01-11T21:05:10.4626659Z ok (3.001s)
2023-01-11T21:05:10.4627007Z   test_dtype_mismatch_issue_cpu (__main__.CpuTests) ... [2023-01-11 20:50:06,310] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 171
2023-01-11T21:05:10.4627273Z [2023-01-11 20:50:09,081] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 171
2023-01-11T21:05:10.4627278Z 
2023-01-11T21:05:10.4627371Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4627438Z import torch
2023-01-11T21:05:10.4627506Z import random
2023-01-11T21:05:10.4627608Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4627728Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4627733Z 
2023-01-11T21:05:10.4627812Z aten = torch.ops.aten
2023-01-11T21:05:10.4627944Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4628036Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4628041Z 
2023-01-11T21:05:10.4628045Z 
2023-01-11T21:05:10.4628179Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4628382Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4628500Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.4628592Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4628690Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4628789Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4628849Z {
2023-01-11T21:05:10.4628934Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.4629029Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4629090Z     {
2023-01-11T21:05:10.4629153Z         #pragma omp for 
2023-01-11T21:05:10.4629238Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:05:10.4629300Z         {
2023-01-11T21:05:10.4629362Z             {
2023-01-11T21:05:10.4629425Z                 {
2023-01-11T21:05:10.4629685Z                     float tmp5 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.4629777Z                     for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.4629832Z                     {
2023-01-11T21:05:10.4629899Z                         {
2023-01-11T21:05:10.4630007Z                             auto tmp0 = static_cast<long>(i1);
2023-01-11T21:05:10.4630115Z                             auto tmp1 = static_cast<long>(63);
2023-01-11T21:05:10.4630210Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.4630298Z                             float tmp3 = 0.0;
2023-01-11T21:05:10.4630373Z                             if(tmp2)
2023-01-11T21:05:10.4630429Z                             {
2023-01-11T21:05:10.4630534Z                                 auto tmp4 = in_ptr0[i1 + (63*i0)];
2023-01-11T21:05:10.4630653Z                                 tmp3 = tmp4;
2023-01-11T21:05:10.4630722Z                             }
2023-01-11T21:05:10.4630827Z                             tmp5 = std::max(tmp5, tmp3);
2023-01-11T21:05:10.4630897Z                         }
2023-01-11T21:05:10.4630965Z                     }
2023-01-11T21:05:10.4631036Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.4631100Z                 }
2023-01-11T21:05:10.4631163Z             }
2023-01-11T21:05:10.4631225Z         }
2023-01-11T21:05:10.4631301Z         #pragma omp for 
2023-01-11T21:05:10.4631383Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:05:10.4631443Z         {
2023-01-11T21:05:10.4631493Z             {
2023-01-11T21:05:10.4631554Z                 {
2023-01-11T21:05:10.4631632Z                     float tmp8 = 0;
2023-01-11T21:05:10.4631724Z                     for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.4631789Z                     {
2023-01-11T21:05:10.4631856Z                         {
2023-01-11T21:05:10.4631943Z                             auto tmp5 = out_ptr0[i0];
2023-01-11T21:05:10.4632049Z                             auto tmp0 = static_cast<long>(i1);
2023-01-11T21:05:10.4632189Z                             auto tmp1 = static_cast<long>(63);
2023-01-11T21:05:10.4632286Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.4632373Z                             float tmp3 = 0.0;
2023-01-11T21:05:10.4632451Z                             if(tmp2)
2023-01-11T21:05:10.4632520Z                             {
2023-01-11T21:05:10.4632624Z                                 auto tmp4 = in_ptr0[i1 + (63*i0)];
2023-01-11T21:05:10.4632696Z                                 tmp3 = tmp4;
2023-01-11T21:05:10.4632768Z                             }
2023-01-11T21:05:10.4632911Z                             auto tmp6 = tmp3 - tmp5;
2023-01-11T21:05:10.4633014Z                             auto tmp7 = std::exp(tmp6);
2023-01-11T21:05:10.4633112Z                             out_ptr1[i1 + (64*i0)] = tmp7;
2023-01-11T21:05:10.4633196Z                             tmp8 += tmp7;
2023-01-11T21:05:10.4633262Z                         }
2023-01-11T21:05:10.4633315Z                     }
2023-01-11T21:05:10.4633400Z                     out_ptr2[i0] = tmp8;
2023-01-11T21:05:10.4633463Z                 }
2023-01-11T21:05:10.4633525Z             }
2023-01-11T21:05:10.4633586Z         }
2023-01-11T21:05:10.4633661Z         #pragma omp for 
2023-01-11T21:05:10.4633742Z         for(long i0=0; i0<4096; i0+=1)
2023-01-11T21:05:10.4633790Z         {
2023-01-11T21:05:10.4633871Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.4633933Z             {
2023-01-11T21:05:10.4634077Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (16*i1) + (64*i0));
2023-01-11T21:05:10.4634204Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:05:10.4634290Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4634399Z                 tmp2.store(in_out_ptr0 + (16*i1) + (64*i0));
2023-01-11T21:05:10.4634450Z             }
2023-01-11T21:05:10.4634540Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.4634625Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:05:10.4634690Z             {
2023-01-11T21:05:10.4634786Z                 auto tmp0 = out_ptr1[i1 + (64*i0)];
2023-01-11T21:05:10.4634873Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:05:10.4634960Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.4635041Z                 in_out_ptr0[i1 + (64*i0)] = tmp2;
2023-01-11T21:05:10.4635103Z             }
2023-01-11T21:05:10.4635164Z         }
2023-01-11T21:05:10.4635224Z     }
2023-01-11T21:05:10.4635284Z }
2023-01-11T21:05:10.4635360Z ''')
2023-01-11T21:05:10.4635365Z 
2023-01-11T21:05:10.4635369Z 
2023-01-11T21:05:10.4635458Z async_compile.wait(globals())
2023-01-11T21:05:10.4635516Z del async_compile
2023-01-11T21:05:10.4635521Z 
2023-01-11T21:05:10.4635590Z def call(args):
2023-01-11T21:05:10.4635656Z     arg0_1, = args
2023-01-11T21:05:10.4635758Z     args.clear()
2023-01-11T21:05:10.4635973Z     buf0 = empty_strided((128, 32, 1), (32, 1, 4096), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4636188Z     buf1 = empty_strided((128, 32, 64), (2048, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4636397Z     buf2 = empty_strided((128, 32, 1), (32, 1, 4096), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4636469Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.4636658Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4636725Z     del arg0_1
2023-01-11T21:05:10.4636796Z     return (buf3, )
2023-01-11T21:05:10.4636801Z 
2023-01-11T21:05:10.4636805Z 
2023-01-11T21:05:10.4636879Z if __name__ == "__main__":
2023-01-11T21:05:10.4636992Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4637115Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4637329Z     arg0_1 = rand_strided((128, 32, 63), (2016, 63, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4637424Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4637442Z 
2023-01-11T21:05:10.4637496Z ok (2.930s)
2023-01-11T21:05:10.4637977Z   test_elu_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4638104Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4638363Z [2023-01-11 20:50:09,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 172
2023-01-11T21:05:10.4638629Z [2023-01-11 20:50:12,042] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 172
2023-01-11T21:05:10.4638637Z 
2023-01-11T21:05:10.4638730Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4638800Z import torch
2023-01-11T21:05:10.4638869Z import random
2023-01-11T21:05:10.4638973Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4639093Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4639098Z 
2023-01-11T21:05:10.4639175Z aten = torch.ops.aten
2023-01-11T21:05:10.4639307Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4639398Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4639403Z 
2023-01-11T21:05:10.4639407Z 
2023-01-11T21:05:10.4639540Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4639743Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4639863Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4639962Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4640045Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4640108Z {
2023-01-11T21:05:10.4640205Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4640267Z     {
2023-01-11T21:05:10.4640343Z         #pragma omp for 
2023-01-11T21:05:10.4640427Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.4640475Z         {
2023-01-11T21:05:10.4640537Z             {
2023-01-11T21:05:10.4640715Z                 {
2023-01-11T21:05:10.4640810Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4640916Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:05:10.4641008Z                     auto tmp2 = tmp0 > tmp1;
2023-01-11T21:05:10.4641122Z                     auto tmp3 = static_cast<float>(1.0507009873554805);
2023-01-11T21:05:10.4641200Z                     auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4641307Z                     auto tmp5 = static_cast<float>(1.0);
2023-01-11T21:05:10.4641398Z                     auto tmp6 = tmp0 * tmp5;
2023-01-11T21:05:10.4641565Z                     auto tmp7 = std::expm1(tmp6);
2023-01-11T21:05:10.4641679Z                     auto tmp8 = static_cast<float>(1.7580993408473766);
2023-01-11T21:05:10.4641769Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.4641871Z                     auto tmp10 = tmp2 ? tmp4 : tmp9;
2023-01-11T21:05:10.4641978Z                     auto tmp11 = static_cast<float>(2);
2023-01-11T21:05:10.4642058Z                     auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.4642160Z                     auto tmp13 = static_cast<float>(1);
2023-01-11T21:05:10.4642254Z                     auto tmp14 = tmp0 + tmp13;
2023-01-11T21:05:10.4642347Z                     auto tmp15 = tmp14 > tmp1;
2023-01-11T21:05:10.4642451Z                     auto tmp16 = static_cast<float>(3);
2023-01-11T21:05:10.4642543Z                     auto tmp17 = tmp14 * tmp16;
2023-01-11T21:05:10.4642648Z                     auto tmp18 = static_cast<float>(4);
2023-01-11T21:05:10.4642725Z                     auto tmp19 = tmp14 * tmp18;
2023-01-11T21:05:10.4642829Z                     auto tmp20 = std::expm1(tmp19);
2023-01-11T21:05:10.4642929Z                     auto tmp21 = static_cast<float>(6);
2023-01-11T21:05:10.4643062Z                     auto tmp22 = tmp20 * tmp21;
2023-01-11T21:05:10.4643161Z                     auto tmp23 = tmp15 ? tmp17 : tmp22;
2023-01-11T21:05:10.4643246Z                     out_ptr0[i0] = tmp12;
2023-01-11T21:05:10.4643329Z                     out_ptr1[i0] = tmp23;
2023-01-11T21:05:10.4643380Z                 }
2023-01-11T21:05:10.4643443Z             }
2023-01-11T21:05:10.4643504Z         }
2023-01-11T21:05:10.4643565Z     }
2023-01-11T21:05:10.4643627Z }
2023-01-11T21:05:10.4643708Z ''')
2023-01-11T21:05:10.4643716Z 
2023-01-11T21:05:10.4643720Z 
2023-01-11T21:05:10.4643809Z async_compile.wait(globals())
2023-01-11T21:05:10.4643867Z del async_compile
2023-01-11T21:05:10.4643885Z 
2023-01-11T21:05:10.4643942Z def call(args):
2023-01-11T21:05:10.4644012Z     arg0_1, = args
2023-01-11T21:05:10.4644084Z     args.clear()
2023-01-11T21:05:10.4644285Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4644484Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4644651Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4644720Z     del arg0_1
2023-01-11T21:05:10.4644783Z     return (buf0, buf1, )
2023-01-11T21:05:10.4644788Z 
2023-01-11T21:05:10.4644793Z 
2023-01-11T21:05:10.4644868Z if __name__ == "__main__":
2023-01-11T21:05:10.4644982Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4645105Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4645304Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4645413Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4645417Z 
2023-01-11T21:05:10.4645484Z ok (2.867s)
2023-01-11T21:05:10.4645943Z   test_embedding_bag_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4646073Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4646320Z [2023-01-11 20:50:12,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 173
2023-01-11T21:05:10.4646552Z [2023-01-11 20:50:12,159] torch._inductor.ir: [WARNING] Using FallbackKernel: aten._embedding_bag
2023-01-11T21:05:10.4646815Z [2023-01-11 20:50:12,165] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 173
2023-01-11T21:05:10.4646821Z 
2023-01-11T21:05:10.4646915Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4646984Z import torch
2023-01-11T21:05:10.4647085Z import random
2023-01-11T21:05:10.4647203Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4647324Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4647329Z 
2023-01-11T21:05:10.4647395Z aten = torch.ops.aten
2023-01-11T21:05:10.4647529Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4647620Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4647625Z 
2023-01-11T21:05:10.4647630Z 
2023-01-11T21:05:10.4647717Z async_compile.wait(globals())
2023-01-11T21:05:10.4647789Z del async_compile
2023-01-11T21:05:10.4647794Z 
2023-01-11T21:05:10.4647864Z def call(args):
2023-01-11T21:05:10.4647945Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4648016Z     args.clear()
2023-01-11T21:05:10.4648112Z     buf0 = aten._embedding_bag(arg0_1, arg1_1, arg2_1)
2023-01-11T21:05:10.4648179Z     del arg0_1
2023-01-11T21:05:10.4648246Z     del arg1_1
2023-01-11T21:05:10.4648311Z     del arg2_1
2023-01-11T21:05:10.4648381Z     buf1 = buf0[0]
2023-01-11T21:05:10.4648480Z     assert_size_stride(buf1, (3, 4), (4, 1))
2023-01-11T21:05:10.4648535Z     buf2 = buf0[1]
2023-01-11T21:05:10.4648627Z     assert_size_stride(buf2, (0, ), (1, ))
2023-01-11T21:05:10.4648739Z     buf3 = buf0[2]
2023-01-11T21:05:10.4648836Z     assert_size_stride(buf3, (3, ), (1, ))
2023-01-11T21:05:10.4648901Z     buf4 = buf0[3]
2023-01-11T21:05:10.4648991Z     assert_size_stride(buf4, (3, ), (1, ))
2023-01-11T21:05:10.4649054Z     del buf0
2023-01-11T21:05:10.4649127Z     return (buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.4649132Z 
2023-01-11T21:05:10.4649148Z 
2023-01-11T21:05:10.4649209Z if __name__ == "__main__":
2023-01-11T21:05:10.4649321Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4649444Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4649645Z     arg0_1 = rand_strided((10, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4649834Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4650025Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4650148Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4650156Z 
2023-01-11T21:05:10.4650208Z ok (0.120s)
2023-01-11T21:05:10.4650656Z   test_embedding_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4650784Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4651042Z [2023-01-11 20:50:12,386] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 174
2023-01-11T21:05:10.4651306Z [2023-01-11 20:50:15,118] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 174
2023-01-11T21:05:10.4651313Z 
2023-01-11T21:05:10.4651407Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4651477Z import torch
2023-01-11T21:05:10.4651546Z import random
2023-01-11T21:05:10.4651665Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4651773Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4651792Z 
2023-01-11T21:05:10.4651857Z aten = torch.ops.aten
2023-01-11T21:05:10.4651992Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4652082Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4652087Z 
2023-01-11T21:05:10.4652091Z 
2023-01-11T21:05:10.4652225Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4652429Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4652549Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4652654Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4652784Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4652867Z                        bool* __restrict__ out_ptr1,
2023-01-11T21:05:10.4652963Z                        long* __restrict__ out_ptr2)
2023-01-11T21:05:10.4653022Z {
2023-01-11T21:05:10.4653121Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4653180Z     {
2023-01-11T21:05:10.4653256Z         #pragma omp for 
2023-01-11T21:05:10.4653325Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4653386Z         {
2023-01-11T21:05:10.4653464Z             #pragma GCC ivdep
2023-01-11T21:05:10.4653546Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.4653608Z             {
2023-01-11T21:05:10.4653671Z                 {
2023-01-11T21:05:10.4653735Z                     {
2023-01-11T21:05:10.4653819Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4653922Z                         auto tmp1 = in_ptr1[i1 + (4*tmp0)];
2023-01-11T21:05:10.4654021Z                         auto tmp2 = tmp1 * (tmp1>0);
2023-01-11T21:05:10.4654116Z                         out_ptr0[i1 + (4*i0)] = tmp2;
2023-01-11T21:05:10.4654181Z                     }
2023-01-11T21:05:10.4654270Z                 }
2023-01-11T21:05:10.4654333Z             }
2023-01-11T21:05:10.4654381Z         }
2023-01-11T21:05:10.4654456Z         #pragma omp for 
2023-01-11T21:05:10.4654536Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4654597Z         {
2023-01-11T21:05:10.4654659Z             {
2023-01-11T21:05:10.4654721Z                 {
2023-01-11T21:05:10.4654813Z                     auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.4654904Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:05:10.4654994Z                     auto tmp2 = tmp0 <= tmp1;
2023-01-11T21:05:10.4655078Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.4655140Z                 }
2023-01-11T21:05:10.4655205Z             }
2023-01-11T21:05:10.4655265Z         }
2023-01-11T21:05:10.4655328Z         #pragma omp for 
2023-01-11T21:05:10.4655408Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4655469Z         {
2023-01-11T21:05:10.4655530Z             {
2023-01-11T21:05:10.4655592Z                 {
2023-01-11T21:05:10.4655685Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4655792Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4655885Z                     auto tmp2 = static_cast<long>(tmp1);
2023-01-11T21:05:10.4655968Z                     out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.4656029Z                 }
2023-01-11T21:05:10.4656091Z             }
2023-01-11T21:05:10.4656151Z         }
2023-01-11T21:05:10.4656211Z     }
2023-01-11T21:05:10.4656267Z }
2023-01-11T21:05:10.4662130Z ''')
2023-01-11T21:05:10.4662144Z 
2023-01-11T21:05:10.4662148Z 
2023-01-11T21:05:10.4662304Z async_compile.wait(globals())
2023-01-11T21:05:10.4662367Z del async_compile
2023-01-11T21:05:10.4662372Z 
2023-01-11T21:05:10.4662444Z def call(args):
2023-01-11T21:05:10.4662541Z     primals_1, primals_2 = args
2023-01-11T21:05:10.4662612Z     args.clear()
2023-01-11T21:05:10.4662852Z     buf0 = empty_strided((2, 8, 4), (32, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4663054Z     buf1 = empty_strided((2, 8, 4), (32, 4, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.4663245Z     buf2 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4663475Z     kernel_cpp_0(c_void_p(primals_2.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4663535Z     del primals_1
2023-01-11T21:05:10.4663604Z     del primals_2
2023-01-11T21:05:10.4663685Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4663690Z 
2023-01-11T21:05:10.4663695Z 
2023-01-11T21:05:10.4663770Z if __name__ == "__main__":
2023-01-11T21:05:10.4663885Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4664009Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4664322Z     primals_1 = rand_strided((10, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4664524Z     primals_2 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4664639Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:05:10.4664646Z 
2023-01-11T21:05:10.4664711Z ok (2.953s)
2023-01-11T21:05:10.4665158Z   test_exp_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4665286Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4665553Z [2023-01-11 20:50:15,159] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 175
2023-01-11T21:05:10.4665822Z [2023-01-11 20:50:17,902] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 175
2023-01-11T21:05:10.4665828Z 
2023-01-11T21:05:10.4665965Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4666036Z import torch
2023-01-11T21:05:10.4666103Z import random
2023-01-11T21:05:10.4666207Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4666327Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4666332Z 
2023-01-11T21:05:10.4666407Z aten = torch.ops.aten
2023-01-11T21:05:10.4666542Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4666635Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4666639Z 
2023-01-11T21:05:10.4666643Z 
2023-01-11T21:05:10.4666777Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4666980Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4667100Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4667192Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4667291Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4667390Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4667450Z {
2023-01-11T21:05:10.4667541Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4667591Z     {
2023-01-11T21:05:10.4667671Z         #pragma omp for 
2023-01-11T21:05:10.4667756Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4667817Z         {
2023-01-11T21:05:10.4667963Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4668096Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.4668179Z             auto tmp1 = tmp0.exp();
2023-01-11T21:05:10.4668249Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.4668330Z             auto tmp4 = tmp3.exp();
2023-01-11T21:05:10.4668425Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4668516Z             tmp4.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4668577Z         }
2023-01-11T21:05:10.4668672Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4668755Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.4668804Z         {
2023-01-11T21:05:10.4668887Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4668967Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.4669056Z             auto tmp1 = std::exp(tmp0);
2023-01-11T21:05:10.4669139Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.4669226Z             auto tmp4 = std::exp(tmp3);
2023-01-11T21:05:10.4669304Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4669367Z             out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.4669427Z         }
2023-01-11T21:05:10.4669487Z     }
2023-01-11T21:05:10.4669547Z }
2023-01-11T21:05:10.4669624Z ''')
2023-01-11T21:05:10.4669631Z 
2023-01-11T21:05:10.4669635Z 
2023-01-11T21:05:10.4669754Z async_compile.wait(globals())
2023-01-11T21:05:10.4669825Z del async_compile
2023-01-11T21:05:10.4669830Z 
2023-01-11T21:05:10.4669886Z def call(args):
2023-01-11T21:05:10.4669960Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4670030Z     args.clear()
2023-01-11T21:05:10.4670225Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4670414Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4670604Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4670671Z     del arg0_1
2023-01-11T21:05:10.4670735Z     del arg1_1
2023-01-11T21:05:10.4670798Z     return (buf0, buf1, )
2023-01-11T21:05:10.4670803Z 
2023-01-11T21:05:10.4670807Z 
2023-01-11T21:05:10.4670880Z if __name__ == "__main__":
2023-01-11T21:05:10.4670994Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4671118Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4671316Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4671509Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4671651Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4671657Z 
2023-01-11T21:05:10.4671722Z ok (2.786s)
2023-01-11T21:05:10.4672149Z   test_expand_as_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4672276Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4672535Z [2023-01-11 20:50:17,986] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 176
2023-01-11T21:05:10.4672802Z [2023-01-11 20:50:20,719] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 176
2023-01-11T21:05:10.4672808Z 
2023-01-11T21:05:10.4672900Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4672972Z import torch
2023-01-11T21:05:10.4673042Z import random
2023-01-11T21:05:10.4673156Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4673276Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4673281Z 
2023-01-11T21:05:10.4673345Z aten = torch.ops.aten
2023-01-11T21:05:10.4673477Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4673567Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4673572Z 
2023-01-11T21:05:10.4673576Z 
2023-01-11T21:05:10.4673708Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4673913Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4674031Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4674132Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4674190Z {
2023-01-11T21:05:10.4674274Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4674336Z     {
2023-01-11T21:05:10.4674425Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4674504Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.4674565Z         {
2023-01-11T21:05:10.4674651Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:05:10.4674701Z             {
2023-01-11T21:05:10.4674788Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:05:10.4674850Z                 {
2023-01-11T21:05:10.4674995Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i2) + (100*i0));
2023-01-11T21:05:10.4675133Z                     auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4675224Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4675313Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:05:10.4675457Z                     tmp3.store(out_ptr0 + (16*i2) + (100*i1) + (12800*i0));
2023-01-11T21:05:10.4675507Z                 }
2023-01-11T21:05:10.4675601Z                 #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.4675691Z                 for(long i2=96; i2<100; i2+=1)
2023-01-11T21:05:10.4675753Z                 {
2023-01-11T21:05:10.4675852Z                     auto tmp0 = in_ptr0[i2 + (100*i0)];
2023-01-11T21:05:10.4675953Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4676042Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4676117Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:05:10.4676218Z                     out_ptr0[i2 + (100*i1) + (12800*i0)] = tmp3;
2023-01-11T21:05:10.4676281Z                 }
2023-01-11T21:05:10.4676343Z             }
2023-01-11T21:05:10.4676404Z         }
2023-01-11T21:05:10.4676464Z     }
2023-01-11T21:05:10.4676526Z }
2023-01-11T21:05:10.4676591Z ''')
2023-01-11T21:05:10.4676598Z 
2023-01-11T21:05:10.4676602Z 
2023-01-11T21:05:10.4676690Z async_compile.wait(globals())
2023-01-11T21:05:10.4676762Z del async_compile
2023-01-11T21:05:10.4676767Z 
2023-01-11T21:05:10.4676837Z def call(args):
2023-01-11T21:05:10.4676937Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4677008Z     args.clear()
2023-01-11T21:05:10.4677223Z     buf0 = empty_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4677341Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4677452Z     return (as_strided(arg0_1, (6, 128, 100), (100, 0, 1)), buf0, )
2023-01-11T21:05:10.4677457Z 
2023-01-11T21:05:10.4677461Z 
2023-01-11T21:05:10.4677535Z if __name__ == "__main__":
2023-01-11T21:05:10.4677647Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4677770Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4677979Z     arg0_1 = rand_strided((6, 1, 100), (100, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4678194Z     arg1_1 = rand_strided((6, 128, 100), (12800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4678307Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4678315Z 
2023-01-11T21:05:10.4678380Z ok (2.909s)
2023-01-11T21:05:10.4678804Z   test_expand_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4678931Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4679191Z [2023-01-11 20:50:20,866] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 177
2023-01-11T21:05:10.4679454Z [2023-01-11 20:50:23,601] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 177
2023-01-11T21:05:10.4679461Z 
2023-01-11T21:05:10.4679556Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4679625Z import torch
2023-01-11T21:05:10.4679694Z import random
2023-01-11T21:05:10.4679811Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4679931Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4679936Z 
2023-01-11T21:05:10.4680000Z aten = torch.ops.aten
2023-01-11T21:05:10.4680134Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4680227Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4680231Z 
2023-01-11T21:05:10.4680236Z 
2023-01-11T21:05:10.4680369Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4680574Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4680866Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4680966Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4681130Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4681177Z {
2023-01-11T21:05:10.4681275Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4681337Z     {
2023-01-11T21:05:10.4681417Z         #pragma omp for 
2023-01-11T21:05:10.4681500Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4681563Z         {
2023-01-11T21:05:10.4681642Z             #pragma GCC ivdep
2023-01-11T21:05:10.4681709Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.4681773Z             {
2023-01-11T21:05:10.4681854Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4681945Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:05:10.4682010Z                 {
2023-01-11T21:05:10.4682095Z                     #pragma GCC ivdep
2023-01-11T21:05:10.4682171Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:05:10.4682237Z                     {
2023-01-11T21:05:10.4682305Z                         {
2023-01-11T21:05:10.4682377Z                             {
2023-01-11T21:05:10.4682484Z                                 auto tmp0 = in_ptr0[i3 + (2*i1)];
2023-01-11T21:05:10.4682596Z                                 auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4682730Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4682841Z                                 auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.4682926Z                                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.4683035Z                                 out_ptr0[i3 + (2*i2) + (6*i1) + (12*i0)] = tmp4;
2023-01-11T21:05:10.4683105Z                             }
2023-01-11T21:05:10.4683173Z                         }
2023-01-11T21:05:10.4683239Z                     }
2023-01-11T21:05:10.4683306Z                 }
2023-01-11T21:05:10.4683368Z             }
2023-01-11T21:05:10.4683420Z         }
2023-01-11T21:05:10.4683508Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4683588Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.4683650Z         {
2023-01-11T21:05:10.4683731Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.4683793Z             {
2023-01-11T21:05:10.4683860Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4683952Z                 for(long i2=0; i2<3; i2+=1)
2023-01-11T21:05:10.4684015Z                 {
2023-01-11T21:05:10.4684096Z                     #pragma GCC ivdep
2023-01-11T21:05:10.4684186Z                     for(long i3=0; i3<2; i3+=1)
2023-01-11T21:05:10.4684249Z                     {
2023-01-11T21:05:10.4684316Z                         {
2023-01-11T21:05:10.4684370Z                             {
2023-01-11T21:05:10.4684471Z                                 auto tmp0 = in_ptr0[i3 + (2*i1)];
2023-01-11T21:05:10.4684579Z                                 auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.4684674Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4684784Z                                 out_ptr1[i3 + (2*i2) + (6*i1) + (12*i0)] = tmp2;
2023-01-11T21:05:10.4684853Z                             }
2023-01-11T21:05:10.4684920Z                         }
2023-01-11T21:05:10.4684971Z                     }
2023-01-11T21:05:10.4685033Z                 }
2023-01-11T21:05:10.4685099Z             }
2023-01-11T21:05:10.4685159Z         }
2023-01-11T21:05:10.4685220Z     }
2023-01-11T21:05:10.4685279Z }
2023-01-11T21:05:10.4685362Z ''')
2023-01-11T21:05:10.4685366Z 
2023-01-11T21:05:10.4685371Z 
2023-01-11T21:05:10.4685444Z async_compile.wait(globals())
2023-01-11T21:05:10.4685514Z del async_compile
2023-01-11T21:05:10.4685518Z 
2023-01-11T21:05:10.4685586Z def call(args):
2023-01-11T21:05:10.4685654Z     arg0_1, = args
2023-01-11T21:05:10.4685722Z     args.clear()
2023-01-11T21:05:10.4685940Z     buf0 = empty_strided((3, 4, 2, 3, 2), (48, 12, 6, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4686155Z     buf1 = empty_strided((2, 1, 2, 3, 2), (12, 12, 6, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4686349Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4686453Z     return (buf0, buf1, as_strided(arg0_1, (2, 2, 5, 2), (0, 2, 0, 1)), )
2023-01-11T21:05:10.4686458Z 
2023-01-11T21:05:10.4686479Z 
2023-01-11T21:05:10.4686540Z if __name__ == "__main__":
2023-01-11T21:05:10.4686653Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4686775Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4686977Z     arg0_1 = rand_strided((2, 1, 2), (2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4687083Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4687088Z 
2023-01-11T21:05:10.4687152Z ok (2.790s)
2023-01-11T21:05:10.4687827Z   test_expanded_reduction_cpu (__main__.CpuTests) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/87157 for platform(s) linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.001s)
2023-01-11T21:05:10.4688294Z   test_expm1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4688423Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4688672Z [2023-01-11 20:50:23,644] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 178
2023-01-11T21:05:10.4688937Z [2023-01-11 20:50:26,348] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 178
2023-01-11T21:05:10.4689332Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4689461Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4689714Z [2023-01-11 20:50:26,398] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 179
2023-01-11T21:05:10.4689973Z [2023-01-11 20:50:29,094] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 179
2023-01-11T21:05:10.4690370Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4690494Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4690750Z [2023-01-11 20:50:29,141] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 180
2023-01-11T21:05:10.4691017Z [2023-01-11 20:50:31,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 180
2023-01-11T21:05:10.4691416Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4691539Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4691779Z [2023-01-11 20:50:31,867] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 181
2023-01-11T21:05:10.4692039Z [2023-01-11 20:50:34,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 181
2023-01-11T21:05:10.4692466Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4692591Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4692842Z [2023-01-11 20:50:34,642] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 182
2023-01-11T21:05:10.4692847Z 
2023-01-11T21:05:10.4692939Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4693009Z import torch
2023-01-11T21:05:10.4693077Z import random
2023-01-11T21:05:10.4693193Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4693300Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4693305Z 
2023-01-11T21:05:10.4693380Z aten = torch.ops.aten
2023-01-11T21:05:10.4693517Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4693607Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4693613Z 
2023-01-11T21:05:10.4693617Z 
2023-01-11T21:05:10.4693792Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4693999Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4694117Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:05:10.4694213Z                        half* __restrict__ out_ptr0,
2023-01-11T21:05:10.4694294Z                        half* __restrict__ out_ptr1)
2023-01-11T21:05:10.4694353Z {
2023-01-11T21:05:10.4694449Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4694509Z     {
2023-01-11T21:05:10.4694585Z         #pragma omp for 
2023-01-11T21:05:10.4694666Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4694727Z         {
2023-01-11T21:05:10.4694776Z             {
2023-01-11T21:05:10.4694843Z                 {
2023-01-11T21:05:10.4694957Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.4695064Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:05:10.4695171Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:05:10.4695263Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4695350Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4695419Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4695484Z                 }
2023-01-11T21:05:10.4695548Z             }
2023-01-11T21:05:10.4695609Z         }
2023-01-11T21:05:10.4695669Z     }
2023-01-11T21:05:10.4695727Z }
2023-01-11T21:05:10.4695793Z ''')
2023-01-11T21:05:10.4695810Z 
2023-01-11T21:05:10.4695814Z 
2023-01-11T21:05:10.4695889Z async_compile.wait(globals())
2023-01-11T21:05:10.4695959Z del async_compile
2023-01-11T21:05:10.4695964Z 
2023-01-11T21:05:10.4696032Z def call(args):
2023-01-11T21:05:10.4696099Z     arg0_1, = args
2023-01-11T21:05:10.4696171Z     args.clear()
2023-01-11T21:05:10.4696364Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.4696555Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.4696706Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4696774Z     del arg0_1
2023-01-11T21:05:10.4696851Z     return (buf0, buf1, )
2023-01-11T21:05:10.4696856Z 
2023-01-11T21:05:10.4696860Z 
2023-01-11T21:05:10.4696934Z if __name__ == "__main__":
2023-01-11T21:05:10.4697046Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4697166Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4697357Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.4697463Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4697468Z 
2023-01-11T21:05:10.4697473Z 
2023-01-11T21:05:10.4697563Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4697649Z import torch
2023-01-11T21:05:10.4697717Z import random
2023-01-11T21:05:10.4697829Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4697951Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4697956Z 
2023-01-11T21:05:10.4698033Z aten = torch.ops.aten
2023-01-11T21:05:10.4698165Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4698255Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4698260Z 
2023-01-11T21:05:10.4698264Z 
2023-01-11T21:05:10.4698382Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4698685Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4698805Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:05:10.4698903Z                        half* __restrict__ out_ptr0,
2023-01-11T21:05:10.4698999Z                        half* __restrict__ out_ptr1)
2023-01-11T21:05:10.4699064Z {
2023-01-11T21:05:10.4699163Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4699211Z     {
2023-01-11T21:05:10.4699287Z         #pragma omp for 
2023-01-11T21:05:10.4699370Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.4699463Z         {
2023-01-11T21:05:10.4699529Z             {
2023-01-11T21:05:10.4699593Z                 {
2023-01-11T21:05:10.4699708Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.4699797Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:05:10.4699898Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:05:10.4699988Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4700070Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4700152Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4700217Z                 }
2023-01-11T21:05:10.4700278Z             }
2023-01-11T21:05:10.4700325Z         }
2023-01-11T21:05:10.4700385Z     }
2023-01-11T21:05:10.4700445Z }
2023-01-11T21:05:10.4700525Z ''')
2023-01-11T21:05:10.4700530Z 
2023-01-11T21:05:10.4700534Z 
2023-01-11T21:05:10.4700621Z async_compile.wait(globals())
2023-01-11T21:05:10.4700693Z del async_compile
2023-01-11T21:05:10.4700698Z 
2023-01-11T21:05:10.4700768Z def call(args):
2023-01-11T21:05:10.4700824Z     arg0_1, = args
2023-01-11T21:05:10.4700893Z     args.clear()
2023-01-11T21:05:10.4701085Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.4701276Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.4701437Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4701505Z     del arg0_1
2023-01-11T21:05:10.4701582Z     return (buf0, buf1, )
2023-01-11T21:05:10.4701587Z 
2023-01-11T21:05:10.4701591Z 
2023-01-11T21:05:10.4701665Z if __name__ == "__main__":
2023-01-11T21:05:10.4701764Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4701888Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4702082Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.4702190Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4702195Z 
2023-01-11T21:05:10.4702199Z 
2023-01-11T21:05:10.4702291Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4702359Z import torch
2023-01-11T21:05:10.4702427Z import random
2023-01-11T21:05:10.4702542Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4702647Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4702652Z 
2023-01-11T21:05:10.4702729Z aten = torch.ops.aten
2023-01-11T21:05:10.4702861Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4702950Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4702955Z 
2023-01-11T21:05:10.4702960Z 
2023-01-11T21:05:10.4703090Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4703293Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4703475Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4703575Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4703658Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4703718Z {
2023-01-11T21:05:10.4703814Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4703874Z     {
2023-01-11T21:05:10.4703950Z         #pragma omp for 
2023-01-11T21:05:10.4704032Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4704096Z         {
2023-01-11T21:05:10.4704216Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4704303Z             auto tmp1 = tmp0.expm1();
2023-01-11T21:05:10.4704438Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.4704522Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4704615Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4704708Z             tmp3.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4704771Z         }
2023-01-11T21:05:10.4704851Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4704969Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.4705033Z         {
2023-01-11T21:05:10.4705115Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4705208Z             auto tmp1 = std::expm1(tmp0);
2023-01-11T21:05:10.4705307Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.4705389Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4705455Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4705531Z             out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4705591Z         }
2023-01-11T21:05:10.4705652Z     }
2023-01-11T21:05:10.4705711Z }
2023-01-11T21:05:10.4705789Z ''')
2023-01-11T21:05:10.4705795Z 
2023-01-11T21:05:10.4705799Z 
2023-01-11T21:05:10.4705886Z async_compile.wait(globals())
2023-01-11T21:05:10.4705944Z del async_compile
2023-01-11T21:05:10.4705951Z 
2023-01-11T21:05:10.4706020Z def call(args):
2023-01-11T21:05:10.4706088Z     arg0_1, = args
2023-01-11T21:05:10.4706155Z     args.clear()
2023-01-11T21:05:10.4706348Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4706539Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4706702Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4706756Z     del arg0_1
2023-01-11T21:05:10.4706832Z     return (buf0, buf1, )
2023-01-11T21:05:10.4706837Z 
2023-01-11T21:05:10.4706842Z 
2023-01-11T21:05:10.4706916Z if __name__ == "__main__":
2023-01-11T21:05:10.4707029Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4707150Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4707341Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4707445Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4707452Z 
2023-01-11T21:05:10.4707456Z 
2023-01-11T21:05:10.4707551Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4707619Z import torch
2023-01-11T21:05:10.4707675Z import random
2023-01-11T21:05:10.4707790Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4707909Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4707913Z 
2023-01-11T21:05:10.4707989Z aten = torch.ops.aten
2023-01-11T21:05:10.4708120Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4708211Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4708215Z 
2023-01-11T21:05:10.4708219Z 
2023-01-11T21:05:10.4708351Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4708550Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4708654Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4708752Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4708879Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4708940Z {
2023-01-11T21:05:10.4709035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4709096Z     {
2023-01-11T21:05:10.4709171Z         #pragma omp for 
2023-01-11T21:05:10.4709240Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4709302Z         {
2023-01-11T21:05:10.4709432Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4709519Z             auto tmp1 = tmp0.expm1();
2023-01-11T21:05:10.4709650Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.4709734Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4709823Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4709900Z             tmp3.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4709962Z         }
2023-01-11T21:05:10.4710055Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4710139Z         for(long i0=192; i0<201; i0+=1)
2023-01-11T21:05:10.4710199Z         {
2023-01-11T21:05:10.4710281Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4710373Z             auto tmp1 = std::expm1(tmp0);
2023-01-11T21:05:10.4710486Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.4710569Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4710647Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4710725Z             out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4710785Z         }
2023-01-11T21:05:10.4710845Z     }
2023-01-11T21:05:10.4710903Z }
2023-01-11T21:05:10.4710967Z ''')
2023-01-11T21:05:10.4710971Z 
2023-01-11T21:05:10.4710976Z 
2023-01-11T21:05:10.4711064Z async_compile.wait(globals())
2023-01-11T21:05:10.4711135Z del async_compile
2023-01-11T21:05:10.4711140Z 
2023-01-11T21:05:10.4711209Z def call(args):
2023-01-11T21:05:10.4711278Z     arg0_1, = args
2023-01-11T21:05:10.4711348Z     args.clear()
2023-01-11T21:05:10.4711543Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4711723Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4711887Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4711958Z     del arg0_1
2023-01-11T21:05:10.4712035Z     return (buf0, buf1, )
2023-01-11T21:05:10.4712040Z 
2023-01-11T21:05:10.4712044Z 
2023-01-11T21:05:10.4712118Z if __name__ == "__main__":
2023-01-11T21:05:10.4712234Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4712356Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4712554Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4712646Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4712651Z 
2023-01-11T21:05:10.4712669Z 
2023-01-11T21:05:10.4712748Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4712822Z import torch
2023-01-11T21:05:10.4712893Z import random
2023-01-11T21:05:10.4713008Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4713126Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4713130Z 
2023-01-11T21:05:10.4713212Z aten = torch.ops.aten
2023-01-11T21:05:10.4713345Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4713422Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4713427Z 
2023-01-11T21:05:10.4713431Z 
2023-01-11T21:05:10.4713563Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4713766Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4713886Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.4713988Z                        double* __restrict__ out_ptr0,
2023-01-11T21:05:10.4714085Z                        double* __restrict__ out_ptr1)
2023-01-11T21:05:10.4714145Z {
2023-01-11T21:05:10.4714241Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4714317Z     {
2023-01-11T21:05:10.4714394Z         #pragma omp for 
2023-01-11T21:05:10.4714477Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4714540Z         {
2023-01-11T21:05:10.4714605Z             {
2023-01-11T21:05:10.4714670Z                 {
2023-01-11T21:05:10.4714748Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4714848Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:05:10.4714951Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:05:10.4715042Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4715125Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4715210Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4715273Z                 }
2023-01-11T21:05:10.4715322Z             }
2023-01-11T21:05:10.4715384Z         }
2023-01-11T21:05:10.4715445Z     }
2023-01-11T21:05:10.4715505Z }
2023-01-11T21:05:10.4715586Z ''')
2023-01-11T21:05:10.4715591Z 
2023-01-11T21:05:10.4715598Z 
2023-01-11T21:05:10.4715687Z async_compile.wait(globals())
2023-01-11T21:05:10.4715759Z del async_compile
2023-01-11T21:05:10.4715764Z 
2023-01-11T21:05:10.4715834Z def call(args):
2023-01-11T21:05:10.4715889Z     arg0_1, = args
2023-01-11T21:05:10.4715986Z     args.clear()
2023-01-11T21:05:10.4716182Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.4716374Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.4716535Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4716603Z     del arg0_1
2023-01-11T21:05:10.4716679Z     return (buf0, buf1, )
2023-01-11T21:05:10.4716684Z 
2023-01-11T21:05:10.4716688Z 
2023-01-11T21:05:10.4716750Z if __name__ == "__main__":
2023-01-11T21:05:10.4716862Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4716982Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4717177Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.4717283Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4717551Z [2023-01-11 20:50:37,349] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 182
2023-01-11T21:05:10.4717949Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4718075Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4718329Z [2023-01-11 20:50:37,386] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 183
2023-01-11T21:05:10.4718575Z [2023-01-11 20:50:40,074] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 183
2023-01-11T21:05:10.4718978Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4719103Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4719357Z [2023-01-11 20:50:40,131] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 184
2023-01-11T21:05:10.4719619Z [2023-01-11 20:50:42,982] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 184
2023-01-11T21:05:10.4720017Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4720168Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4720422Z [2023-01-11 20:50:43,019] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 185
2023-01-11T21:05:10.4720825Z [2023-01-11 20:50:45,687] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 185
2023-01-11T21:05:10.4721224Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4721348Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4721602Z [2023-01-11 20:50:45,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 186
2023-01-11T21:05:10.4721852Z [2023-01-11 20:50:48,463] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 186
2023-01-11T21:05:10.4722302Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4722427Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4722680Z [2023-01-11 20:50:48,508] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 187
2023-01-11T21:05:10.4722685Z 
2023-01-11T21:05:10.4722690Z 
2023-01-11T21:05:10.4722784Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4722852Z import torch
2023-01-11T21:05:10.4722920Z import random
2023-01-11T21:05:10.4723035Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4723157Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4723162Z 
2023-01-11T21:05:10.4723226Z aten = torch.ops.aten
2023-01-11T21:05:10.4723362Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4723458Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4723464Z 
2023-01-11T21:05:10.4723468Z 
2023-01-11T21:05:10.4723603Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4723809Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4723928Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.4724031Z                        double* __restrict__ out_ptr0,
2023-01-11T21:05:10.4724130Z                        double* __restrict__ out_ptr1)
2023-01-11T21:05:10.4724175Z {
2023-01-11T21:05:10.4724274Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4724335Z     {
2023-01-11T21:05:10.4724412Z         #pragma omp for 
2023-01-11T21:05:10.4724498Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.4724562Z         {
2023-01-11T21:05:10.4724624Z             {
2023-01-11T21:05:10.4724674Z                 {
2023-01-11T21:05:10.4724768Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4724869Z                     auto tmp1 = std::expm1(tmp0);
2023-01-11T21:05:10.4724972Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:05:10.4725061Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.4725145Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.4725229Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.4725279Z                 }
2023-01-11T21:05:10.4725341Z             }
2023-01-11T21:05:10.4725402Z         }
2023-01-11T21:05:10.4725462Z     }
2023-01-11T21:05:10.4725521Z }
2023-01-11T21:05:10.4725598Z ''')
2023-01-11T21:05:10.4725602Z 
2023-01-11T21:05:10.4725606Z 
2023-01-11T21:05:10.4725694Z async_compile.wait(globals())
2023-01-11T21:05:10.4725790Z del async_compile
2023-01-11T21:05:10.4725795Z 
2023-01-11T21:05:10.4725863Z def call(args):
2023-01-11T21:05:10.4725933Z     arg0_1, = args
2023-01-11T21:05:10.4726002Z     args.clear()
2023-01-11T21:05:10.4726200Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.4726391Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.4726551Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4726605Z     del arg0_1
2023-01-11T21:05:10.4726683Z     return (buf0, buf1, )
2023-01-11T21:05:10.4726688Z 
2023-01-11T21:05:10.4726692Z 
2023-01-11T21:05:10.4726767Z if __name__ == "__main__":
2023-01-11T21:05:10.4726878Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4726999Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4727192Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.4727300Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4727305Z 
2023-01-11T21:05:10.4727310Z 
2023-01-11T21:05:10.4727401Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4727456Z import torch
2023-01-11T21:05:10.4727562Z import random
2023-01-11T21:05:10.4727678Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4727796Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4727801Z 
2023-01-11T21:05:10.4727877Z aten = torch.ops.aten
2023-01-11T21:05:10.4728009Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4728099Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4728104Z 
2023-01-11T21:05:10.4728108Z 
2023-01-11T21:05:10.4728240Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4728430Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4728546Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.4728648Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4728744Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4728803Z {
2023-01-11T21:05:10.4728901Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4728961Z     {
2023-01-11T21:05:10.4729024Z         #pragma omp for 
2023-01-11T21:05:10.4729105Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4729167Z         {
2023-01-11T21:05:10.4729228Z             {
2023-01-11T21:05:10.4729293Z                 {
2023-01-11T21:05:10.4729386Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4729494Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4729581Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:05:10.4729685Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.4729776Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.4729861Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4729949Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.4730013Z                 }
2023-01-11T21:05:10.4730074Z             }
2023-01-11T21:05:10.4730122Z         }
2023-01-11T21:05:10.4730181Z     }
2023-01-11T21:05:10.4730242Z }
2023-01-11T21:05:10.4730320Z ''')
2023-01-11T21:05:10.4730328Z 
2023-01-11T21:05:10.4730332Z 
2023-01-11T21:05:10.4730419Z async_compile.wait(globals())
2023-01-11T21:05:10.4730491Z del async_compile
2023-01-11T21:05:10.4730495Z 
2023-01-11T21:05:10.4730566Z def call(args):
2023-01-11T21:05:10.4730621Z     arg0_1, = args
2023-01-11T21:05:10.4730693Z     args.clear()
2023-01-11T21:05:10.4730886Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4731079Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4731241Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4731309Z     del arg0_1
2023-01-11T21:05:10.4731416Z     return (buf0, buf1, )
2023-01-11T21:05:10.4731421Z 
2023-01-11T21:05:10.4731425Z 
2023-01-11T21:05:10.4731500Z if __name__ == "__main__":
2023-01-11T21:05:10.4731600Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4731724Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4731914Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.4732020Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4732025Z 
2023-01-11T21:05:10.4732029Z 
2023-01-11T21:05:10.4732120Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4732189Z import torch
2023-01-11T21:05:10.4732257Z import random
2023-01-11T21:05:10.4732370Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4732476Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4732481Z 
2023-01-11T21:05:10.4732557Z aten = torch.ops.aten
2023-01-11T21:05:10.4732688Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4732781Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4732786Z 
2023-01-11T21:05:10.4732791Z 
2023-01-11T21:05:10.4732922Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4733170Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4733286Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.4733384Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4733467Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4733526Z {
2023-01-11T21:05:10.4733623Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4733683Z     {
2023-01-11T21:05:10.4733757Z         #pragma omp for 
2023-01-11T21:05:10.4733838Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.4733886Z         {
2023-01-11T21:05:10.4733947Z             {
2023-01-11T21:05:10.4734009Z                 {
2023-01-11T21:05:10.4734100Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4734208Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4734306Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:05:10.4734412Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.4734489Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.4734572Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4734653Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.4734715Z                 }
2023-01-11T21:05:10.4734777Z             }
2023-01-11T21:05:10.4734837Z         }
2023-01-11T21:05:10.4734896Z     }
2023-01-11T21:05:10.4734941Z }
2023-01-11T21:05:10.4735018Z ''')
2023-01-11T21:05:10.4735022Z 
2023-01-11T21:05:10.4735026Z 
2023-01-11T21:05:10.4735114Z async_compile.wait(globals())
2023-01-11T21:05:10.4735184Z del async_compile
2023-01-11T21:05:10.4735189Z 
2023-01-11T21:05:10.4735258Z def call(args):
2023-01-11T21:05:10.4735326Z     arg0_1, = args
2023-01-11T21:05:10.4735397Z     args.clear()
2023-01-11T21:05:10.4735575Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4735765Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4735927Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4735994Z     del arg0_1
2023-01-11T21:05:10.4736070Z     return (buf0, buf1, )
2023-01-11T21:05:10.4736075Z 
2023-01-11T21:05:10.4736079Z 
2023-01-11T21:05:10.4736154Z if __name__ == "__main__":
2023-01-11T21:05:10.4736268Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4736390Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4736568Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.4736674Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4736679Z 
2023-01-11T21:05:10.4736683Z 
2023-01-11T21:05:10.4736773Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4736872Z import torch
2023-01-11T21:05:10.4736940Z import random
2023-01-11T21:05:10.4737052Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4737173Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4737177Z 
2023-01-11T21:05:10.4737253Z aten = torch.ops.aten
2023-01-11T21:05:10.4737372Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4737462Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4737467Z 
2023-01-11T21:05:10.4737471Z 
2023-01-11T21:05:10.4737603Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4737805Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4737922Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4738020Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4738114Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4738175Z {
2023-01-11T21:05:10.4738260Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4738320Z     {
2023-01-11T21:05:10.4738395Z         #pragma omp for 
2023-01-11T21:05:10.4738577Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4738674Z         {
2023-01-11T21:05:10.4738740Z             {
2023-01-11T21:05:10.4738806Z                 {
2023-01-11T21:05:10.4738885Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4738993Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4739094Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:05:10.4739198Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.4739289Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.4739373Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4739459Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.4739509Z                 }
2023-01-11T21:05:10.4739571Z             }
2023-01-11T21:05:10.4739633Z         }
2023-01-11T21:05:10.4739697Z     }
2023-01-11T21:05:10.4739758Z }
2023-01-11T21:05:10.4739840Z ''')
2023-01-11T21:05:10.4739845Z 
2023-01-11T21:05:10.4739849Z 
2023-01-11T21:05:10.4739938Z async_compile.wait(globals())
2023-01-11T21:05:10.4739998Z del async_compile
2023-01-11T21:05:10.4740003Z 
2023-01-11T21:05:10.4740074Z def call(args):
2023-01-11T21:05:10.4740142Z     arg0_1, = args
2023-01-11T21:05:10.4740213Z     args.clear()
2023-01-11T21:05:10.4740408Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4740596Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4740756Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4740810Z     del arg0_1
2023-01-11T21:05:10.4740887Z     return (buf0, buf1, )
2023-01-11T21:05:10.4740891Z 
2023-01-11T21:05:10.4740895Z 
2023-01-11T21:05:10.4740968Z if __name__ == "__main__":
2023-01-11T21:05:10.4741082Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4741205Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4741395Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4741503Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4741509Z 
2023-01-11T21:05:10.4741513Z 
2023-01-11T21:05:10.4741603Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4741658Z import torch
2023-01-11T21:05:10.4741726Z import random
2023-01-11T21:05:10.4741840Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4741958Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4741962Z 
2023-01-11T21:05:10.4742038Z aten = torch.ops.aten
2023-01-11T21:05:10.4742169Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4742259Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4742263Z 
2023-01-11T21:05:10.4742267Z 
2023-01-11T21:05:10.4742398Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4742618Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4742735Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4742836Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4742930Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4742990Z {
2023-01-11T21:05:10.4743085Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4743146Z     {
2023-01-11T21:05:10.4743210Z         #pragma omp for 
2023-01-11T21:05:10.4743291Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.4743354Z         {
2023-01-11T21:05:10.4743418Z             {
2023-01-11T21:05:10.4743482Z                 {
2023-01-11T21:05:10.4743573Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4743682Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.4743769Z                     auto tmp2 = std::expm1(tmp1);
2023-01-11T21:05:10.4743875Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.4743965Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.4744049Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4744168Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.4744233Z                 }
2023-01-11T21:05:10.4744299Z             }
2023-01-11T21:05:10.4744347Z         }
2023-01-11T21:05:10.4744409Z     }
2023-01-11T21:05:10.4744469Z }
2023-01-11T21:05:10.4744550Z ''')
2023-01-11T21:05:10.4744555Z 
2023-01-11T21:05:10.4744559Z 
2023-01-11T21:05:10.4744647Z async_compile.wait(globals())
2023-01-11T21:05:10.4744720Z del async_compile
2023-01-11T21:05:10.4744725Z 
2023-01-11T21:05:10.4744794Z def call(args):
2023-01-11T21:05:10.4744847Z     arg0_1, = args
2023-01-11T21:05:10.4744917Z     args.clear()
2023-01-11T21:05:10.4745110Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4745302Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4745465Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4745534Z     del arg0_1
2023-01-11T21:05:10.4745612Z     return (buf0, buf1, )
2023-01-11T21:05:10.4745617Z 
2023-01-11T21:05:10.4745622Z 
2023-01-11T21:05:10.4745696Z if __name__ == "__main__":
2023-01-11T21:05:10.4745796Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4745916Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4746106Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4746212Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4746480Z [2023-01-11 20:50:51,192] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 187
2023-01-11T21:05:10.4746485Z 
2023-01-11T21:05:10.4746552Z ok (27.589s)
2023-01-11T21:05:10.4746991Z   test_fill1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4747119Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4747375Z [2023-01-11 20:50:51,316] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 188
2023-01-11T21:05:10.4747625Z [2023-01-11 20:50:54,053] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 188
2023-01-11T21:05:10.4747644Z 
2023-01-11T21:05:10.4747724Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4747792Z import torch
2023-01-11T21:05:10.4747861Z import random
2023-01-11T21:05:10.4747975Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4748094Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4748129Z 
2023-01-11T21:05:10.4748208Z aten = torch.ops.aten
2023-01-11T21:05:10.4748340Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4748418Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4748425Z 
2023-01-11T21:05:10.4748442Z 
2023-01-11T21:05:10.4748562Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4748765Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4748877Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4748973Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4749033Z {
2023-01-11T21:05:10.4749131Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4749192Z     {
2023-01-11T21:05:10.4749254Z         #pragma omp for 
2023-01-11T21:05:10.4749336Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4749397Z         {
2023-01-11T21:05:10.4749534Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4749628Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4749691Z         }
2023-01-11T21:05:10.4749784Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4749881Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.4749944Z         {
2023-01-11T21:05:10.4750042Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:05:10.4750121Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.4750181Z         }
2023-01-11T21:05:10.4750254Z         #pragma omp for 
2023-01-11T21:05:10.4750321Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4750381Z         {
2023-01-11T21:05:10.4750515Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.4750605Z             tmp0.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4750665Z         }
2023-01-11T21:05:10.4750759Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4750841Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.4750891Z         {
2023-01-11T21:05:10.4750987Z             auto tmp0 = static_cast<float>(2);
2023-01-11T21:05:10.4751066Z             out_ptr1[i0] = tmp0;
2023-01-11T21:05:10.4751125Z         }
2023-01-11T21:05:10.4751187Z     }
2023-01-11T21:05:10.4751248Z }
2023-01-11T21:05:10.4751326Z ''')
2023-01-11T21:05:10.4751332Z 
2023-01-11T21:05:10.4751336Z 
2023-01-11T21:05:10.4751410Z async_compile.wait(globals())
2023-01-11T21:05:10.4751481Z del async_compile
2023-01-11T21:05:10.4751486Z 
2023-01-11T21:05:10.4751554Z def call(args):
2023-01-11T21:05:10.4751621Z     arg0_1, = args
2023-01-11T21:05:10.4751690Z     args.clear()
2023-01-11T21:05:10.4751891Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4752087Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4752219Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4752282Z     return (buf0, buf1, )
2023-01-11T21:05:10.4752289Z 
2023-01-11T21:05:10.4752292Z 
2023-01-11T21:05:10.4752366Z if __name__ == "__main__":
2023-01-11T21:05:10.4752479Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4752601Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4752797Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4752903Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4752908Z 
2023-01-11T21:05:10.4752973Z ok (2.862s)
2023-01-11T21:05:10.4753407Z   test_fill2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4753531Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4753812Z [2023-01-11 20:50:54,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 189
2023-01-11T21:05:10.4754078Z [2023-01-11 20:50:56,886] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 189
2023-01-11T21:05:10.4754084Z 
2023-01-11T21:05:10.4754176Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4754244Z import torch
2023-01-11T21:05:10.4754312Z import random
2023-01-11T21:05:10.4754426Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4754545Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4754550Z 
2023-01-11T21:05:10.4754626Z aten = torch.ops.aten
2023-01-11T21:05:10.4754746Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4754835Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4754840Z 
2023-01-11T21:05:10.4754844Z 
2023-01-11T21:05:10.4754974Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4755178Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4755295Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4755392Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4755480Z {
2023-01-11T21:05:10.4755577Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4755625Z     {
2023-01-11T21:05:10.4755700Z         #pragma omp for 
2023-01-11T21:05:10.4755780Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4755843Z         {
2023-01-11T21:05:10.4755975Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4756066Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4756128Z         }
2023-01-11T21:05:10.4756210Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4756290Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.4756350Z         {
2023-01-11T21:05:10.4756447Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:05:10.4756530Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.4756590Z         }
2023-01-11T21:05:10.4756651Z         #pragma omp for 
2023-01-11T21:05:10.4756731Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4756791Z         {
2023-01-11T21:05:10.4756928Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(3.0));
2023-01-11T21:05:10.4757018Z             tmp0.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4757078Z         }
2023-01-11T21:05:10.4757172Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4757239Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.4757298Z         {
2023-01-11T21:05:10.4757398Z             auto tmp0 = static_cast<float>(3.0);
2023-01-11T21:05:10.4757476Z             out_ptr1[i0] = tmp0;
2023-01-11T21:05:10.4757536Z         }
2023-01-11T21:05:10.4757596Z     }
2023-01-11T21:05:10.4757655Z }
2023-01-11T21:05:10.4757720Z ''')
2023-01-11T21:05:10.4757724Z 
2023-01-11T21:05:10.4757742Z 
2023-01-11T21:05:10.4757816Z async_compile.wait(globals())
2023-01-11T21:05:10.4757889Z del async_compile
2023-01-11T21:05:10.4757894Z 
2023-01-11T21:05:10.4757962Z def call(args):
2023-01-11T21:05:10.4758029Z     arg0_1, = args
2023-01-11T21:05:10.4758099Z     args.clear()
2023-01-11T21:05:10.4758300Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4758495Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4758616Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4758693Z     return (buf0, buf1, )
2023-01-11T21:05:10.4758698Z 
2023-01-11T21:05:10.4758702Z 
2023-01-11T21:05:10.4758775Z if __name__ == "__main__":
2023-01-11T21:05:10.4758888Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4759009Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4759206Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4759312Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4759348Z 
2023-01-11T21:05:10.4759414Z ok (2.834s)
2023-01-11T21:05:10.4759840Z   test_flip_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4759965Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4760222Z [2023-01-11 20:50:56,984] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 190
2023-01-11T21:05:10.4760484Z [2023-01-11 20:50:59,735] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 190
2023-01-11T21:05:10.4760489Z 
2023-01-11T21:05:10.4760581Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4760774Z import torch
2023-01-11T21:05:10.4760848Z import random
2023-01-11T21:05:10.4760965Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4761085Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4761091Z 
2023-01-11T21:05:10.4761207Z aten = torch.ops.aten
2023-01-11T21:05:10.4761342Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4761434Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4761438Z 
2023-01-11T21:05:10.4761443Z 
2023-01-11T21:05:10.4761579Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4761782Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4761902Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4762003Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4762101Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4762148Z {
2023-01-11T21:05:10.4762246Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4762311Z     {
2023-01-11T21:05:10.4762388Z         #pragma omp for 
2023-01-11T21:05:10.4762470Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.4762534Z         {
2023-01-11T21:05:10.4762601Z             #pragma GCC ivdep
2023-01-11T21:05:10.4762686Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:05:10.4762749Z             {
2023-01-11T21:05:10.4762813Z                 {
2023-01-11T21:05:10.4762880Z                     {
2023-01-11T21:05:10.4763050Z                         auto tmp0 = in_ptr0[5 + ((-1)*i1) + (6*i0)];
2023-01-11T21:05:10.4763145Z                         out_ptr0[i1 + (6*i0)] = tmp0;
2023-01-11T21:05:10.4763196Z                     }
2023-01-11T21:05:10.4763259Z                 }
2023-01-11T21:05:10.4763321Z             }
2023-01-11T21:05:10.4763381Z         }
2023-01-11T21:05:10.4763468Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.4763547Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.4763607Z         {
2023-01-11T21:05:10.4763675Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:05:10.4763737Z             {
2023-01-11T21:05:10.4763821Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4763909Z                 for(long i2=0; i2<6; i2+=1)
2023-01-11T21:05:10.4763974Z                 {
2023-01-11T21:05:10.4764038Z                     {
2023-01-11T21:05:10.4764105Z                         {
2023-01-11T21:05:10.4764273Z                             auto tmp0 = in_ptr0[30 + i2 + ((-6)*i1) + (36*i0)];
2023-01-11T21:05:10.4764381Z                             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.4764526Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.4764628Z                             out_ptr1[i2 + (6*i1) + (36*i0)] = tmp2;
2023-01-11T21:05:10.4764695Z                         }
2023-01-11T21:05:10.4764758Z                     }
2023-01-11T21:05:10.4764821Z                 }
2023-01-11T21:05:10.4764869Z             }
2023-01-11T21:05:10.4764929Z         }
2023-01-11T21:05:10.4765029Z     }
2023-01-11T21:05:10.4765089Z }
2023-01-11T21:05:10.4765167Z ''')
2023-01-11T21:05:10.4765173Z 
2023-01-11T21:05:10.4765177Z 
2023-01-11T21:05:10.4765265Z async_compile.wait(globals())
2023-01-11T21:05:10.4765335Z del async_compile
2023-01-11T21:05:10.4765342Z 
2023-01-11T21:05:10.4765400Z def call(args):
2023-01-11T21:05:10.4765467Z     arg0_1, = args
2023-01-11T21:05:10.4765536Z     args.clear()
2023-01-11T21:05:10.4765746Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4765953Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4766114Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4766180Z     del arg0_1
2023-01-11T21:05:10.4766243Z     return (buf0, buf1, )
2023-01-11T21:05:10.4766261Z 
2023-01-11T21:05:10.4766265Z 
2023-01-11T21:05:10.4766326Z if __name__ == "__main__":
2023-01-11T21:05:10.4766442Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4766564Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4766806Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4766916Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4766921Z 
2023-01-11T21:05:10.4766985Z ok (2.847s)
2023-01-11T21:05:10.4767418Z   test_fmod_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4767545Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4767804Z [2023-01-11 20:50:59,796] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 191
2023-01-11T21:05:10.4768054Z [2023-01-11 20:51:02,556] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 191
2023-01-11T21:05:10.4768059Z 
2023-01-11T21:05:10.4768152Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4768222Z import torch
2023-01-11T21:05:10.4768290Z import random
2023-01-11T21:05:10.4768404Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4768523Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4768528Z 
2023-01-11T21:05:10.4768604Z aten = torch.ops.aten
2023-01-11T21:05:10.4768736Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4768814Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4768818Z 
2023-01-11T21:05:10.4768822Z 
2023-01-11T21:05:10.4768954Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4769156Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4769275Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4769381Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4769479Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4769580Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4769627Z {
2023-01-11T21:05:10.4769723Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4769783Z     {
2023-01-11T21:05:10.4769858Z         #pragma omp for 
2023-01-11T21:05:10.4769940Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4770000Z         {
2023-01-11T21:05:10.4770135Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4770268Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.4770346Z             auto tmp2 = tmp0.fmod(tmp1);
2023-01-11T21:05:10.4770480Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(3.0));
2023-01-11T21:05:10.4770563Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4770681Z             auto tmp5 = tmp4.fmod(tmp1);
2023-01-11T21:05:10.4770816Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(2.0));
2023-01-11T21:05:10.4770941Z             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:05:10.4771031Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4771107Z             tmp7.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4771169Z         }
2023-01-11T21:05:10.4771261Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4771342Z         for(long i0=64; i0<72; i0+=1)
2023-01-11T21:05:10.4771403Z         {
2023-01-11T21:05:10.4771484Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4771565Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4771652Z             auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:05:10.4771752Z             auto tmp3 = static_cast<float>(3.0);
2023-01-11T21:05:10.4771833Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4771931Z             auto tmp5 = std::fmod(tmp4, tmp1);
2023-01-11T21:05:10.4772033Z             auto tmp6 = static_cast<float>(2.0);
2023-01-11T21:05:10.4772154Z             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:05:10.4772233Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4772338Z             out_ptr1[i0] = tmp7;
2023-01-11T21:05:10.4772402Z         }
2023-01-11T21:05:10.4772462Z     }
2023-01-11T21:05:10.4772521Z }
2023-01-11T21:05:10.4772599Z ''')
2023-01-11T21:05:10.4772604Z 
2023-01-11T21:05:10.4772608Z 
2023-01-11T21:05:10.4772696Z async_compile.wait(globals())
2023-01-11T21:05:10.4772766Z del async_compile
2023-01-11T21:05:10.4772771Z 
2023-01-11T21:05:10.4772827Z def call(args):
2023-01-11T21:05:10.4772900Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4772969Z     args.clear()
2023-01-11T21:05:10.4773181Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4773389Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4773582Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4773648Z     del arg0_1
2023-01-11T21:05:10.4773712Z     del arg1_1
2023-01-11T21:05:10.4773776Z     return (buf0, buf1, )
2023-01-11T21:05:10.4773781Z 
2023-01-11T21:05:10.4773785Z 
2023-01-11T21:05:10.4773858Z if __name__ == "__main__":
2023-01-11T21:05:10.4773972Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4774096Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4774307Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4774518Z     arg1_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4774632Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4774637Z 
2023-01-11T21:05:10.4774703Z ok (2.823s)
2023-01-11T21:05:10.4775135Z   test_fmod_zero_dim_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4775264Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4775523Z [2023-01-11 20:51:02,597] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 192
2023-01-11T21:05:10.4775784Z [2023-01-11 20:51:05,270] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 192
2023-01-11T21:05:10.4776184Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4776343Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4776597Z [2023-01-11 20:51:05,307] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 193
2023-01-11T21:05:10.4776860Z [2023-01-11 20:51:08,019] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 193
2023-01-11T21:05:10.4776865Z 
2023-01-11T21:05:10.4776958Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4777028Z import torch
2023-01-11T21:05:10.4777082Z import random
2023-01-11T21:05:10.4777199Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4777320Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4777325Z 
2023-01-11T21:05:10.4777402Z aten = torch.ops.aten
2023-01-11T21:05:10.4777538Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4777631Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4777638Z 
2023-01-11T21:05:10.4777643Z 
2023-01-11T21:05:10.4777776Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4777978Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4778111Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4778216Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4778314Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4778377Z {
2023-01-11T21:05:10.4778562Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4778630Z     {
2023-01-11T21:05:10.4778707Z         #pragma omp for 
2023-01-11T21:05:10.4778776Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.4778838Z         {
2023-01-11T21:05:10.4778901Z             {
2023-01-11T21:05:10.4778965Z                 {
2023-01-11T21:05:10.4779058Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4779153Z                     auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.4779264Z                     auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:05:10.4779335Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4779400Z                 }
2023-01-11T21:05:10.4779463Z             }
2023-01-11T21:05:10.4779526Z         }
2023-01-11T21:05:10.4779587Z     }
2023-01-11T21:05:10.4779647Z }
2023-01-11T21:05:10.4779729Z ''')
2023-01-11T21:05:10.4779736Z 
2023-01-11T21:05:10.4779740Z 
2023-01-11T21:05:10.4779814Z async_compile.wait(globals())
2023-01-11T21:05:10.4779885Z del async_compile
2023-01-11T21:05:10.4779890Z 
2023-01-11T21:05:10.4779960Z def call(args):
2023-01-11T21:05:10.4780034Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4780106Z     args.clear()
2023-01-11T21:05:10.4780300Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4780462Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4780516Z     del arg0_1
2023-01-11T21:05:10.4780584Z     del arg1_1
2023-01-11T21:05:10.4780655Z     return (buf0, )
2023-01-11T21:05:10.4780659Z 
2023-01-11T21:05:10.4780663Z 
2023-01-11T21:05:10.4780738Z if __name__ == "__main__":
2023-01-11T21:05:10.4780853Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4780975Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4781169Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4781353Z     arg1_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4781454Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4781471Z 
2023-01-11T21:05:10.4781475Z 
2023-01-11T21:05:10.4781555Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4781624Z import torch
2023-01-11T21:05:10.4781694Z import random
2023-01-11T21:05:10.4781806Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4781925Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4781962Z 
2023-01-11T21:05:10.4782043Z aten = torch.ops.aten
2023-01-11T21:05:10.4782174Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4782251Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4782255Z 
2023-01-11T21:05:10.4782274Z 
2023-01-11T21:05:10.4782395Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4782598Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4782713Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4782815Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4782913Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4782973Z {
2023-01-11T21:05:10.4783069Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4783116Z     {
2023-01-11T21:05:10.4783191Z         #pragma omp for 
2023-01-11T21:05:10.4783273Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.4783334Z         {
2023-01-11T21:05:10.4783398Z             {
2023-01-11T21:05:10.4783461Z                 {
2023-01-11T21:05:10.4783537Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.4783631Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4783767Z                     auto tmp2 = std::fmod(tmp0, tmp1);
2023-01-11T21:05:10.4783853Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4783914Z                 }
2023-01-11T21:05:10.4783975Z             }
2023-01-11T21:05:10.4784035Z         }
2023-01-11T21:05:10.4784082Z     }
2023-01-11T21:05:10.4784141Z }
2023-01-11T21:05:10.4784218Z ''')
2023-01-11T21:05:10.4784223Z 
2023-01-11T21:05:10.4784227Z 
2023-01-11T21:05:10.4784315Z async_compile.wait(globals())
2023-01-11T21:05:10.4784385Z del async_compile
2023-01-11T21:05:10.4784390Z 
2023-01-11T21:05:10.4784458Z def call(args):
2023-01-11T21:05:10.4784531Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4784599Z     args.clear()
2023-01-11T21:05:10.4784779Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4784940Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4785007Z     del arg0_1
2023-01-11T21:05:10.4785074Z     del arg1_1
2023-01-11T21:05:10.4785144Z     return (buf0, )
2023-01-11T21:05:10.4785149Z 
2023-01-11T21:05:10.4785153Z 
2023-01-11T21:05:10.4785228Z if __name__ == "__main__":
2023-01-11T21:05:10.4785339Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4785448Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4785632Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4785823Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4785937Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4785942Z 
2023-01-11T21:05:10.4786007Z ok (5.461s)
2023-01-11T21:05:10.4786455Z   test_forced_buffer_realize_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4786582Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4786841Z [2023-01-11 20:51:08,116] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 194
2023-01-11T21:05:10.4787105Z [2023-01-11 20:51:10,832] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 194
2023-01-11T21:05:10.4787111Z 
2023-01-11T21:05:10.4787203Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4787259Z import torch
2023-01-11T21:05:10.4787326Z import random
2023-01-11T21:05:10.4787440Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4787557Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4787592Z 
2023-01-11T21:05:10.4787670Z aten = torch.ops.aten
2023-01-11T21:05:10.4787802Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4787893Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4787898Z 
2023-01-11T21:05:10.4787902Z 
2023-01-11T21:05:10.4788035Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4788224Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4788341Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.4788444Z                        const float* __restrict__ in_ptr0)
2023-01-11T21:05:10.4788504Z {
2023-01-11T21:05:10.4788599Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4788659Z     {
2023-01-11T21:05:10.4788733Z         #pragma omp for 
2023-01-11T21:05:10.4788801Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.4788862Z         {
2023-01-11T21:05:10.4788924Z             {
2023-01-11T21:05:10.4788990Z                 {
2023-01-11T21:05:10.4789082Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4789185Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.4789326Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.4789417Z                     auto tmp3 = tmp2 * tmp1;
2023-01-11T21:05:10.4789505Z                     in_out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.4789569Z                 }
2023-01-11T21:05:10.4789631Z             }
2023-01-11T21:05:10.4789691Z         }
2023-01-11T21:05:10.4789753Z     }
2023-01-11T21:05:10.4789799Z }
2023-01-11T21:05:10.4789877Z ''')
2023-01-11T21:05:10.4789882Z 
2023-01-11T21:05:10.4789885Z 
2023-01-11T21:05:10.4789973Z async_compile.wait(globals())
2023-01-11T21:05:10.4790045Z del async_compile
2023-01-11T21:05:10.4790049Z 
2023-01-11T21:05:10.4790119Z def call(args):
2023-01-11T21:05:10.4790187Z     arg0_1, = args
2023-01-11T21:05:10.4790258Z     args.clear()
2023-01-11T21:05:10.4790438Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4790525Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.4790659Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:05:10.4790731Z     del arg0_1
2023-01-11T21:05:10.4790802Z     return (buf1, )
2023-01-11T21:05:10.4790807Z 
2023-01-11T21:05:10.4790811Z 
2023-01-11T21:05:10.4790887Z if __name__ == "__main__":
2023-01-11T21:05:10.4791001Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4791126Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4791307Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4791414Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4791419Z 
2023-01-11T21:05:10.4791487Z ok (2.811s)
2023-01-11T21:05:10.4791922Z   test_full_like_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4792053Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4792310Z [2023-01-11 20:51:10,894] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 195
2023-01-11T21:05:10.4792570Z [2023-01-11 20:51:13,586] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 195
2023-01-11T21:05:10.4792575Z 
2023-01-11T21:05:10.4792666Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4792734Z import torch
2023-01-11T21:05:10.4792788Z import random
2023-01-11T21:05:10.4792902Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4793020Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4793025Z 
2023-01-11T21:05:10.4793101Z aten = torch.ops.aten
2023-01-11T21:05:10.4793262Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4793352Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4793357Z 
2023-01-11T21:05:10.4793361Z 
2023-01-11T21:05:10.4793494Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4793697Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4793798Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4793857Z {
2023-01-11T21:05:10.4793952Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4794012Z     {
2023-01-11T21:05:10.4794087Z         #pragma omp for 
2023-01-11T21:05:10.4794168Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.4794228Z         {
2023-01-11T21:05:10.4794277Z             {
2023-01-11T21:05:10.4794339Z                 {
2023-01-11T21:05:10.4794445Z                     auto tmp0 = static_cast<float>(7.777);
2023-01-11T21:05:10.4794547Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4794684Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.4794768Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4794831Z                 }
2023-01-11T21:05:10.4794906Z             }
2023-01-11T21:05:10.4794969Z         }
2023-01-11T21:05:10.4795029Z     }
2023-01-11T21:05:10.4795089Z }
2023-01-11T21:05:10.4795166Z ''')
2023-01-11T21:05:10.4795170Z 
2023-01-11T21:05:10.4795175Z 
2023-01-11T21:05:10.4795262Z async_compile.wait(globals())
2023-01-11T21:05:10.4795331Z del async_compile
2023-01-11T21:05:10.4795336Z 
2023-01-11T21:05:10.4795393Z def call(args):
2023-01-11T21:05:10.4795461Z     arg0_1, = args
2023-01-11T21:05:10.4795530Z     args.clear()
2023-01-11T21:05:10.4795718Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4795818Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4795887Z     return (buf0, )
2023-01-11T21:05:10.4795892Z 
2023-01-11T21:05:10.4795898Z 
2023-01-11T21:05:10.4795971Z if __name__ == "__main__":
2023-01-11T21:05:10.4796083Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4796190Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4796382Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4796490Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4796495Z 
2023-01-11T21:05:10.4796560Z ok (2.754s)
2023-01-11T21:05:10.4796999Z   test_fuse_tiled_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4797127Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4797383Z [2023-01-11 20:51:13,634] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 196
2023-01-11T21:05:10.4797648Z [2023-01-11 20:51:16,393] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 196
2023-01-11T21:05:10.4797655Z 
2023-01-11T21:05:10.4797748Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4797803Z import torch
2023-01-11T21:05:10.4797871Z import random
2023-01-11T21:05:10.4797984Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4798104Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4798109Z 
2023-01-11T21:05:10.4798186Z aten = torch.ops.aten
2023-01-11T21:05:10.4798319Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4798409Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4798414Z 
2023-01-11T21:05:10.4798418Z 
2023-01-11T21:05:10.4798551Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4798742Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4798892Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4798995Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4799098Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.4799196Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4799293Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4799353Z {
2023-01-11T21:05:10.4799437Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4799498Z     {
2023-01-11T21:05:10.4799573Z         #pragma omp for 
2023-01-11T21:05:10.4799654Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.4799715Z         {
2023-01-11T21:05:10.4799796Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.4799859Z             {
2023-01-11T21:05:10.4799972Z                 auto tmp0 = at::vec::Vectorized<float>(in_ptr0[i0]);
2023-01-11T21:05:10.4800104Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i1);
2023-01-11T21:05:10.4800194Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4800297Z                 tmp2.store(out_ptr0 + (16*i1) + (128*i0));
2023-01-11T21:05:10.4800403Z             }
2023-01-11T21:05:10.4800498Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.4800695Z             for(long i1=128; i1<128; i1+=1)
2023-01-11T21:05:10.4800747Z             {
2023-01-11T21:05:10.4800833Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4800919Z                 auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.4801005Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4801098Z                 out_ptr0[i1 + (128*i0)] = tmp2;
2023-01-11T21:05:10.4801162Z             }
2023-01-11T21:05:10.4801225Z         }
2023-01-11T21:05:10.4801288Z         #pragma omp for 
2023-01-11T21:05:10.4801370Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.4801432Z         {
2023-01-11T21:05:10.4801567Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i0);
2023-01-11T21:05:10.4801703Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4801789Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4801884Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4801931Z         }
2023-01-11T21:05:10.4802025Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4802112Z         for(long i0=16384; i0<16384; i0+=1)
2023-01-11T21:05:10.4802174Z         {
2023-01-11T21:05:10.4802258Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:05:10.4802357Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4802441Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4802507Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.4802569Z         }
2023-01-11T21:05:10.4802628Z     }
2023-01-11T21:05:10.4802687Z }
2023-01-11T21:05:10.4802769Z ''')
2023-01-11T21:05:10.4802774Z 
2023-01-11T21:05:10.4802779Z 
2023-01-11T21:05:10.4802866Z async_compile.wait(globals())
2023-01-11T21:05:10.4802940Z del async_compile
2023-01-11T21:05:10.4802944Z 
2023-01-11T21:05:10.4803001Z def call(args):
2023-01-11T21:05:10.4803081Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4803150Z     args.clear()
2023-01-11T21:05:10.4803356Z     buf0 = empty_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4803555Z     buf1 = empty_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4803767Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4803833Z     del arg0_1
2023-01-11T21:05:10.4803898Z     del arg1_1
2023-01-11T21:05:10.4803949Z     del arg2_1
2023-01-11T21:05:10.4804025Z     return (buf0, buf1, )
2023-01-11T21:05:10.4804030Z 
2023-01-11T21:05:10.4804034Z 
2023-01-11T21:05:10.4804109Z if __name__ == "__main__":
2023-01-11T21:05:10.4804225Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4804403Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4804603Z     arg0_1 = rand_strided((128, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4804808Z     arg1_1 = rand_strided((1, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4805010Z     arg2_1 = rand_strided((128, 128), (128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4805120Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4805124Z 
2023-01-11T21:05:10.4805190Z ok (2.850s)
2023-01-11T21:05:10.4805629Z   test_gather1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4805754Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4806015Z [2023-01-11 20:51:16,509] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 197
2023-01-11T21:05:10.4806312Z [2023-01-11 20:51:19,230] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 197
2023-01-11T21:05:10.4806318Z 
2023-01-11T21:05:10.4806416Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4806485Z import torch
2023-01-11T21:05:10.4806553Z import random
2023-01-11T21:05:10.4806654Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4806772Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4806778Z 
2023-01-11T21:05:10.4806853Z aten = torch.ops.aten
2023-01-11T21:05:10.4806985Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4807074Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4807078Z 
2023-01-11T21:05:10.4807082Z 
2023-01-11T21:05:10.4807216Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4807421Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4807539Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4807632Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4807729Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4807825Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4807886Z {
2023-01-11T21:05:10.4807982Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4808042Z     {
2023-01-11T21:05:10.4808118Z         #pragma omp for 
2023-01-11T21:05:10.4808186Z         for(long i0=0; i0<20; i0+=1)
2023-01-11T21:05:10.4808249Z         {
2023-01-11T21:05:10.4808328Z             #pragma GCC ivdep
2023-01-11T21:05:10.4808413Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.4808477Z             {
2023-01-11T21:05:10.4808542Z                 {
2023-01-11T21:05:10.4808614Z                     {
2023-01-11T21:05:10.4808705Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.4808809Z                         auto tmp1 = static_cast<long>(1);
2023-01-11T21:05:10.4808905Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4809010Z                         auto tmp3 = in_ptr1[tmp2 + (6*i1)];
2023-01-11T21:05:10.4809106Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.4809201Z                         out_ptr1[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.4809266Z                     }
2023-01-11T21:05:10.4809316Z                 }
2023-01-11T21:05:10.4809378Z             }
2023-01-11T21:05:10.4809439Z         }
2023-01-11T21:05:10.4809499Z     }
2023-01-11T21:05:10.4809560Z }
2023-01-11T21:05:10.4809637Z ''')
2023-01-11T21:05:10.4809642Z 
2023-01-11T21:05:10.4809647Z 
2023-01-11T21:05:10.4809734Z async_compile.wait(globals())
2023-01-11T21:05:10.4809791Z del async_compile
2023-01-11T21:05:10.4809796Z 
2023-01-11T21:05:10.4809896Z def call(args):
2023-01-11T21:05:10.4809970Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4810039Z     args.clear()
2023-01-11T21:05:10.4810255Z     buf0 = empty_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4810464Z     buf1 = empty_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4810651Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4810706Z     del arg0_1
2023-01-11T21:05:10.4810772Z     del arg1_1
2023-01-11T21:05:10.4810849Z     return (buf0, buf1, )
2023-01-11T21:05:10.4810854Z 
2023-01-11T21:05:10.4810858Z 
2023-01-11T21:05:10.4810932Z if __name__ == "__main__":
2023-01-11T21:05:10.4811047Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4811169Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4811381Z     arg0_1 = rand_strided((1, 1, 10, 6), (60, 60, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4811590Z     arg1_1 = rand_strided((4, 5, 10, 1), (50, 10, 1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4811736Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4811754Z 
2023-01-11T21:05:10.4811807Z ok (2.798s)
2023-01-11T21:05:10.4811917Z   test_gather2_cpu (__main__.CpuTests) ... ok (0.003s)
2023-01-11T21:05:10.4812363Z   test_gather_scatter_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4812488Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4812748Z [2023-01-11 20:51:19,493] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 198
2023-01-11T21:05:10.4813013Z [2023-01-11 20:51:22,236] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 198
2023-01-11T21:05:10.4813018Z 
2023-01-11T21:05:10.4813114Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4813182Z import torch
2023-01-11T21:05:10.4813237Z import random
2023-01-11T21:05:10.4813350Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4813471Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4813476Z 
2023-01-11T21:05:10.4813555Z aten = torch.ops.aten
2023-01-11T21:05:10.4813688Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4813779Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4813785Z 
2023-01-11T21:05:10.4813789Z 
2023-01-11T21:05:10.4813921Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4814128Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4814249Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4814340Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4814439Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4814501Z {
2023-01-11T21:05:10.4814598Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4814658Z     {
2023-01-11T21:05:10.4814736Z         #pragma omp for 
2023-01-11T21:05:10.4814803Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.4814864Z         {
2023-01-11T21:05:10.4814998Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:05:10.4815089Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4815150Z         }
2023-01-11T21:05:10.4815244Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4815324Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:05:10.4815371Z         {
2023-01-11T21:05:10.4815469Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:05:10.4815551Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.4815643Z         }
2023-01-11T21:05:10.4815717Z         #pragma omp for 
2023-01-11T21:05:10.4815797Z         for(long i0=0; i0<80; i0+=1)
2023-01-11T21:05:10.4815859Z         {
2023-01-11T21:05:10.4815926Z             #pragma GCC ivdep
2023-01-11T21:05:10.4816012Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:05:10.4816075Z             {
2023-01-11T21:05:10.4816139Z                 {
2023-01-11T21:05:10.4816203Z                     {
2023-01-11T21:05:10.4816302Z                         auto tmp0 = in_ptr0[80 + i0];
2023-01-11T21:05:10.4816395Z                         auto tmp1 = in_ptr0[i0];
2023-01-11T21:05:10.4816485Z                         auto tmp2 = in_ptr1[i1 + (32*tmp1)];
2023-01-11T21:05:10.4816587Z                         auto tmp3 = in_ptr1[i1 + (32*tmp0)];
2023-01-11T21:05:10.4816727Z                         auto tmp4 = tmp2 - tmp3;
2023-01-11T21:05:10.4816832Z                         auto tmp5 = static_cast<float>(1);
2023-01-11T21:05:10.4816926Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.4817040Z                         atomic_add(&out_ptr0[i1 + (32*tmp0)], tmp6);
2023-01-11T21:05:10.4817105Z                     }
2023-01-11T21:05:10.4817194Z                 }
2023-01-11T21:05:10.4817258Z             }
2023-01-11T21:05:10.4817319Z         }
2023-01-11T21:05:10.4817380Z     }
2023-01-11T21:05:10.4817438Z }
2023-01-11T21:05:10.4817515Z ''')
2023-01-11T21:05:10.4817520Z 
2023-01-11T21:05:10.4817524Z 
2023-01-11T21:05:10.4817612Z async_compile.wait(globals())
2023-01-11T21:05:10.4817671Z del async_compile
2023-01-11T21:05:10.4817675Z 
2023-01-11T21:05:10.4817744Z def call(args):
2023-01-11T21:05:10.4817817Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4817886Z     args.clear()
2023-01-11T21:05:10.4818086Z     buf0 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4818247Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4818319Z     del arg0_1
2023-01-11T21:05:10.4818370Z     del arg1_1
2023-01-11T21:05:10.4818440Z     return (buf0, )
2023-01-11T21:05:10.4818445Z 
2023-01-11T21:05:10.4818449Z 
2023-01-11T21:05:10.4818614Z if __name__ == "__main__":
2023-01-11T21:05:10.4818734Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4818858Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4819062Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4819258Z     arg1_1 = rand_strided((2, 80), (80, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4819374Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4819379Z 
2023-01-11T21:05:10.4819446Z ok (2.999s)
2023-01-11T21:05:10.4819869Z   test_gelu_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4819998Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4820261Z [2023-01-11 20:51:22,321] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 199
2023-01-11T21:05:10.4820529Z [2023-01-11 20:51:25,071] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 199
2023-01-11T21:05:10.4820534Z 
2023-01-11T21:05:10.4820630Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4820699Z import torch
2023-01-11T21:05:10.4820768Z import random
2023-01-11T21:05:10.4820884Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4820990Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4821009Z 
2023-01-11T21:05:10.4821073Z aten = torch.ops.aten
2023-01-11T21:05:10.4821205Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4821331Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4821338Z 
2023-01-11T21:05:10.4821342Z 
2023-01-11T21:05:10.4821474Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4821679Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4821797Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4821896Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4821991Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4822038Z {
2023-01-11T21:05:10.4822132Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4822192Z     {
2023-01-11T21:05:10.4822267Z         #pragma omp for 
2023-01-11T21:05:10.4822348Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.4822409Z         {
2023-01-11T21:05:10.4822529Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4822663Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(0.5));
2023-01-11T21:05:10.4822748Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.4822914Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.7071067811865476));
2023-01-11T21:05:10.4822999Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4823083Z             auto tmp5 = tmp4.erf();
2023-01-11T21:05:10.4823215Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4823300Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.4823367Z             auto tmp8 = tmp2 * tmp7;
2023-01-11T21:05:10.4823498Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.4823581Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.4823661Z             auto tmp11 = tmp0 + tmp6;
2023-01-11T21:05:10.4823743Z             auto tmp12 = tmp11 * tmp1;
2023-01-11T21:05:10.4823824Z             auto tmp13 = tmp11 * tmp3;
2023-01-11T21:05:10.4823909Z             auto tmp14 = tmp13.erf();
2023-01-11T21:05:10.4823978Z             auto tmp15 = tmp14 + tmp6;
2023-01-11T21:05:10.4824060Z             auto tmp16 = tmp12 * tmp15;
2023-01-11T21:05:10.4824153Z             tmp10.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4824246Z             tmp16.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4824308Z         }
2023-01-11T21:05:10.4824402Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4824482Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.4824530Z         {
2023-01-11T21:05:10.4824612Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4824709Z             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:05:10.4824790Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.4824898Z             auto tmp3 = static_cast<float>(0.7071067811865476);
2023-01-11T21:05:10.4824980Z             auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4825067Z             auto tmp5 = std::erf(tmp4);
2023-01-11T21:05:10.4825151Z             auto tmp6 = static_cast<float>(1);
2023-01-11T21:05:10.4825233Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.4825312Z             auto tmp8 = tmp2 * tmp7;
2023-01-11T21:05:10.4825410Z             auto tmp9 = static_cast<float>(2);
2023-01-11T21:05:10.4825493Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.4825573Z             auto tmp11 = tmp0 + tmp6;
2023-01-11T21:05:10.4825658Z             auto tmp12 = tmp11 * tmp1;
2023-01-11T21:05:10.4825728Z             auto tmp13 = tmp11 * tmp3;
2023-01-11T21:05:10.4825818Z             auto tmp14 = std::erf(tmp13);
2023-01-11T21:05:10.4825899Z             auto tmp15 = tmp14 + tmp6;
2023-01-11T21:05:10.4825981Z             auto tmp16 = tmp12 * tmp15;
2023-01-11T21:05:10.4826059Z             out_ptr0[i0] = tmp10;
2023-01-11T21:05:10.4826137Z             out_ptr1[i0] = tmp16;
2023-01-11T21:05:10.4826196Z         }
2023-01-11T21:05:10.4826243Z     }
2023-01-11T21:05:10.4826301Z }
2023-01-11T21:05:10.4826380Z ''')
2023-01-11T21:05:10.4826385Z 
2023-01-11T21:05:10.4826424Z 
2023-01-11T21:05:10.4826512Z async_compile.wait(globals())
2023-01-11T21:05:10.4826586Z del async_compile
2023-01-11T21:05:10.4826591Z 
2023-01-11T21:05:10.4826661Z def call(args):
2023-01-11T21:05:10.4826729Z     arg0_1, = args
2023-01-11T21:05:10.4826801Z     args.clear()
2023-01-11T21:05:10.4826989Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4827183Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4827349Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4827416Z     del arg0_1
2023-01-11T21:05:10.4827493Z     return (buf0, buf1, )
2023-01-11T21:05:10.4827499Z 
2023-01-11T21:05:10.4827503Z 
2023-01-11T21:05:10.4827576Z if __name__ == "__main__":
2023-01-11T21:05:10.4827689Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4827811Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4827996Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4828102Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4828107Z 
2023-01-11T21:05:10.4828172Z ok (2.836s)
2023-01-11T21:05:10.4828628Z   test_glu_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4828756Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4829015Z [2023-01-11 20:51:25,158] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 200
2023-01-11T21:05:10.4829278Z [2023-01-11 20:51:27,967] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 200
2023-01-11T21:05:10.4829285Z 
2023-01-11T21:05:10.4829377Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4829446Z import torch
2023-01-11T21:05:10.4829502Z import random
2023-01-11T21:05:10.4829618Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4829735Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4829740Z 
2023-01-11T21:05:10.4829823Z aten = torch.ops.aten
2023-01-11T21:05:10.4830006Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4830137Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4830144Z 
2023-01-11T21:05:10.4830151Z 
2023-01-11T21:05:10.4830296Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4830500Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4830605Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4830703Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4830799Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4830895Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4830955Z {
2023-01-11T21:05:10.4831051Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4831112Z     {
2023-01-11T21:05:10.4831174Z         #pragma omp for 
2023-01-11T21:05:10.4831256Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.4831316Z         {
2023-01-11T21:05:10.4831395Z             #pragma GCC ivdep
2023-01-11T21:05:10.4831476Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.4831538Z             {
2023-01-11T21:05:10.4831601Z                 {
2023-01-11T21:05:10.4831653Z                     {
2023-01-11T21:05:10.4831753Z                         auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.4831857Z                         auto tmp1 = in_ptr0[4 + i1 + (8*i0)];
2023-01-11T21:05:10.4832009Z                         auto tmp2 = std::exp(-tmp1);
2023-01-11T21:05:10.4832099Z                         auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:05:10.4832246Z                         auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4832340Z                         out_ptr0[i1 + (4*i0)] = tmp4;
2023-01-11T21:05:10.4832392Z                     }
2023-01-11T21:05:10.4832457Z                 }
2023-01-11T21:05:10.4832518Z             }
2023-01-11T21:05:10.4832578Z         }
2023-01-11T21:05:10.4832654Z         #pragma omp for 
2023-01-11T21:05:10.4832734Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.4832782Z         {
2023-01-11T21:05:10.4832868Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:05:10.4832933Z             {
2023-01-11T21:05:10.4833078Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (1024*i0));
2023-01-11T21:05:10.4833223Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 512 + (16*i1) + (1024*i0));
2023-01-11T21:05:10.4833359Z                 auto tmp2 = decltype(tmp1)(1)/(decltype(tmp1)(1) + tmp1.neg().exp());
2023-01-11T21:05:10.4833447Z                 auto tmp3 = tmp0 * tmp2;
2023-01-11T21:05:10.4833555Z                 tmp3.store(out_ptr1 + (16*i1) + (512*i0));
2023-01-11T21:05:10.4833605Z             }
2023-01-11T21:05:10.4833697Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.4833818Z             for(long i1=512; i1<512; i1+=1)
2023-01-11T21:05:10.4833883Z             {
2023-01-11T21:05:10.4833980Z                 auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:05:10.4834079Z                 auto tmp1 = in_ptr0[512 + i1 + (1024*i0)];
2023-01-11T21:05:10.4834222Z                 auto tmp2 = std::exp(-tmp1);
2023-01-11T21:05:10.4834295Z                 auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:05:10.4834379Z                 auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4834470Z                 out_ptr1[i1 + (512*i0)] = tmp4;
2023-01-11T21:05:10.4834532Z             }
2023-01-11T21:05:10.4834593Z         }
2023-01-11T21:05:10.4834667Z         #pragma omp for 
2023-01-11T21:05:10.4834747Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.4834798Z         {
2023-01-11T21:05:10.4834878Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.4834939Z             {
2023-01-11T21:05:10.4835079Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (64*i0));
2023-01-11T21:05:10.4835220Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 32 + (16*i1) + (64*i0));
2023-01-11T21:05:10.4835355Z                 auto tmp2 = decltype(tmp1)(1)/(decltype(tmp1)(1) + tmp1.neg().exp());
2023-01-11T21:05:10.4835439Z                 auto tmp3 = tmp0 * tmp2;
2023-01-11T21:05:10.4835540Z                 tmp3.store(out_ptr2 + (16*i1) + (32*i0));
2023-01-11T21:05:10.4835588Z             }
2023-01-11T21:05:10.4835679Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.4835763Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:05:10.4835825Z             {
2023-01-11T21:05:10.4835919Z                 auto tmp0 = in_ptr0[i1 + (64*i0)];
2023-01-11T21:05:10.4836017Z                 auto tmp1 = in_ptr0[32 + i1 + (64*i0)];
2023-01-11T21:05:10.4836158Z                 auto tmp2 = std::exp(-tmp1);
2023-01-11T21:05:10.4836233Z                 auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:05:10.4836320Z                 auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.4836410Z                 out_ptr2[i1 + (32*i0)] = tmp4;
2023-01-11T21:05:10.4836471Z             }
2023-01-11T21:05:10.4836531Z         }
2023-01-11T21:05:10.4836592Z     }
2023-01-11T21:05:10.4836638Z }
2023-01-11T21:05:10.4836715Z ''')
2023-01-11T21:05:10.4836720Z 
2023-01-11T21:05:10.4836724Z 
2023-01-11T21:05:10.4836812Z async_compile.wait(globals())
2023-01-11T21:05:10.4836882Z del async_compile
2023-01-11T21:05:10.4836887Z 
2023-01-11T21:05:10.4836956Z def call(args):
2023-01-11T21:05:10.4837025Z     arg0_1, = args
2023-01-11T21:05:10.4837095Z     args.clear()
2023-01-11T21:05:10.4837310Z     buf0 = empty_strided((8, 16, 8, 4), (512, 32, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4837508Z     buf1 = empty_strided((8, 8, 8, 8), (512, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4837751Z     buf2 = empty_strided((8, 16, 4, 8), (512, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4837943Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4838012Z     del arg0_1
2023-01-11T21:05:10.4838095Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4838101Z 
2023-01-11T21:05:10.4838105Z 
2023-01-11T21:05:10.4838180Z if __name__ == "__main__":
2023-01-11T21:05:10.4838295Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4838418Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4838619Z     arg0_1 = rand_strided((8, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4838726Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4838730Z 
2023-01-11T21:05:10.4838796Z ok (2.913s)
2023-01-11T21:05:10.4839271Z   test_grid_sampler_2d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4839401Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4839661Z [2023-01-11 20:51:30,820] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 201
2023-01-11T21:05:10.4839869Z [2023-01-11 20:51:31,677] torch._inductor.scheduler: [DEBUG] remove_buffer('buf3')
2023-01-11T21:05:10.4840068Z [2023-01-11 20:51:31,677] torch._inductor.scheduler: [DEBUG] remove_buffer('buf7')
2023-01-11T21:05:10.4840265Z [2023-01-11 20:51:31,677] torch._inductor.scheduler: [DEBUG] remove_buffer('buf5')
2023-01-11T21:05:10.4840270Z 
2023-01-11T21:05:10.4840362Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4840420Z import torch
2023-01-11T21:05:10.4840489Z import random
2023-01-11T21:05:10.4840731Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4840855Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4840860Z 
2023-01-11T21:05:10.4840940Z aten = torch.ops.aten
2023-01-11T21:05:10.4841074Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4841164Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4841170Z 
2023-01-11T21:05:10.4841173Z 
2023-01-11T21:05:10.4841308Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4841500Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4841614Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.4841716Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.4841820Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4841926Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4842024Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4842121Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4842203Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.4842297Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.4842392Z                        long* __restrict__ out_ptr4,
2023-01-11T21:05:10.4842486Z                        long* __restrict__ out_ptr5,
2023-01-11T21:05:10.4842579Z                        float* __restrict__ out_ptr6,
2023-01-11T21:05:10.4842672Z                        long* __restrict__ out_ptr7,
2023-01-11T21:05:10.4842763Z                        long* __restrict__ out_ptr8,
2023-01-11T21:05:10.4842841Z                        float* __restrict__ out_ptr9,
2023-01-11T21:05:10.4842935Z                        long* __restrict__ out_ptr10,
2023-01-11T21:05:10.4843028Z                        long* __restrict__ out_ptr11,
2023-01-11T21:05:10.4843187Z                        float* __restrict__ out_ptr12,
2023-01-11T21:05:10.4843279Z                        long* __restrict__ out_ptr13,
2023-01-11T21:05:10.4843374Z                        long* __restrict__ out_ptr14,
2023-01-11T21:05:10.4843471Z                        float* __restrict__ out_ptr15)
2023-01-11T21:05:10.4843532Z {
2023-01-11T21:05:10.4843616Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4843676Z     {
2023-01-11T21:05:10.4843752Z         #pragma omp for 
2023-01-11T21:05:10.4843838Z         for(long i0=0; i0<495616; i0+=1)
2023-01-11T21:05:10.4843901Z         {
2023-01-11T21:05:10.4843964Z             {
2023-01-11T21:05:10.4844015Z                 {
2023-01-11T21:05:10.4844108Z                     auto tmp0 = in_ptr0[2*i0];
2023-01-11T21:05:10.4844203Z                     auto tmp9 = in_ptr0[1 + (2*i0)];
2023-01-11T21:05:10.4844309Z                     auto tmp1 = static_cast<float>(175.5);
2023-01-11T21:05:10.4844402Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.4844496Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:05:10.4844596Z                     auto tmp4 = std::floor(tmp3);
2023-01-11T21:05:10.4844722Z                     auto tmp5 = static_cast<float>(0);
2023-01-11T21:05:10.4844815Z                     auto tmp6 = tmp4 >= tmp5;
2023-01-11T21:05:10.4844918Z                     auto tmp7 = static_cast<float>(352);
2023-01-11T21:05:10.4845006Z                     auto tmp8 = tmp4 < tmp7;
2023-01-11T21:05:10.4845094Z                     auto tmp10 = tmp9 * tmp1;
2023-01-11T21:05:10.4845183Z                     auto tmp11 = tmp10 + tmp1;
2023-01-11T21:05:10.4845281Z                     auto tmp12 = std::floor(tmp11);
2023-01-11T21:05:10.4845372Z                     auto tmp13 = tmp12 >= tmp5;
2023-01-11T21:05:10.4845451Z                     auto tmp14 = tmp12 < tmp7;
2023-01-11T21:05:10.4845542Z                     auto tmp15 = tmp13 && tmp14;
2023-01-11T21:05:10.4845633Z                     auto tmp16 = tmp8 && tmp15;
2023-01-11T21:05:10.4845725Z                     auto tmp17 = tmp6 && tmp16;
2023-01-11T21:05:10.4845826Z                     auto tmp18 = static_cast<float>(1);
2023-01-11T21:05:10.4845917Z                     auto tmp19 = tmp4 + tmp18;
2023-01-11T21:05:10.4846058Z                     auto tmp20 = tmp19 - tmp3;
2023-01-11T21:05:10.4846137Z                     auto tmp21 = tmp12 + tmp18;
2023-01-11T21:05:10.4846274Z                     auto tmp22 = tmp21 - tmp11;
2023-01-11T21:05:10.4846362Z                     auto tmp23 = tmp20 * tmp22;
2023-01-11T21:05:10.4846460Z                     auto tmp24 = tmp17 ? tmp23 : tmp5;
2023-01-11T21:05:10.4846548Z                     auto tmp25 = tmp19 >= tmp5;
2023-01-11T21:05:10.4846636Z                     auto tmp26 = tmp19 < tmp7;
2023-01-11T21:05:10.4846728Z                     auto tmp27 = tmp26 && tmp15;
2023-01-11T21:05:10.4846805Z                     auto tmp28 = tmp25 && tmp27;
2023-01-11T21:05:10.4846939Z                     auto tmp29 = tmp3 - tmp4;
2023-01-11T21:05:10.4847032Z                     auto tmp30 = tmp29 * tmp22;
2023-01-11T21:05:10.4847129Z                     auto tmp31 = tmp28 ? tmp30 : tmp5;
2023-01-11T21:05:10.4847219Z                     auto tmp32 = tmp21 >= tmp5;
2023-01-11T21:05:10.4847309Z                     auto tmp33 = tmp21 < tmp7;
2023-01-11T21:05:10.4847400Z                     auto tmp34 = tmp32 && tmp33;
2023-01-11T21:05:10.4847477Z                     auto tmp35 = tmp8 && tmp34;
2023-01-11T21:05:10.4847565Z                     auto tmp36 = tmp6 && tmp35;
2023-01-11T21:05:10.4847699Z                     auto tmp37 = tmp11 - tmp12;
2023-01-11T21:05:10.4847787Z                     auto tmp38 = tmp20 * tmp37;
2023-01-11T21:05:10.4847882Z                     auto tmp39 = tmp36 ? tmp38 : tmp5;
2023-01-11T21:05:10.4847973Z                     auto tmp40 = tmp26 && tmp34;
2023-01-11T21:05:10.4848063Z                     auto tmp41 = tmp25 && tmp40;
2023-01-11T21:05:10.4848152Z                     auto tmp42 = tmp29 * tmp37;
2023-01-11T21:05:10.4848267Z                     auto tmp43 = tmp41 ? tmp42 : tmp5;
2023-01-11T21:05:10.4848373Z                     auto tmp44 = static_cast<float>(176.0);
2023-01-11T21:05:10.4848466Z                     auto tmp45 = tmp0 * tmp44;
2023-01-11T21:05:10.4848555Z                     auto tmp46 = tmp45 + tmp1;
2023-01-11T21:05:10.4848659Z                     auto tmp47 = static_cast<float>(0.0);
2023-01-11T21:05:10.4848787Z                     auto tmp48 = (tmp47 != tmp47) ? tmp47 : std::max(tmp46, tmp47);
2023-01-11T21:05:10.4848890Z                     auto tmp49 = static_cast<float>(351.0);
2023-01-11T21:05:10.4849005Z                     auto tmp50 = (tmp49 != tmp49) ? tmp49 : std::min(tmp48, tmp49);
2023-01-11T21:05:10.4849103Z                     auto tmp51 = std::floor(tmp50);
2023-01-11T21:05:10.4849191Z                     auto tmp52 = tmp51 >= tmp5;
2023-01-11T21:05:10.4849280Z                     auto tmp53 = tmp51 < tmp7;
2023-01-11T21:05:10.4849369Z                     auto tmp54 = tmp9 * tmp44;
2023-01-11T21:05:10.4849462Z                     auto tmp55 = tmp54 + tmp1;
2023-01-11T21:05:10.4849584Z                     auto tmp56 = (tmp47 != tmp47) ? tmp47 : std::max(tmp55, tmp47);
2023-01-11T21:05:10.4849746Z                     auto tmp57 = (tmp49 != tmp49) ? tmp49 : std::min(tmp56, tmp49);
2023-01-11T21:05:10.4849835Z                     auto tmp58 = std::floor(tmp57);
2023-01-11T21:05:10.4849924Z                     auto tmp59 = tmp58 >= tmp5;
2023-01-11T21:05:10.4850012Z                     auto tmp60 = tmp58 < tmp7;
2023-01-11T21:05:10.4850105Z                     auto tmp61 = tmp59 && tmp60;
2023-01-11T21:05:10.4850195Z                     auto tmp62 = tmp53 && tmp61;
2023-01-11T21:05:10.4850288Z                     auto tmp63 = tmp52 && tmp62;
2023-01-11T21:05:10.4850392Z                     auto tmp64 = static_cast<long>(tmp51);
2023-01-11T21:05:10.4850493Z                     auto tmp65 = static_cast<long>(0);
2023-01-11T21:05:10.4850579Z                     auto tmp66 = tmp63 ? tmp64 : tmp65;
2023-01-11T21:05:10.4850686Z                     auto tmp67 = static_cast<long>(tmp58);
2023-01-11T21:05:10.4850795Z                     auto tmp68 = tmp63 ? tmp67 : tmp65;
2023-01-11T21:05:10.4850933Z                     auto tmp69 = tmp51 + tmp18;
2023-01-11T21:05:10.4851121Z                     auto tmp70 = tmp69 - tmp50;
2023-01-11T21:05:10.4851211Z                     auto tmp71 = tmp58 + tmp18;
2023-01-11T21:05:10.4851348Z                     auto tmp72 = tmp71 - tmp57;
2023-01-11T21:05:10.4851424Z                     auto tmp73 = tmp70 * tmp72;
2023-01-11T21:05:10.4851521Z                     auto tmp74 = tmp63 ? tmp73 : tmp5;
2023-01-11T21:05:10.4851610Z                     auto tmp75 = tmp69 >= tmp5;
2023-01-11T21:05:10.4851700Z                     auto tmp76 = tmp69 < tmp7;
2023-01-11T21:05:10.4851791Z                     auto tmp77 = tmp76 && tmp61;
2023-01-11T21:05:10.4851882Z                     auto tmp78 = tmp75 && tmp77;
2023-01-11T21:05:10.4851986Z                     auto tmp79 = static_cast<long>(tmp69);
2023-01-11T21:05:10.4852075Z                     auto tmp80 = tmp78 ? tmp79 : tmp65;
2023-01-11T21:05:10.4852171Z                     auto tmp81 = tmp78 ? tmp67 : tmp65;
2023-01-11T21:05:10.4852308Z                     auto tmp82 = tmp50 - tmp51;
2023-01-11T21:05:10.4852397Z                     auto tmp83 = tmp82 * tmp72;
2023-01-11T21:05:10.4852494Z                     auto tmp84 = tmp78 ? tmp83 : tmp5;
2023-01-11T21:05:10.4852584Z                     auto tmp85 = tmp71 >= tmp5;
2023-01-11T21:05:10.4852674Z                     auto tmp86 = tmp71 < tmp7;
2023-01-11T21:05:10.4852754Z                     auto tmp87 = tmp85 && tmp86;
2023-01-11T21:05:10.4852844Z                     auto tmp88 = tmp53 && tmp87;
2023-01-11T21:05:10.4852934Z                     auto tmp89 = tmp52 && tmp88;
2023-01-11T21:05:10.4853032Z                     auto tmp90 = tmp89 ? tmp64 : tmp65;
2023-01-11T21:05:10.4853138Z                     auto tmp91 = static_cast<long>(tmp71);
2023-01-11T21:05:10.4853274Z                     auto tmp92 = tmp89 ? tmp91 : tmp65;
2023-01-11T21:05:10.4853411Z                     auto tmp93 = tmp57 - tmp58;
2023-01-11T21:05:10.4853500Z                     auto tmp94 = tmp70 * tmp93;
2023-01-11T21:05:10.4853587Z                     auto tmp95 = tmp89 ? tmp94 : tmp5;
2023-01-11T21:05:10.4853678Z                     auto tmp96 = tmp76 && tmp87;
2023-01-11T21:05:10.4853767Z                     auto tmp97 = tmp75 && tmp96;
2023-01-11T21:05:10.4853863Z                     auto tmp98 = tmp97 ? tmp79 : tmp65;
2023-01-11T21:05:10.4853958Z                     auto tmp99 = tmp97 ? tmp91 : tmp65;
2023-01-11T21:05:10.4854051Z                     auto tmp100 = tmp82 * tmp93;
2023-01-11T21:05:10.4854153Z                     auto tmp101 = tmp97 ? tmp100 : tmp5;
2023-01-11T21:05:10.4854224Z                     out_ptr0[i0] = tmp24;
2023-01-11T21:05:10.4854306Z                     out_ptr1[i0] = tmp31;
2023-01-11T21:05:10.4854388Z                     out_ptr2[i0] = tmp39;
2023-01-11T21:05:10.4854472Z                     out_ptr3[i0] = tmp43;
2023-01-11T21:05:10.4854552Z                     out_ptr4[i0] = tmp66;
2023-01-11T21:05:10.4854632Z                     out_ptr5[i0] = tmp68;
2023-01-11T21:05:10.4854740Z                     out_ptr6[i0] = tmp74;
2023-01-11T21:05:10.4854810Z                     out_ptr7[i0] = tmp80;
2023-01-11T21:05:10.4854891Z                     out_ptr8[i0] = tmp81;
2023-01-11T21:05:10.4854970Z                     out_ptr9[i0] = tmp84;
2023-01-11T21:05:10.4855054Z                     out_ptr10[i0] = tmp90;
2023-01-11T21:05:10.4855139Z                     out_ptr11[i0] = tmp92;
2023-01-11T21:05:10.4855220Z                     out_ptr12[i0] = tmp95;
2023-01-11T21:05:10.4855302Z                     out_ptr13[i0] = tmp98;
2023-01-11T21:05:10.4855369Z                     out_ptr14[i0] = tmp99;
2023-01-11T21:05:10.4855454Z                     out_ptr15[i0] = tmp101;
2023-01-11T21:05:10.4855522Z                 }
2023-01-11T21:05:10.4855584Z             }
2023-01-11T21:05:10.4855649Z         }
2023-01-11T21:05:10.4855726Z         #pragma omp for 
2023-01-11T21:05:10.4855806Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4855854Z         {
2023-01-11T21:05:10.4855934Z             #pragma GCC ivdep
2023-01-11T21:05:10.4856016Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4856080Z             {
2023-01-11T21:05:10.4856160Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4856252Z                 for(long i2=0; i2<123904; i2+=1)
2023-01-11T21:05:10.4856315Z                 {
2023-01-11T21:05:10.4856366Z                     {
2023-01-11T21:05:10.4856433Z                         {
2023-01-11T21:05:10.4856542Z                             auto tmp2 = in_ptr0[(2*i2) + (247808*i0)];
2023-01-11T21:05:10.4856654Z                             auto tmp11 = in_ptr0[1 + (2*i2) + (247808*i0)];
2023-01-11T21:05:10.4856764Z                             auto tmp51 = out_ptr0[i2 + (123904*i0)];
2023-01-11T21:05:10.4856867Z                             auto tmp53 = out_ptr1[i2 + (123904*i0)];
2023-01-11T21:05:10.4856970Z                             auto tmp56 = out_ptr2[i2 + (123904*i0)];
2023-01-11T21:05:10.4857057Z                             auto tmp59 = out_ptr3[i2 + (123904*i0)];
2023-01-11T21:05:10.4857164Z                             auto tmp0 = static_cast<long>(i0);
2023-01-11T21:05:10.4857270Z                             auto tmp1 = static_cast<long>(i1);
2023-01-11T21:05:10.4857380Z                             auto tmp3 = static_cast<float>(175.5);
2023-01-11T21:05:10.4857474Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.4857566Z                             auto tmp5 = tmp4 + tmp3;
2023-01-11T21:05:10.4857672Z                             auto tmp6 = std::floor(tmp5);
2023-01-11T21:05:10.4857776Z                             auto tmp7 = static_cast<float>(0);
2023-01-11T21:05:10.4857858Z                             auto tmp8 = tmp6 >= tmp7;
2023-01-11T21:05:10.4857964Z                             auto tmp9 = static_cast<float>(352);
2023-01-11T21:05:10.4858091Z                             auto tmp10 = tmp6 < tmp9;
2023-01-11T21:05:10.4858186Z                             auto tmp12 = tmp11 * tmp3;
2023-01-11T21:05:10.4858281Z                             auto tmp13 = tmp12 + tmp3;
2023-01-11T21:05:10.4858390Z                             auto tmp14 = std::floor(tmp13);
2023-01-11T21:05:10.4858567Z                             auto tmp15 = tmp14 >= tmp7;
2023-01-11T21:05:10.4858651Z                             auto tmp16 = tmp14 < tmp9;
2023-01-11T21:05:10.4858749Z                             auto tmp17 = tmp15 && tmp16;
2023-01-11T21:05:10.4858847Z                             auto tmp18 = tmp10 && tmp17;
2023-01-11T21:05:10.4858943Z                             auto tmp19 = tmp8 && tmp18;
2023-01-11T21:05:10.4859053Z                             auto tmp20 = static_cast<long>(tmp14);
2023-01-11T21:05:10.4859161Z                             auto tmp21 = static_cast<long>(0);
2023-01-11T21:05:10.4859266Z                             auto tmp22 = tmp19 ? tmp20 : tmp21;
2023-01-11T21:05:10.4859381Z                             auto tmp23 = static_cast<long>(tmp6);
2023-01-11T21:05:10.4859470Z                             auto tmp24 = tmp19 ? tmp23 : tmp21;
2023-01-11T21:05:10.4859636Z                             auto tmp25 = in_ptr1[tmp24 + (352*tmp22) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4859746Z                             auto tmp26 = static_cast<float>(1);
2023-01-11T21:05:10.4859843Z                             auto tmp27 = tmp6 + tmp26;
2023-01-11T21:05:10.4859939Z                             auto tmp28 = tmp27 >= tmp7;
2023-01-11T21:05:10.4860035Z                             auto tmp29 = tmp27 < tmp9;
2023-01-11T21:05:10.4860133Z                             auto tmp30 = tmp29 && tmp17;
2023-01-11T21:05:10.4860216Z                             auto tmp31 = tmp28 && tmp30;
2023-01-11T21:05:10.4860318Z                             auto tmp32 = tmp31 ? tmp20 : tmp21;
2023-01-11T21:05:10.4860427Z                             auto tmp33 = static_cast<long>(tmp27);
2023-01-11T21:05:10.4860533Z                             auto tmp34 = tmp31 ? tmp33 : tmp21;
2023-01-11T21:05:10.4860663Z                             auto tmp35 = in_ptr1[tmp34 + (352*tmp32) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4860760Z                             auto tmp36 = tmp14 + tmp26;
2023-01-11T21:05:10.4860854Z                             auto tmp37 = tmp36 >= tmp7;
2023-01-11T21:05:10.4860948Z                             auto tmp38 = tmp36 < tmp9;
2023-01-11T21:05:10.4861031Z                             auto tmp39 = tmp37 && tmp38;
2023-01-11T21:05:10.4861125Z                             auto tmp40 = tmp10 && tmp39;
2023-01-11T21:05:10.4861217Z                             auto tmp41 = tmp8 && tmp40;
2023-01-11T21:05:10.4861325Z                             auto tmp42 = static_cast<long>(tmp36);
2023-01-11T21:05:10.4861427Z                             auto tmp43 = tmp41 ? tmp42 : tmp21;
2023-01-11T21:05:10.4861527Z                             auto tmp44 = tmp41 ? tmp23 : tmp21;
2023-01-11T21:05:10.4861658Z                             auto tmp45 = in_ptr1[tmp44 + (352*tmp43) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4861755Z                             auto tmp46 = tmp29 && tmp39;
2023-01-11T21:05:10.4861840Z                             auto tmp47 = tmp28 && tmp46;
2023-01-11T21:05:10.4861942Z                             auto tmp48 = tmp47 ? tmp42 : tmp21;
2023-01-11T21:05:10.4862043Z                             auto tmp49 = tmp47 ? tmp33 : tmp21;
2023-01-11T21:05:10.4862171Z                             auto tmp50 = in_ptr1[tmp49 + (352*tmp48) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4862267Z                             auto tmp52 = tmp25 * tmp51;
2023-01-11T21:05:10.4862360Z                             auto tmp54 = tmp35 * tmp53;
2023-01-11T21:05:10.4862453Z                             auto tmp55 = tmp52 + tmp54;
2023-01-11T21:05:10.4862534Z                             auto tmp57 = tmp45 * tmp56;
2023-01-11T21:05:10.4862626Z                             auto tmp58 = tmp55 + tmp57;
2023-01-11T21:05:10.4862747Z                             auto tmp60 = tmp50 * tmp59;
2023-01-11T21:05:10.4862839Z                             auto tmp61 = tmp58 + tmp60;
2023-01-11T21:05:10.4862951Z                             in_out_ptr0[i2 + (123904*i1) + (371712*i0)] = tmp61;
2023-01-11T21:05:10.4863019Z                         }
2023-01-11T21:05:10.4863083Z                     }
2023-01-11T21:05:10.4863145Z                 }
2023-01-11T21:05:10.4863194Z             }
2023-01-11T21:05:10.4863255Z         }
2023-01-11T21:05:10.4863330Z         #pragma omp for 
2023-01-11T21:05:10.4863411Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4863472Z         {
2023-01-11T21:05:10.4863551Z             #pragma GCC ivdep
2023-01-11T21:05:10.4863620Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.4863682Z             {
2023-01-11T21:05:10.4863761Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4863852Z                 for(long i2=0; i2<123904; i2+=1)
2023-01-11T21:05:10.4863916Z                 {
2023-01-11T21:05:10.4863980Z                     {
2023-01-11T21:05:10.4864046Z                         {
2023-01-11T21:05:10.4864140Z                             auto tmp2 = out_ptr5[i2 + (123904*i0)];
2023-01-11T21:05:10.4864274Z                             auto tmp3 = out_ptr4[i2 + (123904*i0)];
2023-01-11T21:05:10.4864378Z                             auto tmp5 = out_ptr6[i2 + (123904*i0)];
2023-01-11T21:05:10.4864478Z                             auto tmp7 = out_ptr8[i2 + (123904*i0)];
2023-01-11T21:05:10.4864576Z                             auto tmp8 = out_ptr7[i2 + (123904*i0)];
2023-01-11T21:05:10.4864682Z                             auto tmp10 = out_ptr9[i2 + (123904*i0)];
2023-01-11T21:05:10.4864789Z                             auto tmp13 = out_ptr11[i2 + (123904*i0)];
2023-01-11T21:05:10.4864892Z                             auto tmp14 = out_ptr10[i2 + (123904*i0)];
2023-01-11T21:05:10.4864980Z                             auto tmp16 = out_ptr12[i2 + (123904*i0)];
2023-01-11T21:05:10.4865082Z                             auto tmp19 = out_ptr14[i2 + (123904*i0)];
2023-01-11T21:05:10.4865180Z                             auto tmp20 = out_ptr13[i2 + (123904*i0)];
2023-01-11T21:05:10.4865280Z                             auto tmp22 = out_ptr15[i2 + (123904*i0)];
2023-01-11T21:05:10.4865387Z                             auto tmp0 = static_cast<long>(i0);
2023-01-11T21:05:10.4865494Z                             auto tmp1 = static_cast<long>(i1);
2023-01-11T21:05:10.4865623Z                             auto tmp4 = in_ptr1[tmp3 + (352*tmp2) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4865718Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.4865829Z                             auto tmp9 = in_ptr1[tmp8 + (352*tmp7) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4865925Z                             auto tmp11 = tmp9 * tmp10;
2023-01-11T21:05:10.4866020Z                             auto tmp12 = tmp6 + tmp11;
2023-01-11T21:05:10.4866153Z                             auto tmp15 = in_ptr1[tmp14 + (352*tmp13) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4866249Z                             auto tmp17 = tmp15 * tmp16;
2023-01-11T21:05:10.4866345Z                             auto tmp18 = tmp12 + tmp17;
2023-01-11T21:05:10.4866473Z                             auto tmp21 = in_ptr1[tmp20 + (352*tmp19) + (123904*tmp1) + (371712*tmp0)];
2023-01-11T21:05:10.4866557Z                             auto tmp23 = tmp21 * tmp22;
2023-01-11T21:05:10.4866650Z                             auto tmp24 = tmp18 + tmp23;
2023-01-11T21:05:10.4866762Z                             in_out_ptr1[i2 + (123904*i1) + (371712*i0)] = tmp24;
2023-01-11T21:05:10.4866830Z                         }
2023-01-11T21:05:10.4866896Z                     }
2023-01-11T21:05:10.4866959Z                 }
2023-01-11T21:05:10.4867021Z             }
2023-01-11T21:05:10.4867068Z         }
2023-01-11T21:05:10.4867129Z     }
2023-01-11T21:05:10.4867188Z }
2023-01-11T21:05:10.4867283Z ''')
2023-01-11T21:05:10.4867322Z 
2023-01-11T21:05:10.4867327Z 
2023-01-11T21:05:10.4867417Z async_compile.wait(globals())
2023-01-11T21:05:10.4867489Z del async_compile
2023-01-11T21:05:10.4867494Z 
2023-01-11T21:05:10.4867563Z def call(args):
2023-01-11T21:05:10.4867640Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4867697Z     args.clear()
2023-01-11T21:05:10.4867917Z     buf0 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4868134Z     buf2 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4868342Z     buf4 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4868551Z     buf6 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4868760Z     buf9 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4868970Z     buf10 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4869188Z     buf11 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4869415Z     buf12 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4869623Z     buf13 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4869834Z     buf14 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4870038Z     buf15 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4870242Z     buf16 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4870451Z     buf17 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4870653Z     buf19 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4870858Z     buf20 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4871053Z     buf21 = empty_strided((4, 352, 352), (123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4871281Z     buf1 = empty_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4871367Z     buf8 = buf1; del buf1  # reuse
2023-01-11T21:05:10.4871592Z     buf18 = empty_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4871679Z     buf22 = buf18; del buf18  # reuse
2023-01-11T21:05:10.4872236Z     kernel_cpp_0(c_void_p(buf8.data_ptr()), c_void_p(buf22.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf13.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf15.data_ptr()), c_void_p(buf16.data_ptr()), c_void_p(buf17.data_ptr()), c_void_p(buf19.data_ptr()), c_void_p(buf20.data_ptr()), c_void_p(buf21.data_ptr()))
2023-01-11T21:05:10.4872311Z     del arg0_1
2023-01-11T21:05:10.4872379Z     del arg1_1
2023-01-11T21:05:10.4872456Z     return (buf8, buf22, )
2023-01-11T21:05:10.4872462Z 
2023-01-11T21:05:10.4872466Z 
2023-01-11T21:05:10.4872541Z if __name__ == "__main__":
2023-01-11T21:05:10.4872644Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4872768Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4872994Z     arg0_1 = rand_strided((4, 3, 352, 352), (371712, 123904, 352, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4873214Z     arg1_1 = rand_strided((4, 352, 352, 2), (247808, 704, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4873328Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4873596Z [2023-01-11 20:51:34,458] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 201
2023-01-11T21:05:10.4873670Z 
2023-01-11T21:05:10.4873737Z ok (7.915s)
2023-01-11T21:05:10.4874181Z   test_hardsigmoid_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4874308Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4874554Z [2023-01-11 20:51:36,007] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 202
2023-01-11T21:05:10.4874819Z [2023-01-11 20:51:38,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 202
2023-01-11T21:05:10.4874825Z 
2023-01-11T21:05:10.4874918Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4874989Z import torch
2023-01-11T21:05:10.4875060Z import random
2023-01-11T21:05:10.4875176Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4875326Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4875331Z 
2023-01-11T21:05:10.4875414Z aten = torch.ops.aten
2023-01-11T21:05:10.4875537Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4875630Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4875635Z 
2023-01-11T21:05:10.4875639Z 
2023-01-11T21:05:10.4875774Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4875979Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4876099Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4876199Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4876297Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4876394Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4876441Z {
2023-01-11T21:05:10.4876537Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4876599Z     {
2023-01-11T21:05:10.4876675Z         #pragma omp for 
2023-01-11T21:05:10.4876755Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4876817Z         {
2023-01-11T21:05:10.4876953Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4877073Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.4877158Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4877290Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:05:10.4877397Z             auto tmp4 = at::vec::maximum(tmp2, tmp3);
2023-01-11T21:05:10.4877530Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(6.0));
2023-01-11T21:05:10.4877638Z             auto tmp6 = at::vec::minimum(tmp4, tmp5);
2023-01-11T21:05:10.4877771Z             auto tmp7 = at::vec::Vectorized<float>(static_cast<float>(6));
2023-01-11T21:05:10.4877855Z             auto tmp8 = tmp6 / tmp7;
2023-01-11T21:05:10.4877922Z             auto tmp9 = tmp2 + tmp1;
2023-01-11T21:05:10.4878031Z             auto tmp10 = at::vec::maximum(tmp9, tmp3);
2023-01-11T21:05:10.4878139Z             auto tmp11 = at::vec::minimum(tmp10, tmp5);
2023-01-11T21:05:10.4878223Z             auto tmp12 = tmp11 / tmp7;
2023-01-11T21:05:10.4878347Z             auto tmp13 = tmp0 - tmp1;
2023-01-11T21:05:10.4878430Z             auto tmp14 = tmp13 + tmp1;
2023-01-11T21:05:10.4878537Z             auto tmp15 = at::vec::maximum(tmp14, tmp3);
2023-01-11T21:05:10.4878641Z             auto tmp16 = at::vec::minimum(tmp15, tmp5);
2023-01-11T21:05:10.4878711Z             auto tmp17 = tmp16 / tmp7;
2023-01-11T21:05:10.4878801Z             tmp8.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4878894Z             tmp12.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4878984Z             tmp17.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.4879076Z         }
2023-01-11T21:05:10.4879171Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4879252Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.4879300Z         {
2023-01-11T21:05:10.4879385Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4879484Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.4879566Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4879665Z             auto tmp3 = static_cast<float>(0.0);
2023-01-11T21:05:10.4879787Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::max(tmp2, tmp3);
2023-01-11T21:05:10.4879884Z             auto tmp5 = static_cast<float>(6.0);
2023-01-11T21:05:10.4879992Z             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::min(tmp4, tmp5);
2023-01-11T21:05:10.4880089Z             auto tmp7 = static_cast<float>(6);
2023-01-11T21:05:10.4880171Z             auto tmp8 = tmp6 / tmp7;
2023-01-11T21:05:10.4880253Z             auto tmp9 = tmp2 + tmp1;
2023-01-11T21:05:10.4880374Z             auto tmp10 = (tmp3 != tmp3) ? tmp3 : std::max(tmp9, tmp3);
2023-01-11T21:05:10.4880495Z             auto tmp11 = (tmp5 != tmp5) ? tmp5 : std::min(tmp10, tmp5);
2023-01-11T21:05:10.4880741Z             auto tmp12 = tmp11 / tmp7;
2023-01-11T21:05:10.4880874Z             auto tmp13 = tmp0 - tmp1;
2023-01-11T21:05:10.4880943Z             auto tmp14 = tmp13 + tmp1;
2023-01-11T21:05:10.4881065Z             auto tmp15 = (tmp3 != tmp3) ? tmp3 : std::max(tmp14, tmp3);
2023-01-11T21:05:10.4881186Z             auto tmp16 = (tmp5 != tmp5) ? tmp5 : std::min(tmp15, tmp5);
2023-01-11T21:05:10.4881269Z             auto tmp17 = tmp16 / tmp7;
2023-01-11T21:05:10.4881349Z             out_ptr0[i0] = tmp8;
2023-01-11T21:05:10.4881431Z             out_ptr1[i0] = tmp12;
2023-01-11T21:05:10.4881508Z             out_ptr2[i0] = tmp17;
2023-01-11T21:05:10.4881556Z         }
2023-01-11T21:05:10.4881618Z     }
2023-01-11T21:05:10.4881678Z }
2023-01-11T21:05:10.4881756Z ''')
2023-01-11T21:05:10.4881764Z 
2023-01-11T21:05:10.4881768Z 
2023-01-11T21:05:10.4881857Z async_compile.wait(globals())
2023-01-11T21:05:10.4881929Z del async_compile
2023-01-11T21:05:10.4881934Z 
2023-01-11T21:05:10.4882004Z def call(args):
2023-01-11T21:05:10.4882062Z     arg0_1, = args
2023-01-11T21:05:10.4882134Z     args.clear()
2023-01-11T21:05:10.4882330Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4882522Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4882709Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4882901Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4882970Z     del arg0_1
2023-01-11T21:05:10.4883051Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4883055Z 
2023-01-11T21:05:10.4883059Z 
2023-01-11T21:05:10.4883121Z if __name__ == "__main__":
2023-01-11T21:05:10.4883238Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4883358Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4883550Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4883659Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4883663Z 
2023-01-11T21:05:10.4883728Z ok (2.840s)
2023-01-11T21:05:10.4884169Z   test_hardswish_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4884295Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4884551Z [2023-01-11 20:51:38,859] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 203
2023-01-11T21:05:10.4884865Z [2023-01-11 20:51:41,650] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 203
2023-01-11T21:05:10.4884882Z 
2023-01-11T21:05:10.4884962Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4885036Z import torch
2023-01-11T21:05:10.4885109Z import random
2023-01-11T21:05:10.4885224Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4885344Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4885349Z 
2023-01-11T21:05:10.4885427Z aten = torch.ops.aten
2023-01-11T21:05:10.4885561Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4885638Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4885643Z 
2023-01-11T21:05:10.4885662Z 
2023-01-11T21:05:10.4885780Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4885984Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4886102Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4886205Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4886302Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4886447Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4886509Z {
2023-01-11T21:05:10.4886593Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4886653Z     {
2023-01-11T21:05:10.4886728Z         #pragma omp for 
2023-01-11T21:05:10.4886809Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4886870Z         {
2023-01-11T21:05:10.4887002Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4887135Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.4887205Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4887337Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(0.0));
2023-01-11T21:05:10.4887444Z             auto tmp4 = at::vec::maximum(tmp2, tmp3);
2023-01-11T21:05:10.4887580Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(6.0));
2023-01-11T21:05:10.4887686Z             auto tmp6 = at::vec::minimum(tmp4, tmp5);
2023-01-11T21:05:10.4887769Z             auto tmp7 = tmp0 * tmp6;
2023-01-11T21:05:10.4887899Z             auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(6));
2023-01-11T21:05:10.4887982Z             auto tmp9 = tmp7 / tmp8;
2023-01-11T21:05:10.4888050Z             auto tmp10 = tmp2 + tmp1;
2023-01-11T21:05:10.4888158Z             auto tmp11 = at::vec::maximum(tmp10, tmp3);
2023-01-11T21:05:10.4888266Z             auto tmp12 = at::vec::minimum(tmp11, tmp5);
2023-01-11T21:05:10.4888351Z             auto tmp13 = tmp2 * tmp12;
2023-01-11T21:05:10.4888434Z             auto tmp14 = tmp13 / tmp8;
2023-01-11T21:05:10.4888560Z             auto tmp15 = tmp0 - tmp1;
2023-01-11T21:05:10.4888643Z             auto tmp16 = tmp15 + tmp1;
2023-01-11T21:05:10.4888736Z             auto tmp17 = at::vec::maximum(tmp16, tmp3);
2023-01-11T21:05:10.4888844Z             auto tmp18 = at::vec::minimum(tmp17, tmp5);
2023-01-11T21:05:10.4888927Z             auto tmp19 = tmp15 * tmp18;
2023-01-11T21:05:10.4889008Z             auto tmp20 = tmp19 / tmp8;
2023-01-11T21:05:10.4889102Z             tmp9.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4889195Z             tmp14.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4889283Z             tmp20.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.4889344Z         }
2023-01-11T21:05:10.4889425Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4889505Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.4889565Z         {
2023-01-11T21:05:10.4889648Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4889745Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.4889825Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4889912Z             auto tmp3 = static_cast<float>(0.0);
2023-01-11T21:05:10.4890032Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::max(tmp2, tmp3);
2023-01-11T21:05:10.4890168Z             auto tmp5 = static_cast<float>(6.0);
2023-01-11T21:05:10.4890289Z             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::min(tmp4, tmp5);
2023-01-11T21:05:10.4890373Z             auto tmp7 = tmp0 * tmp6;
2023-01-11T21:05:10.4890471Z             auto tmp8 = static_cast<float>(6);
2023-01-11T21:05:10.4890552Z             auto tmp9 = tmp7 / tmp8;
2023-01-11T21:05:10.4890634Z             auto tmp10 = tmp2 + tmp1;
2023-01-11T21:05:10.4890743Z             auto tmp11 = (tmp3 != tmp3) ? tmp3 : std::max(tmp10, tmp3);
2023-01-11T21:05:10.4890861Z             auto tmp12 = (tmp5 != tmp5) ? tmp5 : std::min(tmp11, tmp5);
2023-01-11T21:05:10.4890943Z             auto tmp13 = tmp2 * tmp12;
2023-01-11T21:05:10.4891025Z             auto tmp14 = tmp13 / tmp8;
2023-01-11T21:05:10.4891148Z             auto tmp15 = tmp0 - tmp1;
2023-01-11T21:05:10.4891229Z             auto tmp16 = tmp15 + tmp1;
2023-01-11T21:05:10.4891347Z             auto tmp17 = (tmp3 != tmp3) ? tmp3 : std::max(tmp16, tmp3);
2023-01-11T21:05:10.4891469Z             auto tmp18 = (tmp5 != tmp5) ? tmp5 : std::min(tmp17, tmp5);
2023-01-11T21:05:10.4891540Z             auto tmp19 = tmp15 * tmp18;
2023-01-11T21:05:10.4891649Z             auto tmp20 = tmp19 / tmp8;
2023-01-11T21:05:10.4891728Z             out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.4891808Z             out_ptr1[i0] = tmp14;
2023-01-11T21:05:10.4891884Z             out_ptr2[i0] = tmp20;
2023-01-11T21:05:10.4891945Z         }
2023-01-11T21:05:10.4892005Z     }
2023-01-11T21:05:10.4892052Z }
2023-01-11T21:05:10.4892129Z ''')
2023-01-11T21:05:10.4892134Z 
2023-01-11T21:05:10.4892138Z 
2023-01-11T21:05:10.4892226Z async_compile.wait(globals())
2023-01-11T21:05:10.4892297Z del async_compile
2023-01-11T21:05:10.4892302Z 
2023-01-11T21:05:10.4892371Z def call(args):
2023-01-11T21:05:10.4892440Z     arg0_1, = args
2023-01-11T21:05:10.4892510Z     args.clear()
2023-01-11T21:05:10.4892690Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4892882Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4893066Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4893258Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4893326Z     del arg0_1
2023-01-11T21:05:10.4893408Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4893413Z 
2023-01-11T21:05:10.4893417Z 
2023-01-11T21:05:10.4893491Z if __name__ == "__main__":
2023-01-11T21:05:10.4893605Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4893714Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4893906Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4894013Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4894017Z 
2023-01-11T21:05:10.4894081Z ok (2.911s)
2023-01-11T21:05:10.4894527Z   test_hardtanh_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4894653Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4894911Z [2023-01-11 20:51:41,730] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 204
2023-01-11T21:05:10.4895174Z [2023-01-11 20:51:44,465] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 204
2023-01-11T21:05:10.4895179Z 
2023-01-11T21:05:10.4895273Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4895328Z import torch
2023-01-11T21:05:10.4895397Z import random
2023-01-11T21:05:10.4895509Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4895660Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4895665Z 
2023-01-11T21:05:10.4895741Z aten = torch.ops.aten
2023-01-11T21:05:10.4895876Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4895965Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4895970Z 
2023-01-11T21:05:10.4895974Z 
2023-01-11T21:05:10.4896106Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4896296Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4896414Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4896513Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4896609Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4896703Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4896763Z {
2023-01-11T21:05:10.4896858Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4896907Z     {
2023-01-11T21:05:10.4896983Z         #pragma omp for 
2023-01-11T21:05:10.4897062Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4897124Z         {
2023-01-11T21:05:10.4897285Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4897487Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.0));
2023-01-11T21:05:10.4897594Z             auto tmp2 = at::vec::maximum(tmp0, tmp1);
2023-01-11T21:05:10.4897724Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:05:10.4897817Z             auto tmp4 = at::vec::minimum(tmp2, tmp3);
2023-01-11T21:05:10.4897947Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4898031Z             auto tmp6 = tmp0 + tmp5;
2023-01-11T21:05:10.4898137Z             auto tmp7 = at::vec::maximum(tmp6, tmp1);
2023-01-11T21:05:10.4898237Z             auto tmp8 = at::vec::minimum(tmp7, tmp3);
2023-01-11T21:05:10.4898357Z             auto tmp9 = tmp0 - tmp5;
2023-01-11T21:05:10.4898539Z             auto tmp10 = at::vec::maximum(tmp9, tmp1);
2023-01-11T21:05:10.4898661Z             auto tmp11 = at::vec::minimum(tmp10, tmp3);
2023-01-11T21:05:10.4898743Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4898836Z             tmp8.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4898933Z             tmp11.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.4898995Z         }
2023-01-11T21:05:10.4899089Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4899172Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.4899235Z         {
2023-01-11T21:05:10.4899305Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4899456Z             auto tmp1 = static_cast<float>(-1.0);
2023-01-11T21:05:10.4899578Z             auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:05:10.4899680Z             auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.4899801Z             auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:05:10.4899903Z             auto tmp5 = static_cast<float>(1);
2023-01-11T21:05:10.4899991Z             auto tmp6 = tmp0 + tmp5;
2023-01-11T21:05:10.4900112Z             auto tmp7 = (tmp1 != tmp1) ? tmp1 : std::max(tmp6, tmp1);
2023-01-11T21:05:10.4900216Z             auto tmp8 = (tmp3 != tmp3) ? tmp3 : std::min(tmp7, tmp3);
2023-01-11T21:05:10.4900337Z             auto tmp9 = tmp0 - tmp5;
2023-01-11T21:05:10.4900457Z             auto tmp10 = (tmp1 != tmp1) ? tmp1 : std::max(tmp9, tmp1);
2023-01-11T21:05:10.4900577Z             auto tmp11 = (tmp3 != tmp3) ? tmp3 : std::min(tmp10, tmp3);
2023-01-11T21:05:10.4900657Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.4900733Z             out_ptr1[i0] = tmp8;
2023-01-11T21:05:10.4900810Z             out_ptr2[i0] = tmp11;
2023-01-11T21:05:10.4900859Z         }
2023-01-11T21:05:10.4900919Z     }
2023-01-11T21:05:10.4900979Z }
2023-01-11T21:05:10.4901057Z ''')
2023-01-11T21:05:10.4901062Z 
2023-01-11T21:05:10.4901066Z 
2023-01-11T21:05:10.4901192Z async_compile.wait(globals())
2023-01-11T21:05:10.4901263Z del async_compile
2023-01-11T21:05:10.4901268Z 
2023-01-11T21:05:10.4901338Z def call(args):
2023-01-11T21:05:10.4901394Z     arg0_1, = args
2023-01-11T21:05:10.4901466Z     args.clear()
2023-01-11T21:05:10.4901663Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4901854Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4902041Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4902228Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4902296Z     del arg0_1
2023-01-11T21:05:10.4902378Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4902384Z 
2023-01-11T21:05:10.4902388Z 
2023-01-11T21:05:10.4902449Z if __name__ == "__main__":
2023-01-11T21:05:10.4902564Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4902690Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4902882Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4903024Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.4903030Z 
2023-01-11T21:05:10.4903097Z ok (2.815s)
2023-01-11T21:05:10.4903545Z   test_horizonal_fusion1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4903672Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4903931Z [2023-01-11 20:51:44,509] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 205
2023-01-11T21:05:10.4904193Z [2023-01-11 20:51:47,264] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 205
2023-01-11T21:05:10.4904201Z 
2023-01-11T21:05:10.4904280Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4904353Z import torch
2023-01-11T21:05:10.4904424Z import random
2023-01-11T21:05:10.4904539Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4904658Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4904663Z 
2023-01-11T21:05:10.4904739Z aten = torch.ops.aten
2023-01-11T21:05:10.4904873Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4904950Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4904955Z 
2023-01-11T21:05:10.4904973Z 
2023-01-11T21:05:10.4905093Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4905295Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4905414Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4905519Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4905619Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.4905720Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4905816Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4905897Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4905956Z {
2023-01-11T21:05:10.4906052Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4906113Z     {
2023-01-11T21:05:10.4906188Z         #pragma omp for 
2023-01-11T21:05:10.4906270Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.4906331Z         {
2023-01-11T21:05:10.4906450Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4906580Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.4906665Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4906755Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4906847Z         }
2023-01-11T21:05:10.4906941Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4907027Z         for(long i0=2048; i0<2048; i0+=1)
2023-01-11T21:05:10.4907076Z         {
2023-01-11T21:05:10.4907158Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4907238Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4907319Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4907399Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4907460Z         }
2023-01-11T21:05:10.4907534Z         #pragma omp for 
2023-01-11T21:05:10.4907600Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.4907660Z         {
2023-01-11T21:05:10.4907738Z             #pragma GCC ivdep
2023-01-11T21:05:10.4907821Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.4907883Z             {
2023-01-11T21:05:10.4907971Z                 for(long i2=0; i2<1; i2+=1)
2023-01-11T21:05:10.4908034Z                 {
2023-01-11T21:05:10.4908174Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (16*i2) + (256*i0));
2023-01-11T21:05:10.4908299Z                     auto tmp1 = at::vec::Vectorized<float>(in_ptr2[i1]);
2023-01-11T21:05:10.4908474Z                     auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i1) + (16*i2) + (256*i0));
2023-01-11T21:05:10.4908613Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.4908703Z                     auto tmp4 = tmp3 * tmp1;
2023-01-11T21:05:10.4908813Z                     tmp2.store(out_ptr1 + (16*i1) + (16*i2) + (256*i0));
2023-01-11T21:05:10.4908921Z                     tmp4.store(out_ptr2 + (16*i1) + (16*i2) + (256*i0));
2023-01-11T21:05:10.4908985Z                 }
2023-01-11T21:05:10.4909067Z                 #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.4909156Z                 for(long i2=16; i2<16; i2+=1)
2023-01-11T21:05:10.4909219Z                 {
2023-01-11T21:05:10.4909323Z                     auto tmp0 = in_ptr0[i2 + (16*i1) + (256*i0)];
2023-01-11T21:05:10.4909414Z                     auto tmp1 = in_ptr2[i1];
2023-01-11T21:05:10.4909517Z                     auto tmp3 = in_ptr1[i2 + (16*i1) + (256*i0)];
2023-01-11T21:05:10.4909653Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.4909728Z                     auto tmp4 = tmp3 * tmp1;
2023-01-11T21:05:10.4909827Z                     out_ptr1[i2 + (16*i1) + (256*i0)] = tmp2;
2023-01-11T21:05:10.4909926Z                     out_ptr2[i2 + (16*i1) + (256*i0)] = tmp4;
2023-01-11T21:05:10.4909992Z                 }
2023-01-11T21:05:10.4910054Z             }
2023-01-11T21:05:10.4910115Z         }
2023-01-11T21:05:10.4910175Z     }
2023-01-11T21:05:10.4910221Z }
2023-01-11T21:05:10.4910299Z ''')
2023-01-11T21:05:10.4910305Z 
2023-01-11T21:05:10.4910309Z 
2023-01-11T21:05:10.4910397Z async_compile.wait(globals())
2023-01-11T21:05:10.4910469Z del async_compile
2023-01-11T21:05:10.4910474Z 
2023-01-11T21:05:10.4910542Z def call(args):
2023-01-11T21:05:10.4910622Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4910694Z     args.clear()
2023-01-11T21:05:10.4910893Z     buf0 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4911104Z     buf1 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4911307Z     buf2 = empty_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4911543Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4911611Z     del arg0_1
2023-01-11T21:05:10.4911676Z     del arg1_1
2023-01-11T21:05:10.4911738Z     del arg2_1
2023-01-11T21:05:10.4911818Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4911825Z 
2023-01-11T21:05:10.4911829Z 
2023-01-11T21:05:10.4911891Z if __name__ == "__main__":
2023-01-11T21:05:10.4912005Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4912163Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4912371Z     arg0_1 = rand_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4912580Z     arg1_1 = rand_strided((8, 16, 16), (256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4912786Z     arg2_1 = rand_strided((1, 16, 1), (16, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4912905Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4912910Z 
2023-01-11T21:05:10.4912976Z ok (2.806s)
2023-01-11T21:05:10.4913421Z   test_horizonal_fusion2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4913548Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4913792Z [2023-01-11 20:51:47,317] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 206
2023-01-11T21:05:10.4914085Z [2023-01-11 20:51:50,059] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 206
2023-01-11T21:05:10.4914091Z 
2023-01-11T21:05:10.4914184Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4914252Z import torch
2023-01-11T21:05:10.4914320Z import random
2023-01-11T21:05:10.4914435Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4914556Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4914561Z 
2023-01-11T21:05:10.4914638Z aten = torch.ops.aten
2023-01-11T21:05:10.4914758Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4914850Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4914855Z 
2023-01-11T21:05:10.4914860Z 
2023-01-11T21:05:10.4914995Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4915197Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4915318Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4915422Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4915522Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.4915619Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4915703Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4915796Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4915855Z {
2023-01-11T21:05:10.4915951Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4916011Z     {
2023-01-11T21:05:10.4916086Z         #pragma omp for 
2023-01-11T21:05:10.4916153Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.4916214Z         {
2023-01-11T21:05:10.4916344Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4916481Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4916564Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4916658Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4916719Z         }
2023-01-11T21:05:10.4916812Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4916884Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:05:10.4916944Z         {
2023-01-11T21:05:10.4917027Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4917124Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4917206Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4917284Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.4917332Z         }
2023-01-11T21:05:10.4917407Z         #pragma omp for 
2023-01-11T21:05:10.4917486Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.4917547Z         {
2023-01-11T21:05:10.4917676Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.4917838Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.4917920Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4918012Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4918061Z         }
2023-01-11T21:05:10.4918153Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4918234Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:05:10.4918294Z         {
2023-01-11T21:05:10.4918376Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.4918474Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.4918555Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4918620Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.4918680Z         }
2023-01-11T21:05:10.4918753Z         #pragma omp for 
2023-01-11T21:05:10.4918831Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.4918891Z         {
2023-01-11T21:05:10.4919018Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i0);
2023-01-11T21:05:10.4919150Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.4919219Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4919334Z             tmp2.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.4919396Z         }
2023-01-11T21:05:10.4919490Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4919569Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:05:10.4919629Z         {
2023-01-11T21:05:10.4919711Z             auto tmp0 = in_ptr2[i0];
2023-01-11T21:05:10.4919795Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.4919874Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4919952Z             out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.4920012Z         }
2023-01-11T21:05:10.4920072Z     }
2023-01-11T21:05:10.4920130Z }
2023-01-11T21:05:10.4920197Z ''')
2023-01-11T21:05:10.4920202Z 
2023-01-11T21:05:10.4920219Z 
2023-01-11T21:05:10.4920293Z async_compile.wait(globals())
2023-01-11T21:05:10.4920366Z del async_compile
2023-01-11T21:05:10.4920372Z 
2023-01-11T21:05:10.4920440Z def call(args):
2023-01-11T21:05:10.4920522Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4920759Z     args.clear()
2023-01-11T21:05:10.4920977Z     buf0 = empty_strided((8, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4921173Z     buf1 = empty_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4921353Z     buf2 = empty_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4921589Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.4921658Z     del arg0_1
2023-01-11T21:05:10.4921724Z     del arg1_1
2023-01-11T21:05:10.4921791Z     del arg2_1
2023-01-11T21:05:10.4921873Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.4921878Z 
2023-01-11T21:05:10.4921886Z 
2023-01-11T21:05:10.4921964Z if __name__ == "__main__":
2023-01-11T21:05:10.4922082Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4922191Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4922400Z     arg0_1 = rand_strided((8, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4922597Z     arg1_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4922791Z     arg2_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4922913Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4922919Z 
2023-01-11T21:05:10.4922985Z ok (2.794s)
2023-01-11T21:05:10.4923424Z   test_index1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4923603Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4923866Z [2023-01-11 20:51:50,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 207
2023-01-11T21:05:10.4924115Z [2023-01-11 20:51:52,899] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 207
2023-01-11T21:05:10.4924513Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4924639Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4924894Z [2023-01-11 20:51:53,009] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 208
2023-01-11T21:05:10.4925159Z [2023-01-11 20:51:55,778] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 208
2023-01-11T21:05:10.4925164Z 
2023-01-11T21:05:10.4925312Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4925383Z import torch
2023-01-11T21:05:10.4925451Z import random
2023-01-11T21:05:10.4925567Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4925674Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4925692Z 
2023-01-11T21:05:10.4925755Z aten = torch.ops.aten
2023-01-11T21:05:10.4925887Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4925978Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4925983Z 
2023-01-11T21:05:10.4925988Z 
2023-01-11T21:05:10.4926121Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4926327Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4926450Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4926554Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4926646Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.4926746Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4926806Z {
2023-01-11T21:05:10.4926905Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4926967Z     {
2023-01-11T21:05:10.4927044Z         #pragma omp for 
2023-01-11T21:05:10.4927126Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4927174Z         {
2023-01-11T21:05:10.4927254Z             #pragma GCC ivdep
2023-01-11T21:05:10.4927340Z             for(long i1=0; i1<12; i1+=1)
2023-01-11T21:05:10.4927403Z             {
2023-01-11T21:05:10.4927467Z                 {
2023-01-11T21:05:10.4927533Z                     {
2023-01-11T21:05:10.4927628Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4927710Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4927821Z                         auto tmp2 = in_ptr2[i1 + (12*tmp1) + (96*tmp0)];
2023-01-11T21:05:10.4927916Z                         out_ptr0[i1 + (12*i0)] = tmp2;
2023-01-11T21:05:10.4927983Z                     }
2023-01-11T21:05:10.4928046Z                 }
2023-01-11T21:05:10.4928108Z             }
2023-01-11T21:05:10.4928170Z         }
2023-01-11T21:05:10.4928217Z     }
2023-01-11T21:05:10.4928276Z }
2023-01-11T21:05:10.4928352Z ''')
2023-01-11T21:05:10.4928357Z 
2023-01-11T21:05:10.4928361Z 
2023-01-11T21:05:10.4928449Z async_compile.wait(globals())
2023-01-11T21:05:10.4928519Z del async_compile
2023-01-11T21:05:10.4928524Z 
2023-01-11T21:05:10.4928592Z def call(args):
2023-01-11T21:05:10.4928672Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4928729Z     args.clear()
2023-01-11T21:05:10.4928929Z     buf0 = empty_strided((4, 12), (12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4929117Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4929216Z     del arg0_1
2023-01-11T21:05:10.4929281Z     del arg1_1
2023-01-11T21:05:10.4929345Z     del arg2_1
2023-01-11T21:05:10.4929417Z     return (buf0, )
2023-01-11T21:05:10.4929422Z 
2023-01-11T21:05:10.4929426Z 
2023-01-11T21:05:10.4929499Z if __name__ == "__main__":
2023-01-11T21:05:10.4929600Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4929724Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4929930Z     arg0_1 = rand_strided((8, 8, 12), (96, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4930116Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4930299Z     arg2_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4930420Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4930425Z 
2023-01-11T21:05:10.4930431Z 
2023-01-11T21:05:10.4930524Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4930592Z import torch
2023-01-11T21:05:10.4930648Z import random
2023-01-11T21:05:10.4930763Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4930918Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4930924Z 
2023-01-11T21:05:10.4931004Z aten = torch.ops.aten
2023-01-11T21:05:10.4931139Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4931231Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4931235Z 
2023-01-11T21:05:10.4931239Z 
2023-01-11T21:05:10.4931370Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4931575Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4931683Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4931787Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4931889Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.4931989Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.4932050Z {
2023-01-11T21:05:10.4932148Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4932212Z     {
2023-01-11T21:05:10.4932275Z         #pragma omp for 
2023-01-11T21:05:10.4932357Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4932419Z         {
2023-01-11T21:05:10.4932499Z             #pragma GCC ivdep
2023-01-11T21:05:10.4932581Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.4932643Z             {
2023-01-11T21:05:10.4932710Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4932803Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:05:10.4932866Z                 {
2023-01-11T21:05:10.4932930Z                     {
2023-01-11T21:05:10.4932997Z                         {
2023-01-11T21:05:10.4933092Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.4933183Z                             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.4933285Z                             auto tmp2 = in_ptr2[i2 + (12*tmp1) + (96*tmp0)];
2023-01-11T21:05:10.4933389Z                             out_ptr0[i2 + (12*i1) + (48*i0)] = tmp2;
2023-01-11T21:05:10.4933458Z                         }
2023-01-11T21:05:10.4933523Z                     }
2023-01-11T21:05:10.4933587Z                 }
2023-01-11T21:05:10.4933649Z             }
2023-01-11T21:05:10.4933710Z         }
2023-01-11T21:05:10.4933757Z     }
2023-01-11T21:05:10.4933816Z }
2023-01-11T21:05:10.4933893Z ''')
2023-01-11T21:05:10.4933898Z 
2023-01-11T21:05:10.4933902Z 
2023-01-11T21:05:10.4933992Z async_compile.wait(globals())
2023-01-11T21:05:10.4934062Z del async_compile
2023-01-11T21:05:10.4934067Z 
2023-01-11T21:05:10.4934137Z def call(args):
2023-01-11T21:05:10.4934218Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4934274Z     args.clear()
2023-01-11T21:05:10.4934478Z     buf0 = empty_strided((4, 4, 12), (48, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4934696Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.4934763Z     del arg0_1
2023-01-11T21:05:10.4934830Z     del arg1_1
2023-01-11T21:05:10.4934896Z     del arg2_1
2023-01-11T21:05:10.4934967Z     return (buf0, )
2023-01-11T21:05:10.4934972Z 
2023-01-11T21:05:10.4934976Z 
2023-01-11T21:05:10.4935051Z if __name__ == "__main__":
2023-01-11T21:05:10.4935150Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4935274Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4935479Z     arg0_1 = rand_strided((8, 8, 12), (96, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4935668Z     arg1_1 = rand_strided((1, 4), (4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4935859Z     arg2_1 = rand_strided((4, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4935980Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4935988Z 
2023-01-11T21:05:10.4936054Z ok (5.713s)
2023-01-11T21:05:10.4936519Z   test_index2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4936645Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4936891Z [2023-01-11 20:51:55,959] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 209
2023-01-11T21:05:10.4937154Z [2023-01-11 20:51:58,667] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 209
2023-01-11T21:05:10.4937160Z 
2023-01-11T21:05:10.4937253Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4937322Z import torch
2023-01-11T21:05:10.4937395Z import random
2023-01-11T21:05:10.4937511Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4937631Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4937636Z 
2023-01-11T21:05:10.4937715Z aten = torch.ops.aten
2023-01-11T21:05:10.4937834Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4937924Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4937929Z 
2023-01-11T21:05:10.4937934Z 
2023-01-11T21:05:10.4938065Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4938270Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4938386Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.4938577Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.4938682Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4938777Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.4938826Z {
2023-01-11T21:05:10.4938924Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4938985Z     {
2023-01-11T21:05:10.4939062Z         #pragma omp for 
2023-01-11T21:05:10.4939143Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4939210Z         {
2023-01-11T21:05:10.4939290Z             #pragma GCC ivdep
2023-01-11T21:05:10.4939361Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.4939425Z             {
2023-01-11T21:05:10.4939489Z                 {
2023-01-11T21:05:10.4939556Z                     {
2023-01-11T21:05:10.4939656Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4939759Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:05:10.4939856Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:05:10.4939908Z                     }
2023-01-11T21:05:10.4939973Z                 }
2023-01-11T21:05:10.4940034Z             }
2023-01-11T21:05:10.4940093Z         }
2023-01-11T21:05:10.4940169Z         #pragma omp for 
2023-01-11T21:05:10.4940283Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.4940331Z         {
2023-01-11T21:05:10.4940409Z             #pragma GCC ivdep
2023-01-11T21:05:10.4940489Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.4940553Z             {
2023-01-11T21:05:10.4940631Z                 #pragma GCC ivdep
2023-01-11T21:05:10.4940718Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:05:10.4940781Z                 {
2023-01-11T21:05:10.4940832Z                     {
2023-01-11T21:05:10.4940901Z                         {
2023-01-11T21:05:10.4940996Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.4941105Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:05:10.4941207Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:05:10.4941272Z                         }
2023-01-11T21:05:10.4941335Z                     }
2023-01-11T21:05:10.4941384Z                 }
2023-01-11T21:05:10.4941449Z             }
2023-01-11T21:05:10.4941508Z         }
2023-01-11T21:05:10.4941567Z     }
2023-01-11T21:05:10.4941625Z }
2023-01-11T21:05:10.4941705Z ''')
2023-01-11T21:05:10.4941710Z 
2023-01-11T21:05:10.4941715Z 
2023-01-11T21:05:10.4941831Z async_compile.wait(globals())
2023-01-11T21:05:10.4941890Z del async_compile
2023-01-11T21:05:10.4941894Z 
2023-01-11T21:05:10.4941963Z def call(args):
2023-01-11T21:05:10.4942038Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.4942107Z     args.clear()
2023-01-11T21:05:10.4942319Z     buf0 = empty_strided((1, 4, 8, 8), (256, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4942528Z     buf1 = empty_strided((8, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4942716Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.4942783Z     del arg0_1
2023-01-11T21:05:10.4942835Z     del arg1_1
2023-01-11T21:05:10.4942909Z     return (buf0, buf1, )
2023-01-11T21:05:10.4942916Z 
2023-01-11T21:05:10.4942921Z 
2023-01-11T21:05:10.4942995Z if __name__ == "__main__":
2023-01-11T21:05:10.4943107Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4943232Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4943438Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4943628Z     arg1_1 = rand_strided((1, 4), (4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4943728Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.4943746Z 
2023-01-11T21:05:10.4943799Z ok (2.890s)
2023-01-11T21:05:10.4944229Z   test_index3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4944356Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4944616Z [2023-01-11 20:51:58,783] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 210
2023-01-11T21:05:10.4944832Z [2023-01-11 20:51:58,837] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.index
2023-01-11T21:05:10.4945092Z [2023-01-11 20:51:58,842] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 210
2023-01-11T21:05:10.4945098Z 
2023-01-11T21:05:10.4945190Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4945260Z import torch
2023-01-11T21:05:10.4945329Z import random
2023-01-11T21:05:10.4945430Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4945548Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4945553Z 
2023-01-11T21:05:10.4945631Z aten = torch.ops.aten
2023-01-11T21:05:10.4945764Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4945885Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4945890Z 
2023-01-11T21:05:10.4945894Z 
2023-01-11T21:05:10.4945980Z async_compile.wait(globals())
2023-01-11T21:05:10.4946053Z del async_compile
2023-01-11T21:05:10.4946057Z 
2023-01-11T21:05:10.4946125Z def call(args):
2023-01-11T21:05:10.4946193Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4946263Z     args.clear()
2023-01-11T21:05:10.4946407Z     buf0 = aten.index(as_strided(arg0_1, (3, 4, 1, 4, 3), (192, 48, 0, 12, 1)), [None, arg1_1, None, arg2_1])
2023-01-11T21:05:10.4946472Z     del arg0_1
2023-01-11T21:05:10.4946536Z     del arg1_1
2023-01-11T21:05:10.4946599Z     del arg2_1
2023-01-11T21:05:10.4946652Z     buf1 = buf0
2023-01-11T21:05:10.4946756Z     assert_size_stride(buf1, (3, 3, 1, 3), (9, 3, 3, 1))
2023-01-11T21:05:10.4946818Z     del buf0
2023-01-11T21:05:10.4954352Z     return (buf1, )
2023-01-11T21:05:10.4954364Z 
2023-01-11T21:05:10.4954369Z 
2023-01-11T21:05:10.4954467Z if __name__ == "__main__":
2023-01-11T21:05:10.4954594Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4954722Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4955059Z     arg0_1 = rand_strided((3, 4, 4, 4, 3), (192, 48, 12, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4955257Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4955449Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4955561Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4955580Z 
2023-01-11T21:05:10.4955635Z ok (0.171s)
2023-01-11T21:05:10.4956080Z   test_index_put1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4956213Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4956481Z [2023-01-11 20:51:59,781] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 211
2023-01-11T21:05:10.4956753Z [2023-01-11 20:52:02,537] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 211
2023-01-11T21:05:10.4957157Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.4957285Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.4957546Z [2023-01-11 20:52:10,593] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 212
2023-01-11T21:05:10.4957814Z [2023-01-11 20:52:13,344] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 212
2023-01-11T21:05:10.4957820Z 
2023-01-11T21:05:10.4957918Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4957976Z import torch
2023-01-11T21:05:10.4958046Z import random
2023-01-11T21:05:10.4958161Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4958282Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4958287Z 
2023-01-11T21:05:10.4958365Z aten = torch.ops.aten
2023-01-11T21:05:10.4958504Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4958597Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4958603Z 
2023-01-11T21:05:10.4958607Z 
2023-01-11T21:05:10.4958741Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4958935Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4959056Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4959207Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4959308Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.4959410Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4959509Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4959604Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4959652Z {
2023-01-11T21:05:10.4959748Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4959808Z     {
2023-01-11T21:05:10.4959885Z         #pragma omp for 
2023-01-11T21:05:10.4959966Z         for(long i0=0; i0<627200; i0+=1)
2023-01-11T21:05:10.4960036Z         {
2023-01-11T21:05:10.4960173Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4960311Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4960397Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4960493Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4960781Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4960847Z         }
2023-01-11T21:05:10.4960999Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4961082Z         for(long i0=10035200; i0<10035200; i0+=1)
2023-01-11T21:05:10.4961145Z         {
2023-01-11T21:05:10.4961233Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4961337Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4961423Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4961508Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.4961587Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.4961635Z         }
2023-01-11T21:05:10.4961714Z         #pragma omp for 
2023-01-11T21:05:10.4961798Z         for(long i0=0; i0<601; i0+=1)
2023-01-11T21:05:10.4961862Z         {
2023-01-11T21:05:10.4961947Z             #pragma GCC ivdep
2023-01-11T21:05:10.4962038Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:05:10.4962105Z             {
2023-01-11T21:05:10.4962158Z                 {
2023-01-11T21:05:10.4962225Z                     {
2023-01-11T21:05:10.4962321Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.4962428Z                         auto tmp1 = in_ptr2[i1 + (12544*i0)];
2023-01-11T21:05:10.4962532Z                         auto tmp2 = static_cast<long>(1);
2023-01-11T21:05:10.4962627Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.4962731Z                         auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.4962813Z                         auto tmp5 = tmp1 + tmp4;
2023-01-11T21:05:10.4962914Z                         out_ptr0[i1 + (12544*tmp0)] = tmp1;
2023-01-11T21:05:10.4963015Z                         out_ptr1[i1 + (12544*tmp3)] = tmp5;
2023-01-11T21:05:10.4963082Z                     }
2023-01-11T21:05:10.4963147Z                 }
2023-01-11T21:05:10.4963210Z             }
2023-01-11T21:05:10.4963274Z         }
2023-01-11T21:05:10.4963339Z         #pragma omp for 
2023-01-11T21:05:10.4963426Z         for(long i0=0; i0<627200; i0+=1)
2023-01-11T21:05:10.4963489Z         {
2023-01-11T21:05:10.4963628Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4963761Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4963845Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4963938Z             tmp2.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.4963988Z         }
2023-01-11T21:05:10.4964082Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4964174Z         for(long i0=10035200; i0<10035200; i0+=1)
2023-01-11T21:05:10.4964237Z         {
2023-01-11T21:05:10.4964322Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.4964420Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4964504Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4964570Z             out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.4964674Z         }
2023-01-11T21:05:10.4964737Z     }
2023-01-11T21:05:10.4964797Z }
2023-01-11T21:05:10.4964884Z ''')
2023-01-11T21:05:10.4964889Z 
2023-01-11T21:05:10.4964893Z 
2023-01-11T21:05:10.4964983Z async_compile.wait(globals())
2023-01-11T21:05:10.4965059Z del async_compile
2023-01-11T21:05:10.4965064Z 
2023-01-11T21:05:10.4965121Z def call(args):
2023-01-11T21:05:10.4965204Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4965276Z     args.clear()
2023-01-11T21:05:10.4965499Z     buf0 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4965724Z     buf2 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4965943Z     buf4 = empty_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4966182Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4966257Z     del arg0_1
2023-01-11T21:05:10.4966311Z     del arg1_1
2023-01-11T21:05:10.4966377Z     del arg2_1
2023-01-11T21:05:10.4966477Z     return (buf0, buf4, )
2023-01-11T21:05:10.4966549Z 
2023-01-11T21:05:10.4966554Z 
2023-01-11T21:05:10.4966632Z if __name__ == "__main__":
2023-01-11T21:05:10.4966751Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4966878Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.4967109Z     arg0_1 = rand_strided((800, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4967301Z     arg1_1 = rand_strided((601, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.4967506Z     arg2_1 = rand_strided((601, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4967630Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.4967636Z 
2023-01-11T21:05:10.4967640Z 
2023-01-11T21:05:10.4967740Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.4967814Z import torch
2023-01-11T21:05:10.4967889Z import random
2023-01-11T21:05:10.4968008Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.4968133Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.4968138Z 
2023-01-11T21:05:10.4968223Z aten = torch.ops.aten
2023-01-11T21:05:10.4968345Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.4968437Z async_compile = AsyncCompile()
2023-01-11T21:05:10.4968442Z 
2023-01-11T21:05:10.4968447Z 
2023-01-11T21:05:10.4968583Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.4968789Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.4968908Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.4969015Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.4969118Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.4969220Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.4969304Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.4969402Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.4969465Z {
2023-01-11T21:05:10.4969562Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.4969628Z     {
2023-01-11T21:05:10.4969704Z         #pragma omp for 
2023-01-11T21:05:10.4969775Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.4969838Z         {
2023-01-11T21:05:10.4969974Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.4970110Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4970197Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4970290Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.4970381Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4970443Z         }
2023-01-11T21:05:10.4970554Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4970641Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:05:10.4970703Z         {
2023-01-11T21:05:10.4970787Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.4970890Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4970976Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4971057Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.4971122Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.4971183Z         }
2023-01-11T21:05:10.4971260Z         #pragma omp for 
2023-01-11T21:05:10.4971342Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.4971405Z         {
2023-01-11T21:05:10.4971486Z             #pragma GCC ivdep
2023-01-11T21:05:10.4971554Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.4971619Z             {
2023-01-11T21:05:10.4971684Z                 {
2023-01-11T21:05:10.4971753Z                     {
2023-01-11T21:05:10.4995573Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.4995727Z                         auto tmp1 = in_ptr2[i0];
2023-01-11T21:05:10.4995841Z                         auto tmp2 = static_cast<long>(1);
2023-01-11T21:05:10.4996006Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.4996115Z                         auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.4996210Z                         auto tmp5 = tmp1 + tmp4;
2023-01-11T21:05:10.4996311Z                         out_ptr0[i1 + (8*tmp0)] = tmp1;
2023-01-11T21:05:10.4996409Z                         out_ptr1[i1 + (8*tmp3)] = tmp5;
2023-01-11T21:05:10.4996479Z                     }
2023-01-11T21:05:10.4996543Z                 }
2023-01-11T21:05:10.4996592Z             }
2023-01-11T21:05:10.4996656Z         }
2023-01-11T21:05:10.4996737Z         #pragma omp for 
2023-01-11T21:05:10.4996824Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.4996887Z         {
2023-01-11T21:05:10.4997029Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.4997161Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.4997234Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4997315Z             tmp2.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.4997367Z         }
2023-01-11T21:05:10.4997452Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.4997528Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:05:10.4997583Z         {
2023-01-11T21:05:10.4997659Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.4997744Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.4997819Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.4997891Z             out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.4997943Z         }
2023-01-11T21:05:10.4997995Z     }
2023-01-11T21:05:10.4998045Z }
2023-01-11T21:05:10.4998143Z ''')
2023-01-11T21:05:10.4998150Z 
2023-01-11T21:05:10.4998154Z 
2023-01-11T21:05:10.4998235Z async_compile.wait(globals())
2023-01-11T21:05:10.4998297Z del async_compile
2023-01-11T21:05:10.4998303Z 
2023-01-11T21:05:10.4998369Z def call(args):
2023-01-11T21:05:10.4998442Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.4998505Z     args.clear()
2023-01-11T21:05:10.4998708Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4998902Z     buf2 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4999092Z     buf4 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.4999315Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.4999376Z     del arg0_1
2023-01-11T21:05:10.4999433Z     del arg1_1
2023-01-11T21:05:10.4999490Z     del arg2_1
2023-01-11T21:05:10.4999559Z     return (buf0, buf4, )
2023-01-11T21:05:10.4999564Z 
2023-01-11T21:05:10.4999607Z 
2023-01-11T21:05:10.4999675Z if __name__ == "__main__":
2023-01-11T21:05:10.4999782Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.4999895Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5000092Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5000271Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5000465Z     arg2_1 = rand_strided((4, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5000579Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5000585Z 
2023-01-11T21:05:10.5000810Z ok (14.529s)
2023-01-11T21:05:10.5001246Z   test_index_put2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5001367Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5001681Z [2023-01-11 20:52:13,765] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 213
2023-01-11T21:05:10.5001949Z [2023-01-11 20:52:16,520] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 213
2023-01-11T21:05:10.5001955Z 
2023-01-11T21:05:10.5002047Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5002106Z import torch
2023-01-11T21:05:10.5002168Z import random
2023-01-11T21:05:10.5002274Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5002387Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5002392Z 
2023-01-11T21:05:10.5002462Z aten = torch.ops.aten
2023-01-11T21:05:10.5002586Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5002669Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5002677Z 
2023-01-11T21:05:10.5002681Z 
2023-01-11T21:05:10.5002806Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5003001Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5003113Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5003209Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.5003304Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.5003393Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5003446Z {
2023-01-11T21:05:10.5003534Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5003581Z     {
2023-01-11T21:05:10.5003648Z         #pragma omp for 
2023-01-11T21:05:10.5003722Z         for(long i0=0; i0<78400; i0+=1)
2023-01-11T21:05:10.5003776Z         {
2023-01-11T21:05:10.5003904Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5003991Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5004044Z         }
2023-01-11T21:05:10.5004126Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5004211Z         for(long i0=1254400; i0<1254400; i0+=1)
2023-01-11T21:05:10.5004266Z         {
2023-01-11T21:05:10.5004340Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5004410Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.5004464Z         }
2023-01-11T21:05:10.5004525Z         #pragma omp for 
2023-01-11T21:05:10.5004599Z         for(long i0=0; i0<600; i0+=1)
2023-01-11T21:05:10.5004652Z         {
2023-01-11T21:05:10.5004722Z             #pragma GCC ivdep
2023-01-11T21:05:10.5004806Z             for(long i1=0; i1<12544; i1+=1)
2023-01-11T21:05:10.5004861Z             {
2023-01-11T21:05:10.5004916Z                 {
2023-01-11T21:05:10.5004969Z                     {
2023-01-11T21:05:10.5005054Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.5005149Z                         auto tmp1 = in_ptr2[i1 + (12544*i0)];
2023-01-11T21:05:10.5005300Z                         atomic_add(&out_ptr0[i1 + (12544*tmp0)], tmp1);
2023-01-11T21:05:10.5005358Z                     }
2023-01-11T21:05:10.5005413Z                 }
2023-01-11T21:05:10.5005471Z             }
2023-01-11T21:05:10.5005520Z         }
2023-01-11T21:05:10.5005572Z     }
2023-01-11T21:05:10.5005625Z }
2023-01-11T21:05:10.5005695Z ''')
2023-01-11T21:05:10.5005701Z 
2023-01-11T21:05:10.5005705Z 
2023-01-11T21:05:10.5005788Z async_compile.wait(globals())
2023-01-11T21:05:10.5005853Z del async_compile
2023-01-11T21:05:10.5005858Z 
2023-01-11T21:05:10.5005921Z def call(args):
2023-01-11T21:05:10.5005988Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5006050Z     args.clear()
2023-01-11T21:05:10.5006269Z     buf0 = empty_strided((100, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5006453Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5006517Z     del arg0_1
2023-01-11T21:05:10.5006575Z     del arg1_1
2023-01-11T21:05:10.5006632Z     del arg2_1
2023-01-11T21:05:10.5006690Z     return (buf0, )
2023-01-11T21:05:10.5006699Z 
2023-01-11T21:05:10.5006744Z 
2023-01-11T21:05:10.5006807Z if __name__ == "__main__":
2023-01-11T21:05:10.5006914Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5007028Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5007245Z     arg0_1 = rand_strided((100, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5007431Z     arg1_1 = rand_strided((600, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5007644Z     arg2_1 = rand_strided((600, 256, 7, 7), (12544, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5007757Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5007762Z 
2023-01-11T21:05:10.5007822Z ok (5.339s)
2023-01-11T21:05:10.5008253Z   test_index_put3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5008373Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5008631Z [2023-01-11 20:52:18,901] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 214
2023-01-11T21:05:10.5008889Z [2023-01-11 20:52:21,736] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 214
2023-01-11T21:05:10.5008895Z 
2023-01-11T21:05:10.5008979Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5009043Z import torch
2023-01-11T21:05:10.5009108Z import random
2023-01-11T21:05:10.5009214Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5009330Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5009336Z 
2023-01-11T21:05:10.5009400Z aten = torch.ops.aten
2023-01-11T21:05:10.5009528Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5009612Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5009617Z 
2023-01-11T21:05:10.5009621Z 
2023-01-11T21:05:10.5009746Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5009948Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5010061Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5010157Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5010249Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5010332Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5010386Z {
2023-01-11T21:05:10.5010477Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5010564Z     {
2023-01-11T21:05:10.5010635Z         #pragma omp for 
2023-01-11T21:05:10.5010712Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.5010766Z         {
2023-01-11T21:05:10.5010833Z             #pragma GCC ivdep
2023-01-11T21:05:10.5010911Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.5010967Z             {
2023-01-11T21:05:10.5011041Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5011124Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:05:10.5011182Z                 {
2023-01-11T21:05:10.5011233Z                     {
2023-01-11T21:05:10.5011293Z                         {
2023-01-11T21:05:10.5011384Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5011480Z                             auto tmp1 = in_ptr1[i2 + (2*i0)];
2023-01-11T21:05:10.5011580Z                             out_ptr0[i2 + (2*tmp0) + (8*i0)] = tmp1;
2023-01-11T21:05:10.5032261Z                         }
2023-01-11T21:05:10.5032342Z                     }
2023-01-11T21:05:10.5032404Z                 }
2023-01-11T21:05:10.5032453Z             }
2023-01-11T21:05:10.5032508Z         }
2023-01-11T21:05:10.5032583Z         #pragma omp for 
2023-01-11T21:05:10.5032660Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.5032760Z         {
2023-01-11T21:05:10.5032893Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5033020Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.5033090Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5033175Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.5033231Z         }
2023-01-11T21:05:10.5033320Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5033400Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:05:10.5033455Z         {
2023-01-11T21:05:10.5033525Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5033618Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.5033706Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5033777Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.5033840Z         }
2023-01-11T21:05:10.5033915Z         #pragma omp for 
2023-01-11T21:05:10.5034000Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.5034048Z         {
2023-01-11T21:05:10.5034127Z             #pragma GCC ivdep
2023-01-11T21:05:10.5034208Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.5034271Z             {
2023-01-11T21:05:10.5034349Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5034438Z                 for(long i2=0; i2<2; i2+=1)
2023-01-11T21:05:10.5034502Z                 {
2023-01-11T21:05:10.5034553Z                     {
2023-01-11T21:05:10.5034621Z                         {
2023-01-11T21:05:10.5034717Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5034820Z                             auto tmp3 = in_ptr1[i2 + (2*i0)];
2023-01-11T21:05:10.5034926Z                             auto tmp1 = static_cast<long>(1);
2023-01-11T21:05:10.5035028Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5035137Z                             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.5035222Z                             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.5035327Z                             out_ptr1[i2 + (2*tmp2) + (8*i0)] = tmp5;
2023-01-11T21:05:10.5035395Z                         }
2023-01-11T21:05:10.5035460Z                     }
2023-01-11T21:05:10.5035524Z                 }
2023-01-11T21:05:10.5035586Z             }
2023-01-11T21:05:10.5035647Z         }
2023-01-11T21:05:10.5035694Z     }
2023-01-11T21:05:10.5035753Z }
2023-01-11T21:05:10.5035838Z ''')
2023-01-11T21:05:10.5035843Z 
2023-01-11T21:05:10.5035847Z 
2023-01-11T21:05:10.5035935Z async_compile.wait(globals())
2023-01-11T21:05:10.5036007Z del async_compile
2023-01-11T21:05:10.5036012Z 
2023-01-11T21:05:10.5036083Z def call(args):
2023-01-11T21:05:10.5036162Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5036219Z     args.clear()
2023-01-11T21:05:10.5036463Z     buf1 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5036653Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5036720Z     del arg0_1
2023-01-11T21:05:10.5036785Z     del arg1_1
2023-01-11T21:05:10.5036849Z     del arg2_1
2023-01-11T21:05:10.5036918Z     return (buf1, )
2023-01-11T21:05:10.5036923Z 
2023-01-11T21:05:10.5036927Z 
2023-01-11T21:05:10.5037006Z if __name__ == "__main__":
2023-01-11T21:05:10.5037107Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5037229Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5037435Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5037621Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5037829Z     arg2_1 = rand_strided((1024, 1, 2), (2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5037950Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5037955Z 
2023-01-11T21:05:10.5038020Z ok (3.041s)
2023-01-11T21:05:10.5038507Z   test_index_put_as_masked_fill_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5038633Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5038884Z [2023-01-11 20:52:21,857] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 215
2023-01-11T21:05:10.5039149Z [2023-01-11 20:52:24,641] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 215
2023-01-11T21:05:10.5039553Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5039679Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5039937Z [2023-01-11 20:52:24,779] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 216
2023-01-11T21:05:10.5040200Z [2023-01-11 20:52:27,487] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 216
2023-01-11T21:05:10.5040206Z 
2023-01-11T21:05:10.5040299Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5040368Z import torch
2023-01-11T21:05:10.5040436Z import random
2023-01-11T21:05:10.5040537Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5040877Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5040885Z 
2023-01-11T21:05:10.5040963Z aten = torch.ops.aten
2023-01-11T21:05:10.5041098Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5041192Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5041198Z 
2023-01-11T21:05:10.5041202Z 
2023-01-11T21:05:10.5041340Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5041546Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5041667Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.5041759Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5041863Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.5041963Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5042025Z {
2023-01-11T21:05:10.5042123Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5042186Z     {
2023-01-11T21:05:10.5042318Z         #pragma omp for 
2023-01-11T21:05:10.5042387Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.5042449Z         {
2023-01-11T21:05:10.5042550Z             float g_tmp_buffer_in_ptr0[16] = {0};
2023-01-11T21:05:10.5042674Z             flag_to_float(in_ptr0 + 16*i0, g_tmp_buffer_in_ptr0, 16);
2023-01-11T21:05:10.5042816Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:05:10.5042935Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:05:10.5043066Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i0);
2023-01-11T21:05:10.5043191Z             auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:05:10.5043269Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5043332Z         }
2023-01-11T21:05:10.5043427Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5043513Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:05:10.5043577Z         {
2023-01-11T21:05:10.5043664Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5043746Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.5043815Z             auto tmp2 = in_ptr2[i0];
2023-01-11T21:05:10.5043949Z             auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:05:10.5044034Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.5044095Z         }
2023-01-11T21:05:10.5044154Z     }
2023-01-11T21:05:10.5044214Z }
2023-01-11T21:05:10.5044292Z ''')
2023-01-11T21:05:10.5044298Z 
2023-01-11T21:05:10.5044303Z 
2023-01-11T21:05:10.5044379Z async_compile.wait(globals())
2023-01-11T21:05:10.5044450Z del async_compile
2023-01-11T21:05:10.5044455Z 
2023-01-11T21:05:10.5044524Z def call(args):
2023-01-11T21:05:10.5044606Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5044676Z     args.clear()
2023-01-11T21:05:10.5044884Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5045074Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5045143Z     del arg0_1
2023-01-11T21:05:10.5045196Z     del arg1_1
2023-01-11T21:05:10.5045260Z     del arg2_1
2023-01-11T21:05:10.5045332Z     return (buf0, )
2023-01-11T21:05:10.5045338Z 
2023-01-11T21:05:10.5045343Z 
2023-01-11T21:05:10.5045417Z if __name__ == "__main__":
2023-01-11T21:05:10.5045529Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5045652Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5045864Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5046053Z     arg1_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5046236Z     arg2_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5046358Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5046363Z 
2023-01-11T21:05:10.5046367Z 
2023-01-11T21:05:10.5046463Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5046531Z import torch
2023-01-11T21:05:10.5046599Z import random
2023-01-11T21:05:10.5046713Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5046834Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5046838Z 
2023-01-11T21:05:10.5046905Z aten = torch.ops.aten
2023-01-11T21:05:10.5047037Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5047126Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5047131Z 
2023-01-11T21:05:10.5047135Z 
2023-01-11T21:05:10.5047267Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5047473Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5047590Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.5047694Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5047797Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.5047918Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5047979Z {
2023-01-11T21:05:10.5048076Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5048136Z     {
2023-01-11T21:05:10.5048214Z         #pragma omp for 
2023-01-11T21:05:10.5048294Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.5048355Z         {
2023-01-11T21:05:10.5048442Z             float g_tmp_buffer_in_ptr0[16] = {0};
2023-01-11T21:05:10.5048564Z             flag_to_float(in_ptr0 + 16*i0, g_tmp_buffer_in_ptr0, 16);
2023-01-11T21:05:10.5048706Z             auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:05:10.5048840Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.5048958Z             auto tmp2 = at::vec::Vectorized<float>(in_ptr2[0]);
2023-01-11T21:05:10.5049043Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5049167Z             auto tmp4 = decltype(tmp3)::blendv(tmp1, tmp3, tmp0);
2023-01-11T21:05:10.5049260Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5049309Z         }
2023-01-11T21:05:10.5049404Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5049518Z         for(long i0=8192; i0<8192; i0+=1)
2023-01-11T21:05:10.5049582Z         {
2023-01-11T21:05:10.5049664Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5049746Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5049824Z             auto tmp2 = in_ptr2[0];
2023-01-11T21:05:10.5049892Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5049984Z             auto tmp4 = tmp0 ? tmp3 : tmp1;
2023-01-11T21:05:10.5050063Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.5050124Z         }
2023-01-11T21:05:10.5050183Z     }
2023-01-11T21:05:10.5050242Z }
2023-01-11T21:05:10.5050307Z ''')
2023-01-11T21:05:10.5050324Z 
2023-01-11T21:05:10.5050329Z 
2023-01-11T21:05:10.5050403Z async_compile.wait(globals())
2023-01-11T21:05:10.5050477Z del async_compile
2023-01-11T21:05:10.5050484Z 
2023-01-11T21:05:10.5050553Z def call(args):
2023-01-11T21:05:10.5050633Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5050703Z     args.clear()
2023-01-11T21:05:10.5050913Z     buf0 = empty_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5051099Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5051153Z     del arg0_1
2023-01-11T21:05:10.5051216Z     del arg1_1
2023-01-11T21:05:10.5051280Z     del arg2_1
2023-01-11T21:05:10.5051351Z     return (buf0, )
2023-01-11T21:05:10.5051356Z 
2023-01-11T21:05:10.5051361Z 
2023-01-11T21:05:10.5051434Z if __name__ == "__main__":
2023-01-11T21:05:10.5051549Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5051671Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5051879Z     arg0_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5052068Z     arg1_1 = rand_strided((1024, 4, 2), (8, 2, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5052251Z     arg2_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5052373Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5052378Z 
2023-01-11T21:05:10.5052443Z ok (5.747s)
2023-01-11T21:05:10.5052891Z   test_index_put_fallback1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5053017Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5053276Z [2023-01-11 20:52:27,602] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 217
2023-01-11T21:05:10.5053577Z [2023-01-11 20:52:30,220] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 217
2023-01-11T21:05:10.5053976Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5054101Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5054344Z [2023-01-11 20:52:30,327] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 218
2023-01-11T21:05:10.5054606Z [2023-01-11 20:52:30,339] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 218
2023-01-11T21:05:10.5054611Z 
2023-01-11T21:05:10.5054704Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5054775Z import torch
2023-01-11T21:05:10.5054846Z import random
2023-01-11T21:05:10.5054963Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5055081Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5055087Z 
2023-01-11T21:05:10.5055212Z aten = torch.ops.aten
2023-01-11T21:05:10.5055334Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5055424Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5055429Z 
2023-01-11T21:05:10.5055434Z 
2023-01-11T21:05:10.5055567Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5055770Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5055888Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5055987Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5056047Z {
2023-01-11T21:05:10.5056122Z     #pragma GCC ivdep
2023-01-11T21:05:10.5056188Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.5056247Z     {
2023-01-11T21:05:10.5056313Z         {
2023-01-11T21:05:10.5056375Z             {
2023-01-11T21:05:10.5056462Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5056544Z                 out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.5056595Z             }
2023-01-11T21:05:10.5056656Z         }
2023-01-11T21:05:10.5056719Z     }
2023-01-11T21:05:10.5056784Z }
2023-01-11T21:05:10.5056865Z ''')
2023-01-11T21:05:10.5056872Z 
2023-01-11T21:05:10.5056876Z 
2023-01-11T21:05:10.5056965Z async_compile.wait(globals())
2023-01-11T21:05:10.5057036Z del async_compile
2023-01-11T21:05:10.5057041Z 
2023-01-11T21:05:10.5057108Z def call(args):
2023-01-11T21:05:10.5057175Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5057243Z     args.clear()
2023-01-11T21:05:10.5057433Z     buf0 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5057564Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5057631Z     del arg0_1
2023-01-11T21:05:10.5057735Z     aten.index_put_(buf0, [arg1_1], arg2_1, False)
2023-01-11T21:05:10.5057803Z     del arg1_1
2023-01-11T21:05:10.5057854Z     del arg2_1
2023-01-11T21:05:10.5057923Z     return (buf0, )
2023-01-11T21:05:10.5057928Z 
2023-01-11T21:05:10.5057932Z 
2023-01-11T21:05:10.5058008Z if __name__ == "__main__":
2023-01-11T21:05:10.5058119Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5058241Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5058432Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5058700Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5058878Z     arg2_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5059001Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5059006Z 
2023-01-11T21:05:10.5059010Z 
2023-01-11T21:05:10.5059105Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5059176Z import torch
2023-01-11T21:05:10.5059288Z import random
2023-01-11T21:05:10.5059403Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5059525Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5059529Z 
2023-01-11T21:05:10.5059611Z aten = torch.ops.aten
2023-01-11T21:05:10.5059729Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5059822Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5059826Z 
2023-01-11T21:05:10.5059830Z 
2023-01-11T21:05:10.5059961Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5060163Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5060280Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5060379Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5060441Z {
2023-01-11T21:05:10.5060518Z     #pragma GCC ivdep
2023-01-11T21:05:10.5060582Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.5060647Z     {
2023-01-11T21:05:10.5060711Z         {
2023-01-11T21:05:10.5060775Z             {
2023-01-11T21:05:10.5060863Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5060944Z                 out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.5061044Z             }
2023-01-11T21:05:10.5061093Z         }
2023-01-11T21:05:10.5061154Z     }
2023-01-11T21:05:10.5061214Z }
2023-01-11T21:05:10.5061292Z ''')
2023-01-11T21:05:10.5061297Z 
2023-01-11T21:05:10.5061301Z 
2023-01-11T21:05:10.5061387Z async_compile.wait(globals())
2023-01-11T21:05:10.5061458Z del async_compile
2023-01-11T21:05:10.5061463Z 
2023-01-11T21:05:10.5061531Z def call(args):
2023-01-11T21:05:10.5061598Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5061667Z     args.clear()
2023-01-11T21:05:10.5061856Z     buf0 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5061989Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5062054Z     del arg0_1
2023-01-11T21:05:10.5062159Z     aten.index_put_(buf0, [arg1_1], arg2_1, True)
2023-01-11T21:05:10.5062225Z     del arg1_1
2023-01-11T21:05:10.5062277Z     del arg2_1
2023-01-11T21:05:10.5062347Z     return (buf0, )
2023-01-11T21:05:10.5062352Z 
2023-01-11T21:05:10.5062359Z 
2023-01-11T21:05:10.5062432Z if __name__ == "__main__":
2023-01-11T21:05:10.5062543Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5062665Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5062858Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5063042Z     arg1_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5063234Z     arg2_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5063341Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5063346Z 
2023-01-11T21:05:10.5063412Z ok (2.839s)
2023-01-11T21:05:10.5063858Z   test_index_put_fallback2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5063988Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5064245Z [2023-01-11 20:52:30,446] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 219
2023-01-11T21:05:10.5064510Z [2023-01-11 20:52:33,127] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 219
2023-01-11T21:05:10.5064907Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5065067Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5065325Z [2023-01-11 20:52:33,265] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 220
2023-01-11T21:05:10.5065589Z [2023-01-11 20:52:33,285] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 220
2023-01-11T21:05:10.5065594Z 
2023-01-11T21:05:10.5065688Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5065743Z import torch
2023-01-11T21:05:10.5065812Z import random
2023-01-11T21:05:10.5065929Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5066050Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5066055Z 
2023-01-11T21:05:10.5066133Z aten = torch.ops.aten
2023-01-11T21:05:10.5066267Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5066362Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5066369Z 
2023-01-11T21:05:10.5066373Z 
2023-01-11T21:05:10.5066494Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5066699Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5066855Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5066955Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5067016Z {
2023-01-11T21:05:10.5067115Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5067176Z     {
2023-01-11T21:05:10.5067239Z         #pragma omp for 
2023-01-11T21:05:10.5067320Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.5067382Z         {
2023-01-11T21:05:10.5067446Z             {
2023-01-11T21:05:10.5067510Z                 {
2023-01-11T21:05:10.5067603Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5067688Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.5067738Z                 }
2023-01-11T21:05:10.5067803Z             }
2023-01-11T21:05:10.5067867Z         }
2023-01-11T21:05:10.5067929Z     }
2023-01-11T21:05:10.5067989Z }
2023-01-11T21:05:10.5068068Z ''')
2023-01-11T21:05:10.5068073Z 
2023-01-11T21:05:10.5068077Z 
2023-01-11T21:05:10.5068167Z async_compile.wait(globals())
2023-01-11T21:05:10.5068224Z del async_compile
2023-01-11T21:05:10.5068229Z 
2023-01-11T21:05:10.5068297Z def call(args):
2023-01-11T21:05:10.5068386Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5068456Z     args.clear()
2023-01-11T21:05:10.5068659Z     buf0 = empty_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5068792Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5068859Z     del arg0_1
2023-01-11T21:05:10.5068964Z     aten.index_put_(buf0, [None,arg1_1,arg2_1], arg3_1, False)
2023-01-11T21:05:10.5069031Z     del arg1_1
2023-01-11T21:05:10.5069097Z     del arg2_1
2023-01-11T21:05:10.5069159Z     del arg3_1
2023-01-11T21:05:10.5069230Z     return (buf0, )
2023-01-11T21:05:10.5069237Z 
2023-01-11T21:05:10.5069241Z 
2023-01-11T21:05:10.5069315Z if __name__ == "__main__":
2023-01-11T21:05:10.5069426Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5069548Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5069738Z     arg0_1 = rand_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5069924Z     arg1_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5070107Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5070289Z     arg3_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5070415Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5070420Z 
2023-01-11T21:05:10.5070425Z 
2023-01-11T21:05:10.5070515Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5070583Z import torch
2023-01-11T21:05:10.5070650Z import random
2023-01-11T21:05:10.5070792Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5070913Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5070918Z 
2023-01-11T21:05:10.5070994Z aten = torch.ops.aten
2023-01-11T21:05:10.5071127Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5071216Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5071221Z 
2023-01-11T21:05:10.5071226Z 
2023-01-11T21:05:10.5071357Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5071561Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5071679Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5071765Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5071825Z {
2023-01-11T21:05:10.5071923Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5071984Z     {
2023-01-11T21:05:10.5072059Z         #pragma omp for 
2023-01-11T21:05:10.5072139Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.5072202Z         {
2023-01-11T21:05:10.5072252Z             {
2023-01-11T21:05:10.5072314Z                 {
2023-01-11T21:05:10.5072406Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5072524Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.5072588Z                 }
2023-01-11T21:05:10.5072650Z             }
2023-01-11T21:05:10.5072698Z         }
2023-01-11T21:05:10.5072759Z     }
2023-01-11T21:05:10.5072817Z }
2023-01-11T21:05:10.5072894Z ''')
2023-01-11T21:05:10.5072899Z 
2023-01-11T21:05:10.5072903Z 
2023-01-11T21:05:10.5072994Z async_compile.wait(globals())
2023-01-11T21:05:10.5073064Z del async_compile
2023-01-11T21:05:10.5073069Z 
2023-01-11T21:05:10.5073140Z def call(args):
2023-01-11T21:05:10.5073215Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5073283Z     args.clear()
2023-01-11T21:05:10.5073483Z     buf0 = empty_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5073616Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5073687Z     del arg0_1
2023-01-11T21:05:10.5073804Z     aten.index_put_(buf0, [None,arg1_1,arg2_1], arg3_1, True)
2023-01-11T21:05:10.5073872Z     del arg1_1
2023-01-11T21:05:10.5073936Z     del arg2_1
2023-01-11T21:05:10.5073987Z     del arg3_1
2023-01-11T21:05:10.5074057Z     return (buf0, )
2023-01-11T21:05:10.5074061Z 
2023-01-11T21:05:10.5074065Z 
2023-01-11T21:05:10.5074142Z if __name__ == "__main__":
2023-01-11T21:05:10.5074254Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5074375Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5074579Z     arg0_1 = rand_strided((1, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5074764Z     arg1_1 = rand_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5074934Z     arg2_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5075117Z     arg3_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5075243Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5075248Z 
2023-01-11T21:05:10.5075313Z ok (2.951s)
2023-01-11T21:05:10.5075756Z   test_index_select_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5075880Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5076141Z [2023-01-11 20:52:33,406] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 221
2023-01-11T21:05:10.5076402Z [2023-01-11 20:52:36,242] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 221
2023-01-11T21:05:10.5076839Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5076963Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5077220Z [2023-01-11 20:52:36,327] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 222
2023-01-11T21:05:10.5077470Z [2023-01-11 20:52:39,110] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 222
2023-01-11T21:05:10.5077474Z 
2023-01-11T21:05:10.5077569Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5077639Z import torch
2023-01-11T21:05:10.5077707Z import random
2023-01-11T21:05:10.5077821Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5077942Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5077948Z 
2023-01-11T21:05:10.5078026Z aten = torch.ops.aten
2023-01-11T21:05:10.5078158Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5078267Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5078273Z 
2023-01-11T21:05:10.5078277Z 
2023-01-11T21:05:10.5078411Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5078612Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5078728Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.5078833Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5078931Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5079027Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.5079120Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.5079167Z {
2023-01-11T21:05:10.5079263Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5079326Z     {
2023-01-11T21:05:10.5079401Z         #pragma omp for 
2023-01-11T21:05:10.5079483Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5079545Z         {
2023-01-11T21:05:10.5079612Z             #pragma GCC ivdep
2023-01-11T21:05:10.5079696Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.5079758Z             {
2023-01-11T21:05:10.5079822Z                 {
2023-01-11T21:05:10.5079887Z                     {
2023-01-11T21:05:10.5079981Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5080084Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:05:10.5080167Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:05:10.5080232Z                     }
2023-01-11T21:05:10.5080295Z                 }
2023-01-11T21:05:10.5080356Z             }
2023-01-11T21:05:10.5080421Z         }
2023-01-11T21:05:10.5080495Z         #pragma omp for 
2023-01-11T21:05:10.5080574Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5080737Z         {
2023-01-11T21:05:10.5080816Z             #pragma GCC ivdep
2023-01-11T21:05:10.5080898Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.5080961Z             {
2023-01-11T21:05:10.5081048Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5081139Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:05:10.5081190Z                 {
2023-01-11T21:05:10.5081256Z                     {
2023-01-11T21:05:10.5081324Z                         {
2023-01-11T21:05:10.5081421Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5081538Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:05:10.5081642Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:05:10.5081711Z                         }
2023-01-11T21:05:10.5081762Z                     }
2023-01-11T21:05:10.5081827Z                 }
2023-01-11T21:05:10.5081890Z             }
2023-01-11T21:05:10.5082016Z         }
2023-01-11T21:05:10.5082093Z         #pragma omp for 
2023-01-11T21:05:10.5082173Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5082235Z         {
2023-01-11T21:05:10.5082299Z             #pragma GCC ivdep
2023-01-11T21:05:10.5082385Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.5082448Z             {
2023-01-11T21:05:10.5082528Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5082617Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:05:10.5082684Z                 {
2023-01-11T21:05:10.5082750Z                     {
2023-01-11T21:05:10.5082804Z                         {
2023-01-11T21:05:10.5082899Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5082995Z                             auto tmp1 = in_ptr0[i2];
2023-01-11T21:05:10.5083109Z                             auto tmp2 = in_ptr1[tmp1 + (8*tmp0) + (64*i0)];
2023-01-11T21:05:10.5083215Z                             out_ptr2[i2 + (4*i1) + (16*i0)] = tmp2;
2023-01-11T21:05:10.5083286Z                         }
2023-01-11T21:05:10.5083355Z                     }
2023-01-11T21:05:10.5083405Z                 }
2023-01-11T21:05:10.5083468Z             }
2023-01-11T21:05:10.5083530Z         }
2023-01-11T21:05:10.5083592Z     }
2023-01-11T21:05:10.5083696Z }
2023-01-11T21:05:10.5083778Z ''')
2023-01-11T21:05:10.5083784Z 
2023-01-11T21:05:10.5083788Z 
2023-01-11T21:05:10.5083877Z async_compile.wait(globals())
2023-01-11T21:05:10.5083935Z del async_compile
2023-01-11T21:05:10.5083940Z 
2023-01-11T21:05:10.5084009Z def call(args):
2023-01-11T21:05:10.5084082Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5084150Z     args.clear()
2023-01-11T21:05:10.5084353Z     buf0 = empty_strided((4, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5084553Z     buf1 = empty_strided((8, 4, 8), (32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5084745Z     buf2 = empty_strided((8, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5084957Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.5085017Z     del arg0_1
2023-01-11T21:05:10.5085084Z     del arg1_1
2023-01-11T21:05:10.5085170Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.5085175Z 
2023-01-11T21:05:10.5085179Z 
2023-01-11T21:05:10.5085257Z if __name__ == "__main__":
2023-01-11T21:05:10.5085373Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5085496Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5085698Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5085872Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.5085987Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5085993Z 
2023-01-11T21:05:10.5085997Z 
2023-01-11T21:05:10.5086090Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5086160Z import torch
2023-01-11T21:05:10.5086229Z import random
2023-01-11T21:05:10.5086344Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5086464Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5086470Z 
2023-01-11T21:05:10.5086549Z aten = torch.ops.aten
2023-01-11T21:05:10.5086668Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5086758Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5086763Z 
2023-01-11T21:05:10.5086767Z 
2023-01-11T21:05:10.5086899Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5087102Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5087222Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5087325Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5087423Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5087517Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.5087637Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.5087696Z {
2023-01-11T21:05:10.5087791Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5087852Z     {
2023-01-11T21:05:10.5087929Z         #pragma omp for 
2023-01-11T21:05:10.5088010Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5088072Z         {
2023-01-11T21:05:10.5088139Z             #pragma GCC ivdep
2023-01-11T21:05:10.5088223Z             for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.5088285Z             {
2023-01-11T21:05:10.5088348Z                 {
2023-01-11T21:05:10.5088412Z                     {
2023-01-11T21:05:10.5088507Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5088609Z                         auto tmp1 = in_ptr1[i1 + (64*tmp0)];
2023-01-11T21:05:10.5088693Z                         out_ptr0[i1 + (64*i0)] = tmp1;
2023-01-11T21:05:10.5088763Z                     }
2023-01-11T21:05:10.5088827Z                 }
2023-01-11T21:05:10.5088890Z             }
2023-01-11T21:05:10.5088951Z         }
2023-01-11T21:05:10.5089027Z         #pragma omp for 
2023-01-11T21:05:10.5089094Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5089154Z         {
2023-01-11T21:05:10.5089261Z             #pragma GCC ivdep
2023-01-11T21:05:10.5089345Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.5089406Z             {
2023-01-11T21:05:10.5089486Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5089573Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:05:10.5089623Z                 {
2023-01-11T21:05:10.5089687Z                     {
2023-01-11T21:05:10.5089753Z                         {
2023-01-11T21:05:10.5089848Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5089959Z                             auto tmp1 = in_ptr1[i2 + (8*tmp0) + (64*i0)];
2023-01-11T21:05:10.5090062Z                             out_ptr1[i2 + (8*i1) + (32*i0)] = tmp1;
2023-01-11T21:05:10.5090128Z                         }
2023-01-11T21:05:10.5090182Z                     }
2023-01-11T21:05:10.5090244Z                 }
2023-01-11T21:05:10.5090305Z             }
2023-01-11T21:05:10.5090366Z         }
2023-01-11T21:05:10.5090441Z         #pragma omp for 
2023-01-11T21:05:10.5090520Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5090581Z         {
2023-01-11T21:05:10.5090645Z             #pragma GCC ivdep
2023-01-11T21:05:10.5090726Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.5090788Z             {
2023-01-11T21:05:10.5090867Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5090954Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:05:10.5091017Z                 {
2023-01-11T21:05:10.5091082Z                     {
2023-01-11T21:05:10.5091135Z                         {
2023-01-11T21:05:10.5091230Z                             auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5091322Z                             auto tmp1 = in_ptr0[i2];
2023-01-11T21:05:10.5091434Z                             auto tmp2 = in_ptr1[tmp1 + (8*tmp0) + (64*i0)];
2023-01-11T21:05:10.5091538Z                             out_ptr2[i2 + (4*i1) + (16*i0)] = tmp2;
2023-01-11T21:05:10.5091605Z                         }
2023-01-11T21:05:10.5091670Z                     }
2023-01-11T21:05:10.5091724Z                 }
2023-01-11T21:05:10.5091785Z             }
2023-01-11T21:05:10.5091845Z         }
2023-01-11T21:05:10.5091906Z     }
2023-01-11T21:05:10.5091965Z }
2023-01-11T21:05:10.5092043Z ''')
2023-01-11T21:05:10.5092048Z 
2023-01-11T21:05:10.5092053Z 
2023-01-11T21:05:10.5092141Z async_compile.wait(globals())
2023-01-11T21:05:10.5092199Z del async_compile
2023-01-11T21:05:10.5092203Z 
2023-01-11T21:05:10.5092272Z def call(args):
2023-01-11T21:05:10.5092345Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5092420Z     args.clear()
2023-01-11T21:05:10.5092623Z     buf0 = empty_strided((4, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5092821Z     buf1 = empty_strided((8, 4, 8), (32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5093057Z     buf2 = empty_strided((8, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5093257Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.5093326Z     del arg0_1
2023-01-11T21:05:10.5093392Z     del arg1_1
2023-01-11T21:05:10.5093475Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.5093480Z 
2023-01-11T21:05:10.5093484Z 
2023-01-11T21:05:10.5093560Z if __name__ == "__main__":
2023-01-11T21:05:10.5093675Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5093799Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5094005Z     arg0_1 = rand_strided((8, 8, 8), (64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5094178Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5094292Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5094299Z 
2023-01-11T21:05:10.5094367Z ok (5.826s)
2023-01-11T21:05:10.5094875Z   test_indirect_load_broadcast_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5095005Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5095268Z [2023-01-11 20:52:39,295] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 223
2023-01-11T21:05:10.5095534Z [2023-01-11 20:52:42,061] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 223
2023-01-11T21:05:10.5095540Z 
2023-01-11T21:05:10.5095635Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5095704Z import torch
2023-01-11T21:05:10.5095764Z import random
2023-01-11T21:05:10.5095878Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5095998Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5096003Z 
2023-01-11T21:05:10.5096083Z aten = torch.ops.aten
2023-01-11T21:05:10.5096217Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5096309Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5096314Z 
2023-01-11T21:05:10.5096319Z 
2023-01-11T21:05:10.5096452Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5096658Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5096775Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5096867Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5096970Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.5097073Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5097137Z {
2023-01-11T21:05:10.5097234Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5097296Z     {
2023-01-11T21:05:10.5097359Z         #pragma omp for 
2023-01-11T21:05:10.5097443Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.5097505Z         {
2023-01-11T21:05:10.5097587Z             #pragma GCC ivdep
2023-01-11T21:05:10.5097672Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:05:10.5097735Z             {
2023-01-11T21:05:10.5097799Z                 {
2023-01-11T21:05:10.5097851Z                     {
2023-01-11T21:05:10.5097955Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:05:10.5098049Z                         auto tmp2 = in_ptr2[i0];
2023-01-11T21:05:10.5098154Z                         auto tmp1 = in_ptr1[i1 + (512*tmp0)];
2023-01-11T21:05:10.5098247Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5098342Z                         out_ptr0[i1 + (21*i0)] = tmp3;
2023-01-11T21:05:10.5098576Z                     }
2023-01-11T21:05:10.5098631Z                 }
2023-01-11T21:05:10.5098693Z             }
2023-01-11T21:05:10.5098759Z         }
2023-01-11T21:05:10.5098821Z     }
2023-01-11T21:05:10.5098881Z }
2023-01-11T21:05:10.5098966Z ''')
2023-01-11T21:05:10.5098973Z 
2023-01-11T21:05:10.5098977Z 
2023-01-11T21:05:10.5099068Z async_compile.wait(globals())
2023-01-11T21:05:10.5099126Z del async_compile
2023-01-11T21:05:10.5099145Z 
2023-01-11T21:05:10.5099202Z def call(args):
2023-01-11T21:05:10.5099284Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5099355Z     args.clear()
2023-01-11T21:05:10.5099555Z     buf0 = empty_strided((32, 21), (21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5099745Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5099814Z     del arg0_1
2023-01-11T21:05:10.5099877Z     del arg1_1
2023-01-11T21:05:10.5099928Z     del arg2_1
2023-01-11T21:05:10.5100000Z     return (buf0, )
2023-01-11T21:05:10.5100005Z 
2023-01-11T21:05:10.5100009Z 
2023-01-11T21:05:10.5100083Z if __name__ == "__main__":
2023-01-11T21:05:10.5100196Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5100353Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5100551Z     arg0_1 = rand_strided((32, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5100754Z     arg1_1 = rand_strided((9521, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5100934Z     arg2_1 = rand_strided((32, 21), (1, 32), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5101054Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5101058Z 
2023-01-11T21:05:10.5101122Z ok (4.101s)
2023-01-11T21:05:10.5101553Z   test_inf_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5101682Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5101940Z [2023-01-11 20:52:43,252] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 224
2023-01-11T21:05:10.5102202Z [2023-01-11 20:52:46,002] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 224
2023-01-11T21:05:10.5102208Z 
2023-01-11T21:05:10.5102300Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5102368Z import torch
2023-01-11T21:05:10.5102435Z import random
2023-01-11T21:05:10.5102536Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5102655Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5102660Z 
2023-01-11T21:05:10.5102737Z aten = torch.ops.aten
2023-01-11T21:05:10.5102870Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5102961Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5102966Z 
2023-01-11T21:05:10.5102970Z 
2023-01-11T21:05:10.5103106Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5103311Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5103429Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5103515Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5103610Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.5103703Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.5103763Z {
2023-01-11T21:05:10.5103860Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5103921Z     {
2023-01-11T21:05:10.5103995Z         #pragma omp for 
2023-01-11T21:05:10.5104063Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5104123Z         {
2023-01-11T21:05:10.5104186Z             {
2023-01-11T21:05:10.5104277Z                 {
2023-01-11T21:05:10.5104371Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5104503Z                     auto tmp1 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5104596Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5104830Z                     auto tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5104921Z                     auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.5105011Z                     auto tmp5 = tmp0 * tmp3;
2023-01-11T21:05:10.5105095Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5105177Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.5105257Z                     out_ptr2[i0] = tmp5;
2023-01-11T21:05:10.5105322Z                 }
2023-01-11T21:05:10.5105370Z             }
2023-01-11T21:05:10.5105431Z         }
2023-01-11T21:05:10.5105491Z     }
2023-01-11T21:05:10.5105550Z }
2023-01-11T21:05:10.5105627Z ''')
2023-01-11T21:05:10.5105631Z 
2023-01-11T21:05:10.5105638Z 
2023-01-11T21:05:10.5105725Z async_compile.wait(globals())
2023-01-11T21:05:10.5105795Z del async_compile
2023-01-11T21:05:10.5105800Z 
2023-01-11T21:05:10.5105856Z def call(args):
2023-01-11T21:05:10.5105922Z     arg0_1, = args
2023-01-11T21:05:10.5106023Z     args.clear()
2023-01-11T21:05:10.5106217Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5106404Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5106593Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5106781Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.5106835Z     del arg0_1
2023-01-11T21:05:10.5106917Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.5106922Z 
2023-01-11T21:05:10.5106926Z 
2023-01-11T21:05:10.5106999Z if __name__ == "__main__":
2023-01-11T21:05:10.5107113Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5107237Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5107425Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5107533Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5107538Z 
2023-01-11T21:05:10.5107604Z ok (2.789s)
2023-01-11T21:05:10.5108052Z   test_inplace_activations_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5108167Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5108425Z [2023-01-11 20:52:46,375] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 225
2023-01-11T21:05:10.5108688Z [2023-01-11 20:52:49,183] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 225
2023-01-11T21:05:10.5108694Z 
2023-01-11T21:05:10.5108786Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5108857Z import torch
2023-01-11T21:05:10.5108926Z import random
2023-01-11T21:05:10.5109040Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5109158Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5109163Z 
2023-01-11T21:05:10.5109239Z aten = torch.ops.aten
2023-01-11T21:05:10.5109359Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5109448Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5109453Z 
2023-01-11T21:05:10.5109457Z 
2023-01-11T21:05:10.5109592Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5109796Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5109917Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5110050Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5110145Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.5110227Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.5110320Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.5110411Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.5110501Z                        float* __restrict__ out_ptr5,
2023-01-11T21:05:10.5110592Z                        float* __restrict__ out_ptr6)
2023-01-11T21:05:10.5110653Z {
2023-01-11T21:05:10.5110748Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5110795Z     {
2023-01-11T21:05:10.5110873Z         #pragma omp for 
2023-01-11T21:05:10.5110954Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.5111017Z         {
2023-01-11T21:05:10.5111079Z             {
2023-01-11T21:05:10.5111143Z                 {
2023-01-11T21:05:10.5111235Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5111327Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.5111418Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5111546Z                     auto tmp3 = static_cast<float>(3);
2023-01-11T21:05:10.5111638Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.5111741Z                     auto tmp5 = static_cast<float>(0.0);
2023-01-11T21:05:10.5111867Z                     auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp4, tmp5);
2023-01-11T21:05:10.5111973Z                     auto tmp7 = static_cast<float>(6.0);
2023-01-11T21:05:10.5112097Z                     auto tmp8 = (tmp7 != tmp7) ? tmp7 : std::min(tmp6, tmp7);
2023-01-11T21:05:10.5112173Z                     auto tmp9 = tmp2 * tmp8;
2023-01-11T21:05:10.5112275Z                     auto tmp10 = static_cast<float>(6);
2023-01-11T21:05:10.5112368Z                     auto tmp11 = tmp9 / tmp10;
2023-01-11T21:05:10.5112527Z                     auto tmp12 = static_cast<float>(-1.0);
2023-01-11T21:05:10.5112656Z                     auto tmp13 = (tmp12 != tmp12) ? tmp12 : std::max(tmp2, tmp12);
2023-01-11T21:05:10.5112763Z                     auto tmp14 = static_cast<float>(1.0);
2023-01-11T21:05:10.5112894Z                     auto tmp15 = (tmp14 != tmp14) ? tmp14 : std::min(tmp13, tmp14);
2023-01-11T21:05:10.5112995Z                     auto tmp16 = static_cast<float>(0);
2023-01-11T21:05:10.5113074Z                     auto tmp17 = tmp2 > tmp16;
2023-01-11T21:05:10.5113179Z                     auto tmp18 = static_cast<float>(0.01);
2023-01-11T21:05:10.5113270Z                     auto tmp19 = tmp2 * tmp18;
2023-01-11T21:05:10.5113368Z                     auto tmp20 = tmp17 ? tmp2 : tmp19;
2023-01-11T21:05:10.5113515Z                     auto tmp21 = std::exp(-tmp2);
2023-01-11T21:05:10.5113606Z                     auto tmp22 = 1 / (1 + tmp21);
2023-01-11T21:05:10.5113697Z                     auto tmp23 = tmp2 * tmp22;
2023-01-11T21:05:10.5113785Z                     auto tmp24 = std::log1p(tmp2);
2023-01-11T21:05:10.5113887Z                     auto tmp25 = static_cast<bool>(0);
2023-01-11T21:05:10.5113991Z                     auto tmp26 = static_cast<float>(99.0);
2023-01-11T21:05:10.5114091Z                     auto tmp27 = tmp25 ? tmp26 : tmp2;
2023-01-11T21:05:10.5114190Z                     auto tmp28 = static_cast<bool>(1);
2023-01-11T21:05:10.5114287Z                     auto tmp29 = tmp28 ? tmp26 : tmp2;
2023-01-11T21:05:10.5114371Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.5114443Z                     out_ptr1[i0] = tmp15;
2023-01-11T21:05:10.5114525Z                     out_ptr2[i0] = tmp20;
2023-01-11T21:05:10.5114609Z                     out_ptr3[i0] = tmp23;
2023-01-11T21:05:10.5114689Z                     out_ptr4[i0] = tmp24;
2023-01-11T21:05:10.5114769Z                     out_ptr5[i0] = tmp27;
2023-01-11T21:05:10.5114850Z                     out_ptr6[i0] = tmp29;
2023-01-11T21:05:10.5114913Z                 }
2023-01-11T21:05:10.5114991Z             }
2023-01-11T21:05:10.5115053Z         }
2023-01-11T21:05:10.5115112Z     }
2023-01-11T21:05:10.5115171Z }
2023-01-11T21:05:10.5115250Z ''')
2023-01-11T21:05:10.5115255Z 
2023-01-11T21:05:10.5115259Z 
2023-01-11T21:05:10.5115352Z async_compile.wait(globals())
2023-01-11T21:05:10.5115423Z del async_compile
2023-01-11T21:05:10.5115428Z 
2023-01-11T21:05:10.5115496Z def call(args):
2023-01-11T21:05:10.5115551Z     arg0_1, = args
2023-01-11T21:05:10.5115621Z     args.clear()
2023-01-11T21:05:10.5115816Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5116005Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5116189Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5116373Z     buf3 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5116556Z     buf4 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5116729Z     buf5 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5116910Z     buf6 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5117225Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:05:10.5117296Z     del arg0_1
2023-01-11T21:05:10.5117403Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, )
2023-01-11T21:05:10.5117409Z 
2023-01-11T21:05:10.5117413Z 
2023-01-11T21:05:10.5117490Z if __name__ == "__main__":
2023-01-11T21:05:10.5117602Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5117724Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5117915Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5118013Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5118019Z 
2023-01-11T21:05:10.5118086Z ok (3.185s)
2023-01-11T21:05:10.5118418Z   test_inplace_add_cpu (__main__.CpuTests) ... [2023-01-11 20:52:49,217] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 226
2023-01-11T21:05:10.5118683Z [2023-01-11 20:52:52,029] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 226
2023-01-11T21:05:10.5118688Z 
2023-01-11T21:05:10.5118781Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5118850Z import torch
2023-01-11T21:05:10.5118918Z import random
2023-01-11T21:05:10.5119031Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5119138Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5119143Z 
2023-01-11T21:05:10.5119220Z aten = torch.ops.aten
2023-01-11T21:05:10.5119353Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5119445Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5119453Z 
2023-01-11T21:05:10.5119458Z 
2023-01-11T21:05:10.5119591Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5119796Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5119919Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5120023Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5120109Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5120168Z {
2023-01-11T21:05:10.5120264Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5120327Z     {
2023-01-11T21:05:10.5120405Z         #pragma omp for 
2023-01-11T21:05:10.5120486Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.5120547Z         {
2023-01-11T21:05:10.5120864Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5120998Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.5121140Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5121234Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5121297Z         }
2023-01-11T21:05:10.5121393Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5121478Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.5121527Z         {
2023-01-11T21:05:10.5121610Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5121695Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5121780Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5121859Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5121922Z         }
2023-01-11T21:05:10.5121982Z     }
2023-01-11T21:05:10.5122028Z }
2023-01-11T21:05:10.5122111Z ''')
2023-01-11T21:05:10.5122116Z 
2023-01-11T21:05:10.5122121Z 
2023-01-11T21:05:10.5122211Z async_compile.wait(globals())
2023-01-11T21:05:10.5122284Z del async_compile
2023-01-11T21:05:10.5122291Z 
2023-01-11T21:05:10.5122362Z def call(args):
2023-01-11T21:05:10.5122436Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5122510Z     args.clear()
2023-01-11T21:05:10.5122658Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:05:10.5122726Z     del arg1_1
2023-01-11T21:05:10.5122839Z     return (arg0_1, )
2023-01-11T21:05:10.5122845Z 
2023-01-11T21:05:10.5122849Z 
2023-01-11T21:05:10.5122927Z if __name__ == "__main__":
2023-01-11T21:05:10.5123040Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5123162Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5123361Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5123554Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5123656Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5123661Z 
2023-01-11T21:05:10.5123727Z ok (2.838s)
2023-01-11T21:05:10.5124075Z   test_inplace_mixed_dtype_ops_cpu (__main__.CpuTests) ... [2023-01-11 20:52:52,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 227
2023-01-11T21:05:10.5124341Z [2023-01-11 20:52:54,923] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 227
2023-01-11T21:05:10.5124348Z 
2023-01-11T21:05:10.5124442Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5124511Z import torch
2023-01-11T21:05:10.5124581Z import random
2023-01-11T21:05:10.5124697Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5124805Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5124825Z 
2023-01-11T21:05:10.5124889Z aten = torch.ops.aten
2023-01-11T21:05:10.5125023Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5125114Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5125119Z 
2023-01-11T21:05:10.5125123Z 
2023-01-11T21:05:10.5125256Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5125460Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5125578Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5125682Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5125777Z                        const double* __restrict__ in_ptr1)
2023-01-11T21:05:10.5125837Z {
2023-01-11T21:05:10.5125933Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5125996Z     {
2023-01-11T21:05:10.5126072Z         #pragma omp for 
2023-01-11T21:05:10.5126155Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5126217Z         {
2023-01-11T21:05:10.5126266Z             {
2023-01-11T21:05:10.5126329Z                 {
2023-01-11T21:05:10.5126422Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5126511Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5126618Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.5126710Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.5126852Z                     auto tmp4 = static_cast<double>(tmp3);
2023-01-11T21:05:10.5126927Z                     auto tmp5 = tmp4 + tmp1;
2023-01-11T21:05:10.5127031Z                     auto tmp6 = static_cast<float>(tmp5);
2023-01-11T21:05:10.5127140Z                     auto tmp7 = static_cast<double>(tmp6);
2023-01-11T21:05:10.5127230Z                     auto tmp8 = tmp7 * tmp1;
2023-01-11T21:05:10.5127336Z                     auto tmp9 = static_cast<float>(tmp8);
2023-01-11T21:05:10.5127427Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.5127492Z                 }
2023-01-11T21:05:10.5127541Z             }
2023-01-11T21:05:10.5127603Z         }
2023-01-11T21:05:10.5127666Z     }
2023-01-11T21:05:10.5127726Z }
2023-01-11T21:05:10.5127808Z ''')
2023-01-11T21:05:10.5127813Z 
2023-01-11T21:05:10.5127818Z 
2023-01-11T21:05:10.5127906Z async_compile.wait(globals())
2023-01-11T21:05:10.5127979Z del async_compile
2023-01-11T21:05:10.5127984Z 
2023-01-11T21:05:10.5128040Z def call(args):
2023-01-11T21:05:10.5128117Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5128189Z     args.clear()
2023-01-11T21:05:10.5128382Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5128495Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5128658Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()))
2023-01-11T21:05:10.5128725Z     del arg0_1
2023-01-11T21:05:10.5128776Z     del arg1_1
2023-01-11T21:05:10.5128847Z     return (buf1, )
2023-01-11T21:05:10.5128852Z 
2023-01-11T21:05:10.5128856Z 
2023-01-11T21:05:10.5128931Z if __name__ == "__main__":
2023-01-11T21:05:10.5129046Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5129169Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5129368Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5129564Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5129681Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5129686Z 
2023-01-11T21:05:10.5129752Z ok (2.895s)
2023-01-11T21:05:10.5130074Z   test_input_mutation1_cpu (__main__.CpuTests) ... [2023-01-11 20:52:54,969] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 228
2023-01-11T21:05:10.5130283Z [2023-01-11 20:52:54,988] torch._inductor.scheduler: [DEBUG] remove_buffer('buf0')
2023-01-11T21:05:10.5130482Z [2023-01-11 20:52:54,990] torch._inductor.scheduler: [DEBUG] remove_buffer('buf0')
2023-01-11T21:05:10.5130745Z [2023-01-11 20:52:57,823] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 228
2023-01-11T21:05:10.5130751Z 
2023-01-11T21:05:10.5130844Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5130913Z import torch
2023-01-11T21:05:10.5130983Z import random
2023-01-11T21:05:10.5131098Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5131204Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5131212Z 
2023-01-11T21:05:10.5131289Z aten = torch.ops.aten
2023-01-11T21:05:10.5131421Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5131513Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5131518Z 
2023-01-11T21:05:10.5131522Z 
2023-01-11T21:05:10.5131653Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5131857Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5131973Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5132073Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.5132157Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.5132218Z {
2023-01-11T21:05:10.5132314Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5132375Z     {
2023-01-11T21:05:10.5132450Z         #pragma omp for 
2023-01-11T21:05:10.5132530Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5132611Z         {
2023-01-11T21:05:10.5132743Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5132875Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.5132961Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5133043Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:05:10.5133172Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.5133253Z             auto tmp5 = tmp2 + tmp4;
2023-01-11T21:05:10.5133333Z             auto tmp6 = tmp3 / tmp5;
2023-01-11T21:05:10.5133411Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.5133501Z             tmp6.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.5133561Z         }
2023-01-11T21:05:10.5133655Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5133737Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5133797Z         {
2023-01-11T21:05:10.5133884Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.5133971Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.5134051Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5134131Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:05:10.5134277Z             auto tmp4 = static_cast<float>(2);
2023-01-11T21:05:10.5134362Z             auto tmp5 = tmp2 + tmp4;
2023-01-11T21:05:10.5134440Z             auto tmp6 = tmp3 / tmp5;
2023-01-11T21:05:10.5134518Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.5134581Z             out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.5134640Z         }
2023-01-11T21:05:10.5134700Z     }
2023-01-11T21:05:10.5134761Z }
2023-01-11T21:05:10.5134840Z ''')
2023-01-11T21:05:10.5134844Z 
2023-01-11T21:05:10.5134848Z 
2023-01-11T21:05:10.5134936Z async_compile.wait(globals())
2023-01-11T21:05:10.5135011Z del async_compile
2023-01-11T21:05:10.5135017Z 
2023-01-11T21:05:10.5135072Z def call(args):
2023-01-11T21:05:10.5135144Z     arg0_1, = args
2023-01-11T21:05:10.5135214Z     args.clear()
2023-01-11T21:05:10.5135413Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5135575Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.5135646Z     del arg0_1
2023-01-11T21:05:10.5135717Z     return (buf2, )
2023-01-11T21:05:10.5135722Z 
2023-01-11T21:05:10.5135726Z 
2023-01-11T21:05:10.5135800Z if __name__ == "__main__":
2023-01-11T21:05:10.5135900Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5136025Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5136223Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5136329Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5136334Z 
2023-01-11T21:05:10.5136404Z ok (2.902s)
2023-01-11T21:05:10.5136739Z   test_input_mutation2_cpu (__main__.CpuTests) ... [2023-01-11 20:52:57,963] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 229
2023-01-11T21:05:10.5136961Z [2023-01-11 20:52:57,968] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:05:10.5137054Z   target: aten.expand_copy.default
2023-01-11T21:05:10.5137131Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:05:10.5137203Z     Pointwise(
2023-01-11T21:05:10.5137288Z       'cpu',
2023-01-11T21:05:10.5137361Z       torch.float32,
2023-01-11T21:05:10.5137459Z       tmp0 = constant(66.0, torch.float32)
2023-01-11T21:05:10.5137527Z       return tmp0
2023-01-11T21:05:10.5137575Z       ,
2023-01-11T21:05:10.5137642Z       ranges=[1],
2023-01-11T21:05:10.5137750Z       origins={lift_fresh_copy, _tensor_constant0}
2023-01-11T21:05:10.5137811Z     )
2023-01-11T21:05:10.5137870Z   ))
2023-01-11T21:05:10.5137936Z   args[1]: [64]
2023-01-11T21:05:10.5138199Z [2023-01-11 20:52:57,982] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten.expand_copy.default
2023-01-11T21:05:10.5138446Z [2023-01-11 20:53:00,781] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 229
2023-01-11T21:05:10.5138572Z 
2023-01-11T21:05:10.5138656Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5138727Z import torch
2023-01-11T21:05:10.5138802Z import random
2023-01-11T21:05:10.5138918Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5139038Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5139042Z 
2023-01-11T21:05:10.5139121Z aten = torch.ops.aten
2023-01-11T21:05:10.5139254Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5139331Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5139335Z 
2023-01-11T21:05:10.5139354Z 
2023-01-11T21:05:10.5139479Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5139682Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5139802Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5139904Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5140004Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5140066Z {
2023-01-11T21:05:10.5140163Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5140240Z     {
2023-01-11T21:05:10.5140318Z         #pragma omp for 
2023-01-11T21:05:10.5140400Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5140463Z         {
2023-01-11T21:05:10.5140595Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5140727Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.5140811Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5140889Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5140951Z         }
2023-01-11T21:05:10.5141043Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5141123Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5141183Z         {
2023-01-11T21:05:10.5141263Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5141364Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.5141432Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5141510Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5141573Z         }
2023-01-11T21:05:10.5141649Z         #pragma omp single
2023-01-11T21:05:10.5141710Z         {
2023-01-11T21:05:10.5141776Z             {
2023-01-11T21:05:10.5141826Z                 {
2023-01-11T21:05:10.5141934Z                     auto tmp0 = static_cast<float>(66.0);
2023-01-11T21:05:10.5142015Z                     out_ptr1[0] = tmp0;
2023-01-11T21:05:10.5142079Z                 }
2023-01-11T21:05:10.5142146Z             }
2023-01-11T21:05:10.5142207Z         }
2023-01-11T21:05:10.5142267Z     }
2023-01-11T21:05:10.5142312Z }
2023-01-11T21:05:10.5142391Z ''')
2023-01-11T21:05:10.5142396Z 
2023-01-11T21:05:10.5142400Z 
2023-01-11T21:05:10.5142532Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:05:10.5142733Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5142852Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5142951Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5143011Z {
2023-01-11T21:05:10.5143106Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5143153Z     {
2023-01-11T21:05:10.5143229Z         #pragma omp for 
2023-01-11T21:05:10.5143311Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5143373Z         {
2023-01-11T21:05:10.5143501Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5143631Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.5143713Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5143790Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5143851Z         }
2023-01-11T21:05:10.5143944Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5144024Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5144121Z         {
2023-01-11T21:05:10.5144204Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5144302Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.5144373Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5144452Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5144514Z         }
2023-01-11T21:05:10.5144574Z     }
2023-01-11T21:05:10.5144633Z }
2023-01-11T21:05:10.5144710Z ''')
2023-01-11T21:05:10.5144715Z 
2023-01-11T21:05:10.5144720Z 
2023-01-11T21:05:10.5144807Z async_compile.wait(globals())
2023-01-11T21:05:10.5144865Z del async_compile
2023-01-11T21:05:10.5144869Z 
2023-01-11T21:05:10.5144938Z def call(args):
2023-01-11T21:05:10.5145010Z     primals_1, = args
2023-01-11T21:05:10.5145083Z     args.clear()
2023-01-11T21:05:10.5145279Z     buf0 = empty_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5145468Z     buf1 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5145639Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5145697Z     del primals_1
2023-01-11T21:05:10.5145854Z     buf2 = torch.ops.aten.expand_copy.default(buf1, [64])
2023-01-11T21:05:10.5145922Z     del buf1
2023-01-11T21:05:10.5145989Z     buf3 = buf2
2023-01-11T21:05:10.5146083Z     assert_size_stride(buf3, (64, ), (1, ))
2023-01-11T21:05:10.5146146Z     del buf2
2023-01-11T21:05:10.5146340Z     buf4 = empty_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5146456Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.5146564Z     return (as_strided(buf3, (1, 64), (64, 1)), buf0, buf4, )
2023-01-11T21:05:10.5146570Z 
2023-01-11T21:05:10.5146574Z 
2023-01-11T21:05:10.5146648Z if __name__ == "__main__":
2023-01-11T21:05:10.5146760Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5146882Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5147087Z     primals_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5147197Z     print_performance(lambda: call([primals_1]))
2023-01-11T21:05:10.5147202Z 
2023-01-11T21:05:10.5147270Z ok (2.959s)
2023-01-11T21:05:10.5147593Z   test_input_mutation3_cpu (__main__.CpuTests) ... [2023-01-11 20:53:00,886] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 230
2023-01-11T21:05:10.5147855Z [2023-01-11 20:53:03,716] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 230
2023-01-11T21:05:10.5147860Z 
2023-01-11T21:05:10.5147953Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5148020Z import torch
2023-01-11T21:05:10.5148090Z import random
2023-01-11T21:05:10.5148202Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5148321Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5148326Z 
2023-01-11T21:05:10.5148403Z aten = torch.ops.aten
2023-01-11T21:05:10.5148522Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5148612Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5148617Z 
2023-01-11T21:05:10.5148621Z 
2023-01-11T21:05:10.5148754Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5148957Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5149075Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5149174Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5149234Z {
2023-01-11T21:05:10.5149329Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5149376Z     {
2023-01-11T21:05:10.5149451Z         #pragma omp for 
2023-01-11T21:05:10.5149532Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5149594Z         {
2023-01-11T21:05:10.5149724Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5149856Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.5149973Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5150050Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5150111Z         }
2023-01-11T21:05:10.5150207Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5150288Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5150349Z         {
2023-01-11T21:05:10.5150432Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5150528Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.5150597Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5150676Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5150736Z         }
2023-01-11T21:05:10.5150809Z         #pragma omp for 
2023-01-11T21:05:10.5150890Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5150949Z         {
2023-01-11T21:05:10.5151081Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5151200Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.5151282Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5151372Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5151459Z         }
2023-01-11T21:05:10.5151555Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5151636Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5151697Z         {
2023-01-11T21:05:10.5151765Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5151862Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.5151946Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5152024Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5152086Z         }
2023-01-11T21:05:10.5152159Z         #pragma omp for 
2023-01-11T21:05:10.5152238Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5152286Z         {
2023-01-11T21:05:10.5152419Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5152556Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:05:10.5152646Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5152707Z         }
2023-01-11T21:05:10.5152802Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5152883Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5152930Z         {
2023-01-11T21:05:10.5153013Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5153150Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.5153231Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.5153309Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5153369Z         }
2023-01-11T21:05:10.5153442Z         #pragma omp for 
2023-01-11T21:05:10.5153508Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5153569Z         {
2023-01-11T21:05:10.5153699Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5153830Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.5153914Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5154003Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5154064Z         }
2023-01-11T21:05:10.5154145Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5154229Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5154289Z         {
2023-01-11T21:05:10.5154371Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5154468Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.5154550Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5154628Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5154675Z         }
2023-01-11T21:05:10.5154750Z         #pragma omp for 
2023-01-11T21:05:10.5154828Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5154890Z         {
2023-01-11T21:05:10.5155022Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5155153Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(4));
2023-01-11T21:05:10.5155268Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5155344Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5155404Z         }
2023-01-11T21:05:10.5155502Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5155582Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5155643Z         {
2023-01-11T21:05:10.5155726Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5155823Z             auto tmp1 = static_cast<float>(4);
2023-01-11T21:05:10.5155893Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5155972Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5156033Z         }
2023-01-11T21:05:10.5156108Z         #pragma omp for 
2023-01-11T21:05:10.5156189Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5156250Z         {
2023-01-11T21:05:10.5156383Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5156496Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:05:10.5156588Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5156649Z         }
2023-01-11T21:05:10.5156743Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5156856Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5156917Z         {
2023-01-11T21:05:10.5156998Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5157070Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5157148Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5157209Z         }
2023-01-11T21:05:10.5157270Z     }
2023-01-11T21:05:10.5157331Z }
2023-01-11T21:05:10.5157410Z ''')
2023-01-11T21:05:10.5157417Z 
2023-01-11T21:05:10.5157422Z 
2023-01-11T21:05:10.5157511Z async_compile.wait(globals())
2023-01-11T21:05:10.5157570Z del async_compile
2023-01-11T21:05:10.5157588Z 
2023-01-11T21:05:10.5157644Z def call(args):
2023-01-11T21:05:10.5157712Z     arg0_1, = args
2023-01-11T21:05:10.5157784Z     args.clear()
2023-01-11T21:05:10.5157918Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:05:10.5158019Z     return (as_strided(arg0_1, (64, ), (1, )), )
2023-01-11T21:05:10.5158024Z 
2023-01-11T21:05:10.5158028Z 
2023-01-11T21:05:10.5158105Z if __name__ == "__main__":
2023-01-11T21:05:10.5158219Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5158327Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5158531Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5158638Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5158643Z 
2023-01-11T21:05:10.5158709Z ok (2.932s)
2023-01-11T21:05:10.5159044Z   test_input_mutation4_cpu (__main__.CpuTests) ... [2023-01-11 20:53:03,744] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 231
2023-01-11T21:05:10.5159309Z [2023-01-11 20:53:06,514] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 231
2023-01-11T21:05:10.5159316Z 
2023-01-11T21:05:10.5159410Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5159479Z import torch
2023-01-11T21:05:10.5159534Z import random
2023-01-11T21:05:10.5159649Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5159771Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5159776Z 
2023-01-11T21:05:10.5159852Z aten = torch.ops.aten
2023-01-11T21:05:10.5159986Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5160075Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5160081Z 
2023-01-11T21:05:10.5160085Z 
2023-01-11T21:05:10.5160216Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5160421Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5160527Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5160748Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5160809Z {
2023-01-11T21:05:10.5160965Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5161026Z     {
2023-01-11T21:05:10.5161104Z         #pragma omp for 
2023-01-11T21:05:10.5161187Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5161237Z         {
2023-01-11T21:05:10.5161370Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5161497Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:05:10.5161591Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5161654Z         }
2023-01-11T21:05:10.5161749Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5161830Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5161878Z         {
2023-01-11T21:05:10.5161962Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.5162048Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5162128Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5162193Z         }
2023-01-11T21:05:10.5162255Z     }
2023-01-11T21:05:10.5162318Z }
2023-01-11T21:05:10.5162384Z ''')
2023-01-11T21:05:10.5162389Z 
2023-01-11T21:05:10.5162394Z 
2023-01-11T21:05:10.5162481Z async_compile.wait(globals())
2023-01-11T21:05:10.5162552Z del async_compile
2023-01-11T21:05:10.5162607Z 
2023-01-11T21:05:10.5162679Z def call(args):
2023-01-11T21:05:10.5162746Z     arg0_1, = args
2023-01-11T21:05:10.5162815Z     args.clear()
2023-01-11T21:05:10.5162946Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg0_1.data_ptr()))
2023-01-11T21:05:10.5163008Z     return (arg0_1, )
2023-01-11T21:05:10.5163025Z 
2023-01-11T21:05:10.5163029Z 
2023-01-11T21:05:10.5163090Z if __name__ == "__main__":
2023-01-11T21:05:10.5163201Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5163323Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5163521Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5163628Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5163635Z 
2023-01-11T21:05:10.5163700Z ok (2.798s)
2023-01-11T21:05:10.5164159Z   test_invalid_operand_issue1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5164286Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5164546Z [2023-01-11 20:53:07,808] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 232
2023-01-11T21:05:10.5164797Z [2023-01-11 20:53:10,567] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 232
2023-01-11T21:05:10.5164817Z 
2023-01-11T21:05:10.5164898Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5164967Z import torch
2023-01-11T21:05:10.5165041Z import random
2023-01-11T21:05:10.5165157Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5165277Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5165283Z 
2023-01-11T21:05:10.5165362Z aten = torch.ops.aten
2023-01-11T21:05:10.5165494Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5165572Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5165577Z 
2023-01-11T21:05:10.5165581Z 
2023-01-11T21:05:10.5165712Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5165916Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5166032Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5166134Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.5166233Z                        const long* __restrict__ in_ptr2,
2023-01-11T21:05:10.5166337Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:05:10.5166474Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5166521Z {
2023-01-11T21:05:10.5166616Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5166676Z     {
2023-01-11T21:05:10.5166754Z         #pragma omp for 
2023-01-11T21:05:10.5166833Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5166894Z         {
2023-01-11T21:05:10.5166959Z             #pragma GCC ivdep
2023-01-11T21:05:10.5167045Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:05:10.5167107Z             {
2023-01-11T21:05:10.5167186Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5167274Z                 for(long i2=0; i2<768; i2+=1)
2023-01-11T21:05:10.5167338Z                 {
2023-01-11T21:05:10.5167403Z                     {
2023-01-11T21:05:10.5167457Z                         {
2023-01-11T21:05:10.5167552Z                             auto tmp3 = in_ptr0[i0];
2023-01-11T21:05:10.5167656Z                             auto tmp8 = in_ptr2[i1 + (128*i0)];
2023-01-11T21:05:10.5167765Z                             auto tmp0 = static_cast<int>(i1);
2023-01-11T21:05:10.5167868Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:05:10.5167964Z                             auto tmp2 = tmp0 == tmp1;
2023-01-11T21:05:10.5168097Z                             auto tmp4 = static_cast<int>(1);
2023-01-11T21:05:10.5168182Z                             auto tmp5 = tmp0 >= tmp4;
2023-01-11T21:05:10.5168266Z                             auto tmp6 = 0;
2023-01-11T21:05:10.5168340Z                             if(tmp5)
2023-01-11T21:05:10.5168409Z                             {
2023-01-11T21:05:10.5168587Z                                 auto tmp7 = in_ptr1[(-1) + i1 + (127*i0)];
2023-01-11T21:05:10.5168672Z                                 tmp6 = tmp7;
2023-01-11T21:05:10.5168740Z                             }
2023-01-11T21:05:10.5168841Z                             auto tmp9 = tmp5 ? tmp6 : tmp8;
2023-01-11T21:05:10.5168931Z                             auto tmp10 = tmp2 ? tmp3 : tmp9;
2023-01-11T21:05:10.5169040Z                             auto tmp11 = in_ptr3[i2 + (768*tmp10)];
2023-01-11T21:05:10.5169145Z                             out_ptr0[i2 + (768*i1) + (98304*i0)] = tmp11;
2023-01-11T21:05:10.5169214Z                         }
2023-01-11T21:05:10.5169279Z                     }
2023-01-11T21:05:10.5169343Z                 }
2023-01-11T21:05:10.5169405Z             }
2023-01-11T21:05:10.5169452Z         }
2023-01-11T21:05:10.5169513Z     }
2023-01-11T21:05:10.5169571Z }
2023-01-11T21:05:10.5169648Z ''')
2023-01-11T21:05:10.5169653Z 
2023-01-11T21:05:10.5169657Z 
2023-01-11T21:05:10.5169745Z async_compile.wait(globals())
2023-01-11T21:05:10.5169815Z del async_compile
2023-01-11T21:05:10.5169820Z 
2023-01-11T21:05:10.5169889Z def call(args):
2023-01-11T21:05:10.5169973Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
2023-01-11T21:05:10.5170042Z     args.clear()
2023-01-11T21:05:10.5170259Z     buf0 = empty_strided((8, 128, 768), (98304, 768, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5170470Z     kernel_cpp_0(c_void_p(arg3_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5170537Z     del arg0_1
2023-01-11T21:05:10.5170603Z     del arg2_1
2023-01-11T21:05:10.5170666Z     del arg3_1
2023-01-11T21:05:10.5170717Z     del arg4_1
2023-01-11T21:05:10.5170786Z     return (buf0, )
2023-01-11T21:05:10.5170791Z 
2023-01-11T21:05:10.5170796Z 
2023-01-11T21:05:10.5170871Z if __name__ == "__main__":
2023-01-11T21:05:10.5170986Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5171107Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5171311Z     arg0_1 = rand_strided((50005, 768), (768, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5171507Z     arg1_1 = rand_strided((8, 128), (128, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5171701Z     arg2_1 = rand_strided((8, 127), (127, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5171908Z     arg3_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5172098Z     arg4_1 = rand_strided((8, 128), (128, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5172234Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1]))
2023-01-11T21:05:10.5172239Z 
2023-01-11T21:05:10.5172304Z ok (11.333s)
2023-01-11T21:05:10.5172791Z   test_isinf2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5172971Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5173258Z [2023-01-11 20:53:17,882] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 233
2023-01-11T21:05:10.5173525Z [2023-01-11 20:53:20,617] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 233
2023-01-11T21:05:10.5173531Z 
2023-01-11T21:05:10.5173626Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5173755Z import torch
2023-01-11T21:05:10.5173814Z import random
2023-01-11T21:05:10.5173928Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5174048Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5174053Z 
2023-01-11T21:05:10.5174130Z aten = torch.ops.aten
2023-01-11T21:05:10.5174263Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5174354Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5174359Z 
2023-01-11T21:05:10.5174363Z 
2023-01-11T21:05:10.5174497Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5174700Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5174806Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5174907Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:05:10.5174969Z {
2023-01-11T21:05:10.5175067Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5175129Z     {
2023-01-11T21:05:10.5175208Z         #pragma omp for 
2023-01-11T21:05:10.5175276Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:05:10.5175339Z         {
2023-01-11T21:05:10.5175402Z             {
2023-01-11T21:05:10.5175469Z                 {
2023-01-11T21:05:10.5175563Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5175668Z                     auto tmp1 = static_cast<long>(i0);
2023-01-11T21:05:10.5175771Z                     auto tmp2 = static_cast<long>(2);
2023-01-11T21:05:10.5175847Z                     auto tmp3 = tmp1 < tmp2;
2023-01-11T21:05:10.5175946Z                     auto tmp4 = static_cast<long>(1);
2023-01-11T21:05:10.5176036Z                     auto tmp5 = tmp1 < tmp4;
2023-01-11T21:05:10.5176141Z                     auto tmp6 = static_cast<float>(1.0);
2023-01-11T21:05:10.5176270Z                     auto tmp7 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5176366Z                     auto tmp8 = tmp5 ? tmp6 : tmp7;
2023-01-11T21:05:10.5176465Z                     auto tmp9 = static_cast<long>(3);
2023-01-11T21:05:10.5176556Z                     auto tmp10 = tmp1 < tmp9;
2023-01-11T21:05:10.5176646Z                     auto tmp11 = static_cast<float>(2.0);
2023-01-11T21:05:10.5176748Z                     auto tmp12 = static_cast<long>(4);
2023-01-11T21:05:10.5176837Z                     auto tmp13 = tmp1 < tmp12;
2023-01-11T21:05:10.5177076Z                     auto tmp14 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5177202Z                     auto tmp15 = std::numeric_limits<float>::quiet_NaN();
2023-01-11T21:05:10.5177301Z                     auto tmp16 = tmp13 ? tmp14 : tmp15;
2023-01-11T21:05:10.5177399Z                     auto tmp17 = tmp10 ? tmp11 : tmp16;
2023-01-11T21:05:10.5177497Z                     auto tmp18 = tmp3 ? tmp8 : tmp17;
2023-01-11T21:05:10.5177609Z                     auto tmp19 = tmp0 == tmp18;
2023-01-11T21:05:10.5177691Z                     out_ptr0[i0] = tmp19;
2023-01-11T21:05:10.5177755Z                 }
2023-01-11T21:05:10.5177819Z             }
2023-01-11T21:05:10.5177881Z         }
2023-01-11T21:05:10.5177941Z     }
2023-01-11T21:05:10.5177987Z }
2023-01-11T21:05:10.5178064Z ''')
2023-01-11T21:05:10.5178069Z 
2023-01-11T21:05:10.5178073Z 
2023-01-11T21:05:10.5178162Z async_compile.wait(globals())
2023-01-11T21:05:10.5178232Z del async_compile
2023-01-11T21:05:10.5178237Z 
2023-01-11T21:05:10.5178305Z def call(args):
2023-01-11T21:05:10.5178373Z     arg0_1, = args
2023-01-11T21:05:10.5178443Z     args.clear()
2023-01-11T21:05:10.5178709Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5178829Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5178898Z     del arg0_1
2023-01-11T21:05:10.5178971Z     return (buf0, )
2023-01-11T21:05:10.5178976Z 
2023-01-11T21:05:10.5178980Z 
2023-01-11T21:05:10.5179055Z if __name__ == "__main__":
2023-01-11T21:05:10.5179170Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5179326Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5179519Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5179613Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5179632Z 
2023-01-11T21:05:10.5179684Z ok (2.774s)
2023-01-11T21:05:10.5180118Z   test_isinf_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5180244Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5180512Z [2023-01-11 20:53:20,671] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 234
2023-01-11T21:05:10.5180777Z [2023-01-11 20:53:23,361] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 234
2023-01-11T21:05:10.5181176Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5181299Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5181553Z [2023-01-11 20:53:23,403] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 235
2023-01-11T21:05:10.5181813Z [2023-01-11 20:53:26,181] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 235
2023-01-11T21:05:10.5181821Z 
2023-01-11T21:05:10.5181913Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5181970Z import torch
2023-01-11T21:05:10.5182039Z import random
2023-01-11T21:05:10.5182154Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5182274Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5182279Z 
2023-01-11T21:05:10.5182356Z aten = torch.ops.aten
2023-01-11T21:05:10.5182488Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5182578Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5182583Z 
2023-01-11T21:05:10.5182587Z 
2023-01-11T21:05:10.5182718Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5182909Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5183025Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5183121Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:05:10.5183247Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:05:10.5183309Z {
2023-01-11T21:05:10.5183405Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5183465Z     {
2023-01-11T21:05:10.5183530Z         #pragma omp for 
2023-01-11T21:05:10.5183609Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:05:10.5183670Z         {
2023-01-11T21:05:10.5183733Z             {
2023-01-11T21:05:10.5183796Z                 {
2023-01-11T21:05:10.5183888Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5183988Z                     auto tmp1 = std::isinf(tmp0);
2023-01-11T21:05:10.5184073Z                     auto tmp2 = std::isnan(tmp0);
2023-01-11T21:05:10.5184156Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5184270Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.5184356Z                 }
2023-01-11T21:05:10.5184418Z             }
2023-01-11T21:05:10.5184478Z         }
2023-01-11T21:05:10.5184537Z     }
2023-01-11T21:05:10.5184586Z }
2023-01-11T21:05:10.5184665Z ''')
2023-01-11T21:05:10.5184671Z 
2023-01-11T21:05:10.5184675Z 
2023-01-11T21:05:10.5184762Z async_compile.wait(globals())
2023-01-11T21:05:10.5184832Z del async_compile
2023-01-11T21:05:10.5184837Z 
2023-01-11T21:05:10.5184971Z def call(args):
2023-01-11T21:05:10.5185041Z     arg0_1, = args
2023-01-11T21:05:10.5185109Z     args.clear()
2023-01-11T21:05:10.5185283Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5185464Z     buf1 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5185628Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5185696Z     del arg0_1
2023-01-11T21:05:10.5185775Z     return (buf0, buf1, )
2023-01-11T21:05:10.5185780Z 
2023-01-11T21:05:10.5185784Z 
2023-01-11T21:05:10.5185860Z if __name__ == "__main__":
2023-01-11T21:05:10.5185975Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5186101Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5186281Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5186389Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5186397Z 
2023-01-11T21:05:10.5186402Z 
2023-01-11T21:05:10.5186495Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5186564Z import torch
2023-01-11T21:05:10.5186634Z import random
2023-01-11T21:05:10.5186749Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5186869Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5186873Z 
2023-01-11T21:05:10.5186952Z aten = torch.ops.aten
2023-01-11T21:05:10.5187070Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5187162Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5187167Z 
2023-01-11T21:05:10.5187172Z 
2023-01-11T21:05:10.5187304Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5187509Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5187631Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.5187729Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:05:10.5187824Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:05:10.5187885Z {
2023-01-11T21:05:10.5187968Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5188028Z     {
2023-01-11T21:05:10.5188104Z         #pragma omp for 
2023-01-11T21:05:10.5188187Z         for(long i0=0; i0<5; i0+=1)
2023-01-11T21:05:10.5188249Z         {
2023-01-11T21:05:10.5188311Z             {
2023-01-11T21:05:10.5188360Z                 {
2023-01-11T21:05:10.5188453Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5188554Z                     auto tmp1 = std::isinf(tmp0);
2023-01-11T21:05:10.5188653Z                     auto tmp2 = std::isnan(tmp0);
2023-01-11T21:05:10.5188737Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5188860Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.5188923Z                 }
2023-01-11T21:05:10.5188971Z             }
2023-01-11T21:05:10.5189033Z         }
2023-01-11T21:05:10.5189095Z     }
2023-01-11T21:05:10.5189157Z }
2023-01-11T21:05:10.5189236Z ''')
2023-01-11T21:05:10.5189242Z 
2023-01-11T21:05:10.5189246Z 
2023-01-11T21:05:10.5189337Z async_compile.wait(globals())
2023-01-11T21:05:10.5189407Z del async_compile
2023-01-11T21:05:10.5189412Z 
2023-01-11T21:05:10.5189481Z def call(args):
2023-01-11T21:05:10.5189536Z     arg0_1, = args
2023-01-11T21:05:10.5189606Z     args.clear()
2023-01-11T21:05:10.5189792Z     buf0 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5189973Z     buf1 = empty_strided((5, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5190138Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5190205Z     del arg0_1
2023-01-11T21:05:10.5190281Z     return (buf0, buf1, )
2023-01-11T21:05:10.5190288Z 
2023-01-11T21:05:10.5190293Z 
2023-01-11T21:05:10.5190354Z if __name__ == "__main__":
2023-01-11T21:05:10.5190466Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5190619Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5190813Z     arg0_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5190921Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5190925Z 
2023-01-11T21:05:10.5190992Z ok (5.562s)
2023-01-11T21:05:10.5191323Z   test_kernel_names_cpu (__main__.CpuTests) ... [2023-01-11 20:53:26,214] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 236
2023-01-11T21:05:10.5191593Z [2023-01-11 20:53:28,919] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 236
2023-01-11T21:05:10.5191599Z 
2023-01-11T21:05:10.5191690Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5191745Z import torch
2023-01-11T21:05:10.5191817Z import random
2023-01-11T21:05:10.5191929Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5192050Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5192055Z 
2023-01-11T21:05:10.5192135Z aten = torch.ops.aten
2023-01-11T21:05:10.5192268Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5192357Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5192361Z 
2023-01-11T21:05:10.5192365Z 
2023-01-11T21:05:10.5192484Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5192687Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5192805Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5192905Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5192964Z {
2023-01-11T21:05:10.5193059Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5193121Z     {
2023-01-11T21:05:10.5193185Z         #pragma omp for 
2023-01-11T21:05:10.5193266Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5193327Z         {
2023-01-11T21:05:10.5193388Z             {
2023-01-11T21:05:10.5193451Z                 {
2023-01-11T21:05:10.5193545Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5193647Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.5193724Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5193808Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5193871Z                 }
2023-01-11T21:05:10.5193933Z             }
2023-01-11T21:05:10.5193993Z         }
2023-01-11T21:05:10.5194052Z     }
2023-01-11T21:05:10.5194111Z }
2023-01-11T21:05:10.5194175Z ''')
2023-01-11T21:05:10.5194180Z 
2023-01-11T21:05:10.5194185Z 
2023-01-11T21:05:10.5194272Z async_compile.wait(globals())
2023-01-11T21:05:10.5194341Z del async_compile
2023-01-11T21:05:10.5194346Z 
2023-01-11T21:05:10.5194414Z def call(args):
2023-01-11T21:05:10.5194481Z     arg0_1, = args
2023-01-11T21:05:10.5194582Z     args.clear()
2023-01-11T21:05:10.5194770Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5194893Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5194959Z     del arg0_1
2023-01-11T21:05:10.5195028Z     return (buf0, )
2023-01-11T21:05:10.5195032Z 
2023-01-11T21:05:10.5195036Z 
2023-01-11T21:05:10.5195110Z if __name__ == "__main__":
2023-01-11T21:05:10.5195220Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5195342Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5195533Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5195638Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5195642Z 
2023-01-11T21:05:10.5195694Z ok (2.735s)
2023-01-11T21:05:10.5196186Z   test_kwargs_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5196316Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5196574Z [2023-01-11 20:53:28,993] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 237
2023-01-11T21:05:10.5196790Z [2023-01-11 20:53:28,995] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:05:10.5196896Z   target: aten._histogramdd_bin_edges.default
2023-01-11T21:05:10.5196983Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:05:10.5197223Z     InputBuffer(name='arg0_1', layout=FixedLayout('cpu', torch.float32, size=[4, 2], stride=[2, 1]))
2023-01-11T21:05:10.5197283Z   ))
2023-01-11T21:05:10.5197337Z   args[1]: [3, 3]
2023-01-11T21:05:10.5197496Z   kwargs: {'weight': TensorBox(StorageBox(
2023-01-11T21:05:10.5197727Z     InputBuffer(name='arg1_1', layout=FixedLayout('cpu', torch.float32, size=[4], stride=[1]))
2023-01-11T21:05:10.5197788Z   ))}
2023-01-11T21:05:10.5198066Z [2023-01-11 20:53:29,026] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten._histogramdd_bin_edges.default
2023-01-11T21:05:10.5198282Z [2023-01-11 20:53:29,028] torch._inductor.graph: [WARNING] Creating implicit fallback for:
2023-01-11T21:05:10.5198391Z   target: aten._histogramdd_from_bin_cts.default
2023-01-11T21:05:10.5198478Z   args[0]: TensorBox(StorageBox(
2023-01-11T21:05:10.5198705Z     InputBuffer(name='arg0_1', layout=FixedLayout('cpu', torch.float32, size=[4, 2], stride=[2, 1]))
2023-01-11T21:05:10.5198764Z   ))
2023-01-11T21:05:10.5198831Z   args[1]: [3, 3]
2023-01-11T21:05:10.5198973Z   kwargs: {'weight': TensorBox(StorageBox(
2023-01-11T21:05:10.5199199Z     InputBuffer(name='arg1_1', layout=FixedLayout('cpu', torch.float32, size=[4], stride=[1]))
2023-01-11T21:05:10.5199262Z   ))}
2023-01-11T21:05:10.5199541Z [2023-01-11 20:53:29,058] torch._inductor.ir: [WARNING] Using FallbackKernel: torch.ops.aten._histogramdd_from_bin_cts.default
2023-01-11T21:05:10.5199790Z [2023-01-11 20:53:29,062] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 237
2023-01-11T21:05:10.5199810Z 
2023-01-11T21:05:10.5199890Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5199959Z import torch
2023-01-11T21:05:10.5200029Z import random
2023-01-11T21:05:10.5200144Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5200263Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5200268Z 
2023-01-11T21:05:10.5200346Z aten = torch.ops.aten
2023-01-11T21:05:10.5200478Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5200555Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5200560Z 
2023-01-11T21:05:10.5200577Z 
2023-01-11T21:05:10.5200785Z async_compile.wait(globals())
2023-01-11T21:05:10.5200923Z del async_compile
2023-01-11T21:05:10.5200928Z 
2023-01-11T21:05:10.5200998Z def call(args):
2023-01-11T21:05:10.5201074Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5201145Z     args.clear()
2023-01-11T21:05:10.5201300Z     buf0 = torch.ops.aten._histogramdd_bin_edges.default(arg0_1, [3, 3], weight=arg1_1)
2023-01-11T21:05:10.5201369Z     buf1 = buf0[0]
2023-01-11T21:05:10.5201450Z     assert_size_stride(buf1, (4, ), (1, ))
2023-01-11T21:05:10.5201518Z     buf2 = buf0[1]
2023-01-11T21:05:10.5201612Z     assert_size_stride(buf2, (4, ), (1, ))
2023-01-11T21:05:10.5201676Z     del buf0
2023-01-11T21:05:10.5201829Z     buf3 = torch.ops.aten._histogramdd_from_bin_cts.default(arg0_1, [3, 3], weight=arg1_1)
2023-01-11T21:05:10.5201895Z     del arg0_1
2023-01-11T21:05:10.5201962Z     del arg1_1
2023-01-11T21:05:10.5202016Z     buf4 = buf3
2023-01-11T21:05:10.5202111Z     assert_size_stride(buf4, (3, 3), (3, 1))
2023-01-11T21:05:10.5202176Z     del buf3
2023-01-11T21:05:10.5202258Z     return (buf4, buf1, buf2, )
2023-01-11T21:05:10.5202265Z 
2023-01-11T21:05:10.5202269Z 
2023-01-11T21:05:10.5202347Z if __name__ == "__main__":
2023-01-11T21:05:10.5202461Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5202620Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5202813Z     arg0_1 = rand_strided((4, 2), (2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5203005Z     arg1_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5203119Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5203123Z 
2023-01-11T21:05:10.5203188Z ok (0.145s)
2023-01-11T21:05:10.5203623Z   test_l1_loss_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5203749Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5204011Z [2023-01-11 20:53:29,114] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 238
2023-01-11T21:05:10.5204275Z [2023-01-11 20:53:31,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 238
2023-01-11T21:05:10.5204281Z 
2023-01-11T21:05:10.5204373Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5204428Z import torch
2023-01-11T21:05:10.5204496Z import random
2023-01-11T21:05:10.5204609Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5204727Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5204732Z 
2023-01-11T21:05:10.5204809Z aten = torch.ops.aten
2023-01-11T21:05:10.5204940Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5205029Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5205034Z 
2023-01-11T21:05:10.5205040Z 
2023-01-11T21:05:10.5205171Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5205362Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5205479Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5205579Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.5205687Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5205789Z                        const float* __restrict__ in_ptr1)
2023-01-11T21:05:10.5205848Z {
2023-01-11T21:05:10.5205933Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.5206013Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:05:10.5206061Z     {
2023-01-11T21:05:10.5206248Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.5206322Z         float tmp4 = 0;
2023-01-11T21:05:10.5206436Z         auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:05:10.5206544Z         float tmp6 = 0;
2023-01-11T21:05:10.5206658Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:05:10.5206760Z         #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5206811Z         {
2023-01-11T21:05:10.5206941Z             #pragma omp for reduction(+:tmp4_vec) reduction(+:tmp6_vec) 
2023-01-11T21:05:10.5207026Z             for(long i0=0; i0<96; i0+=1)
2023-01-11T21:05:10.5207088Z             {
2023-01-11T21:05:10.5207221Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5207354Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.5207483Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5207568Z                 auto tmp3 = tmp2.abs();
2023-01-11T21:05:10.5207640Z                 auto tmp5 = tmp2 * tmp2;
2023-01-11T21:05:10.5207720Z                 tmp4_vec += tmp3;
2023-01-11T21:05:10.5207798Z                 tmp6_vec += tmp5;
2023-01-11T21:05:10.5207862Z             }
2023-01-11T21:05:10.5208057Z             tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:05:10.5208276Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:05:10.5208420Z             #pragma omp for simd simdlen(8) reduction(+:tmp4) reduction(+:tmp6) 
2023-01-11T21:05:10.5208509Z             for(long i0=1536; i0<1536; i0+=1)
2023-01-11T21:05:10.5208558Z             {
2023-01-11T21:05:10.5208643Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5208727Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5208854Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5208948Z                 auto tmp3 = std::abs(tmp2);
2023-01-11T21:05:10.5209032Z                 auto tmp5 = tmp2 * tmp2;
2023-01-11T21:05:10.5209107Z                 tmp4 += tmp3;
2023-01-11T21:05:10.5209169Z                 tmp6 += tmp5;
2023-01-11T21:05:10.5209230Z             }
2023-01-11T21:05:10.5209291Z         }
2023-01-11T21:05:10.5209367Z         out_ptr0[0] = tmp4;
2023-01-11T21:05:10.5209442Z         out_ptr1[0] = tmp6;
2023-01-11T21:05:10.5209502Z     }
2023-01-11T21:05:10.5209548Z     {
2023-01-11T21:05:10.5209609Z         {
2023-01-11T21:05:10.5209692Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:05:10.5209793Z             auto tmp1 = static_cast<float>(1536);
2023-01-11T21:05:10.5209875Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.5209954Z             in_out_ptr0[0] = tmp2;
2023-01-11T21:05:10.5210015Z         }
2023-01-11T21:05:10.5210061Z     }
2023-01-11T21:05:10.5210121Z     {
2023-01-11T21:05:10.5210182Z         {
2023-01-11T21:05:10.5210263Z             auto tmp0 = out_ptr1[0];
2023-01-11T21:05:10.5210363Z             auto tmp1 = static_cast<float>(1536);
2023-01-11T21:05:10.5210445Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.5210526Z             in_out_ptr1[0] = tmp2;
2023-01-11T21:05:10.5210573Z         }
2023-01-11T21:05:10.5210632Z     }
2023-01-11T21:05:10.5210691Z }
2023-01-11T21:05:10.5210768Z ''')
2023-01-11T21:05:10.5210774Z 
2023-01-11T21:05:10.5210778Z 
2023-01-11T21:05:10.5210868Z async_compile.wait(globals())
2023-01-11T21:05:10.5210940Z del async_compile
2023-01-11T21:05:10.5210944Z 
2023-01-11T21:05:10.5211014Z def call(args):
2023-01-11T21:05:10.5211074Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5211143Z     args.clear()
2023-01-11T21:05:10.5211329Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5211507Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5211589Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5211669Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.5211854Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()))
2023-01-11T21:05:10.5211939Z     del arg0_1
2023-01-11T21:05:10.5212004Z     del arg1_1
2023-01-11T21:05:10.5212079Z     return (buf2, buf3, )
2023-01-11T21:05:10.5212084Z 
2023-01-11T21:05:10.5212088Z 
2023-01-11T21:05:10.5212163Z if __name__ == "__main__":
2023-01-11T21:05:10.5212277Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5212399Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5212616Z     arg0_1 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5212831Z     arg1_1 = rand_strided((2, 3, 16, 16), (768, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5212933Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5212952Z 
2023-01-11T21:05:10.5213004Z ok (2.811s)
2023-01-11T21:05:10.5213443Z   test_layer_norm_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5213613Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5213873Z [2023-01-11 20:53:32,019] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 239
2023-01-11T21:05:10.5214135Z [2023-01-11 20:53:34,826] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 239
2023-01-11T21:05:10.5214141Z 
2023-01-11T21:05:10.5214233Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5214301Z import torch
2023-01-11T21:05:10.5214369Z import random
2023-01-11T21:05:10.5214470Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5214589Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5214594Z 
2023-01-11T21:05:10.5214670Z aten = torch.ops.aten
2023-01-11T21:05:10.5214802Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5214894Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5214899Z 
2023-01-11T21:05:10.5214903Z 
2023-01-11T21:05:10.5215035Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5215240Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5215356Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5215444Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.5215549Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5215651Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5215752Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.5215850Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5215946Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.5216007Z {
2023-01-11T21:05:10.5216094Z     auto out_ptr2 = in_out_ptr0;
2023-01-11T21:05:10.5216165Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:05:10.5216260Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5216321Z     {
2023-01-11T21:05:10.5216395Z         #pragma omp for 
2023-01-11T21:05:10.5216477Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5216537Z         {
2023-01-11T21:05:10.5216586Z             {
2023-01-11T21:05:10.5216777Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.5216853Z                 float tmp1 = 0;
2023-01-11T21:05:10.5216972Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:05:10.5217060Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.5217124Z                 {
2023-01-11T21:05:10.5217265Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (32*i0));
2023-01-11T21:05:10.5217345Z                     tmp1_vec += tmp0;
2023-01-11T21:05:10.5217425Z                 }
2023-01-11T21:05:10.5217620Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:05:10.5217743Z                 #pragma omp simd simdlen(8)  reduction(+:tmp1)
2023-01-11T21:05:10.5217832Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:05:10.5217895Z                 {
2023-01-11T21:05:10.5217994Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.5218070Z                     tmp1 += tmp0;
2023-01-11T21:05:10.5218131Z                 }
2023-01-11T21:05:10.5218199Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5218260Z             }
2023-01-11T21:05:10.5218321Z         }
2023-01-11T21:05:10.5218396Z         #pragma omp for 
2023-01-11T21:05:10.5218562Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5218629Z         {
2023-01-11T21:05:10.5218678Z             {
2023-01-11T21:05:10.5218869Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.5218950Z                 float tmp6 = 0;
2023-01-11T21:05:10.5219116Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:05:10.5219196Z                 float tmp7 = 0;
2023-01-11T21:05:10.5219316Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:05:10.5219406Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.5219471Z                 {
2023-01-11T21:05:10.5219601Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (32*i0));
2023-01-11T21:05:10.5219726Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:05:10.5219862Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:05:10.5219954Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.5220095Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.5220189Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:05:10.5220271Z                     tmp6_vec += tmp5;
2023-01-11T21:05:10.5220351Z                     tmp7_vec += tmp0;
2023-01-11T21:05:10.5220400Z                 }
2023-01-11T21:05:10.5220596Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:05:10.5220792Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:05:10.5220931Z                 #pragma omp simd simdlen(8)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:05:10.5221022Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:05:10.5221088Z                 {
2023-01-11T21:05:10.5221189Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.5221281Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:05:10.5221370Z                     auto tmp2 = static_cast<float>(32);
2023-01-11T21:05:10.5221463Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.5221597Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.5221688Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.5221766Z                     tmp6 += tmp5;
2023-01-11T21:05:10.5221841Z                     tmp7 += tmp0;
2023-01-11T21:05:10.5221906Z                 }
2023-01-11T21:05:10.5221973Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.5222050Z                 out_ptr2[i0] = tmp7;
2023-01-11T21:05:10.5222113Z             }
2023-01-11T21:05:10.5222175Z         }
2023-01-11T21:05:10.5222251Z         #pragma omp for 
2023-01-11T21:05:10.5222334Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.5222395Z         {
2023-01-11T21:05:10.5222516Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 16*i0);
2023-01-11T21:05:10.5222648Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:05:10.5222764Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.5222859Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5222921Z         }
2023-01-11T21:05:10.5223017Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5223097Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.5223145Z         {
2023-01-11T21:05:10.5223228Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:05:10.5223327Z             auto tmp1 = static_cast<float>(32);
2023-01-11T21:05:10.5223411Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.5223492Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5223553Z         }
2023-01-11T21:05:10.5223629Z         #pragma omp for 
2023-01-11T21:05:10.5223694Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.5223755Z         {
2023-01-11T21:05:10.5223888Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.5224019Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(32));
2023-01-11T21:05:10.5224104Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.5224306Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:05:10.5224420Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.5224507Z             auto tmp5 = tmp4.rsqrt();
2023-01-11T21:05:10.5224591Z             tmp5.store(in_out_ptr1 + 16*i0);
2023-01-11T21:05:10.5224651Z         }
2023-01-11T21:05:10.5224744Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5224822Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.5224881Z         {
2023-01-11T21:05:10.5224963Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.5225049Z             auto tmp1 = static_cast<float>(32);
2023-01-11T21:05:10.5225130Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.5225279Z             auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:05:10.5225359Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.5225452Z             auto tmp5 = 1 / std::sqrt(tmp4);
2023-01-11T21:05:10.5225535Z             in_out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.5225595Z         }
2023-01-11T21:05:10.5225656Z         #pragma omp for 
2023-01-11T21:05:10.5225734Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5225796Z         {
2023-01-11T21:05:10.5225881Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.5225943Z             {
2023-01-11T21:05:10.5226083Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (32*i0));
2023-01-11T21:05:10.5226210Z                 auto tmp1 = at::vec::Vectorized<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5226337Z                 auto tmp3 = at::vec::Vectorized<float>(in_out_ptr1[i0]);
2023-01-11T21:05:10.5226454Z                 auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i1);
2023-01-11T21:05:10.5226582Z                 auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i1);
2023-01-11T21:05:10.5226711Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5226799Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.5226883Z                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.5226968Z                 auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.5227096Z                 auto tmp9 = at::vec::clamp_min(tmp8, decltype(tmp8)(0));
2023-01-11T21:05:10.5227200Z                 tmp9.store(out_ptr3 + (16*i1) + (32*i0));
2023-01-11T21:05:10.5227250Z             }
2023-01-11T21:05:10.5227339Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.5227422Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:05:10.5227485Z             {
2023-01-11T21:05:10.5227579Z                 auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.5227669Z                 auto tmp1 = in_out_ptr0[i0];
2023-01-11T21:05:10.5227761Z                 auto tmp3 = in_out_ptr1[i0];
2023-01-11T21:05:10.5227832Z                 auto tmp5 = in_ptr1[i1];
2023-01-11T21:05:10.5227915Z                 auto tmp7 = in_ptr2[i1];
2023-01-11T21:05:10.5228040Z                 auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5228155Z                 auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.5228236Z                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.5228319Z                 auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.5228410Z                 auto tmp9 = tmp8 * (tmp8>0);
2023-01-11T21:05:10.5228488Z                 out_ptr3[i1 + (32*i0)] = tmp9;
2023-01-11T21:05:10.5228550Z             }
2023-01-11T21:05:10.5228611Z         }
2023-01-11T21:05:10.5228671Z     }
2023-01-11T21:05:10.5228731Z }
2023-01-11T21:05:10.5228807Z ''')
2023-01-11T21:05:10.5228813Z 
2023-01-11T21:05:10.5228818Z 
2023-01-11T21:05:10.5228906Z async_compile.wait(globals())
2023-01-11T21:05:10.5228964Z del async_compile
2023-01-11T21:05:10.5228969Z 
2023-01-11T21:05:10.5229038Z def call(args):
2023-01-11T21:05:10.5229138Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.5229208Z     args.clear()
2023-01-11T21:05:10.5229407Z     buf0 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5229603Z     buf1 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5229792Z     buf2 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5229919Z     buf3 = as_strided(buf2, (16, 1), (1, 1)); del buf2  # reuse
2023-01-11T21:05:10.5230031Z     buf4 = as_strided(buf1, (16, 1), (1, 1)); del buf1  # reuse
2023-01-11T21:05:10.5230225Z     buf5 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5230500Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(primals_3.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:05:10.5230623Z     return (buf5, primals_1, primals_2, primals_3, buf3, buf4, )
2023-01-11T21:05:10.5230628Z 
2023-01-11T21:05:10.5230633Z 
2023-01-11T21:05:10.5230707Z if __name__ == "__main__":
2023-01-11T21:05:10.5230820Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5230942Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5231141Z     primals_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5231327Z     primals_2 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5231530Z     primals_3 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5231664Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.5231670Z 
2023-01-11T21:05:10.5231735Z ok (2.955s)
2023-01-11T21:05:10.5232174Z   test_leaky_relu_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5232302Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5232559Z [2023-01-11 20:53:34,902] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 240
2023-01-11T21:05:10.5232826Z [2023-01-11 20:53:37,652] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 240
2023-01-11T21:05:10.5232831Z 
2023-01-11T21:05:10.5232924Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5232992Z import torch
2023-01-11T21:05:10.5233048Z import random
2023-01-11T21:05:10.5233160Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5233280Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5233286Z 
2023-01-11T21:05:10.5233362Z aten = torch.ops.aten
2023-01-11T21:05:10.5233494Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5233584Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5233589Z 
2023-01-11T21:05:10.5233593Z 
2023-01-11T21:05:10.5233757Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5233960Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5234068Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5234167Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5234260Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5234319Z {
2023-01-11T21:05:10.5234416Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5234476Z     {
2023-01-11T21:05:10.5234551Z         #pragma omp for 
2023-01-11T21:05:10.5234619Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.5234681Z         {
2023-01-11T21:05:10.5234742Z             {
2023-01-11T21:05:10.5234804Z                 {
2023-01-11T21:05:10.5234895Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5234997Z                     auto tmp1 = static_cast<float>(0);
2023-01-11T21:05:10.5235073Z                     auto tmp2 = tmp0 > tmp1;
2023-01-11T21:05:10.5235178Z                     auto tmp3 = static_cast<float>(0.2);
2023-01-11T21:05:10.5235266Z                     auto tmp4 = tmp0 * tmp3;
2023-01-11T21:05:10.5235391Z                     auto tmp5 = tmp2 ? tmp0 : tmp4;
2023-01-11T21:05:10.5235493Z                     auto tmp6 = static_cast<float>(2);
2023-01-11T21:05:10.5235581Z                     auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.5235682Z                     auto tmp8 = static_cast<float>(1);
2023-01-11T21:05:10.5235771Z                     auto tmp9 = tmp0 + tmp8;
2023-01-11T21:05:10.5235847Z                     auto tmp10 = tmp9 > tmp1;
2023-01-11T21:05:10.5235952Z                     auto tmp11 = static_cast<float>(0.01);
2023-01-11T21:05:10.5236042Z                     auto tmp12 = tmp9 * tmp11;
2023-01-11T21:05:10.5236140Z                     auto tmp13 = tmp10 ? tmp9 : tmp12;
2023-01-11T21:05:10.5236225Z                     out_ptr0[i0] = tmp7;
2023-01-11T21:05:10.5236307Z                     out_ptr1[i0] = tmp13;
2023-01-11T21:05:10.5236372Z                 }
2023-01-11T21:05:10.5236422Z             }
2023-01-11T21:05:10.5236482Z         }
2023-01-11T21:05:10.5236541Z     }
2023-01-11T21:05:10.5236599Z }
2023-01-11T21:05:10.5236678Z ''')
2023-01-11T21:05:10.5236686Z 
2023-01-11T21:05:10.5236690Z 
2023-01-11T21:05:10.5236781Z async_compile.wait(globals())
2023-01-11T21:05:10.5236881Z del async_compile
2023-01-11T21:05:10.5236888Z 
2023-01-11T21:05:10.5236974Z def call(args):
2023-01-11T21:05:10.5237062Z     arg0_1, = args
2023-01-11T21:05:10.5237160Z     args.clear()
2023-01-11T21:05:10.5237368Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5237564Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5237727Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5237796Z     del arg0_1
2023-01-11T21:05:10.5237859Z     return (buf0, buf1, )
2023-01-11T21:05:10.5237867Z 
2023-01-11T21:05:10.5237883Z 
2023-01-11T21:05:10.5237945Z if __name__ == "__main__":
2023-01-11T21:05:10.5238058Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5238180Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5238378Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5238483Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5238488Z 
2023-01-11T21:05:10.5238553Z ok (2.827s)
2023-01-11T21:05:10.5238984Z   test_lgamma_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5239108Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5239406Z [2023-01-11 20:53:37,733] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 241
2023-01-11T21:05:10.5239672Z [2023-01-11 20:53:40,500] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 241
2023-01-11T21:05:10.5239677Z 
2023-01-11T21:05:10.5239769Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5239837Z import torch
2023-01-11T21:05:10.5239905Z import random
2023-01-11T21:05:10.5240018Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5240137Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5240142Z 
2023-01-11T21:05:10.5240219Z aten = torch.ops.aten
2023-01-11T21:05:10.5240338Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5240428Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5240433Z 
2023-01-11T21:05:10.5240437Z 
2023-01-11T21:05:10.5240570Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5240898Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5241020Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5241181Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5241280Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5241341Z {
2023-01-11T21:05:10.5241424Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5241486Z     {
2023-01-11T21:05:10.5241562Z         #pragma omp for 
2023-01-11T21:05:10.5241644Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5241708Z         {
2023-01-11T21:05:10.5241845Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5241932Z             auto tmp1 = tmp0.lgamma();
2023-01-11T21:05:10.5242051Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.5242137Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5242269Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.5242355Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.5242438Z             auto tmp6 = tmp5.cos();
2023-01-11T21:05:10.5242535Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5242628Z             tmp6.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.5242676Z         }
2023-01-11T21:05:10.5242772Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5242853Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.5242915Z         {
2023-01-11T21:05:10.5243001Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5243096Z             auto tmp1 = std::lgamma(tmp0);
2023-01-11T21:05:10.5243194Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.5243263Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5243361Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.5243445Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.5243531Z             auto tmp6 = std::cos(tmp5);
2023-01-11T21:05:10.5243612Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.5243689Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.5243748Z         }
2023-01-11T21:05:10.5243795Z     }
2023-01-11T21:05:10.5243856Z }
2023-01-11T21:05:10.5243938Z ''')
2023-01-11T21:05:10.5243944Z 
2023-01-11T21:05:10.5243948Z 
2023-01-11T21:05:10.5244036Z async_compile.wait(globals())
2023-01-11T21:05:10.5244106Z del async_compile
2023-01-11T21:05:10.5244111Z 
2023-01-11T21:05:10.5244180Z def call(args):
2023-01-11T21:05:10.5244246Z     arg0_1, = args
2023-01-11T21:05:10.5244316Z     args.clear()
2023-01-11T21:05:10.5244501Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5244696Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5244859Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5244926Z     del arg0_1
2023-01-11T21:05:10.5245041Z     return (buf0, buf1, )
2023-01-11T21:05:10.5245046Z 
2023-01-11T21:05:10.5245050Z 
2023-01-11T21:05:10.5245125Z if __name__ == "__main__":
2023-01-11T21:05:10.5245237Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5245348Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5245545Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5245652Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5245657Z 
2023-01-11T21:05:10.5245722Z ok (2.848s)
2023-01-11T21:05:10.5246156Z   test_linear1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5246284Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5246543Z [2023-01-11 20:53:40,663] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 242
2023-01-11T21:05:10.5246833Z [2023-01-11 20:53:43,344] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 242
2023-01-11T21:05:10.5246839Z 
2023-01-11T21:05:10.5246932Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5247001Z import torch
2023-01-11T21:05:10.5247057Z import random
2023-01-11T21:05:10.5247173Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5247294Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5247299Z 
2023-01-11T21:05:10.5247377Z aten = torch.ops.aten
2023-01-11T21:05:10.5247514Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5247640Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5247648Z 
2023-01-11T21:05:10.5247654Z 
2023-01-11T21:05:10.5247838Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5248095Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5248199Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5248261Z {
2023-01-11T21:05:10.5248378Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5248492Z     {
2023-01-11T21:05:10.5248572Z         #pragma omp for 
2023-01-11T21:05:10.5248652Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.5248714Z         {
2023-01-11T21:05:10.5248841Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5248973Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:05:10.5249070Z             tmp1.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5249132Z         }
2023-01-11T21:05:10.5249225Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5249305Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.5249367Z         {
2023-01-11T21:05:10.5249444Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.5249584Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.5249668Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.5249754Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5249844Z         }
2023-01-11T21:05:10.5249935Z     }
2023-01-11T21:05:10.5250048Z }
2023-01-11T21:05:10.5250175Z ''')
2023-01-11T21:05:10.5250185Z 
2023-01-11T21:05:10.5250191Z 
2023-01-11T21:05:10.5250322Z async_compile.wait(globals())
2023-01-11T21:05:10.5250416Z del async_compile
2023-01-11T21:05:10.5250422Z 
2023-01-11T21:05:10.5250516Z def call(args):
2023-01-11T21:05:10.5250658Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.5250765Z     args.clear()
2023-01-11T21:05:10.5251057Z     buf0 = empty_strided((2, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5251256Z     aten.addmm.out(primals_2, primals_3, as_strided(primals_1, (8, 16), (1, 8)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5251439Z     del primals_1
2023-01-11T21:05:10.5251514Z     del primals_2
2023-01-11T21:05:10.5251599Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5251702Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5251793Z     return (buf1, primals_3, buf1, )
2023-01-11T21:05:10.5251798Z 
2023-01-11T21:05:10.5251803Z 
2023-01-11T21:05:10.5251878Z if __name__ == "__main__":
2023-01-11T21:05:10.5251991Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5252099Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5252306Z     primals_1 = rand_strided((16, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5252508Z     primals_2 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5252706Z     primals_3 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5252845Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.5252850Z 
2023-01-11T21:05:10.5252917Z ok (2.843s)
2023-01-11T21:05:10.5253407Z   test_linear2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5253535Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5253794Z [2023-01-11 20:53:43,776] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 243
2023-01-11T21:05:10.5254043Z [2023-01-11 20:53:46,627] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 243
2023-01-11T21:05:10.5254064Z 
2023-01-11T21:05:10.5254144Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5254216Z import torch
2023-01-11T21:05:10.5254286Z import random
2023-01-11T21:05:10.5254403Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5254524Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5254529Z 
2023-01-11T21:05:10.5254607Z aten = torch.ops.aten
2023-01-11T21:05:10.5254742Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5254821Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5254825Z 
2023-01-11T21:05:10.5254830Z 
2023-01-11T21:05:10.5254963Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5255166Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5255282Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5255343Z {
2023-01-11T21:05:10.5255441Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5255502Z     {
2023-01-11T21:05:10.5255564Z         #pragma omp for 
2023-01-11T21:05:10.5255646Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.5255707Z         {
2023-01-11T21:05:10.5255853Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5255979Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:05:10.5256077Z             tmp1.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5256141Z         }
2023-01-11T21:05:10.5256241Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5256307Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.5256369Z         {
2023-01-11T21:05:10.5256457Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.5256543Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5256623Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5256684Z         }
2023-01-11T21:05:10.5256731Z     }
2023-01-11T21:05:10.5256790Z }
2023-01-11T21:05:10.5256866Z ''')
2023-01-11T21:05:10.5256871Z 
2023-01-11T21:05:10.5256875Z 
2023-01-11T21:05:10.5257009Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:05:10.5257209Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5257356Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5257414Z {
2023-01-11T21:05:10.5257509Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5257558Z     {
2023-01-11T21:05:10.5257633Z         #pragma omp for 
2023-01-11T21:05:10.5257713Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.5257773Z         {
2023-01-11T21:05:10.5257909Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5258034Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:05:10.5258128Z             tmp1.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5258176Z         }
2023-01-11T21:05:10.5258268Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5258348Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.5258408Z         {
2023-01-11T21:05:10.5258601Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.5258691Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5258775Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5258824Z         }
2023-01-11T21:05:10.5258885Z     }
2023-01-11T21:05:10.5258945Z }
2023-01-11T21:05:10.5259026Z ''')
2023-01-11T21:05:10.5259064Z 
2023-01-11T21:05:10.5259068Z 
2023-01-11T21:05:10.5259204Z kernel_cpp_2 = async_compile.cpp('''
2023-01-11T21:05:10.5259407Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5259523Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5259568Z {
2023-01-11T21:05:10.5259664Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5259725Z     {
2023-01-11T21:05:10.5259801Z         #pragma omp for 
2023-01-11T21:05:10.5259880Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.5259943Z         {
2023-01-11T21:05:10.5260085Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5260195Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:05:10.5260293Z             tmp1.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.5260355Z         }
2023-01-11T21:05:10.5260447Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5260527Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.5260587Z         {
2023-01-11T21:05:10.5260673Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.5260746Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5260826Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5260887Z         }
2023-01-11T21:05:10.5260947Z     }
2023-01-11T21:05:10.5261005Z }
2023-01-11T21:05:10.5261082Z ''')
2023-01-11T21:05:10.5261086Z 
2023-01-11T21:05:10.5261090Z 
2023-01-11T21:05:10.5261221Z kernel_cpp_3 = async_compile.cpp('''
2023-01-11T21:05:10.5261408Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5261521Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5261619Z                        bool* __restrict__ out_ptr0)
2023-01-11T21:05:10.5261679Z {
2023-01-11T21:05:10.5261772Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5261831Z     {
2023-01-11T21:05:10.5261909Z         #pragma omp for 
2023-01-11T21:05:10.5261977Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5262038Z         {
2023-01-11T21:05:10.5262099Z             {
2023-01-11T21:05:10.5262162Z                 {
2023-01-11T21:05:10.5262260Z                     auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.5262353Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5262455Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:05:10.5262534Z                     auto tmp3 = tmp1 <= tmp2;
2023-01-11T21:05:10.5262622Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5262705Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.5262770Z                 }
2023-01-11T21:05:10.5262833Z             }
2023-01-11T21:05:10.5262925Z         }
2023-01-11T21:05:10.5262985Z     }
2023-01-11T21:05:10.5263030Z }
2023-01-11T21:05:10.5263107Z ''')
2023-01-11T21:05:10.5263112Z 
2023-01-11T21:05:10.5263116Z 
2023-01-11T21:05:10.5263204Z async_compile.wait(globals())
2023-01-11T21:05:10.5263276Z del async_compile
2023-01-11T21:05:10.5263281Z 
2023-01-11T21:05:10.5263349Z def call(args):
2023-01-11T21:05:10.5263520Z     primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
2023-01-11T21:05:10.5263589Z     args.clear()
2023-01-11T21:05:10.5263775Z     buf0 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5264000Z     aten.addmm.out(primals_2, primals_9, as_strided(primals_1, (8, 8), (1, 8)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5264080Z     del primals_1
2023-01-11T21:05:10.5264150Z     del primals_2
2023-01-11T21:05:10.5264236Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5264392Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5264768Z     buf2 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5265056Z     aten.addmm.out(primals_4, buf1, as_strided(primals_3, (8, 8), (1, 8)), beta=1, alpha=1, out=buf2)
2023-01-11T21:05:10.5265173Z     del primals_4
2023-01-11T21:05:10.5265259Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:05:10.5265360Z     kernel_cpp_1(c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.5265555Z     buf4 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5265710Z     aten.addmm.out(primals_6, buf3, as_strided(primals_5, (8, 8), (1, 8)), beta=1, alpha=1, out=buf4)
2023-01-11T21:05:10.5265780Z     del primals_6
2023-01-11T21:05:10.5265862Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:05:10.5265949Z     kernel_cpp_2(c_void_p(buf5.data_ptr()))
2023-01-11T21:05:10.5266140Z     buf6 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5266291Z     aten.addmm.out(primals_8, buf5, as_strided(primals_7, (8, 8), (1, 8)), beta=1, alpha=1, out=buf6)
2023-01-11T21:05:10.5266366Z     del primals_8
2023-01-11T21:05:10.5266447Z     buf7 = buf6; del buf6  # reuse
2023-01-11T21:05:10.5266636Z     buf8 = empty_strided((2, 8), (8, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5266766Z     kernel_cpp_3(c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:05:10.5266965Z     return (buf7, primals_9, buf1, buf3, buf5, buf8, as_strided(primals_7, (8, 8), (8, 1)), as_strided(primals_5, (8, 8), (8, 1)), as_strided(primals_3, (8, 8), (8, 1)), )
2023-01-11T21:05:10.5266971Z 
2023-01-11T21:05:10.5266976Z 
2023-01-11T21:05:10.5267039Z if __name__ == "__main__":
2023-01-11T21:05:10.5267150Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5267270Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5267470Z     primals_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5267666Z     primals_2 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5267864Z     primals_3 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5268057Z     primals_4 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5268252Z     primals_5 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5268430Z     primals_6 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5268625Z     primals_7 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5268816Z     primals_8 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5269010Z     primals_9 = rand_strided((2, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5269212Z     print_performance(lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]))
2023-01-11T21:05:10.5269218Z 
2023-01-11T21:05:10.5269284Z ok (3.284s)
2023-01-11T21:05:10.5269757Z   test_linear_binary_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5269882Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5270143Z [2023-01-11 20:53:46,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 244
2023-01-11T21:05:10.5270410Z [2023-01-11 20:53:49,623] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 244
2023-01-11T21:05:10.5270795Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5270922Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5271203Z [2023-01-11 20:53:49,698] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 245
2023-01-11T21:05:10.5271466Z [2023-01-11 20:53:49,716] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 245
2023-01-11T21:05:10.5271860Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5271985Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5272240Z [2023-01-11 20:53:49,943] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 246
2023-01-11T21:05:10.5272505Z [2023-01-11 20:53:52,633] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 246
2023-01-11T21:05:10.5272904Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5273026Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5273283Z [2023-01-11 20:53:52,691] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 247
2023-01-11T21:05:10.5273544Z [2023-01-11 20:53:52,708] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 247
2023-01-11T21:05:10.5273931Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5274058Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5274309Z [2023-01-11 20:53:52,938] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 248
2023-01-11T21:05:10.5274314Z 
2023-01-11T21:05:10.5274408Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5274477Z import torch
2023-01-11T21:05:10.5274546Z import random
2023-01-11T21:05:10.5274660Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5274780Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5274786Z 
2023-01-11T21:05:10.5274849Z aten = torch.ops.aten
2023-01-11T21:05:10.5274983Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5275139Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5275144Z 
2023-01-11T21:05:10.5275149Z 
2023-01-11T21:05:10.5275281Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5275488Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5275608Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5275719Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5275780Z {
2023-01-11T21:05:10.5275863Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5275925Z     {
2023-01-11T21:05:10.5276002Z         #pragma omp for 
2023-01-11T21:05:10.5276086Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5276149Z         {
2023-01-11T21:05:10.5276213Z             {
2023-01-11T21:05:10.5276277Z                 {
2023-01-11T21:05:10.5276384Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5276501Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5276594Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5276684Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5276750Z                 }
2023-01-11T21:05:10.5276840Z             }
2023-01-11T21:05:10.5276903Z         }
2023-01-11T21:05:10.5276951Z     }
2023-01-11T21:05:10.5277009Z }
2023-01-11T21:05:10.5277086Z ''')
2023-01-11T21:05:10.5277091Z 
2023-01-11T21:05:10.5277096Z 
2023-01-11T21:05:10.5277183Z async_compile.wait(globals())
2023-01-11T21:05:10.5277254Z del async_compile
2023-01-11T21:05:10.5277260Z 
2023-01-11T21:05:10.5277329Z def call(args):
2023-01-11T21:05:10.5277417Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5277474Z     args.clear()
2023-01-11T21:05:10.5277674Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5277843Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5277913Z     del arg0_1
2023-01-11T21:05:10.5277977Z     del arg1_1
2023-01-11T21:05:10.5278041Z     del arg2_1
2023-01-11T21:05:10.5278158Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5278277Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5278344Z     del arg3_1
2023-01-11T21:05:10.5278413Z     return (buf1, )
2023-01-11T21:05:10.5278418Z 
2023-01-11T21:05:10.5278423Z 
2023-01-11T21:05:10.5278496Z if __name__ == "__main__":
2023-01-11T21:05:10.5278609Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5278731Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5278933Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5279127Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5279318Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5279525Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5279654Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5279659Z 
2023-01-11T21:05:10.5279663Z 
2023-01-11T21:05:10.5279757Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5279825Z import torch
2023-01-11T21:05:10.5279893Z import random
2023-01-11T21:05:10.5280010Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5280129Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5280134Z 
2023-01-11T21:05:10.5280198Z aten = torch.ops.aten
2023-01-11T21:05:10.5280331Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5280421Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5280425Z 
2023-01-11T21:05:10.5280429Z 
2023-01-11T21:05:10.5280564Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5280972Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5281093Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5281205Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5281268Z {
2023-01-11T21:05:10.5281354Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5281415Z     {
2023-01-11T21:05:10.5281493Z         #pragma omp for 
2023-01-11T21:05:10.5281576Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5281638Z         {
2023-01-11T21:05:10.5281703Z             {
2023-01-11T21:05:10.5281767Z                 {
2023-01-11T21:05:10.5281874Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5281988Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5282078Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5289034Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5289147Z                 }
2023-01-11T21:05:10.5289203Z             }
2023-01-11T21:05:10.5289256Z         }
2023-01-11T21:05:10.5289306Z     }
2023-01-11T21:05:10.5289354Z }
2023-01-11T21:05:10.5289499Z ''')
2023-01-11T21:05:10.5289506Z 
2023-01-11T21:05:10.5289673Z 
2023-01-11T21:05:10.5289849Z async_compile.wait(globals())
2023-01-11T21:05:10.5289973Z del async_compile
2023-01-11T21:05:10.5289983Z 
2023-01-11T21:05:10.5290100Z def call(args):
2023-01-11T21:05:10.5290230Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5290314Z     args.clear()
2023-01-11T21:05:10.5290515Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5290663Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5290732Z     del arg0_1
2023-01-11T21:05:10.5290799Z     del arg1_1
2023-01-11T21:05:10.5290917Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5291052Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5291123Z     del arg2_1
2023-01-11T21:05:10.5291194Z     return (buf1, )
2023-01-11T21:05:10.5291199Z 
2023-01-11T21:05:10.5291204Z 
2023-01-11T21:05:10.5291268Z if __name__ == "__main__":
2023-01-11T21:05:10.5291384Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5291510Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5291716Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5291924Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5292128Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5292252Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5292257Z 
2023-01-11T21:05:10.5292261Z 
2023-01-11T21:05:10.5292354Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5292410Z import torch
2023-01-11T21:05:10.5292482Z import random
2023-01-11T21:05:10.5292598Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5292721Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5292726Z 
2023-01-11T21:05:10.5292806Z aten = torch.ops.aten
2023-01-11T21:05:10.5292940Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5293033Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5293037Z 
2023-01-11T21:05:10.5293042Z 
2023-01-11T21:05:10.5293172Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5293366Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5293487Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5293595Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5293655Z {
2023-01-11T21:05:10.5293752Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5293813Z     {
2023-01-11T21:05:10.5293948Z         #pragma omp for 
2023-01-11T21:05:10.5294018Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5294082Z         {
2023-01-11T21:05:10.5294144Z             {
2023-01-11T21:05:10.5294208Z                 {
2023-01-11T21:05:10.5294334Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5294451Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5294543Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5294622Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5294687Z                 }
2023-01-11T21:05:10.5294746Z             }
2023-01-11T21:05:10.5294810Z         }
2023-01-11T21:05:10.5294871Z     }
2023-01-11T21:05:10.5294932Z }
2023-01-11T21:05:10.5295012Z ''')
2023-01-11T21:05:10.5295017Z 
2023-01-11T21:05:10.5295021Z 
2023-01-11T21:05:10.5295113Z async_compile.wait(globals())
2023-01-11T21:05:10.5295170Z del async_compile
2023-01-11T21:05:10.5295175Z 
2023-01-11T21:05:10.5295247Z def call(args):
2023-01-11T21:05:10.5295342Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5295412Z     args.clear()
2023-01-11T21:05:10.5295612Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5295793Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5295861Z     del arg0_1
2023-01-11T21:05:10.5295912Z     del arg1_1
2023-01-11T21:05:10.5295977Z     del arg2_1
2023-01-11T21:05:10.5296061Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5296192Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5296257Z     del arg3_1
2023-01-11T21:05:10.5296326Z     return (buf1, )
2023-01-11T21:05:10.5296331Z 
2023-01-11T21:05:10.5296335Z 
2023-01-11T21:05:10.5296409Z if __name__ == "__main__":
2023-01-11T21:05:10.5296521Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5296630Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5296833Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5297028Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5297232Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5297428Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5297556Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5297562Z 
2023-01-11T21:05:10.5297566Z 
2023-01-11T21:05:10.5297661Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5297728Z import torch
2023-01-11T21:05:10.5297784Z import random
2023-01-11T21:05:10.5297898Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5298017Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5298022Z 
2023-01-11T21:05:10.5298097Z aten = torch.ops.aten
2023-01-11T21:05:10.5298232Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5298321Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5298326Z 
2023-01-11T21:05:10.5298330Z 
2023-01-11T21:05:10.5298556Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5298767Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5298873Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5298983Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5299045Z {
2023-01-11T21:05:10.5299143Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5299208Z     {
2023-01-11T21:05:10.5299285Z         #pragma omp for 
2023-01-11T21:05:10.5299366Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5299414Z         {
2023-01-11T21:05:10.5299478Z             {
2023-01-11T21:05:10.5299545Z                 {
2023-01-11T21:05:10.5299666Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5299816Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5299910Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5300002Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5300052Z                 }
2023-01-11T21:05:10.5300114Z             }
2023-01-11T21:05:10.5300177Z         }
2023-01-11T21:05:10.5300236Z     }
2023-01-11T21:05:10.5300295Z }
2023-01-11T21:05:10.5300374Z ''')
2023-01-11T21:05:10.5300379Z 
2023-01-11T21:05:10.5300383Z 
2023-01-11T21:05:10.5300471Z async_compile.wait(globals())
2023-01-11T21:05:10.5300529Z del async_compile
2023-01-11T21:05:10.5300534Z 
2023-01-11T21:05:10.5300602Z def call(args):
2023-01-11T21:05:10.5300683Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5300751Z     args.clear()
2023-01-11T21:05:10.5300951Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5301072Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5301140Z     del arg0_1
2023-01-11T21:05:10.5301193Z     del arg1_1
2023-01-11T21:05:10.5301278Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5301457Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5301524Z     del arg2_1
2023-01-11T21:05:10.5301594Z     return (buf1, )
2023-01-11T21:05:10.5301598Z 
2023-01-11T21:05:10.5301602Z 
2023-01-11T21:05:10.5301676Z if __name__ == "__main__":
2023-01-11T21:05:10.5301788Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5301898Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5302102Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5302299Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5302493Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5302615Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5302623Z 
2023-01-11T21:05:10.5302627Z 
2023-01-11T21:05:10.5302721Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5302791Z import torch
2023-01-11T21:05:10.5302863Z import random
2023-01-11T21:05:10.5302964Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5303085Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5303090Z 
2023-01-11T21:05:10.5303167Z aten = torch.ops.aten
2023-01-11T21:05:10.5303300Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5303389Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5303394Z 
2023-01-11T21:05:10.5303398Z 
2023-01-11T21:05:10.5303529Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5303732Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5303850Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5303959Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5304005Z {
2023-01-11T21:05:10.5304102Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5304163Z     {
2023-01-11T21:05:10.5304241Z         #pragma omp for 
2023-01-11T21:05:10.5304322Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5304383Z         {
2023-01-11T21:05:10.5304433Z             {
2023-01-11T21:05:10.5304495Z                 {
2023-01-11T21:05:10.5304608Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5304727Z                     auto tmp1 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5304818Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5304907Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5304970Z                 }
2023-01-11T21:05:10.5305019Z             }
2023-01-11T21:05:10.5305079Z         }
2023-01-11T21:05:10.5305139Z     }
2023-01-11T21:05:10.5305198Z }
2023-01-11T21:05:10.5305274Z ''')
2023-01-11T21:05:10.5305314Z 
2023-01-11T21:05:10.5305318Z 
2023-01-11T21:05:10.5305406Z async_compile.wait(globals())
2023-01-11T21:05:10.5305477Z del async_compile
2023-01-11T21:05:10.5305482Z 
2023-01-11T21:05:10.5305537Z def call(args):
2023-01-11T21:05:10.5305629Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5305698Z     args.clear()
2023-01-11T21:05:10.5305899Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5306070Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5306138Z     del arg0_1
2023-01-11T21:05:10.5306202Z     del arg1_1
2023-01-11T21:05:10.5306253Z     del arg2_1
2023-01-11T21:05:10.5306369Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5306499Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5306563Z     del arg3_1
2023-01-11T21:05:10.5306636Z     return (buf1, )
2023-01-11T21:05:10.5306641Z 
2023-01-11T21:05:10.5306645Z 
2023-01-11T21:05:10.5306720Z if __name__ == "__main__":
2023-01-11T21:05:10.5306835Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5306980Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5307171Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5307368Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5307576Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5307779Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5307907Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5308177Z [2023-01-11 20:53:55,706] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 248
2023-01-11T21:05:10.5308584Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5308708Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5308968Z [2023-01-11 20:53:55,778] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 249
2023-01-11T21:05:10.5309233Z [2023-01-11 20:53:55,796] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 249
2023-01-11T21:05:10.5309617Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5309745Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5310006Z [2023-01-11 20:53:56,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 250
2023-01-11T21:05:10.5310268Z [2023-01-11 20:53:58,747] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 250
2023-01-11T21:05:10.5310665Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5310790Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5311045Z [2023-01-11 20:53:58,819] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 251
2023-01-11T21:05:10.5311339Z [2023-01-11 20:53:58,862] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 251
2023-01-11T21:05:10.5311738Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5311863Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5312119Z [2023-01-11 20:53:59,073] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 252
2023-01-11T21:05:10.5312363Z [2023-01-11 20:53:59,085] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 252
2023-01-11T21:05:10.5312760Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5312917Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5313174Z [2023-01-11 20:53:59,133] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 253
2023-01-11T21:05:10.5313179Z 
2023-01-11T21:05:10.5313184Z 
2023-01-11T21:05:10.5313279Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5313349Z import torch
2023-01-11T21:05:10.5313419Z import random
2023-01-11T21:05:10.5313533Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5313653Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5313658Z 
2023-01-11T21:05:10.5313721Z aten = torch.ops.aten
2023-01-11T21:05:10.5313855Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5313947Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5313954Z 
2023-01-11T21:05:10.5313959Z 
2023-01-11T21:05:10.5314093Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5314299Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5314418Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5314525Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5314584Z {
2023-01-11T21:05:10.5314667Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5314728Z     {
2023-01-11T21:05:10.5314804Z         #pragma omp for 
2023-01-11T21:05:10.5314886Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5314948Z         {
2023-01-11T21:05:10.5315011Z             {
2023-01-11T21:05:10.5315074Z                 {
2023-01-11T21:05:10.5315175Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5315293Z                     auto tmp1 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5315388Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5315476Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5315540Z                 }
2023-01-11T21:05:10.5315602Z             }
2023-01-11T21:05:10.5315665Z         }
2023-01-11T21:05:10.5315713Z     }
2023-01-11T21:05:10.5315771Z }
2023-01-11T21:05:10.5315848Z ''')
2023-01-11T21:05:10.5315852Z 
2023-01-11T21:05:10.5315857Z 
2023-01-11T21:05:10.5315945Z async_compile.wait(globals())
2023-01-11T21:05:10.5316015Z del async_compile
2023-01-11T21:05:10.5316020Z 
2023-01-11T21:05:10.5316087Z def call(args):
2023-01-11T21:05:10.5316168Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5316224Z     args.clear()
2023-01-11T21:05:10.5316425Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5316569Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5316636Z     del arg0_1
2023-01-11T21:05:10.5316733Z     del arg1_1
2023-01-11T21:05:10.5316847Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5316978Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5317033Z     del arg2_1
2023-01-11T21:05:10.5317103Z     return (buf1, )
2023-01-11T21:05:10.5317107Z 
2023-01-11T21:05:10.5317112Z 
2023-01-11T21:05:10.5317185Z if __name__ == "__main__":
2023-01-11T21:05:10.5317297Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5317420Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5317622Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5317828Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5318031Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5318139Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5318159Z 
2023-01-11T21:05:10.5318163Z 
2023-01-11T21:05:10.5318242Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5318310Z import torch
2023-01-11T21:05:10.5318378Z import random
2023-01-11T21:05:10.5318520Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5318641Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5318645Z 
2023-01-11T21:05:10.5318723Z aten = torch.ops.aten
2023-01-11T21:05:10.5318854Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5318931Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5318947Z 
2023-01-11T21:05:10.5318951Z 
2023-01-11T21:05:10.5319070Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5319269Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5319388Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5319497Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5319561Z {
2023-01-11T21:05:10.5319657Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5319718Z     {
2023-01-11T21:05:10.5319780Z         #pragma omp for 
2023-01-11T21:05:10.5319863Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5319925Z         {
2023-01-11T21:05:10.5319986Z             {
2023-01-11T21:05:10.5320050Z                 {
2023-01-11T21:05:10.5320164Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5320282Z                     auto tmp1 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5320358Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5320449Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5320512Z                 }
2023-01-11T21:05:10.5320574Z             }
2023-01-11T21:05:10.5320799Z         }
2023-01-11T21:05:10.5320866Z     }
2023-01-11T21:05:10.5320911Z }
2023-01-11T21:05:10.5320993Z ''')
2023-01-11T21:05:10.5320998Z 
2023-01-11T21:05:10.5321005Z 
2023-01-11T21:05:10.5321095Z async_compile.wait(globals())
2023-01-11T21:05:10.5321169Z del async_compile
2023-01-11T21:05:10.5321174Z 
2023-01-11T21:05:10.5321245Z def call(args):
2023-01-11T21:05:10.5321337Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5321407Z     args.clear()
2023-01-11T21:05:10.5321608Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5321745Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5321812Z     del arg0_1
2023-01-11T21:05:10.5321879Z     del arg1_1
2023-01-11T21:05:10.5321944Z     del arg2_1
2023-01-11T21:05:10.5322029Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5322162Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5322230Z     del arg3_1
2023-01-11T21:05:10.5322287Z     return (buf1, )
2023-01-11T21:05:10.5322292Z 
2023-01-11T21:05:10.5322296Z 
2023-01-11T21:05:10.5322431Z if __name__ == "__main__":
2023-01-11T21:05:10.5322544Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5322668Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5322875Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5323072Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5323270Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5323466Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5323580Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5323586Z 
2023-01-11T21:05:10.5323604Z 
2023-01-11T21:05:10.5323684Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5323753Z import torch
2023-01-11T21:05:10.5323825Z import random
2023-01-11T21:05:10.5323941Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5324068Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5324073Z 
2023-01-11T21:05:10.5324155Z aten = torch.ops.aten
2023-01-11T21:05:10.5324340Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5324420Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5324425Z 
2023-01-11T21:05:10.5324443Z 
2023-01-11T21:05:10.5324564Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5324765Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5324885Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5324991Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5325051Z {
2023-01-11T21:05:10.5325147Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5325206Z     {
2023-01-11T21:05:10.5325268Z         #pragma omp for 
2023-01-11T21:05:10.5325348Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5325412Z         {
2023-01-11T21:05:10.5325476Z             {
2023-01-11T21:05:10.5325542Z                 {
2023-01-11T21:05:10.5325656Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5325765Z                     auto tmp1 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5325859Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5325949Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5326014Z                 }
2023-01-11T21:05:10.5326076Z             }
2023-01-11T21:05:10.5326137Z         }
2023-01-11T21:05:10.5326198Z     }
2023-01-11T21:05:10.5326243Z }
2023-01-11T21:05:10.5326321Z ''')
2023-01-11T21:05:10.5326326Z 
2023-01-11T21:05:10.5326331Z 
2023-01-11T21:05:10.5326420Z async_compile.wait(globals())
2023-01-11T21:05:10.5326490Z del async_compile
2023-01-11T21:05:10.5326495Z 
2023-01-11T21:05:10.5326565Z def call(args):
2023-01-11T21:05:10.5326647Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5326716Z     args.clear()
2023-01-11T21:05:10.5326903Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5327024Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5327093Z     del arg0_1
2023-01-11T21:05:10.5327160Z     del arg1_1
2023-01-11T21:05:10.5327242Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5327373Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5327438Z     del arg2_1
2023-01-11T21:05:10.5327495Z     return (buf1, )
2023-01-11T21:05:10.5327513Z 
2023-01-11T21:05:10.5327517Z 
2023-01-11T21:05:10.5327577Z if __name__ == "__main__":
2023-01-11T21:05:10.5327691Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5327812Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5328013Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5328210Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5328438Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5328560Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5328567Z 
2023-01-11T21:05:10.5328572Z 
2023-01-11T21:05:10.5328666Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5328722Z import torch
2023-01-11T21:05:10.5328790Z import random
2023-01-11T21:05:10.5328905Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5329025Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5329030Z 
2023-01-11T21:05:10.5329106Z aten = torch.ops.aten
2023-01-11T21:05:10.5329237Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5329327Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5329332Z 
2023-01-11T21:05:10.5329335Z 
2023-01-11T21:05:10.5329467Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5329656Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5329777Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5329885Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5329972Z {
2023-01-11T21:05:10.5330070Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5330129Z     {
2023-01-11T21:05:10.5330205Z         #pragma omp for 
2023-01-11T21:05:10.5330274Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5330336Z         {
2023-01-11T21:05:10.5330398Z             {
2023-01-11T21:05:10.5330460Z                 {
2023-01-11T21:05:10.5330580Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5330692Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5330784Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5330860Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5330923Z                 }
2023-01-11T21:05:10.5330987Z             }
2023-01-11T21:05:10.5331048Z         }
2023-01-11T21:05:10.5331107Z     }
2023-01-11T21:05:10.5331165Z }
2023-01-11T21:05:10.5331228Z ''')
2023-01-11T21:05:10.5331245Z 
2023-01-11T21:05:10.5331249Z 
2023-01-11T21:05:10.5331327Z async_compile.wait(globals())
2023-01-11T21:05:10.5331398Z del async_compile
2023-01-11T21:05:10.5331403Z 
2023-01-11T21:05:10.5331472Z def call(args):
2023-01-11T21:05:10.5331563Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5331632Z     args.clear()
2023-01-11T21:05:10.5331830Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5332000Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5332055Z     del arg0_1
2023-01-11T21:05:10.5332120Z     del arg1_1
2023-01-11T21:05:10.5332185Z     del arg2_1
2023-01-11T21:05:10.5332303Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5332439Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5332506Z     del arg3_1
2023-01-11T21:05:10.5332577Z     return (buf1, )
2023-01-11T21:05:10.5332582Z 
2023-01-11T21:05:10.5332588Z 
2023-01-11T21:05:10.5332664Z if __name__ == "__main__":
2023-01-11T21:05:10.5332766Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5332887Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5333090Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5333287Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5333490Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5333693Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5333821Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5333855Z 
2023-01-11T21:05:10.5333859Z 
2023-01-11T21:05:10.5333953Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5334009Z import torch
2023-01-11T21:05:10.5334077Z import random
2023-01-11T21:05:10.5334193Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5334312Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5334317Z 
2023-01-11T21:05:10.5334393Z aten = torch.ops.aten
2023-01-11T21:05:10.5334525Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5334614Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5334619Z 
2023-01-11T21:05:10.5334623Z 
2023-01-11T21:05:10.5334755Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5334946Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5335063Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5335170Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5335232Z {
2023-01-11T21:05:10.5335328Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5335390Z     {
2023-01-11T21:05:10.5335464Z         #pragma omp for 
2023-01-11T21:05:10.5335594Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5335686Z         {
2023-01-11T21:05:10.5335773Z             {
2023-01-11T21:05:10.5335843Z                 {
2023-01-11T21:05:10.5335965Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5336081Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5336174Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5336251Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5336316Z                 }
2023-01-11T21:05:10.5336382Z             }
2023-01-11T21:05:10.5336444Z         }
2023-01-11T21:05:10.5336506Z     }
2023-01-11T21:05:10.5336565Z }
2023-01-11T21:05:10.5336632Z ''')
2023-01-11T21:05:10.5336651Z 
2023-01-11T21:05:10.5336658Z 
2023-01-11T21:05:10.5336734Z async_compile.wait(globals())
2023-01-11T21:05:10.5336807Z del async_compile
2023-01-11T21:05:10.5336812Z 
2023-01-11T21:05:10.5336884Z def call(args):
2023-01-11T21:05:10.5336966Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5337040Z     args.clear()
2023-01-11T21:05:10.5337242Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5337385Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5337439Z     del arg0_1
2023-01-11T21:05:10.5337506Z     del arg1_1
2023-01-11T21:05:10.5337622Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5337755Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5337827Z     del arg2_1
2023-01-11T21:05:10.5337900Z     return (buf1, )
2023-01-11T21:05:10.5337905Z 
2023-01-11T21:05:10.5337909Z 
2023-01-11T21:05:10.5337983Z if __name__ == "__main__":
2023-01-11T21:05:10.5338098Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5338210Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5338413Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5338706Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5338916Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5339042Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5339319Z [2023-01-11 20:53:59,144] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 253
2023-01-11T21:05:10.5339733Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5339899Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5340160Z [2023-01-11 20:53:59,328] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 254
2023-01-11T21:05:10.5340411Z [2023-01-11 20:53:59,339] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 254
2023-01-11T21:05:10.5340809Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5340937Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5341194Z [2023-01-11 20:53:59,380] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 255
2023-01-11T21:05:10.5341456Z [2023-01-11 20:53:59,390] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 255
2023-01-11T21:05:10.5341910Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5342039Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5342291Z [2023-01-11 20:53:59,579] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 256
2023-01-11T21:05:10.5342554Z [2023-01-11 20:54:02,338] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 256
2023-01-11T21:05:10.5342949Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5343078Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5343318Z [2023-01-11 20:54:02,408] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 257
2023-01-11T21:05:10.5343578Z [2023-01-11 20:54:02,427] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 257
2023-01-11T21:05:10.5343971Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5344096Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5344352Z [2023-01-11 20:54:02,652] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 258
2023-01-11T21:05:10.5344358Z 
2023-01-11T21:05:10.5344364Z 
2023-01-11T21:05:10.5344462Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5344532Z import torch
2023-01-11T21:05:10.5344602Z import random
2023-01-11T21:05:10.5344722Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5344846Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5344851Z 
2023-01-11T21:05:10.5344914Z aten = torch.ops.aten
2023-01-11T21:05:10.5345047Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5345141Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5345145Z 
2023-01-11T21:05:10.5345150Z 
2023-01-11T21:05:10.5345284Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5345488Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5345637Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5345746Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5345806Z {
2023-01-11T21:05:10.5345894Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5345954Z     {
2023-01-11T21:05:10.5346030Z         #pragma omp for 
2023-01-11T21:05:10.5346111Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5346173Z         {
2023-01-11T21:05:10.5346236Z             {
2023-01-11T21:05:10.5346285Z                 {
2023-01-11T21:05:10.5346406Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5346526Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5346654Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5346746Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5346809Z                 }
2023-01-11T21:05:10.5346871Z             }
2023-01-11T21:05:10.5346921Z         }
2023-01-11T21:05:10.5346982Z     }
2023-01-11T21:05:10.5347041Z }
2023-01-11T21:05:10.5347120Z ''')
2023-01-11T21:05:10.5347125Z 
2023-01-11T21:05:10.5347129Z 
2023-01-11T21:05:10.5347249Z async_compile.wait(globals())
2023-01-11T21:05:10.5347322Z del async_compile
2023-01-11T21:05:10.5347327Z 
2023-01-11T21:05:10.5347395Z def call(args):
2023-01-11T21:05:10.5347484Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5347541Z     args.clear()
2023-01-11T21:05:10.5347742Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5347892Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5347958Z     del arg0_1
2023-01-11T21:05:10.5348023Z     del arg1_1
2023-01-11T21:05:10.5348087Z     del arg2_1
2023-01-11T21:05:10.5348170Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5348288Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5348356Z     del arg3_1
2023-01-11T21:05:10.5348425Z     return (buf1, )
2023-01-11T21:05:10.5348430Z 
2023-01-11T21:05:10.5348434Z 
2023-01-11T21:05:10.5348508Z if __name__ == "__main__":
2023-01-11T21:05:10.5348623Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5348745Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5348946Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5349128Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5349324Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5349518Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5349646Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5349652Z 
2023-01-11T21:05:10.5349656Z 
2023-01-11T21:05:10.5349748Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5349819Z import torch
2023-01-11T21:05:10.5349886Z import random
2023-01-11T21:05:10.5349999Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5350109Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5350127Z 
2023-01-11T21:05:10.5350191Z aten = torch.ops.aten
2023-01-11T21:05:10.5350321Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5350411Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5350416Z 
2023-01-11T21:05:10.5350421Z 
2023-01-11T21:05:10.5350552Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5350756Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5350877Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5350985Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5351030Z {
2023-01-11T21:05:10.5351126Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5351215Z     {
2023-01-11T21:05:10.5351293Z         #pragma omp for 
2023-01-11T21:05:10.5351374Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5351438Z         {
2023-01-11T21:05:10.5351501Z             {
2023-01-11T21:05:10.5351554Z                 {
2023-01-11T21:05:10.5351674Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5351786Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5351877Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5351966Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5352028Z                 }
2023-01-11T21:05:10.5352090Z             }
2023-01-11T21:05:10.5352137Z         }
2023-01-11T21:05:10.5352197Z     }
2023-01-11T21:05:10.5352254Z }
2023-01-11T21:05:10.5352332Z ''')
2023-01-11T21:05:10.5352337Z 
2023-01-11T21:05:10.5352341Z 
2023-01-11T21:05:10.5352429Z async_compile.wait(globals())
2023-01-11T21:05:10.5352499Z del async_compile
2023-01-11T21:05:10.5352506Z 
2023-01-11T21:05:10.5352574Z def call(args):
2023-01-11T21:05:10.5352641Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5352710Z     args.clear()
2023-01-11T21:05:10.5352936Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5353059Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5353124Z     del arg0_1
2023-01-11T21:05:10.5353189Z     del arg1_1
2023-01-11T21:05:10.5353271Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5353388Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5353453Z     del arg2_1
2023-01-11T21:05:10.5353523Z     return (buf1, )
2023-01-11T21:05:10.5353528Z 
2023-01-11T21:05:10.5353532Z 
2023-01-11T21:05:10.5353605Z if __name__ == "__main__":
2023-01-11T21:05:10.5353716Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5353836Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5354042Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5354238Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5354422Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5354544Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5354549Z 
2023-01-11T21:05:10.5354553Z 
2023-01-11T21:05:10.5354645Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5354711Z import torch
2023-01-11T21:05:10.5354780Z import random
2023-01-11T21:05:10.5354896Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5355015Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5355019Z 
2023-01-11T21:05:10.5355095Z aten = torch.ops.aten
2023-01-11T21:05:10.5355213Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5355302Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5355308Z 
2023-01-11T21:05:10.5355312Z 
2023-01-11T21:05:10.5355446Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5355647Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5355771Z extern "C" void kernel(const bfloat16* __restrict__ in_ptr0,
2023-01-11T21:05:10.5355876Z                        const bfloat16* __restrict__ in_ptr1,
2023-01-11T21:05:10.5355976Z                        bfloat16* __restrict__ out_ptr0)
2023-01-11T21:05:10.5356035Z {
2023-01-11T21:05:10.5356118Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5356176Z     {
2023-01-11T21:05:10.5356251Z         #pragma omp for 
2023-01-11T21:05:10.5356332Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5356395Z         {
2023-01-11T21:05:10.5356456Z             {
2023-01-11T21:05:10.5356521Z                 {
2023-01-11T21:05:10.5356622Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5356735Z                     auto tmp1 = static_cast<float>(in_ptr1[i0]);
2023-01-11T21:05:10.5356855Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5356940Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5357004Z                 }
2023-01-11T21:05:10.5357068Z             }
2023-01-11T21:05:10.5357120Z         }
2023-01-11T21:05:10.5357180Z     }
2023-01-11T21:05:10.5357238Z }
2023-01-11T21:05:10.5357317Z ''')
2023-01-11T21:05:10.5357322Z 
2023-01-11T21:05:10.5357326Z 
2023-01-11T21:05:10.5357414Z async_compile.wait(globals())
2023-01-11T21:05:10.5357484Z del async_compile
2023-01-11T21:05:10.5357488Z 
2023-01-11T21:05:10.5357557Z def call(args):
2023-01-11T21:05:10.5357646Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5357703Z     args.clear()
2023-01-11T21:05:10.5357901Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5358070Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5358140Z     del arg0_1
2023-01-11T21:05:10.5358205Z     del arg1_1
2023-01-11T21:05:10.5358269Z     del arg2_1
2023-01-11T21:05:10.5358461Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5358516Z     del arg3_1
2023-01-11T21:05:10.5358619Z     return (as_strided(buf0, (2, 3, 30), (90, 30, 1)), )
2023-01-11T21:05:10.5358625Z 
2023-01-11T21:05:10.5358629Z 
2023-01-11T21:05:10.5358705Z if __name__ == "__main__":
2023-01-11T21:05:10.5358817Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5358936Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5359138Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5359333Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5359539Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5359731Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5359860Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5359867Z 
2023-01-11T21:05:10.5359871Z 
2023-01-11T21:05:10.5359963Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5360033Z import torch
2023-01-11T21:05:10.5360101Z import random
2023-01-11T21:05:10.5360215Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5360333Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5360337Z 
2023-01-11T21:05:10.5360415Z aten = torch.ops.aten
2023-01-11T21:05:10.5360533Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5360758Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5360769Z 
2023-01-11T21:05:10.5360773Z 
2023-01-11T21:05:10.5360915Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5361117Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5361249Z extern "C" void kernel(const bfloat16* __restrict__ in_ptr0,
2023-01-11T21:05:10.5361357Z                        const bfloat16* __restrict__ in_ptr1,
2023-01-11T21:05:10.5361462Z                        bfloat16* __restrict__ out_ptr0)
2023-01-11T21:05:10.5361524Z {
2023-01-11T21:05:10.5361606Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5361667Z     {
2023-01-11T21:05:10.5361744Z         #pragma omp for 
2023-01-11T21:05:10.5361829Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5361892Z         {
2023-01-11T21:05:10.5361956Z             {
2023-01-11T21:05:10.5362021Z                 {
2023-01-11T21:05:10.5362124Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5362236Z                     auto tmp1 = static_cast<float>(in_ptr1[i0]);
2023-01-11T21:05:10.5362331Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5362416Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5362541Z                 }
2023-01-11T21:05:10.5362604Z             }
2023-01-11T21:05:10.5362666Z         }
2023-01-11T21:05:10.5362713Z     }
2023-01-11T21:05:10.5362772Z }
2023-01-11T21:05:10.5362851Z ''')
2023-01-11T21:05:10.5362858Z 
2023-01-11T21:05:10.5362863Z 
2023-01-11T21:05:10.5362953Z async_compile.wait(globals())
2023-01-11T21:05:10.5363025Z del async_compile
2023-01-11T21:05:10.5363030Z 
2023-01-11T21:05:10.5363099Z def call(args):
2023-01-11T21:05:10.5363180Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5363237Z     args.clear()
2023-01-11T21:05:10.5363438Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5363582Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5363650Z     del arg0_1
2023-01-11T21:05:10.5363720Z     del arg1_1
2023-01-11T21:05:10.5363882Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5363951Z     del arg2_1
2023-01-11T21:05:10.5364042Z     return (as_strided(buf0, (2, 3, 30), (90, 30, 1)), )
2023-01-11T21:05:10.5364047Z 
2023-01-11T21:05:10.5364066Z 
2023-01-11T21:05:10.5364165Z if __name__ == "__main__":
2023-01-11T21:05:10.5364283Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5364404Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5364604Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5364811Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5365015Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5365136Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5365141Z 
2023-01-11T21:05:10.5365145Z 
2023-01-11T21:05:10.5365239Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5365297Z import torch
2023-01-11T21:05:10.5365365Z import random
2023-01-11T21:05:10.5365479Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5365598Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5365604Z 
2023-01-11T21:05:10.5365683Z aten = torch.ops.aten
2023-01-11T21:05:10.5365814Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5365904Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5365908Z 
2023-01-11T21:05:10.5365913Z 
2023-01-11T21:05:10.5366043Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5366236Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5366355Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5366463Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5366522Z {
2023-01-11T21:05:10.5366618Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5366678Z     {
2023-01-11T21:05:10.5366755Z         #pragma omp for 
2023-01-11T21:05:10.5366824Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5366885Z         {
2023-01-11T21:05:10.5366948Z             {
2023-01-11T21:05:10.5367011Z                 {
2023-01-11T21:05:10.5367132Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5367246Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5367336Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5367413Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5367475Z                 }
2023-01-11T21:05:10.5367537Z             }
2023-01-11T21:05:10.5367597Z         }
2023-01-11T21:05:10.5367657Z     }
2023-01-11T21:05:10.5367717Z }
2023-01-11T21:05:10.5367781Z ''')
2023-01-11T21:05:10.5367785Z 
2023-01-11T21:05:10.5367803Z 
2023-01-11T21:05:10.5367878Z async_compile.wait(globals())
2023-01-11T21:05:10.5367947Z del async_compile
2023-01-11T21:05:10.5367952Z 
2023-01-11T21:05:10.5368049Z def call(args):
2023-01-11T21:05:10.5368139Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5368208Z     args.clear()
2023-01-11T21:05:10.5368407Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5368559Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5368613Z     del arg0_1
2023-01-11T21:05:10.5368677Z     del arg1_1
2023-01-11T21:05:10.5368741Z     del arg2_1
2023-01-11T21:05:10.5368824Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5368956Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5369022Z     del arg3_1
2023-01-11T21:05:10.5369090Z     return (buf1, )
2023-01-11T21:05:10.5369095Z 
2023-01-11T21:05:10.5369100Z 
2023-01-11T21:05:10.5369161Z if __name__ == "__main__":
2023-01-11T21:05:10.5369273Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5369394Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5369599Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5369793Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5370056Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5370255Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5370383Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5370636Z [2023-01-11 20:54:02,662] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 258
2023-01-11T21:05:10.5371040Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5371167Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5371428Z [2023-01-11 20:54:02,711] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 259
2023-01-11T21:05:10.5371694Z [2023-01-11 20:54:02,724] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 259
2023-01-11T21:05:10.5372092Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5372219Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5372474Z [2023-01-11 20:54:02,958] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 260
2023-01-11T21:05:10.5372739Z [2023-01-11 20:54:05,696] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 260
2023-01-11T21:05:10.5373144Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5373272Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5373527Z [2023-01-11 20:54:05,768] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 261
2023-01-11T21:05:10.5373773Z [2023-01-11 20:54:05,786] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 261
2023-01-11T21:05:10.5374168Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5374326Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5374582Z [2023-01-11 20:54:06,012] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 262
2023-01-11T21:05:10.5374842Z [2023-01-11 20:54:08,746] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 262
2023-01-11T21:05:10.5375238Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5375362Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5375617Z [2023-01-11 20:54:08,806] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 263
2023-01-11T21:05:10.5375623Z 
2023-01-11T21:05:10.5375627Z 
2023-01-11T21:05:10.5375788Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5375909Z import torch
2023-01-11T21:05:10.5376016Z import random
2023-01-11T21:05:10.5376231Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5376451Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5376460Z 
2023-01-11T21:05:10.5376586Z aten = torch.ops.aten
2023-01-11T21:05:10.5376729Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5376821Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5376826Z 
2023-01-11T21:05:10.5376830Z 
2023-01-11T21:05:10.5376966Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5377172Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5377281Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5377391Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5377452Z {
2023-01-11T21:05:10.5377551Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5377612Z     {
2023-01-11T21:05:10.5377688Z         #pragma omp for 
2023-01-11T21:05:10.5377770Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5377819Z         {
2023-01-11T21:05:10.5377882Z             {
2023-01-11T21:05:10.5377945Z                 {
2023-01-11T21:05:10.5378066Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5378179Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5378270Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5378361Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5378412Z                 }
2023-01-11T21:05:10.5378568Z             }
2023-01-11T21:05:10.5378633Z         }
2023-01-11T21:05:10.5378696Z     }
2023-01-11T21:05:10.5378755Z }
2023-01-11T21:05:10.5378838Z ''')
2023-01-11T21:05:10.5378843Z 
2023-01-11T21:05:10.5378847Z 
2023-01-11T21:05:10.5378937Z async_compile.wait(globals())
2023-01-11T21:05:10.5378996Z del async_compile
2023-01-11T21:05:10.5379001Z 
2023-01-11T21:05:10.5379070Z def call(args):
2023-01-11T21:05:10.5379152Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5379223Z     args.clear()
2023-01-11T21:05:10.5379423Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5379546Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5379614Z     del arg0_1
2023-01-11T21:05:10.5379665Z     del arg1_1
2023-01-11T21:05:10.5379748Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5379883Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5379950Z     del arg2_1
2023-01-11T21:05:10.5380023Z     return (buf1, )
2023-01-11T21:05:10.5380065Z 
2023-01-11T21:05:10.5380069Z 
2023-01-11T21:05:10.5380147Z if __name__ == "__main__":
2023-01-11T21:05:10.5380258Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5380382Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5380572Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5380770Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5380966Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5381088Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5381093Z 
2023-01-11T21:05:10.5381097Z 
2023-01-11T21:05:10.5381189Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5381257Z import torch
2023-01-11T21:05:10.5381325Z import random
2023-01-11T21:05:10.5381437Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5381544Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5381551Z 
2023-01-11T21:05:10.5381627Z aten = torch.ops.aten
2023-01-11T21:05:10.5381758Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5381892Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5381898Z 
2023-01-11T21:05:10.5381902Z 
2023-01-11T21:05:10.5382037Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5382239Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5382359Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5382467Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5382513Z {
2023-01-11T21:05:10.5382611Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5382671Z     {
2023-01-11T21:05:10.5382746Z         #pragma omp for 
2023-01-11T21:05:10.5382829Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5382889Z         {
2023-01-11T21:05:10.5382938Z             {
2023-01-11T21:05:10.5383002Z                 {
2023-01-11T21:05:10.5383121Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5383233Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5383375Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5383465Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5383531Z                 }
2023-01-11T21:05:10.5383580Z             }
2023-01-11T21:05:10.5383641Z         }
2023-01-11T21:05:10.5383702Z     }
2023-01-11T21:05:10.5383762Z }
2023-01-11T21:05:10.5383841Z ''')
2023-01-11T21:05:10.5383846Z 
2023-01-11T21:05:10.5383850Z 
2023-01-11T21:05:10.5383940Z async_compile.wait(globals())
2023-01-11T21:05:10.5384013Z del async_compile
2023-01-11T21:05:10.5384019Z 
2023-01-11T21:05:10.5384121Z def call(args):
2023-01-11T21:05:10.5384228Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5384323Z     args.clear()
2023-01-11T21:05:10.5384618Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5384849Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5384950Z     del arg0_1
2023-01-11T21:05:10.5385042Z     del arg1_1
2023-01-11T21:05:10.5385142Z     del arg2_1
2023-01-11T21:05:10.5385329Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5385577Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5385699Z     del arg3_1
2023-01-11T21:05:10.5385828Z     return (buf1, )
2023-01-11T21:05:10.5385836Z 
2023-01-11T21:05:10.5385842Z 
2023-01-11T21:05:10.5385932Z if __name__ == "__main__":
2023-01-11T21:05:10.5386047Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5386168Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5386376Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5386629Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5386835Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5387042Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5387170Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5387176Z 
2023-01-11T21:05:10.5387180Z 
2023-01-11T21:05:10.5387273Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5387344Z import torch
2023-01-11T21:05:10.5387414Z import random
2023-01-11T21:05:10.5387528Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5387635Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5387641Z 
2023-01-11T21:05:10.5387718Z aten = torch.ops.aten
2023-01-11T21:05:10.5387852Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5387943Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5387950Z 
2023-01-11T21:05:10.5387954Z 
2023-01-11T21:05:10.5388092Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5388337Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5388459Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5388568Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5388615Z {
2023-01-11T21:05:10.5388712Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5388773Z     {
2023-01-11T21:05:10.5388848Z         #pragma omp for 
2023-01-11T21:05:10.5388930Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5388991Z         {
2023-01-11T21:05:10.5389052Z             {
2023-01-11T21:05:10.5389102Z                 {
2023-01-11T21:05:10.5389220Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5389336Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5389475Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5389564Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5389627Z                 }
2023-01-11T21:05:10.5389689Z             }
2023-01-11T21:05:10.5389739Z         }
2023-01-11T21:05:10.5389797Z     }
2023-01-11T21:05:10.5389856Z }
2023-01-11T21:05:10.5389932Z ''')
2023-01-11T21:05:10.5389938Z 
2023-01-11T21:05:10.5389942Z 
2023-01-11T21:05:10.5390030Z async_compile.wait(globals())
2023-01-11T21:05:10.5390101Z del async_compile
2023-01-11T21:05:10.5390105Z 
2023-01-11T21:05:10.5390172Z def call(args):
2023-01-11T21:05:10.5390240Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5390309Z     args.clear()
2023-01-11T21:05:10.5390510Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5390654Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5390721Z     del arg0_1
2023-01-11T21:05:10.5390787Z     del arg1_1
2023-01-11T21:05:10.5390903Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5391022Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5391089Z     del arg2_1
2023-01-11T21:05:10.5391159Z     return (buf1, )
2023-01-11T21:05:10.5391164Z 
2023-01-11T21:05:10.5391168Z 
2023-01-11T21:05:10.5391242Z if __name__ == "__main__":
2023-01-11T21:05:10.5391356Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5391478Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5391678Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5391881Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5392071Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5392191Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5392227Z 
2023-01-11T21:05:10.5392231Z 
2023-01-11T21:05:10.5392324Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5392392Z import torch
2023-01-11T21:05:10.5392460Z import random
2023-01-11T21:05:10.5392575Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5392695Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5392700Z 
2023-01-11T21:05:10.5392777Z aten = torch.ops.aten
2023-01-11T21:05:10.5392894Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5392983Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5392988Z 
2023-01-11T21:05:10.5392992Z 
2023-01-11T21:05:10.5393125Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5393328Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5393447Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5393554Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5393615Z {
2023-01-11T21:05:10.5393712Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5393760Z     {
2023-01-11T21:05:10.5393835Z         #pragma omp for 
2023-01-11T21:05:10.5393942Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5394005Z         {
2023-01-11T21:05:10.5394068Z             {
2023-01-11T21:05:10.5394130Z                 {
2023-01-11T21:05:10.5394237Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5394352Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5394489Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5394579Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5394643Z                 }
2023-01-11T21:05:10.5394705Z             }
2023-01-11T21:05:10.5394766Z         }
2023-01-11T21:05:10.5394812Z     }
2023-01-11T21:05:10.5394870Z }
2023-01-11T21:05:10.5394947Z ''')
2023-01-11T21:05:10.5394952Z 
2023-01-11T21:05:10.5394958Z 
2023-01-11T21:05:10.5395047Z async_compile.wait(globals())
2023-01-11T21:05:10.5395118Z del async_compile
2023-01-11T21:05:10.5395123Z 
2023-01-11T21:05:10.5395191Z def call(args):
2023-01-11T21:05:10.5395282Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5395340Z     args.clear()
2023-01-11T21:05:10.5395538Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5395689Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5395759Z     del arg0_1
2023-01-11T21:05:10.5395823Z     del arg1_1
2023-01-11T21:05:10.5395886Z     del arg2_1
2023-01-11T21:05:10.5395969Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5396099Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5396152Z     del arg3_1
2023-01-11T21:05:10.5396220Z     return (buf1, )
2023-01-11T21:05:10.5396225Z 
2023-01-11T21:05:10.5396230Z 
2023-01-11T21:05:10.5396309Z if __name__ == "__main__":
2023-01-11T21:05:10.5396420Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5396542Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5396746Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5396938Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5397121Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5397316Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5397442Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5397447Z 
2023-01-11T21:05:10.5397451Z 
2023-01-11T21:05:10.5397542Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5397610Z import torch
2023-01-11T21:05:10.5397680Z import random
2023-01-11T21:05:10.5397792Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5397945Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5397951Z 
2023-01-11T21:05:10.5398014Z aten = torch.ops.aten
2023-01-11T21:05:10.5398145Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5398235Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5398240Z 
2023-01-11T21:05:10.5398244Z 
2023-01-11T21:05:10.5398376Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5398579Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5398696Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5398804Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5398867Z {
2023-01-11T21:05:10.5398950Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5399009Z     {
2023-01-11T21:05:10.5399085Z         #pragma omp for 
2023-01-11T21:05:10.5399165Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5399228Z         {
2023-01-11T21:05:10.5399290Z             {
2023-01-11T21:05:10.5399353Z                 {
2023-01-11T21:05:10.5399459Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5399610Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5399748Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5399836Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5399900Z                 }
2023-01-11T21:05:10.5399961Z             }
2023-01-11T21:05:10.5400021Z         }
2023-01-11T21:05:10.5400067Z     }
2023-01-11T21:05:10.5400125Z }
2023-01-11T21:05:10.5400201Z ''')
2023-01-11T21:05:10.5400206Z 
2023-01-11T21:05:10.5400210Z 
2023-01-11T21:05:10.5400298Z async_compile.wait(globals())
2023-01-11T21:05:10.5400369Z del async_compile
2023-01-11T21:05:10.5400374Z 
2023-01-11T21:05:10.5400442Z def call(args):
2023-01-11T21:05:10.5400524Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5400580Z     args.clear()
2023-01-11T21:05:10.5400963Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5401088Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5401158Z     del arg0_1
2023-01-11T21:05:10.5401229Z     del arg1_1
2023-01-11T21:05:10.5401312Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5401443Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5401496Z     del arg2_1
2023-01-11T21:05:10.5401568Z     return (buf1, )
2023-01-11T21:05:10.5401573Z 
2023-01-11T21:05:10.5401578Z 
2023-01-11T21:05:10.5401655Z if __name__ == "__main__":
2023-01-11T21:05:10.5401768Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5401894Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5402099Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5402298Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5402499Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5402607Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5402880Z [2023-01-11 20:54:08,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 263
2023-01-11T21:05:10.5403280Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5403407Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5403665Z [2023-01-11 20:54:09,052] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 264
2023-01-11T21:05:10.5403931Z [2023-01-11 20:54:09,064] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 264
2023-01-11T21:05:10.5404393Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5404520Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5404780Z [2023-01-11 20:54:09,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 265
2023-01-11T21:05:10.5405041Z [2023-01-11 20:54:09,139] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 265
2023-01-11T21:05:10.5405435Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5405603Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5405847Z [2023-01-11 20:54:09,366] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 266
2023-01-11T21:05:10.5406113Z [2023-01-11 20:54:09,377] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 266
2023-01-11T21:05:10.5406513Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5406639Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5406896Z [2023-01-11 20:54:09,428] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 267
2023-01-11T21:05:10.5407156Z [2023-01-11 20:54:09,442] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 267
2023-01-11T21:05:10.5407552Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5407677Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5407927Z [2023-01-11 20:54:09,673] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 268
2023-01-11T21:05:10.5407933Z 
2023-01-11T21:05:10.5407938Z 
2023-01-11T21:05:10.5408030Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5408097Z import torch
2023-01-11T21:05:10.5408156Z import random
2023-01-11T21:05:10.5408270Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5408391Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5408397Z 
2023-01-11T21:05:10.5408475Z aten = torch.ops.aten
2023-01-11T21:05:10.5408608Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5408699Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5408704Z 
2023-01-11T21:05:10.5408708Z 
2023-01-11T21:05:10.5408841Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5409032Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5409151Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5409257Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5409318Z {
2023-01-11T21:05:10.5409414Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5409476Z     {
2023-01-11T21:05:10.5409553Z         #pragma omp for 
2023-01-11T21:05:10.5409652Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5409713Z         {
2023-01-11T21:05:10.5409775Z             {
2023-01-11T21:05:10.5409840Z                 {
2023-01-11T21:05:10.5409963Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5410076Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5410212Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5410288Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5410351Z                 }
2023-01-11T21:05:10.5410413Z             }
2023-01-11T21:05:10.5410475Z         }
2023-01-11T21:05:10.5410536Z     }
2023-01-11T21:05:10.5410594Z }
2023-01-11T21:05:10.5410670Z ''')
2023-01-11T21:05:10.5410675Z 
2023-01-11T21:05:10.5410680Z 
2023-01-11T21:05:10.5410757Z async_compile.wait(globals())
2023-01-11T21:05:10.5410828Z del async_compile
2023-01-11T21:05:10.5410833Z 
2023-01-11T21:05:10.5410902Z def call(args):
2023-01-11T21:05:10.5410994Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5411064Z     args.clear()
2023-01-11T21:05:10.5411266Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5411465Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5411534Z     del arg0_1
2023-01-11T21:05:10.5411586Z     del arg1_1
2023-01-11T21:05:10.5411649Z     del arg2_1
2023-01-11T21:05:10.5411765Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5411897Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5411963Z     del arg3_1
2023-01-11T21:05:10.5412034Z     return (buf1, )
2023-01-11T21:05:10.5412039Z 
2023-01-11T21:05:10.5412044Z 
2023-01-11T21:05:10.5412118Z if __name__ == "__main__":
2023-01-11T21:05:10.5412217Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5412341Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5412541Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5412740Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5412945Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5413149Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5413277Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5413283Z 
2023-01-11T21:05:10.5413287Z 
2023-01-11T21:05:10.5413380Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5413448Z import torch
2023-01-11T21:05:10.5413504Z import random
2023-01-11T21:05:10.5413618Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5413738Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5413743Z 
2023-01-11T21:05:10.5413821Z aten = torch.ops.aten
2023-01-11T21:05:10.5413952Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5414041Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5414047Z 
2023-01-11T21:05:10.5414053Z 
2023-01-11T21:05:10.5414184Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5414375Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5414495Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5414604Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5414665Z {
2023-01-11T21:05:10.5414760Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5414824Z     {
2023-01-11T21:05:10.5414901Z         #pragma omp for 
2023-01-11T21:05:10.5414970Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5415032Z         {
2023-01-11T21:05:10.5415093Z             {
2023-01-11T21:05:10.5415157Z                 {
2023-01-11T21:05:10.5415308Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5415422Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5415562Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5415640Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5415703Z                 }
2023-01-11T21:05:10.5415765Z             }
2023-01-11T21:05:10.5415825Z         }
2023-01-11T21:05:10.5415887Z     }
2023-01-11T21:05:10.5415945Z }
2023-01-11T21:05:10.5416020Z ''')
2023-01-11T21:05:10.5416025Z 
2023-01-11T21:05:10.5416029Z 
2023-01-11T21:05:10.5416103Z async_compile.wait(globals())
2023-01-11T21:05:10.5416173Z del async_compile
2023-01-11T21:05:10.5416178Z 
2023-01-11T21:05:10.5416246Z def call(args):
2023-01-11T21:05:10.5416325Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5416393Z     args.clear()
2023-01-11T21:05:10.5416593Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5416739Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5416805Z     del arg0_1
2023-01-11T21:05:10.5416856Z     del arg1_1
2023-01-11T21:05:10.5417005Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5417139Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5417204Z     del arg2_1
2023-01-11T21:05:10.5417276Z     return (buf1, )
2023-01-11T21:05:10.5417280Z 
2023-01-11T21:05:10.5417285Z 
2023-01-11T21:05:10.5417358Z if __name__ == "__main__":
2023-01-11T21:05:10.5417470Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5417579Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5417778Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5417983Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5418187Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5418308Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5418313Z 
2023-01-11T21:05:10.5418320Z 
2023-01-11T21:05:10.5418413Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5418569Z import torch
2023-01-11T21:05:10.5418640Z import random
2023-01-11T21:05:10.5418743Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5418864Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5418869Z 
2023-01-11T21:05:10.5418947Z aten = torch.ops.aten
2023-01-11T21:05:10.5419080Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5419171Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5419176Z 
2023-01-11T21:05:10.5419180Z 
2023-01-11T21:05:10.5419317Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5419522Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5419642Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5419737Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5419798Z {
2023-01-11T21:05:10.5419896Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5419957Z     {
2023-01-11T21:05:10.5420034Z         #pragma omp for 
2023-01-11T21:05:10.5420115Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5420178Z         {
2023-01-11T21:05:10.5420227Z             {
2023-01-11T21:05:10.5420291Z                 {
2023-01-11T21:05:10.5420409Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5420522Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5420657Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5420747Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5420810Z                 }
2023-01-11T21:05:10.5420858Z             }
2023-01-11T21:05:10.5420954Z         }
2023-01-11T21:05:10.5421013Z     }
2023-01-11T21:05:10.5421071Z }
2023-01-11T21:05:10.5421148Z ''')
2023-01-11T21:05:10.5421153Z 
2023-01-11T21:05:10.5421157Z 
2023-01-11T21:05:10.5421251Z async_compile.wait(globals())
2023-01-11T21:05:10.5421320Z del async_compile
2023-01-11T21:05:10.5421325Z 
2023-01-11T21:05:10.5421380Z def call(args):
2023-01-11T21:05:10.5421466Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5421535Z     args.clear()
2023-01-11T21:05:10.5421735Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5421885Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5421952Z     del arg0_1
2023-01-11T21:05:10.5422016Z     del arg1_1
2023-01-11T21:05:10.5422068Z     del arg2_1
2023-01-11T21:05:10.5422150Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5422281Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5422347Z     del arg3_1
2023-01-11T21:05:10.5422419Z     return (buf1, )
2023-01-11T21:05:10.5422424Z 
2023-01-11T21:05:10.5422428Z 
2023-01-11T21:05:10.5422502Z if __name__ == "__main__":
2023-01-11T21:05:10.5422658Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5422781Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5422969Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5423164Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5423359Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5423553Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5423681Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5423687Z 
2023-01-11T21:05:10.5423691Z 
2023-01-11T21:05:10.5423784Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5423854Z import torch
2023-01-11T21:05:10.5423922Z import random
2023-01-11T21:05:10.5424022Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5424144Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5424149Z 
2023-01-11T21:05:10.5424225Z aten = torch.ops.aten
2023-01-11T21:05:10.5424357Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5424446Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5424451Z 
2023-01-11T21:05:10.5424457Z 
2023-01-11T21:05:10.5424589Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5424791Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5424910Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5425004Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5425063Z {
2023-01-11T21:05:10.5425158Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5425221Z     {
2023-01-11T21:05:10.5425295Z         #pragma omp for 
2023-01-11T21:05:10.5425375Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5425436Z         {
2023-01-11T21:05:10.5425486Z             {
2023-01-11T21:05:10.5425552Z                 {
2023-01-11T21:05:10.5425671Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5425784Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5425920Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5426011Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5426073Z                 }
2023-01-11T21:05:10.5426123Z             }
2023-01-11T21:05:10.5426184Z         }
2023-01-11T21:05:10.5426244Z     }
2023-01-11T21:05:10.5426303Z }
2023-01-11T21:05:10.5426379Z ''')
2023-01-11T21:05:10.5426385Z 
2023-01-11T21:05:10.5426389Z 
2023-01-11T21:05:10.5426477Z async_compile.wait(globals())
2023-01-11T21:05:10.5426547Z del async_compile
2023-01-11T21:05:10.5426582Z 
2023-01-11T21:05:10.5426639Z def call(args):
2023-01-11T21:05:10.5426718Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5426787Z     args.clear()
2023-01-11T21:05:10.5426989Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5427110Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5427175Z     del arg0_1
2023-01-11T21:05:10.5427240Z     del arg1_1
2023-01-11T21:05:10.5427309Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5427441Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5427507Z     del arg2_1
2023-01-11T21:05:10.5427578Z     return (buf1, )
2023-01-11T21:05:10.5427583Z 
2023-01-11T21:05:10.5427588Z 
2023-01-11T21:05:10.5427660Z if __name__ == "__main__":
2023-01-11T21:05:10.5427772Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5427895Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5428086Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5428284Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5428509Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5428631Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5428636Z 
2023-01-11T21:05:10.5428639Z 
2023-01-11T21:05:10.5428732Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5428801Z import torch
2023-01-11T21:05:10.5428869Z import random
2023-01-11T21:05:10.5428981Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5429087Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5429105Z 
2023-01-11T21:05:10.5429168Z aten = torch.ops.aten
2023-01-11T21:05:10.5429299Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5429388Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5429394Z 
2023-01-11T21:05:10.5429399Z 
2023-01-11T21:05:10.5429531Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5429734Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5429857Z extern "C" void kernel(const bfloat16* __restrict__ in_ptr0,
2023-01-11T21:05:10.5429966Z                        const bfloat16* __restrict__ in_ptr1,
2023-01-11T21:05:10.5430066Z                        bfloat16* __restrict__ out_ptr0)
2023-01-11T21:05:10.5430113Z {
2023-01-11T21:05:10.5430207Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5430267Z     {
2023-01-11T21:05:10.5430343Z         #pragma omp for 
2023-01-11T21:05:10.5430422Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5430486Z         {
2023-01-11T21:05:10.5430534Z             {
2023-01-11T21:05:10.5430598Z                 {
2023-01-11T21:05:10.5430714Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5430825Z                     auto tmp1 = static_cast<float>(in_ptr1[i0]);
2023-01-11T21:05:10.5430962Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5431046Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5431110Z                 }
2023-01-11T21:05:10.5431161Z             }
2023-01-11T21:05:10.5431223Z         }
2023-01-11T21:05:10.5431283Z     }
2023-01-11T21:05:10.5431341Z }
2023-01-11T21:05:10.5431418Z ''')
2023-01-11T21:05:10.5431424Z 
2023-01-11T21:05:10.5431428Z 
2023-01-11T21:05:10.5431518Z async_compile.wait(globals())
2023-01-11T21:05:10.5431587Z del async_compile
2023-01-11T21:05:10.5431592Z 
2023-01-11T21:05:10.5431647Z def call(args):
2023-01-11T21:05:10.5431736Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5431805Z     args.clear()
2023-01-11T21:05:10.5432003Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5432173Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5432271Z     del arg0_1
2023-01-11T21:05:10.5432335Z     del arg1_1
2023-01-11T21:05:10.5432388Z     del arg2_1
2023-01-11T21:05:10.5432551Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5432616Z     del arg3_1
2023-01-11T21:05:10.5432718Z     return (as_strided(buf0, (2, 3, 30), (90, 30, 1)), )
2023-01-11T21:05:10.5432723Z 
2023-01-11T21:05:10.5432727Z 
2023-01-11T21:05:10.5432801Z if __name__ == "__main__":
2023-01-11T21:05:10.5432917Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5433037Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5433240Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5433423Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5433627Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5433834Z     arg3_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5433961Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5434260Z [2023-01-11 20:54:12,393] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 268
2023-01-11T21:05:10.5434664Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5434788Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5435046Z [2023-01-11 20:54:12,463] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 269
2023-01-11T21:05:10.5435308Z [2023-01-11 20:54:12,482] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 269
2023-01-11T21:05:10.5435708Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5435832Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5436077Z [2023-01-11 20:54:12,707] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 270
2023-01-11T21:05:10.5436336Z [2023-01-11 20:54:12,717] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 270
2023-01-11T21:05:10.5436733Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5436860Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5437114Z [2023-01-11 20:54:12,766] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 271
2023-01-11T21:05:10.5437376Z [2023-01-11 20:54:12,780] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 271
2023-01-11T21:05:10.5437382Z 
2023-01-11T21:05:10.5437386Z 
2023-01-11T21:05:10.5437479Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5437547Z import torch
2023-01-11T21:05:10.5437616Z import random
2023-01-11T21:05:10.5437720Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5437839Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5437844Z 
2023-01-11T21:05:10.5437920Z aten = torch.ops.aten
2023-01-11T21:05:10.5438053Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5438176Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5438182Z 
2023-01-11T21:05:10.5438186Z 
2023-01-11T21:05:10.5438319Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5438524Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5438647Z extern "C" void kernel(const bfloat16* __restrict__ in_ptr0,
2023-01-11T21:05:10.5438753Z                        const bfloat16* __restrict__ in_ptr1,
2023-01-11T21:05:10.5438841Z                        bfloat16* __restrict__ out_ptr0)
2023-01-11T21:05:10.5438901Z {
2023-01-11T21:05:10.5438997Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5439057Z     {
2023-01-11T21:05:10.5439135Z         #pragma omp for 
2023-01-11T21:05:10.5439218Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5439266Z         {
2023-01-11T21:05:10.5439328Z             {
2023-01-11T21:05:10.5439389Z                 {
2023-01-11T21:05:10.5439506Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5439623Z                     auto tmp1 = static_cast<float>(in_ptr1[i0]);
2023-01-11T21:05:10.5439789Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5439874Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5439924Z                 }
2023-01-11T21:05:10.5439985Z             }
2023-01-11T21:05:10.5440046Z         }
2023-01-11T21:05:10.5440106Z     }
2023-01-11T21:05:10.5440164Z }
2023-01-11T21:05:10.5440242Z ''')
2023-01-11T21:05:10.5440248Z 
2023-01-11T21:05:10.5440252Z 
2023-01-11T21:05:10.5440340Z async_compile.wait(globals())
2023-01-11T21:05:10.5440397Z del async_compile
2023-01-11T21:05:10.5440414Z 
2023-01-11T21:05:10.5440470Z def call(args):
2023-01-11T21:05:10.5440550Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5440756Z     args.clear()
2023-01-11T21:05:10.5440977Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5441124Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5441192Z     del arg0_1
2023-01-11T21:05:10.5441257Z     del arg1_1
2023-01-11T21:05:10.5441406Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5441475Z     del arg2_1
2023-01-11T21:05:10.5441579Z     return (as_strided(buf0, (2, 3, 30), (90, 30, 1)), )
2023-01-11T21:05:10.5441584Z 
2023-01-11T21:05:10.5441588Z 
2023-01-11T21:05:10.5441665Z if __name__ == "__main__":
2023-01-11T21:05:10.5441780Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5441903Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5442106Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5442314Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5442507Z     arg2_1 = rand_strided((2, 3, 30), (90, 30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5442632Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5442637Z 
2023-01-11T21:05:10.5442642Z 
2023-01-11T21:05:10.5442740Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5442811Z import torch
2023-01-11T21:05:10.5442880Z import random
2023-01-11T21:05:10.5442994Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5443115Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5443120Z 
2023-01-11T21:05:10.5443197Z aten = torch.ops.aten
2023-01-11T21:05:10.5443315Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5443406Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5443411Z 
2023-01-11T21:05:10.5443415Z 
2023-01-11T21:05:10.5443547Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5443750Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5443929Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5444037Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5444098Z {
2023-01-11T21:05:10.5444185Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5444246Z     {
2023-01-11T21:05:10.5444322Z         #pragma omp for 
2023-01-11T21:05:10.5444404Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5444469Z         {
2023-01-11T21:05:10.5444533Z             {
2023-01-11T21:05:10.5444597Z                 {
2023-01-11T21:05:10.5444705Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5444818Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5444958Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5445050Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5445115Z                 }
2023-01-11T21:05:10.5445178Z             }
2023-01-11T21:05:10.5445239Z         }
2023-01-11T21:05:10.5445289Z     }
2023-01-11T21:05:10.5445348Z }
2023-01-11T21:05:10.5445427Z ''')
2023-01-11T21:05:10.5445432Z 
2023-01-11T21:05:10.5445436Z 
2023-01-11T21:05:10.5445526Z async_compile.wait(globals())
2023-01-11T21:05:10.5445633Z del async_compile
2023-01-11T21:05:10.5445638Z 
2023-01-11T21:05:10.5445709Z def call(args):
2023-01-11T21:05:10.5445799Z     arg0_1, arg1_1, arg2_1, arg3_1 = args
2023-01-11T21:05:10.5445856Z     args.clear()
2023-01-11T21:05:10.5446059Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5446209Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5446278Z     del arg0_1
2023-01-11T21:05:10.5446344Z     del arg1_1
2023-01-11T21:05:10.5446407Z     del arg2_1
2023-01-11T21:05:10.5446492Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5446609Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg3_1.data_ptr()))
2023-01-11T21:05:10.5446673Z     del arg3_1
2023-01-11T21:05:10.5446746Z     return (buf1, )
2023-01-11T21:05:10.5446751Z 
2023-01-11T21:05:10.5446756Z 
2023-01-11T21:05:10.5446829Z if __name__ == "__main__":
2023-01-11T21:05:10.5446941Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5447063Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5447265Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5447459Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5447642Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5447837Z     arg3_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5447963Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]))
2023-01-11T21:05:10.5447969Z 
2023-01-11T21:05:10.5447973Z 
2023-01-11T21:05:10.5448067Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5448138Z import torch
2023-01-11T21:05:10.5448206Z import random
2023-01-11T21:05:10.5448319Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5448438Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5448445Z 
2023-01-11T21:05:10.5448509Z aten = torch.ops.aten
2023-01-11T21:05:10.5448640Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5448729Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5448735Z 
2023-01-11T21:05:10.5448740Z 
2023-01-11T21:05:10.5448871Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5449073Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5449192Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5449300Z                        const bfloat16* __restrict__ in_ptr0)
2023-01-11T21:05:10.5449359Z {
2023-01-11T21:05:10.5449442Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5449501Z     {
2023-01-11T21:05:10.5449607Z         #pragma omp for 
2023-01-11T21:05:10.5449690Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5449754Z         {
2023-01-11T21:05:10.5449818Z             {
2023-01-11T21:05:10.5449882Z                 {
2023-01-11T21:05:10.5449990Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5450103Z                     auto tmp1 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5450239Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5450328Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5450395Z                 }
2023-01-11T21:05:10.5450458Z             }
2023-01-11T21:05:10.5450517Z         }
2023-01-11T21:05:10.5450564Z     }
2023-01-11T21:05:10.5450622Z }
2023-01-11T21:05:10.5450699Z ''')
2023-01-11T21:05:10.5450704Z 
2023-01-11T21:05:10.5450708Z 
2023-01-11T21:05:10.5450796Z async_compile.wait(globals())
2023-01-11T21:05:10.5450867Z del async_compile
2023-01-11T21:05:10.5450871Z 
2023-01-11T21:05:10.5450942Z def call(args):
2023-01-11T21:05:10.5451022Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5451079Z     args.clear()
2023-01-11T21:05:10.5451277Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5451429Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5451497Z     del arg0_1
2023-01-11T21:05:10.5451562Z     del arg1_1
2023-01-11T21:05:10.5451644Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5451773Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.5451825Z     del arg2_1
2023-01-11T21:05:10.5451895Z     return (buf1, )
2023-01-11T21:05:10.5451900Z 
2023-01-11T21:05:10.5451904Z 
2023-01-11T21:05:10.5451976Z if __name__ == "__main__":
2023-01-11T21:05:10.5452091Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5452214Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5452415Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5452615Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5452813Z     arg2_1 = rand_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5452922Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5452927Z 
2023-01-11T21:05:10.5452990Z ok (26.149s)
2023-01-11T21:05:10.5453432Z   test_linear_packed_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5453558Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5453816Z [2023-01-11 20:54:12,853] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 272
2023-01-11T21:05:10.5454081Z [2023-01-11 20:54:12,885] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 272
2023-01-11T21:05:10.5454482Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5454606Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5454860Z [2023-01-11 20:54:12,935] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 273
2023-01-11T21:05:10.5455121Z [2023-01-11 20:54:12,940] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 273
2023-01-11T21:05:10.5455518Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5455678Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5455933Z [2023-01-11 20:54:12,997] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 274
2023-01-11T21:05:10.5456194Z [2023-01-11 20:54:13,001] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 274
2023-01-11T21:05:10.5456590Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5456719Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5456973Z [2023-01-11 20:54:13,028] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 275
2023-01-11T21:05:10.5457264Z [2023-01-11 20:54:13,032] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 275
2023-01-11T21:05:10.5457270Z 
2023-01-11T21:05:10.5457362Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5457431Z import torch
2023-01-11T21:05:10.5457499Z import random
2023-01-11T21:05:10.5457601Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5457720Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5457726Z 
2023-01-11T21:05:10.5457801Z aten = torch.ops.aten
2023-01-11T21:05:10.5457933Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5458023Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5458029Z 
2023-01-11T21:05:10.5458033Z 
2023-01-11T21:05:10.5458121Z async_compile.wait(globals())
2023-01-11T21:05:10.5458191Z del async_compile
2023-01-11T21:05:10.5458196Z 
2023-01-11T21:05:10.5458264Z def call(args):
2023-01-11T21:05:10.5458332Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5458403Z     args.clear()
2023-01-11T21:05:10.5458681Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5458850Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5458919Z     del arg0_1
2023-01-11T21:05:10.5458986Z     del arg1_1
2023-01-11T21:05:10.5459052Z     del arg2_1
2023-01-11T21:05:10.5459143Z     return (as_strided(buf0, (2, 3, 30), (90, 30, 1)), )
2023-01-11T21:05:10.5459148Z 
2023-01-11T21:05:10.5459167Z 
2023-01-11T21:05:10.5459229Z if __name__ == "__main__":
2023-01-11T21:05:10.5459341Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5459463Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5459669Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5459863Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5460072Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5460193Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5460198Z 
2023-01-11T21:05:10.5460202Z 
2023-01-11T21:05:10.5460295Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5460350Z import torch
2023-01-11T21:05:10.5460421Z import random
2023-01-11T21:05:10.5460535Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5460653Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5460658Z 
2023-01-11T21:05:10.5460735Z aten = torch.ops.aten
2023-01-11T21:05:10.5460866Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5460956Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5461028Z 
2023-01-11T21:05:10.5461032Z 
2023-01-11T21:05:10.5461106Z async_compile.wait(globals())
2023-01-11T21:05:10.5461177Z del async_compile
2023-01-11T21:05:10.5461182Z 
2023-01-11T21:05:10.5461254Z def call(args):
2023-01-11T21:05:10.5461327Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5461396Z     args.clear()
2023-01-11T21:05:10.5461592Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5461734Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5461802Z     del arg0_1
2023-01-11T21:05:10.5461855Z     del arg1_1
2023-01-11T21:05:10.5461961Z     return (as_strided(buf0, (2, 3, 30), (90, 30, 1)), )
2023-01-11T21:05:10.5461966Z 
2023-01-11T21:05:10.5461970Z 
2023-01-11T21:05:10.5462045Z if __name__ == "__main__":
2023-01-11T21:05:10.5462158Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5462282Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5462483Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5462690Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5462846Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5462851Z 
2023-01-11T21:05:10.5462855Z 
2023-01-11T21:05:10.5462936Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5463004Z import torch
2023-01-11T21:05:10.5463073Z import random
2023-01-11T21:05:10.5463186Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5463304Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5463308Z 
2023-01-11T21:05:10.5463385Z aten = torch.ops.aten
2023-01-11T21:05:10.5463515Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5463591Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5463609Z 
2023-01-11T21:05:10.5463613Z 
2023-01-11T21:05:10.5463688Z async_compile.wait(globals())
2023-01-11T21:05:10.5463760Z del async_compile
2023-01-11T21:05:10.5463765Z 
2023-01-11T21:05:10.5463833Z def call(args):
2023-01-11T21:05:10.5463912Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5463983Z     args.clear()
2023-01-11T21:05:10.5464177Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5464325Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5464379Z     del arg0_1
2023-01-11T21:05:10.5464444Z     del arg1_1
2023-01-11T21:05:10.5464510Z     del arg2_1
2023-01-11T21:05:10.5464582Z     return (buf0, )
2023-01-11T21:05:10.5464587Z 
2023-01-11T21:05:10.5464592Z 
2023-01-11T21:05:10.5464665Z if __name__ == "__main__":
2023-01-11T21:05:10.5464778Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5464902Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5465102Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5465283Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5465481Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5465602Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5465607Z 
2023-01-11T21:05:10.5465612Z 
2023-01-11T21:05:10.5465704Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5465772Z import torch
2023-01-11T21:05:10.5465840Z import random
2023-01-11T21:05:10.5465952Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5466070Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5466076Z 
2023-01-11T21:05:10.5466139Z aten = torch.ops.aten
2023-01-11T21:05:10.5466268Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5466360Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5466365Z 
2023-01-11T21:05:10.5466369Z 
2023-01-11T21:05:10.5466486Z async_compile.wait(globals())
2023-01-11T21:05:10.5466555Z del async_compile
2023-01-11T21:05:10.5466560Z 
2023-01-11T21:05:10.5466628Z def call(args):
2023-01-11T21:05:10.5466700Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5466770Z     args.clear()
2023-01-11T21:05:10.5466952Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5467071Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5467137Z     del arg0_1
2023-01-11T21:05:10.5467201Z     del arg1_1
2023-01-11T21:05:10.5467272Z     return (buf0, )
2023-01-11T21:05:10.5467277Z 
2023-01-11T21:05:10.5467281Z 
2023-01-11T21:05:10.5467354Z if __name__ == "__main__":
2023-01-11T21:05:10.5467464Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5467572Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5467771Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5467971Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5468085Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5468090Z 
2023-01-11T21:05:10.5468155Z ok (0.249s)
2023-01-11T21:05:10.5468621Z   test_linear_unary_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5468753Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5469013Z [2023-01-11 20:54:13,212] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 276
2023-01-11T21:05:10.5469278Z [2023-01-11 20:54:16,083] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 276
2023-01-11T21:05:10.5469678Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5469791Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5470048Z [2023-01-11 20:54:16,148] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 277
2023-01-11T21:05:10.5470308Z [2023-01-11 20:54:16,164] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 277
2023-01-11T21:05:10.5470703Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5470830Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5471085Z [2023-01-11 20:54:16,399] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 278
2023-01-11T21:05:10.5471343Z [2023-01-11 20:54:19,121] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 278
2023-01-11T21:05:10.5471737Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5471861Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5472114Z [2023-01-11 20:54:19,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 279
2023-01-11T21:05:10.5472407Z [2023-01-11 20:54:19,188] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 279
2023-01-11T21:05:10.5472807Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5472919Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5473169Z [2023-01-11 20:54:19,416] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 280
2023-01-11T21:05:10.5473175Z 
2023-01-11T21:05:10.5473267Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5473336Z import torch
2023-01-11T21:05:10.5473407Z import random
2023-01-11T21:05:10.5473519Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5473641Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5473646Z 
2023-01-11T21:05:10.5473723Z aten = torch.ops.aten
2023-01-11T21:05:10.5473877Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5473968Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5473973Z 
2023-01-11T21:05:10.5473978Z 
2023-01-11T21:05:10.5474110Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5474315Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5474432Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5474494Z {
2023-01-11T21:05:10.5474589Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5474652Z     {
2023-01-11T21:05:10.5474713Z         #pragma omp for 
2023-01-11T21:05:10.5474796Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5474857Z         {
2023-01-11T21:05:10.5474920Z             {
2023-01-11T21:05:10.5474985Z                 {
2023-01-11T21:05:10.5475107Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5475189Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5475281Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5475345Z                 }
2023-01-11T21:05:10.5475408Z             }
2023-01-11T21:05:10.5475470Z         }
2023-01-11T21:05:10.5475532Z     }
2023-01-11T21:05:10.5475590Z }
2023-01-11T21:05:10.5475653Z ''')
2023-01-11T21:05:10.5475658Z 
2023-01-11T21:05:10.5475662Z 
2023-01-11T21:05:10.5475750Z async_compile.wait(globals())
2023-01-11T21:05:10.5475821Z del async_compile
2023-01-11T21:05:10.5475826Z 
2023-01-11T21:05:10.5475895Z def call(args):
2023-01-11T21:05:10.5475975Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5476045Z     args.clear()
2023-01-11T21:05:10.5476244Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5476413Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5476470Z     del arg0_1
2023-01-11T21:05:10.5476535Z     del arg1_1
2023-01-11T21:05:10.5476599Z     del arg2_1
2023-01-11T21:05:10.5476715Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5476817Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5476888Z     return (buf1, )
2023-01-11T21:05:10.5476892Z 
2023-01-11T21:05:10.5476897Z 
2023-01-11T21:05:10.5476970Z if __name__ == "__main__":
2023-01-11T21:05:10.5477069Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5477189Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5477393Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5477588Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5477793Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5477944Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5477949Z 
2023-01-11T21:05:10.5477953Z 
2023-01-11T21:05:10.5478044Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5478118Z import torch
2023-01-11T21:05:10.5478174Z import random
2023-01-11T21:05:10.5478288Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5478408Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5478413Z 
2023-01-11T21:05:10.5478493Z aten = torch.ops.aten
2023-01-11T21:05:10.5478626Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5478716Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5478721Z 
2023-01-11T21:05:10.5478726Z 
2023-01-11T21:05:10.5478860Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5479067Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5479173Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5479238Z {
2023-01-11T21:05:10.5479334Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5479395Z     {
2023-01-11T21:05:10.5479475Z         #pragma omp for 
2023-01-11T21:05:10.5479584Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5479648Z         {
2023-01-11T21:05:10.5479697Z             {
2023-01-11T21:05:10.5479760Z                 {
2023-01-11T21:05:10.5479882Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5479977Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5480067Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5480132Z                 }
2023-01-11T21:05:10.5480195Z             }
2023-01-11T21:05:10.5480243Z         }
2023-01-11T21:05:10.5480303Z     }
2023-01-11T21:05:10.5480361Z }
2023-01-11T21:05:10.5480439Z ''')
2023-01-11T21:05:10.5480444Z 
2023-01-11T21:05:10.5480449Z 
2023-01-11T21:05:10.5480539Z async_compile.wait(globals())
2023-01-11T21:05:10.5480744Z del async_compile
2023-01-11T21:05:10.5480753Z 
2023-01-11T21:05:10.5480833Z def call(args):
2023-01-11T21:05:10.5480894Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5480966Z     args.clear()
2023-01-11T21:05:10.5481178Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5481321Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5481389Z     del arg0_1
2023-01-11T21:05:10.5481454Z     del arg1_1
2023-01-11T21:05:10.5481569Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5481658Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5481733Z     return (buf1, )
2023-01-11T21:05:10.5481738Z 
2023-01-11T21:05:10.5481742Z 
2023-01-11T21:05:10.5481818Z if __name__ == "__main__":
2023-01-11T21:05:10.5481931Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5482054Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5482258Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5482467Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5482582Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5482587Z 
2023-01-11T21:05:10.5482592Z 
2023-01-11T21:05:10.5482683Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5482739Z import torch
2023-01-11T21:05:10.5482809Z import random
2023-01-11T21:05:10.5482923Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5483044Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5483049Z 
2023-01-11T21:05:10.5483126Z aten = torch.ops.aten
2023-01-11T21:05:10.5483258Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5483349Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5483354Z 
2023-01-11T21:05:10.5483358Z 
2023-01-11T21:05:10.5483564Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5483754Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5483875Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5483937Z {
2023-01-11T21:05:10.5484034Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5484096Z     {
2023-01-11T21:05:10.5484173Z         #pragma omp for 
2023-01-11T21:05:10.5484240Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5484303Z         {
2023-01-11T21:05:10.5484366Z             {
2023-01-11T21:05:10.5484429Z                 {
2023-01-11T21:05:10.5484550Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5484644Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5484738Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5484788Z                 }
2023-01-11T21:05:10.5484852Z             }
2023-01-11T21:05:10.5484913Z         }
2023-01-11T21:05:10.5484976Z     }
2023-01-11T21:05:10.5485033Z }
2023-01-11T21:05:10.5485110Z ''')
2023-01-11T21:05:10.5485115Z 
2023-01-11T21:05:10.5485119Z 
2023-01-11T21:05:10.5485210Z async_compile.wait(globals())
2023-01-11T21:05:10.5485307Z del async_compile
2023-01-11T21:05:10.5485325Z 
2023-01-11T21:05:10.5485382Z def call(args):
2023-01-11T21:05:10.5485462Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5485531Z     args.clear()
2023-01-11T21:05:10.5485731Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5485882Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5485948Z     del arg0_1
2023-01-11T21:05:10.5486013Z     del arg1_1
2023-01-11T21:05:10.5486065Z     del arg2_1
2023-01-11T21:05:10.5486147Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5486251Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5486320Z     return (buf1, )
2023-01-11T21:05:10.5486328Z 
2023-01-11T21:05:10.5486332Z 
2023-01-11T21:05:10.5486406Z if __name__ == "__main__":
2023-01-11T21:05:10.5486518Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5486641Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5486829Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5487022Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5487217Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5487338Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5487344Z 
2023-01-11T21:05:10.5487348Z 
2023-01-11T21:05:10.5487439Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5487507Z import torch
2023-01-11T21:05:10.5487577Z import random
2023-01-11T21:05:10.5487691Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5487797Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5487804Z 
2023-01-11T21:05:10.5487881Z aten = torch.ops.aten
2023-01-11T21:05:10.5488014Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5488105Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5488111Z 
2023-01-11T21:05:10.5488115Z 
2023-01-11T21:05:10.5488247Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5488448Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5488566Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5488626Z {
2023-01-11T21:05:10.5488710Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5488771Z     {
2023-01-11T21:05:10.5488846Z         #pragma omp for 
2023-01-11T21:05:10.5488929Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5488994Z         {
2023-01-11T21:05:10.5489056Z             {
2023-01-11T21:05:10.5489119Z                 {
2023-01-11T21:05:10.5489225Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5489350Z                     auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.5489443Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5489511Z                 }
2023-01-11T21:05:10.5489573Z             }
2023-01-11T21:05:10.5489633Z         }
2023-01-11T21:05:10.5489680Z     }
2023-01-11T21:05:10.5489738Z }
2023-01-11T21:05:10.5489817Z ''')
2023-01-11T21:05:10.5489822Z 
2023-01-11T21:05:10.5489826Z 
2023-01-11T21:05:10.5489918Z async_compile.wait(globals())
2023-01-11T21:05:10.5489988Z del async_compile
2023-01-11T21:05:10.5489993Z 
2023-01-11T21:05:10.5490061Z def call(args):
2023-01-11T21:05:10.5490134Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5490203Z     args.clear()
2023-01-11T21:05:10.5490389Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5490510Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5490576Z     del arg0_1
2023-01-11T21:05:10.5490644Z     del arg1_1
2023-01-11T21:05:10.5490726Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5490827Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5490896Z     return (buf1, )
2023-01-11T21:05:10.5490934Z 
2023-01-11T21:05:10.5490939Z 
2023-01-11T21:05:10.5491001Z if __name__ == "__main__":
2023-01-11T21:05:10.5491113Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5491231Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5491431Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5491627Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5491740Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5491745Z 
2023-01-11T21:05:10.5491749Z 
2023-01-11T21:05:10.5491840Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5491908Z import torch
2023-01-11T21:05:10.5491964Z import random
2023-01-11T21:05:10.5492080Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5492198Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5492203Z 
2023-01-11T21:05:10.5492280Z aten = torch.ops.aten
2023-01-11T21:05:10.5492413Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5492504Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5492510Z 
2023-01-11T21:05:10.5492514Z 
2023-01-11T21:05:10.5492644Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5492847Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5492952Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5493012Z {
2023-01-11T21:05:10.5493107Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5493167Z     {
2023-01-11T21:05:10.5493241Z         #pragma omp for 
2023-01-11T21:05:10.5493322Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5493387Z         {
2023-01-11T21:05:10.5493437Z             {
2023-01-11T21:05:10.5493499Z                 {
2023-01-11T21:05:10.5493620Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5493791Z                     auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.5493881Z                     auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.5493970Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5494034Z                 }
2023-01-11T21:05:10.5494083Z             }
2023-01-11T21:05:10.5494145Z         }
2023-01-11T21:05:10.5494207Z     }
2023-01-11T21:05:10.5494266Z }
2023-01-11T21:05:10.5494342Z ''')
2023-01-11T21:05:10.5494347Z 
2023-01-11T21:05:10.5494351Z 
2023-01-11T21:05:10.5494438Z async_compile.wait(globals())
2023-01-11T21:05:10.5494508Z del async_compile
2023-01-11T21:05:10.5494513Z 
2023-01-11T21:05:10.5494568Z def call(args):
2023-01-11T21:05:10.5494647Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5494716Z     args.clear()
2023-01-11T21:05:10.5494952Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5495119Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5495189Z     del arg0_1
2023-01-11T21:05:10.5495255Z     del arg1_1
2023-01-11T21:05:10.5495306Z     del arg2_1
2023-01-11T21:05:10.5495421Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5495523Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5495593Z     return (buf1, )
2023-01-11T21:05:10.5495598Z 
2023-01-11T21:05:10.5495602Z 
2023-01-11T21:05:10.5495676Z if __name__ == "__main__":
2023-01-11T21:05:10.5495788Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5495909Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5496111Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5496293Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5496497Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5496649Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5496920Z [2023-01-11 20:54:22,088] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 280
2023-01-11T21:05:10.5497319Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5497445Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5497701Z [2023-01-11 20:54:22,165] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 281
2023-01-11T21:05:10.5497966Z [2023-01-11 20:54:22,182] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 281
2023-01-11T21:05:10.5498368Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5498580Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5498825Z [2023-01-11 20:54:22,406] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 282
2023-01-11T21:05:10.5499088Z [2023-01-11 20:54:25,127] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 282
2023-01-11T21:05:10.5499484Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5499613Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5499865Z [2023-01-11 20:54:25,185] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 283
2023-01-11T21:05:10.5500125Z [2023-01-11 20:54:25,201] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 283
2023-01-11T21:05:10.5500525Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5500649Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5500942Z [2023-01-11 20:54:25,455] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 284
2023-01-11T21:05:10.5501205Z [2023-01-11 20:54:28,146] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 284
2023-01-11T21:05:10.5501597Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5501719Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5501958Z [2023-01-11 20:54:28,207] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 285
2023-01-11T21:05:10.5501976Z 
2023-01-11T21:05:10.5501981Z 
2023-01-11T21:05:10.5502059Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5502131Z import torch
2023-01-11T21:05:10.5502201Z import random
2023-01-11T21:05:10.5502318Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5502438Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5502493Z 
2023-01-11T21:05:10.5502572Z aten = torch.ops.aten
2023-01-11T21:05:10.5502704Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5502781Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5502786Z 
2023-01-11T21:05:10.5502803Z 
2023-01-11T21:05:10.5502921Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5503127Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5503245Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5503307Z {
2023-01-11T21:05:10.5503402Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5503463Z     {
2023-01-11T21:05:10.5503527Z         #pragma omp for 
2023-01-11T21:05:10.5503610Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5503672Z         {
2023-01-11T21:05:10.5503735Z             {
2023-01-11T21:05:10.5503799Z                 {
2023-01-11T21:05:10.5503923Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5504073Z                     auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.5504150Z                     auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.5504242Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5504308Z                 }
2023-01-11T21:05:10.5504371Z             }
2023-01-11T21:05:10.5504432Z         }
2023-01-11T21:05:10.5504492Z     }
2023-01-11T21:05:10.5504550Z }
2023-01-11T21:05:10.5504613Z ''')
2023-01-11T21:05:10.5504618Z 
2023-01-11T21:05:10.5504622Z 
2023-01-11T21:05:10.5504716Z async_compile.wait(globals())
2023-01-11T21:05:10.5504785Z del async_compile
2023-01-11T21:05:10.5504790Z 
2023-01-11T21:05:10.5504859Z def call(args):
2023-01-11T21:05:10.5504933Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5505004Z     args.clear()
2023-01-11T21:05:10.5505205Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5505351Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5505406Z     del arg0_1
2023-01-11T21:05:10.5505470Z     del arg1_1
2023-01-11T21:05:10.5505586Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5505686Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5505757Z     return (buf1, )
2023-01-11T21:05:10.5505762Z 
2023-01-11T21:05:10.5505767Z 
2023-01-11T21:05:10.5505842Z if __name__ == "__main__":
2023-01-11T21:05:10.5505954Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5506062Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5506262Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5506468Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5506617Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5506622Z 
2023-01-11T21:05:10.5506626Z 
2023-01-11T21:05:10.5506721Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5506790Z import torch
2023-01-11T21:05:10.5506859Z import random
2023-01-11T21:05:10.5506970Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5507076Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5507081Z 
2023-01-11T21:05:10.5507158Z aten = torch.ops.aten
2023-01-11T21:05:10.5507289Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5507378Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5507384Z 
2023-01-11T21:05:10.5507388Z 
2023-01-11T21:05:10.5507519Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5507722Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5507844Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5507904Z {
2023-01-11T21:05:10.5507987Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5508047Z     {
2023-01-11T21:05:10.5508159Z         #pragma omp for 
2023-01-11T21:05:10.5508241Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5508302Z         {
2023-01-11T21:05:10.5508364Z             {
2023-01-11T21:05:10.5508426Z                 {
2023-01-11T21:05:10.5508533Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5508679Z                     auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.5508769Z                     auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.5508858Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5508921Z                 }
2023-01-11T21:05:10.5508983Z             }
2023-01-11T21:05:10.5509047Z         }
2023-01-11T21:05:10.5509094Z     }
2023-01-11T21:05:10.5509155Z }
2023-01-11T21:05:10.5509231Z ''')
2023-01-11T21:05:10.5509239Z 
2023-01-11T21:05:10.5509243Z 
2023-01-11T21:05:10.5509331Z async_compile.wait(globals())
2023-01-11T21:05:10.5509401Z del async_compile
2023-01-11T21:05:10.5509406Z 
2023-01-11T21:05:10.5509474Z def call(args):
2023-01-11T21:05:10.5509558Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5509615Z     args.clear()
2023-01-11T21:05:10.5509817Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5509968Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5510035Z     del arg0_1
2023-01-11T21:05:10.5510101Z     del arg1_1
2023-01-11T21:05:10.5510165Z     del arg2_1
2023-01-11T21:05:10.5510249Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5510338Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5510409Z     return (buf1, )
2023-01-11T21:05:10.5510414Z 
2023-01-11T21:05:10.5510418Z 
2023-01-11T21:05:10.5510497Z if __name__ == "__main__":
2023-01-11T21:05:10.5510612Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5510733Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5510935Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5511130Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5511327Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5511434Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5511439Z 
2023-01-11T21:05:10.5511457Z 
2023-01-11T21:05:10.5511537Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5511605Z import torch
2023-01-11T21:05:10.5511677Z import random
2023-01-11T21:05:10.5511791Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5511912Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5511917Z 
2023-01-11T21:05:10.5511994Z aten = torch.ops.aten
2023-01-11T21:05:10.5512166Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5512243Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5512248Z 
2023-01-11T21:05:10.5512252Z 
2023-01-11T21:05:10.5512385Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5512587Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5512707Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5512769Z {
2023-01-11T21:05:10.5512864Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5512925Z     {
2023-01-11T21:05:10.5512986Z         #pragma omp for 
2023-01-11T21:05:10.5513068Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5513129Z         {
2023-01-11T21:05:10.5513191Z             {
2023-01-11T21:05:10.5513255Z                 {
2023-01-11T21:05:10.5513375Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5513524Z                     auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.5513604Z                     auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.5513695Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5513759Z                 }
2023-01-11T21:05:10.5513855Z             }
2023-01-11T21:05:10.5513918Z         }
2023-01-11T21:05:10.5513979Z     }
2023-01-11T21:05:10.5514038Z }
2023-01-11T21:05:10.5514101Z ''')
2023-01-11T21:05:10.5514106Z 
2023-01-11T21:05:10.5514110Z 
2023-01-11T21:05:10.5514200Z async_compile.wait(globals())
2023-01-11T21:05:10.5514271Z del async_compile
2023-01-11T21:05:10.5514275Z 
2023-01-11T21:05:10.5514344Z def call(args):
2023-01-11T21:05:10.5514418Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5514489Z     args.clear()
2023-01-11T21:05:10.5514688Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5514795Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5514862Z     del arg0_1
2023-01-11T21:05:10.5514931Z     del arg1_1
2023-01-11T21:05:10.5515017Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5515119Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5515191Z     return (buf1, )
2023-01-11T21:05:10.5515196Z 
2023-01-11T21:05:10.5515202Z 
2023-01-11T21:05:10.5515276Z if __name__ == "__main__":
2023-01-11T21:05:10.5515387Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5515496Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5515694Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5515892Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5516005Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5516010Z 
2023-01-11T21:05:10.5516014Z 
2023-01-11T21:05:10.5516106Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5516174Z import torch
2023-01-11T21:05:10.5516242Z import random
2023-01-11T21:05:10.5516355Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5516463Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5516468Z 
2023-01-11T21:05:10.5516543Z aten = torch.ops.aten
2023-01-11T21:05:10.5516676Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5516766Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5516771Z 
2023-01-11T21:05:10.5516776Z 
2023-01-11T21:05:10.5516906Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5517109Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5517227Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5517286Z {
2023-01-11T21:05:10.5517369Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5517428Z     {
2023-01-11T21:05:10.5517503Z         #pragma omp for 
2023-01-11T21:05:10.5517584Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5517645Z         {
2023-01-11T21:05:10.5517734Z             {
2023-01-11T21:05:10.5517784Z                 {
2023-01-11T21:05:10.5517902Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5518004Z                     auto tmp1 = std::tanh(tmp0);
2023-01-11T21:05:10.5518092Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5518155Z                 }
2023-01-11T21:05:10.5518216Z             }
2023-01-11T21:05:10.5518276Z         }
2023-01-11T21:05:10.5518323Z     }
2023-01-11T21:05:10.5518382Z }
2023-01-11T21:05:10.5518459Z ''')
2023-01-11T21:05:10.5518464Z 
2023-01-11T21:05:10.5518468Z 
2023-01-11T21:05:10.5518555Z async_compile.wait(globals())
2023-01-11T21:05:10.5518625Z del async_compile
2023-01-11T21:05:10.5518630Z 
2023-01-11T21:05:10.5518700Z def call(args):
2023-01-11T21:05:10.5518780Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5518849Z     args.clear()
2023-01-11T21:05:10.5519034Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5519204Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5519272Z     del arg0_1
2023-01-11T21:05:10.5519337Z     del arg1_1
2023-01-11T21:05:10.5519435Z     del arg2_1
2023-01-11T21:05:10.5519553Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5519653Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5519711Z     return (buf1, )
2023-01-11T21:05:10.5519716Z 
2023-01-11T21:05:10.5519720Z 
2023-01-11T21:05:10.5519794Z if __name__ == "__main__":
2023-01-11T21:05:10.5519906Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5520030Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5520235Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5520430Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5520778Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5520904Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5520910Z 
2023-01-11T21:05:10.5520914Z 
2023-01-11T21:05:10.5520996Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5521066Z import torch
2023-01-11T21:05:10.5521139Z import random
2023-01-11T21:05:10.5521254Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5521374Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5521379Z 
2023-01-11T21:05:10.5521458Z aten = torch.ops.aten
2023-01-11T21:05:10.5521591Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5521683Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5521688Z 
2023-01-11T21:05:10.5521694Z 
2023-01-11T21:05:10.5521816Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5522020Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5522143Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5522204Z {
2023-01-11T21:05:10.5522299Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5522361Z     {
2023-01-11T21:05:10.5522441Z         #pragma omp for 
2023-01-11T21:05:10.5522511Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5522574Z         {
2023-01-11T21:05:10.5522637Z             {
2023-01-11T21:05:10.5522705Z                 {
2023-01-11T21:05:10.5522825Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5522925Z                     auto tmp1 = std::tanh(tmp0);
2023-01-11T21:05:10.5523015Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5523065Z                 }
2023-01-11T21:05:10.5523127Z             }
2023-01-11T21:05:10.5523187Z         }
2023-01-11T21:05:10.5523246Z     }
2023-01-11T21:05:10.5523303Z }
2023-01-11T21:05:10.5523379Z ''')
2023-01-11T21:05:10.5523384Z 
2023-01-11T21:05:10.5523451Z 
2023-01-11T21:05:10.5523541Z async_compile.wait(globals())
2023-01-11T21:05:10.5523599Z del async_compile
2023-01-11T21:05:10.5523603Z 
2023-01-11T21:05:10.5523673Z def call(args):
2023-01-11T21:05:10.5523747Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5523820Z     args.clear()
2023-01-11T21:05:10.5524021Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5524163Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5524235Z     del arg0_1
2023-01-11T21:05:10.5524287Z     del arg1_1
2023-01-11T21:05:10.5524402Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5524504Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5524576Z     return (buf1, )
2023-01-11T21:05:10.5524581Z 
2023-01-11T21:05:10.5524585Z 
2023-01-11T21:05:10.5524660Z if __name__ == "__main__":
2023-01-11T21:05:10.5524772Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5524900Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5525103Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5525334Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5525450Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5525717Z [2023-01-11 20:54:28,224] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 285
2023-01-11T21:05:10.5526116Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5526241Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5526501Z [2023-01-11 20:54:28,439] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 286
2023-01-11T21:05:10.5526765Z [2023-01-11 20:54:31,089] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 286
2023-01-11T21:05:10.5527165Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5527290Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5527543Z [2023-01-11 20:54:31,140] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 287
2023-01-11T21:05:10.5527802Z [2023-01-11 20:54:31,155] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 287
2023-01-11T21:05:10.5528189Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5528313Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5528565Z [2023-01-11 20:54:31,487] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 288
2023-01-11T21:05:10.5528826Z [2023-01-11 20:54:34,208] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 288
2023-01-11T21:05:10.5529220Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5529377Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5529632Z [2023-01-11 20:54:34,420] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 289
2023-01-11T21:05:10.5529892Z [2023-01-11 20:54:34,446] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 289
2023-01-11T21:05:10.5530283Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5530407Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5530656Z [2023-01-11 20:54:34,704] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 290
2023-01-11T21:05:10.5530664Z 
2023-01-11T21:05:10.5530668Z 
2023-01-11T21:05:10.5530761Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5530816Z import torch
2023-01-11T21:05:10.5530886Z import random
2023-01-11T21:05:10.5531031Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5531152Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5531157Z 
2023-01-11T21:05:10.5531233Z aten = torch.ops.aten
2023-01-11T21:05:10.5531365Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5531454Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5531460Z 
2023-01-11T21:05:10.5531464Z 
2023-01-11T21:05:10.5531597Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5531786Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5531906Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5531966Z {
2023-01-11T21:05:10.5532060Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5532122Z     {
2023-01-11T21:05:10.5532197Z         #pragma omp for 
2023-01-11T21:05:10.5532278Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5532326Z         {
2023-01-11T21:05:10.5532390Z             {
2023-01-11T21:05:10.5532453Z                 {
2023-01-11T21:05:10.5532574Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5532676Z                     auto tmp1 = std::tanh(tmp0);
2023-01-11T21:05:10.5532766Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5532816Z                 }
2023-01-11T21:05:10.5532878Z             }
2023-01-11T21:05:10.5532937Z         }
2023-01-11T21:05:10.5532996Z     }
2023-01-11T21:05:10.5533055Z }
2023-01-11T21:05:10.5533131Z ''')
2023-01-11T21:05:10.5533136Z 
2023-01-11T21:05:10.5533141Z 
2023-01-11T21:05:10.5533229Z async_compile.wait(globals())
2023-01-11T21:05:10.5533287Z del async_compile
2023-01-11T21:05:10.5533304Z 
2023-01-11T21:05:10.5533360Z def call(args):
2023-01-11T21:05:10.5533442Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5533511Z     args.clear()
2023-01-11T21:05:10.5533711Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5533863Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5533930Z     del arg0_1
2023-01-11T21:05:10.5533994Z     del arg1_1
2023-01-11T21:05:10.5534047Z     del arg2_1
2023-01-11T21:05:10.5534132Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5534232Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5534303Z     return (buf1, )
2023-01-11T21:05:10.5534308Z 
2023-01-11T21:05:10.5534312Z 
2023-01-11T21:05:10.5534389Z if __name__ == "__main__":
2023-01-11T21:05:10.5534502Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5534623Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5534811Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5535044Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5535238Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5535360Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5535365Z 
2023-01-11T21:05:10.5535369Z 
2023-01-11T21:05:10.5535462Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5535533Z import torch
2023-01-11T21:05:10.5535601Z import random
2023-01-11T21:05:10.5535714Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5535820Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5535825Z 
2023-01-11T21:05:10.5535902Z aten = torch.ops.aten
2023-01-11T21:05:10.5536033Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5536122Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5536127Z 
2023-01-11T21:05:10.5536131Z 
2023-01-11T21:05:10.5536265Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5536469Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5536621Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5536681Z {
2023-01-11T21:05:10.5536766Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5536830Z     {
2023-01-11T21:05:10.5536905Z         #pragma omp for 
2023-01-11T21:05:10.5536987Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5537048Z         {
2023-01-11T21:05:10.5537112Z             {
2023-01-11T21:05:10.5537174Z                 {
2023-01-11T21:05:10.5537281Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5537380Z                     auto tmp1 = std::tanh(tmp0);
2023-01-11T21:05:10.5537468Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5537531Z                 }
2023-01-11T21:05:10.5537592Z             }
2023-01-11T21:05:10.5537653Z         }
2023-01-11T21:05:10.5537716Z     }
2023-01-11T21:05:10.5537762Z }
2023-01-11T21:05:10.5537839Z ''')
2023-01-11T21:05:10.5537843Z 
2023-01-11T21:05:10.5537848Z 
2023-01-11T21:05:10.5537935Z async_compile.wait(globals())
2023-01-11T21:05:10.5538008Z del async_compile
2023-01-11T21:05:10.5538013Z 
2023-01-11T21:05:10.5538082Z def call(args):
2023-01-11T21:05:10.5538155Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5538225Z     args.clear()
2023-01-11T21:05:10.5538411Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5538619Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5538688Z     del arg0_1
2023-01-11T21:05:10.5538756Z     del arg1_1
2023-01-11T21:05:10.5538841Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5538944Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5539016Z     return (buf1, )
2023-01-11T21:05:10.5539021Z 
2023-01-11T21:05:10.5539025Z 
2023-01-11T21:05:10.5539085Z if __name__ == "__main__":
2023-01-11T21:05:10.5539201Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5539324Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5539533Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5539733Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5539849Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5539854Z 
2023-01-11T21:05:10.5539858Z 
2023-01-11T21:05:10.5539952Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5540021Z import torch
2023-01-11T21:05:10.5540076Z import random
2023-01-11T21:05:10.5540191Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5540313Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5540317Z 
2023-01-11T21:05:10.5540393Z aten = torch.ops.aten
2023-01-11T21:05:10.5540525Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5540649Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5540654Z 
2023-01-11T21:05:10.5540658Z 
2023-01-11T21:05:10.5540790Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5540994Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5541099Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5541159Z {
2023-01-11T21:05:10.5541254Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5541314Z     {
2023-01-11T21:05:10.5541391Z         #pragma omp for 
2023-01-11T21:05:10.5541472Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5541533Z         {
2023-01-11T21:05:10.5541582Z             {
2023-01-11T21:05:10.5541645Z                 {
2023-01-11T21:05:10.5541763Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5541870Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5541973Z                     auto tmp2 = static_cast<float>(3);
2023-01-11T21:05:10.5542066Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5542170Z                     auto tmp4 = static_cast<float>(0.0);
2023-01-11T21:05:10.5542332Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp3, tmp4);
2023-01-11T21:05:10.5542438Z                     auto tmp6 = static_cast<float>(6.0);
2023-01-11T21:05:10.5542561Z                     auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::min(tmp5, tmp6);
2023-01-11T21:05:10.5542651Z                     auto tmp8 = tmp1 * tmp7;
2023-01-11T21:05:10.5542754Z                     auto tmp9 = static_cast<float>(6);
2023-01-11T21:05:10.5542846Z                     auto tmp10 = tmp8 / tmp9;
2023-01-11T21:05:10.5542957Z                     auto tmp11 = static_cast<bfloat16>(tmp10);
2023-01-11T21:05:10.5543048Z                     in_out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.5543099Z                 }
2023-01-11T21:05:10.5543162Z             }
2023-01-11T21:05:10.5543224Z         }
2023-01-11T21:05:10.5543290Z     }
2023-01-11T21:05:10.5543349Z }
2023-01-11T21:05:10.5543426Z ''')
2023-01-11T21:05:10.5543432Z 
2023-01-11T21:05:10.5543436Z 
2023-01-11T21:05:10.5543526Z async_compile.wait(globals())
2023-01-11T21:05:10.5543584Z del async_compile
2023-01-11T21:05:10.5543589Z 
2023-01-11T21:05:10.5543658Z def call(args):
2023-01-11T21:05:10.5543739Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5543810Z     args.clear()
2023-01-11T21:05:10.5544009Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5544178Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5544247Z     del arg0_1
2023-01-11T21:05:10.5544299Z     del arg1_1
2023-01-11T21:05:10.5544364Z     del arg2_1
2023-01-11T21:05:10.5544480Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5544584Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5544658Z     return (buf1, )
2023-01-11T21:05:10.5544662Z 
2023-01-11T21:05:10.5544667Z 
2023-01-11T21:05:10.5544744Z if __name__ == "__main__":
2023-01-11T21:05:10.5544858Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5544982Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5545171Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5545366Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5545572Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5545693Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5545697Z 
2023-01-11T21:05:10.5545702Z 
2023-01-11T21:05:10.5545794Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5545863Z import torch
2023-01-11T21:05:10.5545937Z import random
2023-01-11T21:05:10.5546051Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5546188Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5546193Z 
2023-01-11T21:05:10.5546270Z aten = torch.ops.aten
2023-01-11T21:05:10.5546405Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5546495Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5546500Z 
2023-01-11T21:05:10.5546504Z 
2023-01-11T21:05:10.5546637Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5546841Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5546960Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5547021Z {
2023-01-11T21:05:10.5547105Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5547166Z     {
2023-01-11T21:05:10.5547241Z         #pragma omp for 
2023-01-11T21:05:10.5547322Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5547385Z         {
2023-01-11T21:05:10.5547447Z             {
2023-01-11T21:05:10.5547499Z                 {
2023-01-11T21:05:10.5547620Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5547727Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5547911Z                     auto tmp2 = static_cast<float>(3);
2023-01-11T21:05:10.5548005Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5548110Z                     auto tmp4 = static_cast<float>(0.0);
2023-01-11T21:05:10.5548238Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp3, tmp4);
2023-01-11T21:05:10.5548344Z                     auto tmp6 = static_cast<float>(6.0);
2023-01-11T21:05:10.5548455Z                     auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::min(tmp5, tmp6);
2023-01-11T21:05:10.5548545Z                     auto tmp8 = tmp1 * tmp7;
2023-01-11T21:05:10.5548647Z                     auto tmp9 = static_cast<float>(6);
2023-01-11T21:05:10.5548741Z                     auto tmp10 = tmp8 / tmp9;
2023-01-11T21:05:10.5548854Z                     auto tmp11 = static_cast<bfloat16>(tmp10);
2023-01-11T21:05:10.5548949Z                     in_out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.5549015Z                 }
2023-01-11T21:05:10.5549063Z             }
2023-01-11T21:05:10.5549127Z         }
2023-01-11T21:05:10.5549189Z     }
2023-01-11T21:05:10.5549248Z }
2023-01-11T21:05:10.5549328Z ''')
2023-01-11T21:05:10.5549333Z 
2023-01-11T21:05:10.5549336Z 
2023-01-11T21:05:10.5549425Z async_compile.wait(globals())
2023-01-11T21:05:10.5549502Z del async_compile
2023-01-11T21:05:10.5549507Z 
2023-01-11T21:05:10.5549577Z def call(args):
2023-01-11T21:05:10.5549637Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5549708Z     args.clear()
2023-01-11T21:05:10.5549910Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5550056Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5550123Z     del arg0_1
2023-01-11T21:05:10.5550188Z     del arg1_1
2023-01-11T21:05:10.5550305Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5550394Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5550467Z     return (buf1, )
2023-01-11T21:05:10.5550475Z 
2023-01-11T21:05:10.5550479Z 
2023-01-11T21:05:10.5550552Z if __name__ == "__main__":
2023-01-11T21:05:10.5550664Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5550785Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5550987Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5551193Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5551305Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5551310Z 
2023-01-11T21:05:10.5551314Z 
2023-01-11T21:05:10.5551394Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5551461Z import torch
2023-01-11T21:05:10.5551529Z import random
2023-01-11T21:05:10.5551674Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5551793Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5551798Z 
2023-01-11T21:05:10.5551874Z aten = torch.ops.aten
2023-01-11T21:05:10.5552008Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5552085Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5552104Z 
2023-01-11T21:05:10.5552108Z 
2023-01-11T21:05:10.5552226Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5552430Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5552549Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5552607Z {
2023-01-11T21:05:10.5552702Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5552762Z     {
2023-01-11T21:05:10.5552838Z         #pragma omp for 
2023-01-11T21:05:10.5552907Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5552971Z         {
2023-01-11T21:05:10.5553036Z             {
2023-01-11T21:05:10.5553099Z                 {
2023-01-11T21:05:10.5553219Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5553359Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5553465Z                     auto tmp2 = static_cast<float>(3);
2023-01-11T21:05:10.5553543Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5553647Z                     auto tmp4 = static_cast<float>(0.0);
2023-01-11T21:05:10.5553772Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp3, tmp4);
2023-01-11T21:05:10.5553876Z                     auto tmp6 = static_cast<float>(6.0);
2023-01-11T21:05:10.5554001Z                     auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::min(tmp5, tmp6);
2023-01-11T21:05:10.5554091Z                     auto tmp8 = tmp1 * tmp7;
2023-01-11T21:05:10.5554191Z                     auto tmp9 = static_cast<float>(6);
2023-01-11T21:05:10.5554283Z                     auto tmp10 = tmp8 / tmp9;
2023-01-11T21:05:10.5554385Z                     auto tmp11 = static_cast<bfloat16>(tmp10);
2023-01-11T21:05:10.5554474Z                     in_out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.5554537Z                 }
2023-01-11T21:05:10.5554601Z             }
2023-01-11T21:05:10.5554663Z         }
2023-01-11T21:05:10.5554723Z     }
2023-01-11T21:05:10.5554768Z }
2023-01-11T21:05:10.5554845Z ''')
2023-01-11T21:05:10.5554850Z 
2023-01-11T21:05:10.5554854Z 
2023-01-11T21:05:10.5554944Z async_compile.wait(globals())
2023-01-11T21:05:10.5555015Z del async_compile
2023-01-11T21:05:10.5555020Z 
2023-01-11T21:05:10.5555087Z def call(args):
2023-01-11T21:05:10.5555167Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5555236Z     args.clear()
2023-01-11T21:05:10.5555436Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5555575Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5555644Z     del arg0_1
2023-01-11T21:05:10.5555709Z     del arg1_1
2023-01-11T21:05:10.5555772Z     del arg2_1
2023-01-11T21:05:10.5555855Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5555959Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5556029Z     return (buf1, )
2023-01-11T21:05:10.5556034Z 
2023-01-11T21:05:10.5556038Z 
2023-01-11T21:05:10.5556099Z if __name__ == "__main__":
2023-01-11T21:05:10.5556210Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5556329Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5556530Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5556725Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5556920Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5557040Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5557338Z [2023-01-11 20:54:37,443] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 290
2023-01-11T21:05:10.5557738Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5557850Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5558105Z [2023-01-11 20:54:37,603] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 291
2023-01-11T21:05:10.5558366Z [2023-01-11 20:54:37,625] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 291
2023-01-11T21:05:10.5558763Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5558917Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5559174Z [2023-01-11 20:54:37,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 292
2023-01-11T21:05:10.5559436Z [2023-01-11 20:54:40,547] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 292
2023-01-11T21:05:10.5559831Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5559954Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5560208Z [2023-01-11 20:54:40,636] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 293
2023-01-11T21:05:10.5560469Z [2023-01-11 20:54:40,663] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 293
2023-01-11T21:05:10.5560992Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5561122Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5561380Z [2023-01-11 20:54:40,928] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 294
2023-01-11T21:05:10.5561385Z 
2023-01-11T21:05:10.5561390Z 
2023-01-11T21:05:10.5561487Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5561560Z import torch
2023-01-11T21:05:10.5561630Z import random
2023-01-11T21:05:10.5561745Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5561866Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5561873Z 
2023-01-11T21:05:10.5561952Z aten = torch.ops.aten
2023-01-11T21:05:10.5562072Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5562164Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5562169Z 
2023-01-11T21:05:10.5562174Z 
2023-01-11T21:05:10.5562308Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5562513Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5562633Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5562697Z {
2023-01-11T21:05:10.5562792Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5562839Z     {
2023-01-11T21:05:10.5562914Z         #pragma omp for 
2023-01-11T21:05:10.5562995Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5563112Z         {
2023-01-11T21:05:10.5563175Z             {
2023-01-11T21:05:10.5563242Z                 {
2023-01-11T21:05:10.5563366Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5563460Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5563562Z                     auto tmp2 = static_cast<float>(3);
2023-01-11T21:05:10.5563653Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.5563760Z                     auto tmp4 = static_cast<float>(0.0);
2023-01-11T21:05:10.5563888Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::max(tmp3, tmp4);
2023-01-11T21:05:10.5563992Z                     auto tmp6 = static_cast<float>(6.0);
2023-01-11T21:05:10.5564116Z                     auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::min(tmp5, tmp6);
2023-01-11T21:05:10.5564207Z                     auto tmp8 = tmp1 * tmp7;
2023-01-11T21:05:10.5564296Z                     auto tmp9 = static_cast<float>(6);
2023-01-11T21:05:10.5564390Z                     auto tmp10 = tmp8 / tmp9;
2023-01-11T21:05:10.5564502Z                     auto tmp11 = static_cast<bfloat16>(tmp10);
2023-01-11T21:05:10.5564628Z                     in_out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.5564694Z                 }
2023-01-11T21:05:10.5564755Z             }
2023-01-11T21:05:10.5564815Z         }
2023-01-11T21:05:10.5564862Z     }
2023-01-11T21:05:10.5564921Z }
2023-01-11T21:05:10.5564999Z ''')
2023-01-11T21:05:10.5565004Z 
2023-01-11T21:05:10.5565008Z 
2023-01-11T21:05:10.5565096Z async_compile.wait(globals())
2023-01-11T21:05:10.5565167Z del async_compile
2023-01-11T21:05:10.5565172Z 
2023-01-11T21:05:10.5565241Z def call(args):
2023-01-11T21:05:10.5565315Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5565373Z     args.clear()
2023-01-11T21:05:10.5565573Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5565692Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5565762Z     del arg0_1
2023-01-11T21:05:10.5565828Z     del arg1_1
2023-01-11T21:05:10.5565912Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5566016Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5566075Z     return (buf1, )
2023-01-11T21:05:10.5566081Z 
2023-01-11T21:05:10.5566099Z 
2023-01-11T21:05:10.5566160Z if __name__ == "__main__":
2023-01-11T21:05:10.5566273Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5566395Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5566594Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5566790Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5566907Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5566912Z 
2023-01-11T21:05:10.5566917Z 
2023-01-11T21:05:10.5567007Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5567076Z import torch
2023-01-11T21:05:10.5567132Z import random
2023-01-11T21:05:10.5567245Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5567364Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5567371Z 
2023-01-11T21:05:10.5567451Z aten = torch.ops.aten
2023-01-11T21:05:10.5567582Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5567672Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5567677Z 
2023-01-11T21:05:10.5567682Z 
2023-01-11T21:05:10.5567813Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5568018Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5568125Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5568184Z {
2023-01-11T21:05:10.5568278Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5568340Z     {
2023-01-11T21:05:10.5568416Z         #pragma omp for 
2023-01-11T21:05:10.5568497Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5568574Z         {
2023-01-11T21:05:10.5568636Z             {
2023-01-11T21:05:10.5568698Z                 {
2023-01-11T21:05:10.5568820Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5568926Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5569027Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:05:10.5569118Z                     auto tmp3 = tmp1 > tmp2;
2023-01-11T21:05:10.5569210Z                     auto tmp4 = static_cast<float>(0.1);
2023-01-11T21:05:10.5569304Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5569399Z                     auto tmp6 = tmp3 ? tmp1 : tmp5;
2023-01-11T21:05:10.5569508Z                     auto tmp7 = static_cast<bfloat16>(tmp6);
2023-01-11T21:05:10.5569595Z                     in_out_ptr0[i0] = tmp7;
2023-01-11T21:05:10.5569659Z                 }
2023-01-11T21:05:10.5569721Z             }
2023-01-11T21:05:10.5569771Z         }
2023-01-11T21:05:10.5569831Z     }
2023-01-11T21:05:10.5569889Z }
2023-01-11T21:05:10.5569966Z ''')
2023-01-11T21:05:10.5569971Z 
2023-01-11T21:05:10.5569975Z 
2023-01-11T21:05:10.5570063Z async_compile.wait(globals())
2023-01-11T21:05:10.5570163Z del async_compile
2023-01-11T21:05:10.5570168Z 
2023-01-11T21:05:10.5570238Z def call(args):
2023-01-11T21:05:10.5570319Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5570375Z     args.clear()
2023-01-11T21:05:10.5570577Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5570747Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5570814Z     del arg0_1
2023-01-11T21:05:10.5570881Z     del arg1_1
2023-01-11T21:05:10.5570945Z     del arg2_1
2023-01-11T21:05:10.5571059Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5571148Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5571221Z     return (buf1, )
2023-01-11T21:05:10.5571226Z 
2023-01-11T21:05:10.5571231Z 
2023-01-11T21:05:10.5571304Z if __name__ == "__main__":
2023-01-11T21:05:10.5571418Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5571540Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5571739Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5571931Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5572134Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5572242Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5572247Z 
2023-01-11T21:05:10.5572264Z 
2023-01-11T21:05:10.5572342Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5572410Z import torch
2023-01-11T21:05:10.5572479Z import random
2023-01-11T21:05:10.5572592Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5572714Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5572719Z 
2023-01-11T21:05:10.5572794Z aten = torch.ops.aten
2023-01-11T21:05:10.5572929Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5573007Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5573011Z 
2023-01-11T21:05:10.5573015Z 
2023-01-11T21:05:10.5573146Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5573349Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5573471Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5573531Z {
2023-01-11T21:05:10.5573626Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5573686Z     {
2023-01-11T21:05:10.5573748Z         #pragma omp for 
2023-01-11T21:05:10.5573829Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5573891Z         {
2023-01-11T21:05:10.5573953Z             {
2023-01-11T21:05:10.5574045Z                 {
2023-01-11T21:05:10.5574164Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5574271Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5574363Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:05:10.5574453Z                     auto tmp3 = tmp1 > tmp2;
2023-01-11T21:05:10.5574557Z                     auto tmp4 = static_cast<float>(0.1);
2023-01-11T21:05:10.5574647Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5574745Z                     auto tmp6 = tmp3 ? tmp1 : tmp5;
2023-01-11T21:05:10.5574854Z                     auto tmp7 = static_cast<bfloat16>(tmp6);
2023-01-11T21:05:10.5574944Z                     in_out_ptr0[i0] = tmp7;
2023-01-11T21:05:10.5574994Z                 }
2023-01-11T21:05:10.5575056Z             }
2023-01-11T21:05:10.5575117Z         }
2023-01-11T21:05:10.5575178Z     }
2023-01-11T21:05:10.5575237Z }
2023-01-11T21:05:10.5575315Z ''')
2023-01-11T21:05:10.5575323Z 
2023-01-11T21:05:10.5575327Z 
2023-01-11T21:05:10.5575414Z async_compile.wait(globals())
2023-01-11T21:05:10.5575473Z del async_compile
2023-01-11T21:05:10.5575491Z 
2023-01-11T21:05:10.5575547Z def call(args):
2023-01-11T21:05:10.5575652Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5575725Z     args.clear()
2023-01-11T21:05:10.5575925Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5576067Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5576136Z     del arg0_1
2023-01-11T21:05:10.5576202Z     del arg1_1
2023-01-11T21:05:10.5576303Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5576407Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5576478Z     return (buf1, )
2023-01-11T21:05:10.5576483Z 
2023-01-11T21:05:10.5576487Z 
2023-01-11T21:05:10.5576562Z if __name__ == "__main__":
2023-01-11T21:05:10.5576677Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5576797Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5577001Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5577205Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5577308Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5577313Z 
2023-01-11T21:05:10.5577317Z 
2023-01-11T21:05:10.5577409Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5577478Z import torch
2023-01-11T21:05:10.5577548Z import random
2023-01-11T21:05:10.5577661Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5577781Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5577786Z 
2023-01-11T21:05:10.5577863Z aten = torch.ops.aten
2023-01-11T21:05:10.5577980Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5578073Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5578079Z 
2023-01-11T21:05:10.5578083Z 
2023-01-11T21:05:10.5578216Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5578422Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5578631Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5578693Z {
2023-01-11T21:05:10.5578790Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5578850Z     {
2023-01-11T21:05:10.5578912Z         #pragma omp for 
2023-01-11T21:05:10.5578994Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5579056Z         {
2023-01-11T21:05:10.5579119Z             {
2023-01-11T21:05:10.5579183Z                 {
2023-01-11T21:05:10.5579304Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5579413Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5579502Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:05:10.5579632Z                     auto tmp3 = tmp1 > tmp2;
2023-01-11T21:05:10.5579738Z                     auto tmp4 = static_cast<float>(0.1);
2023-01-11T21:05:10.5579830Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5579930Z                     auto tmp6 = tmp3 ? tmp1 : tmp5;
2023-01-11T21:05:10.5580040Z                     auto tmp7 = static_cast<bfloat16>(tmp6);
2023-01-11T21:05:10.5580131Z                     in_out_ptr0[i0] = tmp7;
2023-01-11T21:05:10.5580182Z                 }
2023-01-11T21:05:10.5580245Z             }
2023-01-11T21:05:10.5580307Z         }
2023-01-11T21:05:10.5580367Z     }
2023-01-11T21:05:10.5580425Z }
2023-01-11T21:05:10.5580502Z ''')
2023-01-11T21:05:10.5580509Z 
2023-01-11T21:05:10.5580514Z 
2023-01-11T21:05:10.5580600Z async_compile.wait(globals())
2023-01-11T21:05:10.5580659Z del async_compile
2023-01-11T21:05:10.5580663Z 
2023-01-11T21:05:10.5580732Z def call(args):
2023-01-11T21:05:10.5580812Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5580885Z     args.clear()
2023-01-11T21:05:10.5581085Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5581291Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5581360Z     del arg0_1
2023-01-11T21:05:10.5581412Z     del arg1_1
2023-01-11T21:05:10.5581476Z     del arg2_1
2023-01-11T21:05:10.5581559Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5581661Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5581730Z     return (buf1, )
2023-01-11T21:05:10.5581736Z 
2023-01-11T21:05:10.5581740Z 
2023-01-11T21:05:10.5581814Z if __name__ == "__main__":
2023-01-11T21:05:10.5581926Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5582047Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5582238Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5582435Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5582637Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5582761Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5583029Z [2023-01-11 20:54:43,663] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 294
2023-01-11T21:05:10.5583430Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5583556Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5583810Z [2023-01-11 20:54:43,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 295
2023-01-11T21:05:10.5584075Z [2023-01-11 20:54:43,751] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 295
2023-01-11T21:05:10.5584471Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5584583Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5584838Z [2023-01-11 20:54:44,048] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 296
2023-01-11T21:05:10.5585097Z [2023-01-11 20:54:46,785] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 296
2023-01-11T21:05:10.5585492Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5585650Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5585903Z [2023-01-11 20:54:46,888] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 297
2023-01-11T21:05:10.5586162Z [2023-01-11 20:54:46,911] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 297
2023-01-11T21:05:10.5586559Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5586684Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5586939Z [2023-01-11 20:54:47,160] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 298
2023-01-11T21:05:10.5587224Z [2023-01-11 20:54:49,856] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 298
2023-01-11T21:05:10.5587604Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5587729Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5587981Z [2023-01-11 20:54:49,949] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 299
2023-01-11T21:05:10.5587986Z 
2023-01-11T21:05:10.5587991Z 
2023-01-11T21:05:10.5588084Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5588155Z import torch
2023-01-11T21:05:10.5588224Z import random
2023-01-11T21:05:10.5588339Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5588459Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5588466Z 
2023-01-11T21:05:10.5588544Z aten = torch.ops.aten
2023-01-11T21:05:10.5588665Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5588755Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5588760Z 
2023-01-11T21:05:10.5588764Z 
2023-01-11T21:05:10.5588898Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5589100Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5589219Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5589280Z {
2023-01-11T21:05:10.5589374Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5589425Z     {
2023-01-11T21:05:10.5594744Z         #pragma omp for 
2023-01-11T21:05:10.5594866Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5594937Z         {
2023-01-11T21:05:10.5595004Z             {
2023-01-11T21:05:10.5595069Z                 {
2023-01-11T21:05:10.5595184Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5595296Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5595399Z                     auto tmp2 = static_cast<float>(0);
2023-01-11T21:05:10.5595491Z                     auto tmp3 = tmp1 > tmp2;
2023-01-11T21:05:10.5595595Z                     auto tmp4 = static_cast<float>(0.1);
2023-01-11T21:05:10.5595685Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5595782Z                     auto tmp6 = tmp3 ? tmp1 : tmp5;
2023-01-11T21:05:10.5595893Z                     auto tmp7 = static_cast<bfloat16>(tmp6);
2023-01-11T21:05:10.5595970Z                     in_out_ptr0[i0] = tmp7;
2023-01-11T21:05:10.5596033Z                 }
2023-01-11T21:05:10.5596095Z             }
2023-01-11T21:05:10.5596240Z         }
2023-01-11T21:05:10.5596301Z     }
2023-01-11T21:05:10.5596359Z }
2023-01-11T21:05:10.5596450Z ''')
2023-01-11T21:05:10.5596457Z 
2023-01-11T21:05:10.5596476Z 
2023-01-11T21:05:10.5596554Z async_compile.wait(globals())
2023-01-11T21:05:10.5596627Z del async_compile
2023-01-11T21:05:10.5596632Z 
2023-01-11T21:05:10.5596701Z def call(args):
2023-01-11T21:05:10.5596776Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5596847Z     args.clear()
2023-01-11T21:05:10.5597059Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5597181Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5597236Z     del arg0_1
2023-01-11T21:05:10.5597300Z     del arg1_1
2023-01-11T21:05:10.5597383Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5597484Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5597554Z     return (buf1, )
2023-01-11T21:05:10.5597559Z 
2023-01-11T21:05:10.5597564Z 
2023-01-11T21:05:10.5597640Z if __name__ == "__main__":
2023-01-11T21:05:10.5597753Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5597863Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5598102Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5598303Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5598418Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5598423Z 
2023-01-11T21:05:10.5598427Z 
2023-01-11T21:05:10.5598519Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5598590Z import torch
2023-01-11T21:05:10.5598660Z import random
2023-01-11T21:05:10.5598772Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5598879Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5598883Z 
2023-01-11T21:05:10.5598960Z aten = torch.ops.aten
2023-01-11T21:05:10.5599090Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5599184Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5599189Z 
2023-01-11T21:05:10.5599193Z 
2023-01-11T21:05:10.5599325Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5599534Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5599654Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5599709Z {
2023-01-11T21:05:10.5599793Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5599858Z     {
2023-01-11T21:05:10.5599935Z         #pragma omp for 
2023-01-11T21:05:10.5600004Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5600066Z         {
2023-01-11T21:05:10.5600127Z             {
2023-01-11T21:05:10.5600191Z                 {
2023-01-11T21:05:10.5600310Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5600417Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5600574Z                     auto tmp2 = static_cast<float>(-0.5);
2023-01-11T21:05:10.5600900Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5601008Z                     auto tmp4 = static_cast<float>(4.0);
2023-01-11T21:05:10.5601137Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5601248Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5601339Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5601404Z                 }
2023-01-11T21:05:10.5601468Z             }
2023-01-11T21:05:10.5601517Z         }
2023-01-11T21:05:10.5601578Z     }
2023-01-11T21:05:10.5601637Z }
2023-01-11T21:05:10.5601718Z ''')
2023-01-11T21:05:10.5601723Z 
2023-01-11T21:05:10.5601727Z 
2023-01-11T21:05:10.5601818Z async_compile.wait(globals())
2023-01-11T21:05:10.5601891Z del async_compile
2023-01-11T21:05:10.5601898Z 
2023-01-11T21:05:10.5601968Z def call(args):
2023-01-11T21:05:10.5602050Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5602177Z     args.clear()
2023-01-11T21:05:10.5602380Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5602552Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5602621Z     del arg0_1
2023-01-11T21:05:10.5602688Z     del arg1_1
2023-01-11T21:05:10.5602754Z     del arg2_1
2023-01-11T21:05:10.5602869Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5602959Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5603030Z     return (buf1, )
2023-01-11T21:05:10.5603035Z 
2023-01-11T21:05:10.5603039Z 
2023-01-11T21:05:10.5603116Z if __name__ == "__main__":
2023-01-11T21:05:10.5603229Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5603351Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5603554Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5603755Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5604005Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5604117Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5604122Z 
2023-01-11T21:05:10.5604127Z 
2023-01-11T21:05:10.5604223Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5604291Z import torch
2023-01-11T21:05:10.5604360Z import random
2023-01-11T21:05:10.5604473Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5604593Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5604598Z 
2023-01-11T21:05:10.5604674Z aten = torch.ops.aten
2023-01-11T21:05:10.5604807Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5604884Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5604889Z 
2023-01-11T21:05:10.5604896Z 
2023-01-11T21:05:10.5605029Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5605232Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5605352Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5605412Z {
2023-01-11T21:05:10.5605508Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5605569Z     {
2023-01-11T21:05:10.5605631Z         #pragma omp for 
2023-01-11T21:05:10.5605714Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5605776Z         {
2023-01-11T21:05:10.5605841Z             {
2023-01-11T21:05:10.5605905Z                 {
2023-01-11T21:05:10.5606024Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5606131Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5606274Z                     auto tmp2 = static_cast<float>(-0.5);
2023-01-11T21:05:10.5606399Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5606505Z                     auto tmp4 = static_cast<float>(4.0);
2023-01-11T21:05:10.5606628Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5606739Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5606828Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5606891Z                 }
2023-01-11T21:05:10.5606952Z             }
2023-01-11T21:05:10.5607001Z         }
2023-01-11T21:05:10.5607061Z     }
2023-01-11T21:05:10.5607119Z }
2023-01-11T21:05:10.5607195Z ''')
2023-01-11T21:05:10.5607200Z 
2023-01-11T21:05:10.5607204Z 
2023-01-11T21:05:10.5607291Z async_compile.wait(globals())
2023-01-11T21:05:10.5607361Z del async_compile
2023-01-11T21:05:10.5607366Z 
2023-01-11T21:05:10.5607433Z def call(args):
2023-01-11T21:05:10.5607493Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5607562Z     args.clear()
2023-01-11T21:05:10.5607759Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5607931Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5608000Z     del arg0_1
2023-01-11T21:05:10.5608068Z     del arg1_1
2023-01-11T21:05:10.5608183Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5608270Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5608340Z     return (buf1, )
2023-01-11T21:05:10.5608345Z 
2023-01-11T21:05:10.5608349Z 
2023-01-11T21:05:10.5608423Z if __name__ == "__main__":
2023-01-11T21:05:10.5608533Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5608653Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5608857Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5609062Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5609178Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5609184Z 
2023-01-11T21:05:10.5609188Z 
2023-01-11T21:05:10.5609280Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5609335Z import torch
2023-01-11T21:05:10.5609432Z import random
2023-01-11T21:05:10.5609546Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5609664Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5609669Z 
2023-01-11T21:05:10.5609745Z aten = torch.ops.aten
2023-01-11T21:05:10.5609877Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5609966Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5609971Z 
2023-01-11T21:05:10.5609975Z 
2023-01-11T21:05:10.5610094Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5610297Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5610415Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5610478Z {
2023-01-11T21:05:10.5610573Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5610636Z     {
2023-01-11T21:05:10.5610711Z         #pragma omp for 
2023-01-11T21:05:10.5610780Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5610845Z         {
2023-01-11T21:05:10.5610907Z             {
2023-01-11T21:05:10.5610971Z                 {
2023-01-11T21:05:10.5611090Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5611200Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5611358Z                     auto tmp2 = static_cast<float>(-0.5);
2023-01-11T21:05:10.5611471Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5611576Z                     auto tmp4 = static_cast<float>(4.0);
2023-01-11T21:05:10.5611698Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5611809Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5611903Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5611967Z                 }
2023-01-11T21:05:10.5612030Z             }
2023-01-11T21:05:10.5612092Z         }
2023-01-11T21:05:10.5612140Z     }
2023-01-11T21:05:10.5612202Z }
2023-01-11T21:05:10.5612280Z ''')
2023-01-11T21:05:10.5612286Z 
2023-01-11T21:05:10.5612290Z 
2023-01-11T21:05:10.5612378Z async_compile.wait(globals())
2023-01-11T21:05:10.5612450Z del async_compile
2023-01-11T21:05:10.5612455Z 
2023-01-11T21:05:10.5612524Z def call(args):
2023-01-11T21:05:10.5612605Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5612661Z     args.clear()
2023-01-11T21:05:10.5612858Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5613008Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5613075Z     del arg0_1
2023-01-11T21:05:10.5613142Z     del arg1_1
2023-01-11T21:05:10.5613206Z     del arg2_1
2023-01-11T21:05:10.5613323Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5613413Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5613483Z     return (buf1, )
2023-01-11T21:05:10.5613488Z 
2023-01-11T21:05:10.5613492Z 
2023-01-11T21:05:10.5613570Z if __name__ == "__main__":
2023-01-11T21:05:10.5613683Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5613804Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5614004Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5614200Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5614397Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5614505Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5614510Z 
2023-01-11T21:05:10.5614530Z 
2023-01-11T21:05:10.5614609Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5614681Z import torch
2023-01-11T21:05:10.5614750Z import random
2023-01-11T21:05:10.5614863Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5614982Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5615016Z 
2023-01-11T21:05:10.5615095Z aten = torch.ops.aten
2023-01-11T21:05:10.5615225Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5615302Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5615307Z 
2023-01-11T21:05:10.5615311Z 
2023-01-11T21:05:10.5615445Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5615648Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5615767Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5615827Z {
2023-01-11T21:05:10.5615929Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5615989Z     {
2023-01-11T21:05:10.5616052Z         #pragma omp for 
2023-01-11T21:05:10.5616133Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5616197Z         {
2023-01-11T21:05:10.5616259Z             {
2023-01-11T21:05:10.5616323Z                 {
2023-01-11T21:05:10.5616446Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5616553Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5616697Z                     auto tmp2 = static_cast<float>(-0.5);
2023-01-11T21:05:10.5616822Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5616925Z                     auto tmp4 = static_cast<float>(4.0);
2023-01-11T21:05:10.5617050Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5617158Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5617248Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5617310Z                 }
2023-01-11T21:05:10.5617373Z             }
2023-01-11T21:05:10.5617422Z         }
2023-01-11T21:05:10.5617483Z     }
2023-01-11T21:05:10.5617543Z }
2023-01-11T21:05:10.5617619Z ''')
2023-01-11T21:05:10.5617624Z 
2023-01-11T21:05:10.5617628Z 
2023-01-11T21:05:10.5617715Z async_compile.wait(globals())
2023-01-11T21:05:10.5617789Z del async_compile
2023-01-11T21:05:10.5617794Z 
2023-01-11T21:05:10.5617862Z def call(args):
2023-01-11T21:05:10.5617923Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5617991Z     args.clear()
2023-01-11T21:05:10.5618190Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5618309Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5618375Z     del arg0_1
2023-01-11T21:05:10.5618440Z     del arg1_1
2023-01-11T21:05:10.5618639Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5618731Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5618802Z     return (buf1, )
2023-01-11T21:05:10.5618809Z 
2023-01-11T21:05:10.5618813Z 
2023-01-11T21:05:10.5618889Z if __name__ == "__main__":
2023-01-11T21:05:10.5619057Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5619177Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5619384Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5619585Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5619700Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5619956Z [2023-01-11 20:54:49,973] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 299
2023-01-11T21:05:10.5620362Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5620490Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5620753Z [2023-01-11 20:54:50,276] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 300
2023-01-11T21:05:10.5621049Z [2023-01-11 20:54:52,968] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 300
2023-01-11T21:05:10.5621453Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5621578Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5621835Z [2023-01-11 20:54:53,069] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 301
2023-01-11T21:05:10.5622098Z [2023-01-11 20:54:53,093] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 301
2023-01-11T21:05:10.5622500Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5622626Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5622868Z [2023-01-11 20:54:53,284] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 302
2023-01-11T21:05:10.5623128Z [2023-01-11 20:54:55,945] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 302
2023-01-11T21:05:10.5623524Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5623647Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5623902Z [2023-01-11 20:54:56,023] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 303
2023-01-11T21:05:10.5623907Z 
2023-01-11T21:05:10.5623912Z 
2023-01-11T21:05:10.5624007Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5624076Z import torch
2023-01-11T21:05:10.5624145Z import random
2023-01-11T21:05:10.5624259Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5624378Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5624383Z 
2023-01-11T21:05:10.5624447Z aten = torch.ops.aten
2023-01-11T21:05:10.5624579Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5624670Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5624675Z 
2023-01-11T21:05:10.5624713Z 
2023-01-11T21:05:10.5624850Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5625053Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5625173Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5625233Z {
2023-01-11T21:05:10.5625328Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5625376Z     {
2023-01-11T21:05:10.5625451Z         #pragma omp for 
2023-01-11T21:05:10.5625534Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5625595Z         {
2023-01-11T21:05:10.5625657Z             {
2023-01-11T21:05:10.5625719Z                 {
2023-01-11T21:05:10.5625826Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5625931Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5626039Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5626129Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5626243Z                     auto tmp4 = static_cast<float>(0.7071067811865476);
2023-01-11T21:05:10.5626333Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5626430Z                     auto tmp6 = std::erf(tmp5);
2023-01-11T21:05:10.5626580Z                     auto tmp7 = static_cast<float>(1);
2023-01-11T21:05:10.5626659Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.5626747Z                     auto tmp9 = tmp3 * tmp8;
2023-01-11T21:05:10.5626855Z                     auto tmp10 = static_cast<bfloat16>(tmp9);
2023-01-11T21:05:10.5626942Z                     in_out_ptr0[i0] = tmp10;
2023-01-11T21:05:10.5627006Z                 }
2023-01-11T21:05:10.5627068Z             }
2023-01-11T21:05:10.5627127Z         }
2023-01-11T21:05:10.5627174Z     }
2023-01-11T21:05:10.5627232Z }
2023-01-11T21:05:10.5627310Z ''')
2023-01-11T21:05:10.5627315Z 
2023-01-11T21:05:10.5627319Z 
2023-01-11T21:05:10.5627408Z async_compile.wait(globals())
2023-01-11T21:05:10.5627478Z del async_compile
2023-01-11T21:05:10.5627485Z 
2023-01-11T21:05:10.5627553Z def call(args):
2023-01-11T21:05:10.5627633Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5627690Z     args.clear()
2023-01-11T21:05:10.5627892Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5628063Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5628130Z     del arg0_1
2023-01-11T21:05:10.5628197Z     del arg1_1
2023-01-11T21:05:10.5628262Z     del arg2_1
2023-01-11T21:05:10.5628379Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5628467Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5628539Z     return (buf1, )
2023-01-11T21:05:10.5628543Z 
2023-01-11T21:05:10.5628548Z 
2023-01-11T21:05:10.5628623Z if __name__ == "__main__":
2023-01-11T21:05:10.5628737Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5628861Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5629061Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5629259Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5629463Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5629572Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5629589Z 
2023-01-11T21:05:10.5629594Z 
2023-01-11T21:05:10.5629674Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5629741Z import torch
2023-01-11T21:05:10.5629809Z import random
2023-01-11T21:05:10.5629920Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5630040Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5630044Z 
2023-01-11T21:05:10.5630121Z aten = torch.ops.aten
2023-01-11T21:05:10.5630253Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5630367Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5630371Z 
2023-01-11T21:05:10.5630388Z 
2023-01-11T21:05:10.5630507Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5630711Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5630830Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5630889Z {
2023-01-11T21:05:10.5630985Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5631045Z     {
2023-01-11T21:05:10.5631120Z         #pragma omp for 
2023-01-11T21:05:10.5631189Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5631251Z         {
2023-01-11T21:05:10.5631313Z             {
2023-01-11T21:05:10.5631375Z                 {
2023-01-11T21:05:10.5631495Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5631602Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5631694Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5631786Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5631896Z                     auto tmp4 = static_cast<float>(0.7071067811865476);
2023-01-11T21:05:10.5632017Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5632117Z                     auto tmp6 = std::erf(tmp5);
2023-01-11T21:05:10.5632219Z                     auto tmp7 = static_cast<float>(1);
2023-01-11T21:05:10.5632308Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.5632398Z                     auto tmp9 = tmp3 * tmp8;
2023-01-11T21:05:10.5632496Z                     auto tmp10 = static_cast<bfloat16>(tmp9);
2023-01-11T21:05:10.5632582Z                     in_out_ptr0[i0] = tmp10;
2023-01-11T21:05:10.5632648Z                 }
2023-01-11T21:05:10.5632710Z             }
2023-01-11T21:05:10.5632770Z         }
2023-01-11T21:05:10.5632831Z     }
2023-01-11T21:05:10.5632877Z }
2023-01-11T21:05:10.5632954Z ''')
2023-01-11T21:05:10.5632961Z 
2023-01-11T21:05:10.5632965Z 
2023-01-11T21:05:10.5633055Z async_compile.wait(globals())
2023-01-11T21:05:10.5633126Z del async_compile
2023-01-11T21:05:10.5633131Z 
2023-01-11T21:05:10.5633199Z def call(args):
2023-01-11T21:05:10.5633275Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5633344Z     args.clear()
2023-01-11T21:05:10.5633543Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5633705Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5633771Z     del arg0_1
2023-01-11T21:05:10.5633835Z     del arg1_1
2023-01-11T21:05:10.5633949Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5634050Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5634120Z     return (buf1, )
2023-01-11T21:05:10.5634125Z 
2023-01-11T21:05:10.5634130Z 
2023-01-11T21:05:10.5634204Z if __name__ == "__main__":
2023-01-11T21:05:10.5634316Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5634427Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5634627Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5634835Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5634949Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5634955Z 
2023-01-11T21:05:10.5634959Z 
2023-01-11T21:05:10.5635051Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5635119Z import torch
2023-01-11T21:05:10.5635187Z import random
2023-01-11T21:05:10.5635300Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5635408Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5635413Z 
2023-01-11T21:05:10.5635488Z aten = torch.ops.aten
2023-01-11T21:05:10.5635619Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5635709Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5635743Z 
2023-01-11T21:05:10.5635748Z 
2023-01-11T21:05:10.5635880Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5636084Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5636202Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5636261Z {
2023-01-11T21:05:10.5636344Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5636405Z     {
2023-01-11T21:05:10.5636480Z         #pragma omp for 
2023-01-11T21:05:10.5636561Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5636624Z         {
2023-01-11T21:05:10.5636685Z             {
2023-01-11T21:05:10.5636735Z                 {
2023-01-11T21:05:10.5636853Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5636960Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5637064Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5637157Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5637268Z                     auto tmp4 = static_cast<float>(0.7071067811865476);
2023-01-11T21:05:10.5637358Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5637484Z                     auto tmp6 = std::erf(tmp5);
2023-01-11T21:05:10.5637576Z                     auto tmp7 = static_cast<float>(1);
2023-01-11T21:05:10.5637664Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.5637751Z                     auto tmp9 = tmp3 * tmp8;
2023-01-11T21:05:10.5637861Z                     auto tmp10 = static_cast<bfloat16>(tmp9);
2023-01-11T21:05:10.5637948Z                     in_out_ptr0[i0] = tmp10;
2023-01-11T21:05:10.5638012Z                 }
2023-01-11T21:05:10.5638073Z             }
2023-01-11T21:05:10.5638121Z         }
2023-01-11T21:05:10.5638181Z     }
2023-01-11T21:05:10.5638239Z }
2023-01-11T21:05:10.5638316Z ''')
2023-01-11T21:05:10.5638321Z 
2023-01-11T21:05:10.5638325Z 
2023-01-11T21:05:10.5638416Z async_compile.wait(globals())
2023-01-11T21:05:10.5638486Z del async_compile
2023-01-11T21:05:10.5638491Z 
2023-01-11T21:05:10.5638562Z def call(args):
2023-01-11T21:05:10.5638628Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5638700Z     args.clear()
2023-01-11T21:05:10.5638900Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5639049Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5639115Z     del arg0_1
2023-01-11T21:05:10.5639179Z     del arg1_1
2023-01-11T21:05:10.5639242Z     del arg2_1
2023-01-11T21:05:10.5639311Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5639413Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5639483Z     return (buf1, )
2023-01-11T21:05:10.5639489Z 
2023-01-11T21:05:10.5639493Z 
2023-01-11T21:05:10.5639567Z if __name__ == "__main__":
2023-01-11T21:05:10.5639678Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5639801Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5640001Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5640195Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5640378Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5640496Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5640501Z 
2023-01-11T21:05:10.5640505Z 
2023-01-11T21:05:10.5640740Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5640813Z import torch
2023-01-11T21:05:10.5640883Z import random
2023-01-11T21:05:10.5640999Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5641119Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5641125Z 
2023-01-11T21:05:10.5641202Z aten = torch.ops.aten
2023-01-11T21:05:10.5641322Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5641506Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5641511Z 
2023-01-11T21:05:10.5641516Z 
2023-01-11T21:05:10.5641652Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5641860Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5641981Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5642042Z {
2023-01-11T21:05:10.5642140Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5642187Z     {
2023-01-11T21:05:10.5642263Z         #pragma omp for 
2023-01-11T21:05:10.5642345Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5642408Z         {
2023-01-11T21:05:10.5642471Z             {
2023-01-11T21:05:10.5642535Z                 {
2023-01-11T21:05:10.5642656Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5642749Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5642854Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5642948Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5643061Z                     auto tmp4 = static_cast<float>(0.7071067811865476);
2023-01-11T21:05:10.5643186Z                     auto tmp5 = tmp1 * tmp4;
2023-01-11T21:05:10.5643286Z                     auto tmp6 = std::erf(tmp5);
2023-01-11T21:05:10.5643389Z                     auto tmp7 = static_cast<float>(1);
2023-01-11T21:05:10.5643467Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.5643555Z                     auto tmp9 = tmp3 * tmp8;
2023-01-11T21:05:10.5643665Z                     auto tmp10 = static_cast<bfloat16>(tmp9);
2023-01-11T21:05:10.5643752Z                     in_out_ptr0[i0] = tmp10;
2023-01-11T21:05:10.5643819Z                 }
2023-01-11T21:05:10.5643881Z             }
2023-01-11T21:05:10.5643944Z         }
2023-01-11T21:05:10.5643991Z     }
2023-01-11T21:05:10.5644050Z }
2023-01-11T21:05:10.5644128Z ''')
2023-01-11T21:05:10.5644134Z 
2023-01-11T21:05:10.5644140Z 
2023-01-11T21:05:10.5644229Z async_compile.wait(globals())
2023-01-11T21:05:10.5644300Z del async_compile
2023-01-11T21:05:10.5644305Z 
2023-01-11T21:05:10.5644374Z def call(args):
2023-01-11T21:05:10.5644451Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5644522Z     args.clear()
2023-01-11T21:05:10.5644710Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5644830Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5644897Z     del arg0_1
2023-01-11T21:05:10.5644963Z     del arg1_1
2023-01-11T21:05:10.5645046Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5645149Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5645219Z     return (buf1, )
2023-01-11T21:05:10.5645223Z 
2023-01-11T21:05:10.5645227Z 
2023-01-11T21:05:10.5645289Z if __name__ == "__main__":
2023-01-11T21:05:10.5645400Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5645523Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5645727Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5645928Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5646043Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5646312Z [2023-01-11 20:54:56,041] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 303
2023-01-11T21:05:10.5646711Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5646836Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5647080Z [2023-01-11 20:54:56,275] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 304
2023-01-11T21:05:10.5647374Z [2023-01-11 20:54:58,904] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 304
2023-01-11T21:05:10.5647774Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5647902Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5648154Z [2023-01-11 20:54:58,991] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 305
2023-01-11T21:05:10.5648416Z [2023-01-11 20:54:59,022] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 305
2023-01-11T21:05:10.5648848Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5648978Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5649232Z [2023-01-11 20:54:59,230] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 306
2023-01-11T21:05:10.5649495Z [2023-01-11 20:55:01,860] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 306
2023-01-11T21:05:10.5649894Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5650006Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5650263Z [2023-01-11 20:55:01,942] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 307
2023-01-11T21:05:10.5650270Z 
2023-01-11T21:05:10.5650275Z 
2023-01-11T21:05:10.5650369Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5650438Z import torch
2023-01-11T21:05:10.5650508Z import random
2023-01-11T21:05:10.5650622Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5650743Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5650748Z 
2023-01-11T21:05:10.5650825Z aten = torch.ops.aten
2023-01-11T21:05:10.5650958Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5651037Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5651043Z 
2023-01-11T21:05:10.5651047Z 
2023-01-11T21:05:10.5651179Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5651381Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5651502Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5651562Z {
2023-01-11T21:05:10.5651657Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5651719Z     {
2023-01-11T21:05:10.5651781Z         #pragma omp for 
2023-01-11T21:05:10.5651863Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5651923Z         {
2023-01-11T21:05:10.5651986Z             {
2023-01-11T21:05:10.5652048Z                 {
2023-01-11T21:05:10.5652167Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5652273Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5652364Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5652455Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5652544Z                     auto tmp4 = tmp1 * tmp1;
2023-01-11T21:05:10.5652630Z                     auto tmp5 = tmp4 * tmp1;
2023-01-11T21:05:10.5652773Z                     auto tmp6 = static_cast<float>(0.044715);
2023-01-11T21:05:10.5652863Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5652950Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:05:10.5653052Z                     auto tmp9 = static_cast<float>(0.7978845608028654);
2023-01-11T21:05:10.5653144Z                     auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.5653241Z                     auto tmp11 = std::tanh(tmp10);
2023-01-11T21:05:10.5653344Z                     auto tmp12 = static_cast<float>(1);
2023-01-11T21:05:10.5653435Z                     auto tmp13 = tmp11 + tmp12;
2023-01-11T21:05:10.5653526Z                     auto tmp14 = tmp3 * tmp13;
2023-01-11T21:05:10.5653635Z                     auto tmp15 = static_cast<bfloat16>(tmp14);
2023-01-11T21:05:10.5653726Z                     in_out_ptr0[i0] = tmp15;
2023-01-11T21:05:10.5653776Z                 }
2023-01-11T21:05:10.5653839Z             }
2023-01-11T21:05:10.5653899Z         }
2023-01-11T21:05:10.5653962Z     }
2023-01-11T21:05:10.5654021Z }
2023-01-11T21:05:10.5654098Z ''')
2023-01-11T21:05:10.5654103Z 
2023-01-11T21:05:10.5654108Z 
2023-01-11T21:05:10.5654197Z async_compile.wait(globals())
2023-01-11T21:05:10.5654281Z del async_compile
2023-01-11T21:05:10.5654287Z 
2023-01-11T21:05:10.5654357Z def call(args):
2023-01-11T21:05:10.5654436Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5654506Z     args.clear()
2023-01-11T21:05:10.5654706Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5654874Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5654940Z     del arg0_1
2023-01-11T21:05:10.5654993Z     del arg1_1
2023-01-11T21:05:10.5655057Z     del arg2_1
2023-01-11T21:05:10.5655172Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5655273Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5655346Z     return (buf1, )
2023-01-11T21:05:10.5655350Z 
2023-01-11T21:05:10.5655355Z 
2023-01-11T21:05:10.5655429Z if __name__ == "__main__":
2023-01-11T21:05:10.5655545Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5655665Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5655855Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5656049Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5656253Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5656374Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5656379Z 
2023-01-11T21:05:10.5656384Z 
2023-01-11T21:05:10.5656476Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5656545Z import torch
2023-01-11T21:05:10.5656613Z import random
2023-01-11T21:05:10.5656714Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5656839Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5656844Z 
2023-01-11T21:05:10.5656919Z aten = torch.ops.aten
2023-01-11T21:05:10.5657055Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5657145Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5657150Z 
2023-01-11T21:05:10.5657154Z 
2023-01-11T21:05:10.5657283Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5657485Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5657602Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5657662Z {
2023-01-11T21:05:10.5657744Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5657804Z     {
2023-01-11T21:05:10.5657879Z         #pragma omp for 
2023-01-11T21:05:10.5657960Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5658022Z         {
2023-01-11T21:05:10.5658083Z             {
2023-01-11T21:05:10.5658162Z                 {
2023-01-11T21:05:10.5658282Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5658387Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5658583Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5658679Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5658769Z                     auto tmp4 = tmp1 * tmp1;
2023-01-11T21:05:10.5658857Z                     auto tmp5 = tmp4 * tmp1;
2023-01-11T21:05:10.5658951Z                     auto tmp6 = static_cast<float>(0.044715);
2023-01-11T21:05:10.5659039Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5659127Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:05:10.5659239Z                     auto tmp9 = static_cast<float>(0.7978845608028654);
2023-01-11T21:05:10.5659332Z                     auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.5659433Z                     auto tmp11 = std::tanh(tmp10);
2023-01-11T21:05:10.5659539Z                     auto tmp12 = static_cast<float>(1);
2023-01-11T21:05:10.5659631Z                     auto tmp13 = tmp11 + tmp12;
2023-01-11T21:05:10.5659710Z                     auto tmp14 = tmp3 * tmp13;
2023-01-11T21:05:10.5659853Z                     auto tmp15 = static_cast<bfloat16>(tmp14);
2023-01-11T21:05:10.5659949Z                     in_out_ptr0[i0] = tmp15;
2023-01-11T21:05:10.5660013Z                 }
2023-01-11T21:05:10.5660077Z             }
2023-01-11T21:05:10.5660139Z         }
2023-01-11T21:05:10.5660198Z     }
2023-01-11T21:05:10.5660243Z }
2023-01-11T21:05:10.5660322Z ''')
2023-01-11T21:05:10.5660327Z 
2023-01-11T21:05:10.5660331Z 
2023-01-11T21:05:10.5660420Z async_compile.wait(globals())
2023-01-11T21:05:10.5660489Z del async_compile
2023-01-11T21:05:10.5660494Z 
2023-01-11T21:05:10.5660563Z def call(args):
2023-01-11T21:05:10.5660635Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5660704Z     args.clear()
2023-01-11T21:05:10.5660888Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5661032Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5661099Z     del arg0_1
2023-01-11T21:05:10.5661166Z     del arg1_1
2023-01-11T21:05:10.5661280Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5661381Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5661451Z     return (buf1, )
2023-01-11T21:05:10.5661455Z 
2023-01-11T21:05:10.5661459Z 
2023-01-11T21:05:10.5661534Z if __name__ == "__main__":
2023-01-11T21:05:10.5661634Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5661756Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5661959Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5662166Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5662286Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5662291Z 
2023-01-11T21:05:10.5662295Z 
2023-01-11T21:05:10.5662387Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5662457Z import torch
2023-01-11T21:05:10.5662527Z import random
2023-01-11T21:05:10.5662628Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5662747Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5662752Z 
2023-01-11T21:05:10.5662828Z aten = torch.ops.aten
2023-01-11T21:05:10.5662960Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5663049Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5663054Z 
2023-01-11T21:05:10.5663058Z 
2023-01-11T21:05:10.5663189Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5663391Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5663510Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5663587Z {
2023-01-11T21:05:10.5663682Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5663742Z     {
2023-01-11T21:05:10.5663816Z         #pragma omp for 
2023-01-11T21:05:10.5663897Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5663960Z         {
2023-01-11T21:05:10.5664010Z             {
2023-01-11T21:05:10.5664072Z                 {
2023-01-11T21:05:10.5664191Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5664297Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5664403Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5664492Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5664580Z                     auto tmp4 = tmp1 * tmp1;
2023-01-11T21:05:10.5664655Z                     auto tmp5 = tmp4 * tmp1;
2023-01-11T21:05:10.5664762Z                     auto tmp6 = static_cast<float>(0.044715);
2023-01-11T21:05:10.5664847Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5664937Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:05:10.5665050Z                     auto tmp9 = static_cast<float>(0.7978845608028654);
2023-01-11T21:05:10.5665185Z                     auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.5665285Z                     auto tmp11 = std::tanh(tmp10);
2023-01-11T21:05:10.5665386Z                     auto tmp12 = static_cast<float>(1);
2023-01-11T21:05:10.5665466Z                     auto tmp13 = tmp11 + tmp12;
2023-01-11T21:05:10.5665556Z                     auto tmp14 = tmp3 * tmp13;
2023-01-11T21:05:10.5665665Z                     auto tmp15 = static_cast<bfloat16>(tmp14);
2023-01-11T21:05:10.5665754Z                     in_out_ptr0[i0] = tmp15;
2023-01-11T21:05:10.5665818Z                 }
2023-01-11T21:05:10.5665880Z             }
2023-01-11T21:05:10.5665941Z         }
2023-01-11T21:05:10.5665988Z     }
2023-01-11T21:05:10.5666046Z }
2023-01-11T21:05:10.5666128Z ''')
2023-01-11T21:05:10.5666133Z 
2023-01-11T21:05:10.5666140Z 
2023-01-11T21:05:10.5666228Z async_compile.wait(globals())
2023-01-11T21:05:10.5666298Z del async_compile
2023-01-11T21:05:10.5666303Z 
2023-01-11T21:05:10.5666372Z def call(args):
2023-01-11T21:05:10.5666455Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5666512Z     args.clear()
2023-01-11T21:05:10.5666713Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5666864Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5666932Z     del arg0_1
2023-01-11T21:05:10.5666998Z     del arg1_1
2023-01-11T21:05:10.5667063Z     del arg2_1
2023-01-11T21:05:10.5667147Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5667235Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5667305Z     return (buf1, )
2023-01-11T21:05:10.5667310Z 
2023-01-11T21:05:10.5667314Z 
2023-01-11T21:05:10.5667388Z if __name__ == "__main__":
2023-01-11T21:05:10.5667499Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5667624Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5667826Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5668024Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5668219Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5668328Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5668333Z 
2023-01-11T21:05:10.5668350Z 
2023-01-11T21:05:10.5668429Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5668496Z import torch
2023-01-11T21:05:10.5668564Z import random
2023-01-11T21:05:10.5668677Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5668797Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5668802Z 
2023-01-11T21:05:10.5668877Z aten = torch.ops.aten
2023-01-11T21:05:10.5669009Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5669116Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5669121Z 
2023-01-11T21:05:10.5669137Z 
2023-01-11T21:05:10.5669257Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5669461Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5669578Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5669638Z {
2023-01-11T21:05:10.5669732Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5669792Z     {
2023-01-11T21:05:10.5669854Z         #pragma omp for 
2023-01-11T21:05:10.5669935Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5669996Z         {
2023-01-11T21:05:10.5670058Z             {
2023-01-11T21:05:10.5670120Z                 {
2023-01-11T21:05:10.5670239Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5670343Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5670437Z                     auto tmp2 = static_cast<float>(0.5);
2023-01-11T21:05:10.5670529Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5670618Z                     auto tmp4 = tmp1 * tmp1;
2023-01-11T21:05:10.5670735Z                     auto tmp5 = tmp4 * tmp1;
2023-01-11T21:05:10.5670845Z                     auto tmp6 = static_cast<float>(0.044715);
2023-01-11T21:05:10.5670932Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5671020Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:05:10.5671133Z                     auto tmp9 = static_cast<float>(0.7978845608028654);
2023-01-11T21:05:10.5671212Z                     auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.5671310Z                     auto tmp11 = std::tanh(tmp10);
2023-01-11T21:05:10.5671411Z                     auto tmp12 = static_cast<float>(1);
2023-01-11T21:05:10.5671502Z                     auto tmp13 = tmp11 + tmp12;
2023-01-11T21:05:10.5671592Z                     auto tmp14 = tmp3 * tmp13;
2023-01-11T21:05:10.5671707Z                     auto tmp15 = static_cast<bfloat16>(tmp14);
2023-01-11T21:05:10.5671797Z                     in_out_ptr0[i0] = tmp15;
2023-01-11T21:05:10.5671848Z                 }
2023-01-11T21:05:10.5671912Z             }
2023-01-11T21:05:10.5671972Z         }
2023-01-11T21:05:10.5672033Z     }
2023-01-11T21:05:10.5672093Z }
2023-01-11T21:05:10.5672169Z ''')
2023-01-11T21:05:10.5672174Z 
2023-01-11T21:05:10.5672179Z 
2023-01-11T21:05:10.5672267Z async_compile.wait(globals())
2023-01-11T21:05:10.5672324Z del async_compile
2023-01-11T21:05:10.5672329Z 
2023-01-11T21:05:10.5672397Z def call(args):
2023-01-11T21:05:10.5672470Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5672540Z     args.clear()
2023-01-11T21:05:10.5672739Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5672858Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5672924Z     del arg0_1
2023-01-11T21:05:10.5672978Z     del arg1_1
2023-01-11T21:05:10.5673060Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5673161Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5673231Z     return (buf1, )
2023-01-11T21:05:10.5673236Z 
2023-01-11T21:05:10.5673242Z 
2023-01-11T21:05:10.5673318Z if __name__ == "__main__":
2023-01-11T21:05:10.5673430Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5673551Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5673751Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5673935Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5674048Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5674317Z [2023-01-11 20:55:01,970] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 307
2023-01-11T21:05:10.5674724Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5674885Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5675143Z [2023-01-11 20:55:02,201] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 308
2023-01-11T21:05:10.5675409Z [2023-01-11 20:55:04,831] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 308
2023-01-11T21:05:10.5675803Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5675930Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5676184Z [2023-01-11 20:55:04,910] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 309
2023-01-11T21:05:10.5676460Z [2023-01-11 20:55:04,930] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 309
2023-01-11T21:05:10.5676860Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5676985Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5677235Z [2023-01-11 20:55:05,130] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 310
2023-01-11T21:05:10.5677499Z [2023-01-11 20:55:07,772] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 310
2023-01-11T21:05:10.5677898Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5678023Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5678279Z [2023-01-11 20:55:07,843] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 311
2023-01-11T21:05:10.5678539Z [2023-01-11 20:55:07,859] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 311
2023-01-11T21:05:10.5678931Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5679061Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5679318Z [2023-01-11 20:55:08,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 312
2023-01-11T21:05:10.5679323Z 
2023-01-11T21:05:10.5679328Z 
2023-01-11T21:05:10.5679409Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5679478Z import torch
2023-01-11T21:05:10.5679548Z import random
2023-01-11T21:05:10.5679663Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5679785Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5679790Z 
2023-01-11T21:05:10.5679869Z aten = torch.ops.aten
2023-01-11T21:05:10.5680004Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5680097Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5680102Z 
2023-01-11T21:05:10.5680137Z 
2023-01-11T21:05:10.5680259Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5680461Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5680704Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5680769Z {
2023-01-11T21:05:10.5680867Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5680928Z     {
2023-01-11T21:05:10.5681009Z         #pragma omp for 
2023-01-11T21:05:10.5681078Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5681140Z         {
2023-01-11T21:05:10.5681203Z             {
2023-01-11T21:05:10.5681268Z                 {
2023-01-11T21:05:10.5681389Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5681497Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5681603Z                     auto tmp2 = static_cast<float>(0.0);
2023-01-11T21:05:10.5681717Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5681825Z                     auto tmp4 = static_cast<float>(6.0);
2023-01-11T21:05:10.5681949Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5682115Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5682205Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5682268Z                 }
2023-01-11T21:05:10.5682331Z             }
2023-01-11T21:05:10.5682380Z         }
2023-01-11T21:05:10.5682441Z     }
2023-01-11T21:05:10.5682503Z }
2023-01-11T21:05:10.5682584Z ''')
2023-01-11T21:05:10.5682591Z 
2023-01-11T21:05:10.5682595Z 
2023-01-11T21:05:10.5682684Z async_compile.wait(globals())
2023-01-11T21:05:10.5682756Z del async_compile
2023-01-11T21:05:10.5682761Z 
2023-01-11T21:05:10.5682830Z def call(args):
2023-01-11T21:05:10.5682913Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5682970Z     args.clear()
2023-01-11T21:05:10.5683170Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5683343Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5683414Z     del arg0_1
2023-01-11T21:05:10.5683480Z     del arg1_1
2023-01-11T21:05:10.5683546Z     del arg2_1
2023-01-11T21:05:10.5683661Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5683750Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5683819Z     return (buf1, )
2023-01-11T21:05:10.5683825Z 
2023-01-11T21:05:10.5683829Z 
2023-01-11T21:05:10.5683903Z if __name__ == "__main__":
2023-01-11T21:05:10.5684014Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5684136Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5684335Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5684527Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5684734Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5684847Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5684852Z 
2023-01-11T21:05:10.5684869Z 
2023-01-11T21:05:10.5684949Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5685016Z import torch
2023-01-11T21:05:10.5685084Z import random
2023-01-11T21:05:10.5685198Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5685316Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5685321Z 
2023-01-11T21:05:10.5685398Z aten = torch.ops.aten
2023-01-11T21:05:10.5685529Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5685606Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5685610Z 
2023-01-11T21:05:10.5685627Z 
2023-01-11T21:05:10.5685746Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5685950Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5686106Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5686168Z {
2023-01-11T21:05:10.5686265Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5686326Z     {
2023-01-11T21:05:10.5686388Z         #pragma omp for 
2023-01-11T21:05:10.5686470Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5686530Z         {
2023-01-11T21:05:10.5686592Z             {
2023-01-11T21:05:10.5686654Z                 {
2023-01-11T21:05:10.5686772Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5686877Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5686968Z                     auto tmp2 = static_cast<float>(0.0);
2023-01-11T21:05:10.5687094Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5687199Z                     auto tmp4 = static_cast<float>(6.0);
2023-01-11T21:05:10.5687326Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5687435Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5687551Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5687615Z                 }
2023-01-11T21:05:10.5687677Z             }
2023-01-11T21:05:10.5687724Z         }
2023-01-11T21:05:10.5687784Z     }
2023-01-11T21:05:10.5687842Z }
2023-01-11T21:05:10.5687920Z ''')
2023-01-11T21:05:10.5687925Z 
2023-01-11T21:05:10.5687929Z 
2023-01-11T21:05:10.5688015Z async_compile.wait(globals())
2023-01-11T21:05:10.5688087Z del async_compile
2023-01-11T21:05:10.5688091Z 
2023-01-11T21:05:10.5688159Z def call(args):
2023-01-11T21:05:10.5688219Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5688288Z     args.clear()
2023-01-11T21:05:10.5688487Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5688629Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5688697Z     del arg0_1
2023-01-11T21:05:10.5688761Z     del arg1_1
2023-01-11T21:05:10.5688876Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5688966Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5689037Z     return (buf1, )
2023-01-11T21:05:10.5689041Z 
2023-01-11T21:05:10.5689046Z 
2023-01-11T21:05:10.5689119Z if __name__ == "__main__":
2023-01-11T21:05:10.5689230Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5689350Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5689552Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5689755Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5689869Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5689874Z 
2023-01-11T21:05:10.5689878Z 
2023-01-11T21:05:10.5689972Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5690027Z import torch
2023-01-11T21:05:10.5690096Z import random
2023-01-11T21:05:10.5690209Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5690329Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5690335Z 
2023-01-11T21:05:10.5690410Z aten = torch.ops.aten
2023-01-11T21:05:10.5690543Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5690631Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5690636Z 
2023-01-11T21:05:10.5690641Z 
2023-01-11T21:05:10.5690770Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5690958Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5691077Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5691136Z {
2023-01-11T21:05:10.5691232Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5691292Z     {
2023-01-11T21:05:10.5691396Z         #pragma omp for 
2023-01-11T21:05:10.5691476Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5691525Z         {
2023-01-11T21:05:10.5691587Z             {
2023-01-11T21:05:10.5691648Z                 {
2023-01-11T21:05:10.5691769Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5691875Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5691979Z                     auto tmp2 = static_cast<float>(0.0);
2023-01-11T21:05:10.5692104Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5692193Z                     auto tmp4 = static_cast<float>(6.0);
2023-01-11T21:05:10.5692315Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5692424Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5692512Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5692576Z                 }
2023-01-11T21:05:10.5692640Z             }
2023-01-11T21:05:10.5692701Z         }
2023-01-11T21:05:10.5692748Z     }
2023-01-11T21:05:10.5692806Z }
2023-01-11T21:05:10.5692882Z ''')
2023-01-11T21:05:10.5692887Z 
2023-01-11T21:05:10.5692892Z 
2023-01-11T21:05:10.5693024Z async_compile.wait(globals())
2023-01-11T21:05:10.5693097Z del async_compile
2023-01-11T21:05:10.5693102Z 
2023-01-11T21:05:10.5693169Z def call(args):
2023-01-11T21:05:10.5693248Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5693304Z     args.clear()
2023-01-11T21:05:10.5693502Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5693653Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5693719Z     del arg0_1
2023-01-11T21:05:10.5693784Z     del arg1_1
2023-01-11T21:05:10.5693847Z     del arg2_1
2023-01-11T21:05:10.5693928Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5694017Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5694090Z     return (buf1, )
2023-01-11T21:05:10.5694095Z 
2023-01-11T21:05:10.5694099Z 
2023-01-11T21:05:10.5694171Z if __name__ == "__main__":
2023-01-11T21:05:10.5694285Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5694407Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5694605Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5694798Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5694992Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5695100Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5695118Z 
2023-01-11T21:05:10.5695122Z 
2023-01-11T21:05:10.5695201Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5695269Z import torch
2023-01-11T21:05:10.5695337Z import random
2023-01-11T21:05:10.5695450Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5695571Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5695577Z 
2023-01-11T21:05:10.5695652Z aten = torch.ops.aten
2023-01-11T21:05:10.5695786Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5695863Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5695868Z 
2023-01-11T21:05:10.5695885Z 
2023-01-11T21:05:10.5696003Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5696206Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5696325Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5696384Z {
2023-01-11T21:05:10.5696480Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5696539Z     {
2023-01-11T21:05:10.5696614Z         #pragma omp for 
2023-01-11T21:05:10.5696683Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5696743Z         {
2023-01-11T21:05:10.5696805Z             {
2023-01-11T21:05:10.5696902Z                 {
2023-01-11T21:05:10.5697022Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5697128Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5697222Z                     auto tmp2 = static_cast<float>(0.0);
2023-01-11T21:05:10.5697347Z                     auto tmp3 = (tmp2 != tmp2) ? tmp2 : std::max(tmp1, tmp2);
2023-01-11T21:05:10.5697448Z                     auto tmp4 = static_cast<float>(6.0);
2023-01-11T21:05:10.5697569Z                     auto tmp5 = (tmp4 != tmp4) ? tmp4 : std::min(tmp3, tmp4);
2023-01-11T21:05:10.5697679Z                     auto tmp6 = static_cast<bfloat16>(tmp5);
2023-01-11T21:05:10.5697767Z                     in_out_ptr0[i0] = tmp6;
2023-01-11T21:05:10.5697830Z                 }
2023-01-11T21:05:10.5697892Z             }
2023-01-11T21:05:10.5697940Z         }
2023-01-11T21:05:10.5698000Z     }
2023-01-11T21:05:10.5698057Z }
2023-01-11T21:05:10.5698134Z ''')
2023-01-11T21:05:10.5698141Z 
2023-01-11T21:05:10.5698145Z 
2023-01-11T21:05:10.5698233Z async_compile.wait(globals())
2023-01-11T21:05:10.5698302Z del async_compile
2023-01-11T21:05:10.5698307Z 
2023-01-11T21:05:10.5698375Z def call(args):
2023-01-11T21:05:10.5698558Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5698644Z     args.clear()
2023-01-11T21:05:10.5698849Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5698969Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5699037Z     del arg0_1
2023-01-11T21:05:10.5699103Z     del arg1_1
2023-01-11T21:05:10.5699187Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5699275Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5699347Z     return (buf1, )
2023-01-11T21:05:10.5699353Z 
2023-01-11T21:05:10.5699357Z 
2023-01-11T21:05:10.5699431Z if __name__ == "__main__":
2023-01-11T21:05:10.5699544Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5699669Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5699872Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5700072Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5700187Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5700192Z 
2023-01-11T21:05:10.5700196Z 
2023-01-11T21:05:10.5700293Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5700348Z import torch
2023-01-11T21:05:10.5700417Z import random
2023-01-11T21:05:10.5700532Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5700654Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5700659Z 
2023-01-11T21:05:10.5700736Z aten = torch.ops.aten
2023-01-11T21:05:10.5700871Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5700962Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5700968Z 
2023-01-11T21:05:10.5700972Z 
2023-01-11T21:05:10.5701091Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5701298Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5701419Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5701481Z {
2023-01-11T21:05:10.5701577Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5701638Z     {
2023-01-11T21:05:10.5701713Z         #pragma omp for 
2023-01-11T21:05:10.5701781Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5701842Z         {
2023-01-11T21:05:10.5701904Z             {
2023-01-11T21:05:10.5701966Z                 {
2023-01-11T21:05:10.5702085Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5702191Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5702337Z                     auto tmp2 = std::exp(-tmp1);
2023-01-11T21:05:10.5702414Z                     auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:05:10.5702549Z                     auto tmp4 = tmp1 * tmp3;
2023-01-11T21:05:10.5702657Z                     auto tmp5 = static_cast<bfloat16>(tmp4);
2023-01-11T21:05:10.5702746Z                     in_out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.5702811Z                 }
2023-01-11T21:05:10.5702874Z             }
2023-01-11T21:05:10.5702935Z         }
2023-01-11T21:05:10.5702983Z     }
2023-01-11T21:05:10.5703040Z }
2023-01-11T21:05:10.5703117Z ''')
2023-01-11T21:05:10.5703122Z 
2023-01-11T21:05:10.5703127Z 
2023-01-11T21:05:10.5703215Z async_compile.wait(globals())
2023-01-11T21:05:10.5703286Z del async_compile
2023-01-11T21:05:10.5703291Z 
2023-01-11T21:05:10.5703360Z def call(args):
2023-01-11T21:05:10.5703440Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5703510Z     args.clear()
2023-01-11T21:05:10.5703697Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5703865Z     aten.addmm.out(arg1_1, as_strided(arg2_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5703935Z     del arg0_1
2023-01-11T21:05:10.5704000Z     del arg1_1
2023-01-11T21:05:10.5704064Z     del arg2_1
2023-01-11T21:05:10.5704220Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5704323Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5704380Z     return (buf1, )
2023-01-11T21:05:10.5704385Z 
2023-01-11T21:05:10.5704389Z 
2023-01-11T21:05:10.5704464Z if __name__ == "__main__":
2023-01-11T21:05:10.5704575Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5704697Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5704898Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5705089Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5705292Z     arg2_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5705417Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5705671Z [2023-01-11 20:55:10,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 312
2023-01-11T21:05:10.5706076Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5706200Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5706455Z [2023-01-11 20:55:10,903] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 313
2023-01-11T21:05:10.5706716Z [2023-01-11 20:55:10,929] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 313
2023-01-11T21:05:10.5707117Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5707246Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5707502Z [2023-01-11 20:55:11,167] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 314
2023-01-11T21:05:10.5707763Z [2023-01-11 20:55:13,793] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 314
2023-01-11T21:05:10.5708157Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5708310Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5708562Z [2023-01-11 20:55:13,920] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 315
2023-01-11T21:05:10.5708810Z [2023-01-11 20:55:13,935] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 315
2023-01-11T21:05:10.5708815Z 
2023-01-11T21:05:10.5708833Z 
2023-01-11T21:05:10.5708912Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5708981Z import torch
2023-01-11T21:05:10.5709049Z import random
2023-01-11T21:05:10.5709165Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5709285Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5709290Z 
2023-01-11T21:05:10.5709367Z aten = torch.ops.aten
2023-01-11T21:05:10.5709501Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5709578Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5709583Z 
2023-01-11T21:05:10.5709589Z 
2023-01-11T21:05:10.5709721Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5709922Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5710069Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5710131Z {
2023-01-11T21:05:10.5710227Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5710287Z     {
2023-01-11T21:05:10.5710349Z         #pragma omp for 
2023-01-11T21:05:10.5710431Z         for(long i0=0; i0<180; i0+=1)
2023-01-11T21:05:10.5710493Z         {
2023-01-11T21:05:10.5710555Z             {
2023-01-11T21:05:10.5710620Z                 {
2023-01-11T21:05:10.5710739Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5710847Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5710981Z                     auto tmp2 = std::exp(-tmp1);
2023-01-11T21:05:10.5711072Z                     auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:05:10.5711167Z                     auto tmp4 = tmp1 * tmp3;
2023-01-11T21:05:10.5711277Z                     auto tmp5 = static_cast<bfloat16>(tmp4);
2023-01-11T21:05:10.5711367Z                     in_out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.5711435Z                 }
2023-01-11T21:05:10.5711497Z             }
2023-01-11T21:05:10.5711546Z         }
2023-01-11T21:05:10.5711606Z     }
2023-01-11T21:05:10.5711665Z }
2023-01-11T21:05:10.5711745Z ''')
2023-01-11T21:05:10.5711752Z 
2023-01-11T21:05:10.5711756Z 
2023-01-11T21:05:10.5711845Z async_compile.wait(globals())
2023-01-11T21:05:10.5711920Z del async_compile
2023-01-11T21:05:10.5711925Z 
2023-01-11T21:05:10.5711994Z def call(args):
2023-01-11T21:05:10.5712055Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5712125Z     args.clear()
2023-01-11T21:05:10.5712325Z     buf0 = empty_strided((6, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5712468Z     aten.mm.out(as_strided(arg1_1, (6, 10), (10, 1)), as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5712539Z     del arg0_1
2023-01-11T21:05:10.5712603Z     del arg1_1
2023-01-11T21:05:10.5712718Z     buf1 = as_strided(buf0, (2, 3, 30), (90, 30, 1)); del buf0  # reuse
2023-01-11T21:05:10.5712822Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5712880Z     return (buf1, )
2023-01-11T21:05:10.5712885Z 
2023-01-11T21:05:10.5712889Z 
2023-01-11T21:05:10.5712964Z if __name__ == "__main__":
2023-01-11T21:05:10.5713077Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5713198Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5713399Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5713604Z     arg1_1 = rand_strided((2, 3, 10), (30, 10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5713717Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5713722Z 
2023-01-11T21:05:10.5713726Z 
2023-01-11T21:05:10.5713818Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5713904Z import torch
2023-01-11T21:05:10.5713974Z import random
2023-01-11T21:05:10.5714087Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5714209Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5714214Z 
2023-01-11T21:05:10.5714291Z aten = torch.ops.aten
2023-01-11T21:05:10.5714422Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5714513Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5714518Z 
2023-01-11T21:05:10.5714522Z 
2023-01-11T21:05:10.5714654Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5714843Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5714963Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5715022Z {
2023-01-11T21:05:10.5715117Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5715177Z     {
2023-01-11T21:05:10.5715252Z         #pragma omp for 
2023-01-11T21:05:10.5715336Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5715384Z         {
2023-01-11T21:05:10.5715445Z             {
2023-01-11T21:05:10.5715508Z                 {
2023-01-11T21:05:10.5715653Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5715761Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5715907Z                     auto tmp2 = std::exp(-tmp1);
2023-01-11T21:05:10.5715998Z                     auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:05:10.5716075Z                     auto tmp4 = tmp1 * tmp3;
2023-01-11T21:05:10.5716185Z                     auto tmp5 = static_cast<bfloat16>(tmp4);
2023-01-11T21:05:10.5716273Z                     in_out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.5716335Z                 }
2023-01-11T21:05:10.5716397Z             }
2023-01-11T21:05:10.5716457Z         }
2023-01-11T21:05:10.5716516Z     }
2023-01-11T21:05:10.5716562Z }
2023-01-11T21:05:10.5716637Z ''')
2023-01-11T21:05:10.5716644Z 
2023-01-11T21:05:10.5716648Z 
2023-01-11T21:05:10.5716737Z async_compile.wait(globals())
2023-01-11T21:05:10.5716810Z del async_compile
2023-01-11T21:05:10.5716815Z 
2023-01-11T21:05:10.5716885Z def call(args):
2023-01-11T21:05:10.5716965Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.5717035Z     args.clear()
2023-01-11T21:05:10.5717221Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5717371Z     aten.addmm.out(arg1_1, arg2_1, as_strided(arg0_1, (10, 30), (1, 10)), beta=1, alpha=1, out=buf0)
2023-01-11T21:05:10.5717437Z     del arg0_1
2023-01-11T21:05:10.5717502Z     del arg1_1
2023-01-11T21:05:10.5717566Z     del arg2_1
2023-01-11T21:05:10.5717651Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5717753Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5717810Z     return (buf1, )
2023-01-11T21:05:10.5717815Z 
2023-01-11T21:05:10.5717832Z 
2023-01-11T21:05:10.5717893Z if __name__ == "__main__":
2023-01-11T21:05:10.5718007Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5718128Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5718330Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5718524Z     arg1_1 = rand_strided((30, ), (1, ), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5718718Z     arg2_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5718839Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.5718843Z 
2023-01-11T21:05:10.5718848Z 
2023-01-11T21:05:10.5718940Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5718994Z import torch
2023-01-11T21:05:10.5719062Z import random
2023-01-11T21:05:10.5719175Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5719293Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5719298Z 
2023-01-11T21:05:10.5719375Z aten = torch.ops.aten
2023-01-11T21:05:10.5719535Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5719624Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5719629Z 
2023-01-11T21:05:10.5719633Z 
2023-01-11T21:05:10.5719753Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5719956Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5720074Z extern "C" void kernel(bfloat16* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.5720134Z {
2023-01-11T21:05:10.5720229Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5720289Z     {
2023-01-11T21:05:10.5720365Z         #pragma omp for 
2023-01-11T21:05:10.5720433Z         for(long i0=0; i0<60; i0+=1)
2023-01-11T21:05:10.5720495Z         {
2023-01-11T21:05:10.5720556Z             {
2023-01-11T21:05:10.5720735Z                 {
2023-01-11T21:05:10.5720856Z                     auto tmp0 = static_cast<float>(in_out_ptr0[i0]);
2023-01-11T21:05:10.5720964Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5721116Z                     auto tmp2 = std::exp(-tmp1);
2023-01-11T21:05:10.5721193Z                     auto tmp3 = 1 / (1 + tmp2);
2023-01-11T21:05:10.5721337Z                     auto tmp4 = tmp1 * tmp3;
2023-01-11T21:05:10.5721451Z                     auto tmp5 = static_cast<bfloat16>(tmp4);
2023-01-11T21:05:10.5721541Z                     in_out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.5721606Z                 }
2023-01-11T21:05:10.5721669Z             }
2023-01-11T21:05:10.5721730Z         }
2023-01-11T21:05:10.5721777Z     }
2023-01-11T21:05:10.5721837Z }
2023-01-11T21:05:10.5721916Z ''')
2023-01-11T21:05:10.5721921Z 
2023-01-11T21:05:10.5721925Z 
2023-01-11T21:05:10.5722016Z async_compile.wait(globals())
2023-01-11T21:05:10.5722088Z del async_compile
2023-01-11T21:05:10.5722093Z 
2023-01-11T21:05:10.5722163Z def call(args):
2023-01-11T21:05:10.5722238Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5722309Z     args.clear()
2023-01-11T21:05:10.5722501Z     buf0 = empty_strided((2, 30), (30, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5722620Z     aten.mm.out(arg1_1, as_strided(arg0_1, (10, 30), (1, 10)), out=buf0)
2023-01-11T21:05:10.5722688Z     del arg0_1
2023-01-11T21:05:10.5722758Z     del arg1_1
2023-01-11T21:05:10.5722845Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5722948Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5723019Z     return (buf1, )
2023-01-11T21:05:10.5723024Z 
2023-01-11T21:05:10.5723028Z 
2023-01-11T21:05:10.5723089Z if __name__ == "__main__":
2023-01-11T21:05:10.5723202Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5723324Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5723524Z     arg0_1 = rand_strided((30, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5723721Z     arg1_1 = rand_strided((2, 10), (10, 1), device='cpu', dtype=torch.bfloat16)
2023-01-11T21:05:10.5723835Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5723841Z 
2023-01-11T21:05:10.5723908Z ok (60.904s)
2023-01-11T21:05:10.5724349Z   test_linspace1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5724474Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5724719Z [2023-01-11 20:55:13,997] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 316
2023-01-11T21:05:10.5724979Z [2023-01-11 20:55:16,621] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 316
2023-01-11T21:05:10.5724985Z 
2023-01-11T21:05:10.5725077Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5725146Z import torch
2023-01-11T21:05:10.5725253Z import random
2023-01-11T21:05:10.5725369Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5725487Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5725492Z 
2023-01-11T21:05:10.5725570Z aten = torch.ops.aten
2023-01-11T21:05:10.5725691Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5725781Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5725786Z 
2023-01-11T21:05:10.5725791Z 
2023-01-11T21:05:10.5725923Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5726125Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5726242Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5726340Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5726399Z {
2023-01-11T21:05:10.5726494Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5726543Z     {
2023-01-11T21:05:10.5726618Z         #pragma omp for 
2023-01-11T21:05:10.5726700Z         for(long i0=0; i0<7; i0+=1)
2023-01-11T21:05:10.5726761Z         {
2023-01-11T21:05:10.5726822Z             {
2023-01-11T21:05:10.5726885Z                 {
2023-01-11T21:05:10.5727030Z                     auto tmp4 = in_ptr0[i0];
2023-01-11T21:05:10.5727139Z                     auto tmp0 = static_cast<float>(0.125);
2023-01-11T21:05:10.5727241Z                     auto tmp1 = static_cast<float>(i0);
2023-01-11T21:05:10.5727332Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5727421Z                     auto tmp3 = tmp2 + tmp0;
2023-01-11T21:05:10.5727508Z                     auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.5727594Z                     out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.5727644Z                 }
2023-01-11T21:05:10.5727707Z             }
2023-01-11T21:05:10.5727769Z         }
2023-01-11T21:05:10.5727833Z     }
2023-01-11T21:05:10.5727892Z }
2023-01-11T21:05:10.5727973Z ''')
2023-01-11T21:05:10.5727978Z 
2023-01-11T21:05:10.5727984Z 
2023-01-11T21:05:10.5728073Z async_compile.wait(globals())
2023-01-11T21:05:10.5728130Z del async_compile
2023-01-11T21:05:10.5728150Z 
2023-01-11T21:05:10.5728206Z def call(args):
2023-01-11T21:05:10.5728274Z     arg0_1, = args
2023-01-11T21:05:10.5728347Z     args.clear()
2023-01-11T21:05:10.5728543Z     buf0 = empty_strided((1, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5728674Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5728741Z     del arg0_1
2023-01-11T21:05:10.5728797Z     return (buf0, )
2023-01-11T21:05:10.5728815Z 
2023-01-11T21:05:10.5728819Z 
2023-01-11T21:05:10.5728880Z if __name__ == "__main__":
2023-01-11T21:05:10.5728992Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5729112Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5729306Z     arg0_1 = rand_strided((1, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5729410Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5729417Z 
2023-01-11T21:05:10.5729482Z ok (2.688s)
2023-01-11T21:05:10.5729921Z   test_linspace2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5730045Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5730306Z [2023-01-11 20:55:16,681] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 317
2023-01-11T21:05:10.5730557Z [2023-01-11 20:55:19,279] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 317
2023-01-11T21:05:10.5730562Z 
2023-01-11T21:05:10.5730653Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5730720Z import torch
2023-01-11T21:05:10.5730817Z import random
2023-01-11T21:05:10.5730930Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5731047Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5731053Z 
2023-01-11T21:05:10.5731129Z aten = torch.ops.aten
2023-01-11T21:05:10.5731260Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5731338Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5731343Z 
2023-01-11T21:05:10.5731347Z 
2023-01-11T21:05:10.5731479Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5731682Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5731799Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5731898Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5731957Z {
2023-01-11T21:05:10.5732017Z     {
2023-01-11T21:05:10.5732065Z         {
2023-01-11T21:05:10.5732150Z             auto tmp4 = in_ptr0[0];
2023-01-11T21:05:10.5732252Z             auto tmp0 = static_cast<float>(0.0);
2023-01-11T21:05:10.5732350Z             auto tmp1 = static_cast<float>(0);
2023-01-11T21:05:10.5732432Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5732536Z             auto tmp3 = tmp2 + tmp1;
2023-01-11T21:05:10.5732617Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.5732680Z             out_ptr0[0] = tmp5;
2023-01-11T21:05:10.5732740Z         }
2023-01-11T21:05:10.5732799Z     }
2023-01-11T21:05:10.5732856Z }
2023-01-11T21:05:10.5732933Z ''')
2023-01-11T21:05:10.5732938Z 
2023-01-11T21:05:10.5732942Z 
2023-01-11T21:05:10.5733029Z async_compile.wait(globals())
2023-01-11T21:05:10.5733100Z del async_compile
2023-01-11T21:05:10.5733105Z 
2023-01-11T21:05:10.5733160Z def call(args):
2023-01-11T21:05:10.5733229Z     arg0_1, = args
2023-01-11T21:05:10.5733297Z     args.clear()
2023-01-11T21:05:10.5733490Z     buf0 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5733620Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5733690Z     del arg0_1
2023-01-11T21:05:10.5733760Z     return (buf0, )
2023-01-11T21:05:10.5733765Z 
2023-01-11T21:05:10.5733769Z 
2023-01-11T21:05:10.5733845Z if __name__ == "__main__":
2023-01-11T21:05:10.5733944Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5734063Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5734257Z     arg0_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5734362Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5734367Z 
2023-01-11T21:05:10.5734432Z ok (2.658s)
2023-01-11T21:05:10.5734877Z   test_linspace3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5735005Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5735266Z [2023-01-11 20:55:19,333] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 318
2023-01-11T21:05:10.5735528Z [2023-01-11 20:55:19,336] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 318
2023-01-11T21:05:10.5735533Z 
2023-01-11T21:05:10.5735612Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5735680Z import torch
2023-01-11T21:05:10.5735748Z import random
2023-01-11T21:05:10.5735862Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5735980Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5735985Z 
2023-01-11T21:05:10.5736061Z aten = torch.ops.aten
2023-01-11T21:05:10.5736192Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5736269Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5736317Z 
2023-01-11T21:05:10.5736321Z 
2023-01-11T21:05:10.5736394Z async_compile.wait(globals())
2023-01-11T21:05:10.5736466Z del async_compile
2023-01-11T21:05:10.5736471Z 
2023-01-11T21:05:10.5736540Z def call(args):
2023-01-11T21:05:10.5736733Z     buf0 = empty_strided((0, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5736805Z     return (buf0, )
2023-01-11T21:05:10.5736810Z 
2023-01-11T21:05:10.5736814Z 
2023-01-11T21:05:10.5736887Z if __name__ == "__main__":
2023-01-11T21:05:10.5737000Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5737122Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5737205Z     print_performance(lambda: call([]))
2023-01-11T21:05:10.5737210Z 
2023-01-11T21:05:10.5737274Z ok (0.053s)
2023-01-11T21:05:10.5737601Z   test_list_clearing_cpu (__main__.CpuTests) ... [2023-01-11 20:55:19,383] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph None
2023-01-11T21:05:10.5737866Z [2023-01-11 20:55:22,057] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph None
2023-01-11T21:05:10.5737872Z 
2023-01-11T21:05:10.5737961Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5738060Z import torch
2023-01-11T21:05:10.5738129Z import random
2023-01-11T21:05:10.5738242Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5738346Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5738351Z 
2023-01-11T21:05:10.5738427Z aten = torch.ops.aten
2023-01-11T21:05:10.5738643Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5738740Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5738745Z 
2023-01-11T21:05:10.5738750Z 
2023-01-11T21:05:10.5738884Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5739089Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5739211Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5739319Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5739404Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5739465Z {
2023-01-11T21:05:10.5739566Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5739627Z     {
2023-01-11T21:05:10.5739705Z         #pragma omp for 
2023-01-11T21:05:10.5739786Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.5739848Z         {
2023-01-11T21:05:10.5739969Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5740100Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.5740185Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5740278Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5740340Z         }
2023-01-11T21:05:10.5740435Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5740517Z         for(long i0=16; i0<25; i0+=1)
2023-01-11T21:05:10.5740567Z         {
2023-01-11T21:05:10.5740650Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5740729Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5740811Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5740891Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5740954Z         }
2023-01-11T21:05:10.5741013Z     }
2023-01-11T21:05:10.5741058Z }
2023-01-11T21:05:10.5741135Z ''')
2023-01-11T21:05:10.5741140Z 
2023-01-11T21:05:10.5741144Z 
2023-01-11T21:05:10.5741231Z async_compile.wait(globals())
2023-01-11T21:05:10.5741305Z del async_compile
2023-01-11T21:05:10.5741310Z 
2023-01-11T21:05:10.5741382Z def call(args):
2023-01-11T21:05:10.5741450Z     x_1, y_1 = args
2023-01-11T21:05:10.5741523Z     args.clear()
2023-01-11T21:05:10.5741702Z     buf0 = empty_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5741857Z     kernel_cpp_0(c_void_p(x_1.data_ptr()), c_void_p(y_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5741924Z     del x_1
2023-01-11T21:05:10.5742025Z     del y_1
2023-01-11T21:05:10.5742219Z     buf1 = empty_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5742308Z     aten.mm.out(buf0, buf0, out=buf1)
2023-01-11T21:05:10.5742381Z     return (buf1, )
2023-01-11T21:05:10.5742388Z 
2023-01-11T21:05:10.5742392Z 
2023-01-11T21:05:10.5742466Z if __name__ == "__main__":
2023-01-11T21:05:10.5742565Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5742687Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5742872Z     x_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5743057Z     y_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5743166Z     print_performance(lambda: call([x_1, y_1]))
2023-01-11T21:05:10.5743171Z 
2023-01-11T21:05:10.5743237Z ok (2.721s)
2023-01-11T21:05:10.5743709Z   test_log1p_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5743840Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5744099Z [2023-01-11 20:55:22,094] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 319
2023-01-11T21:05:10.5744346Z [2023-01-11 20:55:24,769] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 319
2023-01-11T21:05:10.5744744Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5744867Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5745123Z [2023-01-11 20:55:24,825] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 320
2023-01-11T21:05:10.5745387Z [2023-01-11 20:55:27,467] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 320
2023-01-11T21:05:10.5745784Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5745909Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5746165Z [2023-01-11 20:55:27,502] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 321
2023-01-11T21:05:10.5746426Z [2023-01-11 20:55:30,130] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 321
2023-01-11T21:05:10.5746827Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5746953Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5747193Z [2023-01-11 20:55:30,166] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 322
2023-01-11T21:05:10.5747455Z [2023-01-11 20:55:32,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 322
2023-01-11T21:05:10.5747850Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5748006Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5748262Z [2023-01-11 20:55:32,853] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 323
2023-01-11T21:05:10.5748268Z 
2023-01-11T21:05:10.5748362Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5748431Z import torch
2023-01-11T21:05:10.5748500Z import random
2023-01-11T21:05:10.5748615Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5748722Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5748739Z 
2023-01-11T21:05:10.5748803Z aten = torch.ops.aten
2023-01-11T21:05:10.5748934Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5749024Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5749029Z 
2023-01-11T21:05:10.5749033Z 
2023-01-11T21:05:10.5749168Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5749371Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5749524Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:05:10.5749624Z                        half* __restrict__ out_ptr0,
2023-01-11T21:05:10.5749704Z                        half* __restrict__ out_ptr1)
2023-01-11T21:05:10.5749764Z {
2023-01-11T21:05:10.5749859Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5749919Z     {
2023-01-11T21:05:10.5749993Z         #pragma omp for 
2023-01-11T21:05:10.5750074Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.5750136Z         {
2023-01-11T21:05:10.5750186Z             {
2023-01-11T21:05:10.5750248Z                 {
2023-01-11T21:05:10.5750362Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5750465Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:05:10.5750566Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:05:10.5750659Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5750742Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5750815Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5750878Z                 }
2023-01-11T21:05:10.5750940Z             }
2023-01-11T21:05:10.5751001Z         }
2023-01-11T21:05:10.5751060Z     }
2023-01-11T21:05:10.5751118Z }
2023-01-11T21:05:10.5751195Z ''')
2023-01-11T21:05:10.5751200Z 
2023-01-11T21:05:10.5751204Z 
2023-01-11T21:05:10.5751280Z async_compile.wait(globals())
2023-01-11T21:05:10.5751351Z del async_compile
2023-01-11T21:05:10.5751356Z 
2023-01-11T21:05:10.5751424Z def call(args):
2023-01-11T21:05:10.5751491Z     arg0_1, = args
2023-01-11T21:05:10.5751562Z     args.clear()
2023-01-11T21:05:10.5751756Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.5751945Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.5752099Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5752165Z     del arg0_1
2023-01-11T21:05:10.5752241Z     return (buf0, buf1, )
2023-01-11T21:05:10.5752249Z 
2023-01-11T21:05:10.5752253Z 
2023-01-11T21:05:10.5752326Z if __name__ == "__main__":
2023-01-11T21:05:10.5752440Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5752561Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5752751Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.5752856Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5752862Z 
2023-01-11T21:05:10.5752866Z 
2023-01-11T21:05:10.5752958Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5753014Z import torch
2023-01-11T21:05:10.5753083Z import random
2023-01-11T21:05:10.5753195Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5753313Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5753348Z 
2023-01-11T21:05:10.5753424Z aten = torch.ops.aten
2023-01-11T21:05:10.5753557Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5753650Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5753655Z 
2023-01-11T21:05:10.5753659Z 
2023-01-11T21:05:10.5753791Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5753980Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5754097Z extern "C" void kernel(const half* __restrict__ in_ptr0,
2023-01-11T21:05:10.5754192Z                        half* __restrict__ out_ptr0,
2023-01-11T21:05:10.5754285Z                        half* __restrict__ out_ptr1)
2023-01-11T21:05:10.5754345Z {
2023-01-11T21:05:10.5754440Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5754500Z     {
2023-01-11T21:05:10.5754562Z         #pragma omp for 
2023-01-11T21:05:10.5754644Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.5754707Z         {
2023-01-11T21:05:10.5754770Z             {
2023-01-11T21:05:10.5754832Z                 {
2023-01-11T21:05:10.5754946Z                     auto tmp0 = static_cast<float>(in_ptr0[i0]);
2023-01-11T21:05:10.5755074Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:05:10.5755164Z                     auto tmp2 = static_cast<half>(2);
2023-01-11T21:05:10.5755254Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5755338Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5755420Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5755484Z                 }
2023-01-11T21:05:10.5755545Z             }
2023-01-11T21:05:10.5755593Z         }
2023-01-11T21:05:10.5755652Z     }
2023-01-11T21:05:10.5755709Z }
2023-01-11T21:05:10.5755785Z ''')
2023-01-11T21:05:10.5755790Z 
2023-01-11T21:05:10.5755795Z 
2023-01-11T21:05:10.5755882Z async_compile.wait(globals())
2023-01-11T21:05:10.5755953Z del async_compile
2023-01-11T21:05:10.5755959Z 
2023-01-11T21:05:10.5756028Z def call(args):
2023-01-11T21:05:10.5756096Z     arg0_1, = args
2023-01-11T21:05:10.5756152Z     args.clear()
2023-01-11T21:05:10.5756346Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.5756538Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.5756697Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5756764Z     del arg0_1
2023-01-11T21:05:10.5756840Z     return (buf0, buf1, )
2023-01-11T21:05:10.5756845Z 
2023-01-11T21:05:10.5756849Z 
2023-01-11T21:05:10.5756923Z if __name__ == "__main__":
2023-01-11T21:05:10.5757023Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5757145Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5757337Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.5757443Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5757450Z 
2023-01-11T21:05:10.5757454Z 
2023-01-11T21:05:10.5757545Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5757614Z import torch
2023-01-11T21:05:10.5757682Z import random
2023-01-11T21:05:10.5757798Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5757904Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5757921Z 
2023-01-11T21:05:10.5757985Z aten = torch.ops.aten
2023-01-11T21:05:10.5758116Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5758205Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5758211Z 
2023-01-11T21:05:10.5758215Z 
2023-01-11T21:05:10.5758346Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5758548Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5758665Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5758763Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5758877Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5758936Z {
2023-01-11T21:05:10.5759032Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5759093Z     {
2023-01-11T21:05:10.5759168Z         #pragma omp for 
2023-01-11T21:05:10.5759251Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5759312Z         {
2023-01-11T21:05:10.5759431Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5759515Z             auto tmp1 = tmp0.log1p();
2023-01-11T21:05:10.5759646Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.5759730Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5759821Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5759910Z             tmp3.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.5759971Z         }
2023-01-11T21:05:10.5760051Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5760132Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5760193Z         {
2023-01-11T21:05:10.5760275Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5760365Z             auto tmp1 = std::log1p(tmp0);
2023-01-11T21:05:10.5760491Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.5760576Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5760778Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5760857Z             out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5760919Z         }
2023-01-11T21:05:10.5760980Z     }
2023-01-11T21:05:10.5761040Z }
2023-01-11T21:05:10.5761120Z ''')
2023-01-11T21:05:10.5761125Z 
2023-01-11T21:05:10.5761129Z 
2023-01-11T21:05:10.5761219Z async_compile.wait(globals())
2023-01-11T21:05:10.5761278Z del async_compile
2023-01-11T21:05:10.5761296Z 
2023-01-11T21:05:10.5761352Z def call(args):
2023-01-11T21:05:10.5761421Z     arg0_1, = args
2023-01-11T21:05:10.5761491Z     args.clear()
2023-01-11T21:05:10.5761687Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5761878Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5762041Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5762110Z     del arg0_1
2023-01-11T21:05:10.5762172Z     return (buf0, buf1, )
2023-01-11T21:05:10.5762177Z 
2023-01-11T21:05:10.5762181Z 
2023-01-11T21:05:10.5762255Z if __name__ == "__main__":
2023-01-11T21:05:10.5762371Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5762493Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5762688Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5762794Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5762800Z 
2023-01-11T21:05:10.5762804Z 
2023-01-11T21:05:10.5762894Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5762962Z import torch
2023-01-11T21:05:10.5763018Z import random
2023-01-11T21:05:10.5763135Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5763252Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5763257Z 
2023-01-11T21:05:10.5763334Z aten = torch.ops.aten
2023-01-11T21:05:10.5763468Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5763559Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5763564Z 
2023-01-11T21:05:10.5763568Z 
2023-01-11T21:05:10.5763698Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5763902Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5764007Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5764106Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5764200Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5764259Z {
2023-01-11T21:05:10.5764354Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5764471Z     {
2023-01-11T21:05:10.5764549Z         #pragma omp for 
2023-01-11T21:05:10.5764616Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.5764679Z         {
2023-01-11T21:05:10.5764813Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5764901Z             auto tmp1 = tmp0.log1p();
2023-01-11T21:05:10.5765034Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.5765119Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5765220Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5765297Z             tmp3.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.5765360Z         }
2023-01-11T21:05:10.5765455Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5765538Z         for(long i0=192; i0<201; i0+=1)
2023-01-11T21:05:10.5765601Z         {
2023-01-11T21:05:10.5765687Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5765780Z             auto tmp1 = std::log1p(tmp0);
2023-01-11T21:05:10.5765868Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.5765951Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5766031Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5766157Z             out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5766219Z         }
2023-01-11T21:05:10.5766278Z     }
2023-01-11T21:05:10.5766336Z }
2023-01-11T21:05:10.5766401Z ''')
2023-01-11T21:05:10.5766406Z 
2023-01-11T21:05:10.5766410Z 
2023-01-11T21:05:10.5766498Z async_compile.wait(globals())
2023-01-11T21:05:10.5766568Z del async_compile
2023-01-11T21:05:10.5766573Z 
2023-01-11T21:05:10.5766643Z def call(args):
2023-01-11T21:05:10.5766710Z     arg0_1, = args
2023-01-11T21:05:10.5766779Z     args.clear()
2023-01-11T21:05:10.5766974Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5767155Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5767317Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5767388Z     del arg0_1
2023-01-11T21:05:10.5767465Z     return (buf0, buf1, )
2023-01-11T21:05:10.5767470Z 
2023-01-11T21:05:10.5767474Z 
2023-01-11T21:05:10.5767552Z if __name__ == "__main__":
2023-01-11T21:05:10.5767668Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5767791Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5767987Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5768080Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5768099Z 
2023-01-11T21:05:10.5768103Z 
2023-01-11T21:05:10.5768182Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5768252Z import torch
2023-01-11T21:05:10.5768323Z import random
2023-01-11T21:05:10.5768437Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5768556Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5768561Z 
2023-01-11T21:05:10.5768639Z aten = torch.ops.aten
2023-01-11T21:05:10.5768770Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5768848Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5768853Z 
2023-01-11T21:05:10.5768870Z 
2023-01-11T21:05:10.5768992Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5769195Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5769314Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.5769414Z                        double* __restrict__ out_ptr0,
2023-01-11T21:05:10.5769512Z                        double* __restrict__ out_ptr1)
2023-01-11T21:05:10.5769571Z {
2023-01-11T21:05:10.5769667Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5769715Z     {
2023-01-11T21:05:10.5769791Z         #pragma omp for 
2023-01-11T21:05:10.5769871Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.5769932Z         {
2023-01-11T21:05:10.5769994Z             {
2023-01-11T21:05:10.5770090Z                 {
2023-01-11T21:05:10.5770169Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5770270Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:05:10.5770375Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:05:10.5770465Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5770549Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5770631Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5770694Z                 }
2023-01-11T21:05:10.5770756Z             }
2023-01-11T21:05:10.5770805Z         }
2023-01-11T21:05:10.5770869Z     }
2023-01-11T21:05:10.5770927Z }
2023-01-11T21:05:10.5771004Z ''')
2023-01-11T21:05:10.5771009Z 
2023-01-11T21:05:10.5771013Z 
2023-01-11T21:05:10.5771101Z async_compile.wait(globals())
2023-01-11T21:05:10.5771171Z del async_compile
2023-01-11T21:05:10.5771176Z 
2023-01-11T21:05:10.5771244Z def call(args):
2023-01-11T21:05:10.5771300Z     arg0_1, = args
2023-01-11T21:05:10.5771371Z     args.clear()
2023-01-11T21:05:10.5771563Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5771754Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5771946Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5772014Z     del arg0_1
2023-01-11T21:05:10.5772089Z     return (buf0, buf1, )
2023-01-11T21:05:10.5772094Z 
2023-01-11T21:05:10.5772099Z 
2023-01-11T21:05:10.5772160Z if __name__ == "__main__":
2023-01-11T21:05:10.5772273Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5772394Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5772585Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5772692Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5772959Z [2023-01-11 20:55:35,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 323
2023-01-11T21:05:10.5773365Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5773493Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5773751Z [2023-01-11 20:55:35,468] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 324
2023-01-11T21:05:10.5774000Z [2023-01-11 20:55:38,084] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 324
2023-01-11T21:05:10.5774398Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5774524Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5774782Z [2023-01-11 20:55:38,121] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 325
2023-01-11T21:05:10.5775045Z [2023-01-11 20:55:40,797] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 325
2023-01-11T21:05:10.5775443Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5775567Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5775821Z [2023-01-11 20:55:40,839] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 326
2023-01-11T21:05:10.5776112Z [2023-01-11 20:55:43,440] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 326
2023-01-11T21:05:10.5776511Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5776637Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5776890Z [2023-01-11 20:55:43,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 327
2023-01-11T21:05:10.5777136Z [2023-01-11 20:55:46,099] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 327
2023-01-11T21:05:10.5777562Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5777690Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5777944Z [2023-01-11 20:55:46,135] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 328
2023-01-11T21:05:10.5777951Z 
2023-01-11T21:05:10.5777956Z 
2023-01-11T21:05:10.5778049Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5778120Z import torch
2023-01-11T21:05:10.5778189Z import random
2023-01-11T21:05:10.5778305Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5778426Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5778431Z 
2023-01-11T21:05:10.5778578Z aten = torch.ops.aten
2023-01-11T21:05:10.5778716Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5778810Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5778815Z 
2023-01-11T21:05:10.5778820Z 
2023-01-11T21:05:10.5778955Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5779162Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5779287Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.5779388Z                        double* __restrict__ out_ptr0,
2023-01-11T21:05:10.5779484Z                        double* __restrict__ out_ptr1)
2023-01-11T21:05:10.5779530Z {
2023-01-11T21:05:10.5779626Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5779688Z     {
2023-01-11T21:05:10.5779765Z         #pragma omp for 
2023-01-11T21:05:10.5779849Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.5779915Z         {
2023-01-11T21:05:10.5779978Z             {
2023-01-11T21:05:10.5780029Z                 {
2023-01-11T21:05:10.5780125Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5780226Z                     auto tmp1 = std::log1p(tmp0);
2023-01-11T21:05:10.5780331Z                     auto tmp2 = static_cast<double>(2);
2023-01-11T21:05:10.5780424Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5780509Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5780593Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5780643Z                 }
2023-01-11T21:05:10.5780707Z             }
2023-01-11T21:05:10.5780767Z         }
2023-01-11T21:05:10.5780826Z     }
2023-01-11T21:05:10.5780885Z }
2023-01-11T21:05:10.5780962Z ''')
2023-01-11T21:05:10.5780967Z 
2023-01-11T21:05:10.5780971Z 
2023-01-11T21:05:10.5781060Z async_compile.wait(globals())
2023-01-11T21:05:10.5781117Z del async_compile
2023-01-11T21:05:10.5781122Z 
2023-01-11T21:05:10.5781189Z def call(args):
2023-01-11T21:05:10.5781256Z     arg0_1, = args
2023-01-11T21:05:10.5781326Z     args.clear()
2023-01-11T21:05:10.5781522Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5781746Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5781908Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5781964Z     del arg0_1
2023-01-11T21:05:10.5782039Z     return (buf0, buf1, )
2023-01-11T21:05:10.5782044Z 
2023-01-11T21:05:10.5782048Z 
2023-01-11T21:05:10.5782121Z if __name__ == "__main__":
2023-01-11T21:05:10.5782233Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5782353Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5782545Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5782650Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5782655Z 
2023-01-11T21:05:10.5782659Z 
2023-01-11T21:05:10.5782750Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5782818Z import torch
2023-01-11T21:05:10.5782876Z import random
2023-01-11T21:05:10.5782987Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5783107Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5783112Z 
2023-01-11T21:05:10.5783243Z aten = torch.ops.aten
2023-01-11T21:05:10.5783376Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5783465Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5783470Z 
2023-01-11T21:05:10.5783474Z 
2023-01-11T21:05:10.5783606Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5783795Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5783910Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.5784027Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5784158Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5784234Z {
2023-01-11T21:05:10.5784376Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5784466Z     {
2023-01-11T21:05:10.5784552Z         #pragma omp for 
2023-01-11T21:05:10.5784657Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.5784734Z         {
2023-01-11T21:05:10.5784819Z             {
2023-01-11T21:05:10.5784908Z                 {
2023-01-11T21:05:10.5785029Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5785190Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5785307Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:05:10.5785437Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.5785549Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.5785658Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5785778Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.5785890Z                 }
2023-01-11T21:05:10.5785978Z             }
2023-01-11T21:05:10.5786048Z         }
2023-01-11T21:05:10.5786143Z     }
2023-01-11T21:05:10.5786230Z }
2023-01-11T21:05:10.5786358Z ''')
2023-01-11T21:05:10.5786367Z 
2023-01-11T21:05:10.5786372Z 
2023-01-11T21:05:10.5786490Z async_compile.wait(globals())
2023-01-11T21:05:10.5786594Z del async_compile
2023-01-11T21:05:10.5786603Z 
2023-01-11T21:05:10.5786728Z def call(args):
2023-01-11T21:05:10.5786842Z     arg0_1, = args
2023-01-11T21:05:10.5786943Z     args.clear()
2023-01-11T21:05:10.5787238Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5787501Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5787714Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5787806Z     del arg0_1
2023-01-11T21:05:10.5787919Z     return (buf0, buf1, )
2023-01-11T21:05:10.5787927Z 
2023-01-11T21:05:10.5787934Z 
2023-01-11T21:05:10.5788053Z if __name__ == "__main__":
2023-01-11T21:05:10.5788222Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5788483Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5788836Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.5789028Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5789036Z 
2023-01-11T21:05:10.5789043Z 
2023-01-11T21:05:10.5789189Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5789300Z import torch
2023-01-11T21:05:10.5789415Z import random
2023-01-11T21:05:10.5789602Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5789791Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5789800Z 
2023-01-11T21:05:10.5789933Z aten = torch.ops.aten
2023-01-11T21:05:10.5790174Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5790336Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5790345Z 
2023-01-11T21:05:10.5790351Z 
2023-01-11T21:05:10.5790608Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5790930Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5791143Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.5791396Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5791547Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5791655Z {
2023-01-11T21:05:10.5791830Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5791938Z     {
2023-01-11T21:05:10.5792072Z         #pragma omp for 
2023-01-11T21:05:10.5792223Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.5792331Z         {
2023-01-11T21:05:10.5792420Z             {
2023-01-11T21:05:10.5792519Z                 {
2023-01-11T21:05:10.5792647Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5792844Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5793042Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:05:10.5793235Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.5793393Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.5793510Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5793586Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.5793653Z                 }
2023-01-11T21:05:10.5793715Z             }
2023-01-11T21:05:10.5793775Z         }
2023-01-11T21:05:10.5793835Z     }
2023-01-11T21:05:10.5793892Z }
2023-01-11T21:05:10.5793991Z ''')
2023-01-11T21:05:10.5793998Z 
2023-01-11T21:05:10.5794002Z 
2023-01-11T21:05:10.5794079Z async_compile.wait(globals())
2023-01-11T21:05:10.5794149Z del async_compile
2023-01-11T21:05:10.5794154Z 
2023-01-11T21:05:10.5794222Z def call(args):
2023-01-11T21:05:10.5794288Z     arg0_1, = args
2023-01-11T21:05:10.5794357Z     args.clear()
2023-01-11T21:05:10.5794553Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5794742Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5794907Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5794961Z     del arg0_1
2023-01-11T21:05:10.5795038Z     return (buf0, buf1, )
2023-01-11T21:05:10.5795043Z 
2023-01-11T21:05:10.5795049Z 
2023-01-11T21:05:10.5795124Z if __name__ == "__main__":
2023-01-11T21:05:10.5795236Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5795357Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5795550Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.5795655Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5795660Z 
2023-01-11T21:05:10.5795664Z 
2023-01-11T21:05:10.5795756Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5795811Z import torch
2023-01-11T21:05:10.5795880Z import random
2023-01-11T21:05:10.5795994Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5796112Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5796180Z 
2023-01-11T21:05:10.5796258Z aten = torch.ops.aten
2023-01-11T21:05:10.5796391Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5796483Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5796488Z 
2023-01-11T21:05:10.5796492Z 
2023-01-11T21:05:10.5796625Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5796818Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5796936Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5797037Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5797133Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5797194Z {
2023-01-11T21:05:10.5797290Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5797354Z     {
2023-01-11T21:05:10.5797418Z         #pragma omp for 
2023-01-11T21:05:10.5797499Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.5797564Z         {
2023-01-11T21:05:10.5797626Z             {
2023-01-11T21:05:10.5797688Z                 {
2023-01-11T21:05:10.5797779Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5797907Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5798010Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:05:10.5798111Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.5798201Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.5798285Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5798367Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.5798433Z                 }
2023-01-11T21:05:10.5798496Z             }
2023-01-11T21:05:10.5798543Z         }
2023-01-11T21:05:10.5798602Z     }
2023-01-11T21:05:10.5798661Z }
2023-01-11T21:05:10.5798738Z ''')
2023-01-11T21:05:10.5798744Z 
2023-01-11T21:05:10.5798748Z 
2023-01-11T21:05:10.5798835Z async_compile.wait(globals())
2023-01-11T21:05:10.5798908Z del async_compile
2023-01-11T21:05:10.5798913Z 
2023-01-11T21:05:10.5798982Z def call(args):
2023-01-11T21:05:10.5799037Z     arg0_1, = args
2023-01-11T21:05:10.5799107Z     args.clear()
2023-01-11T21:05:10.5799305Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5799495Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5799659Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5799727Z     del arg0_1
2023-01-11T21:05:10.5799803Z     return (buf0, buf1, )
2023-01-11T21:05:10.5799808Z 
2023-01-11T21:05:10.5799812Z 
2023-01-11T21:05:10.5799887Z if __name__ == "__main__":
2023-01-11T21:05:10.5799987Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5800108Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5800295Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5800403Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5800408Z 
2023-01-11T21:05:10.5800412Z 
2023-01-11T21:05:10.5800502Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5800569Z import torch
2023-01-11T21:05:10.5800766Z import random
2023-01-11T21:05:10.5800868Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5800987Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5800992Z 
2023-01-11T21:05:10.5801069Z aten = torch.ops.aten
2023-01-11T21:05:10.5801202Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5801294Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5801299Z 
2023-01-11T21:05:10.5801303Z 
2023-01-11T21:05:10.5801437Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5801641Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5801758Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5801909Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5802006Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5802065Z {
2023-01-11T21:05:10.5802162Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5802226Z     {
2023-01-11T21:05:10.5802301Z         #pragma omp for 
2023-01-11T21:05:10.5802383Z         for(long i0=0; i0<201; i0+=1)
2023-01-11T21:05:10.5802432Z         {
2023-01-11T21:05:10.5802494Z             {
2023-01-11T21:05:10.5802558Z                 {
2023-01-11T21:05:10.5802650Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5802759Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.5802862Z                     auto tmp2 = std::log1p(tmp1);
2023-01-11T21:05:10.5802964Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.5803043Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.5803127Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5803213Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.5803279Z                 }
2023-01-11T21:05:10.5803341Z             }
2023-01-11T21:05:10.5803403Z         }
2023-01-11T21:05:10.5803465Z     }
2023-01-11T21:05:10.5803510Z }
2023-01-11T21:05:10.5803624Z ''')
2023-01-11T21:05:10.5803630Z 
2023-01-11T21:05:10.5803634Z 
2023-01-11T21:05:10.5803723Z async_compile.wait(globals())
2023-01-11T21:05:10.5803793Z del async_compile
2023-01-11T21:05:10.5803798Z 
2023-01-11T21:05:10.5803866Z def call(args):
2023-01-11T21:05:10.5803932Z     arg0_1, = args
2023-01-11T21:05:10.5804002Z     args.clear()
2023-01-11T21:05:10.5804184Z     buf0 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5804373Z     buf1 = empty_strided((201, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5804534Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5804602Z     del arg0_1
2023-01-11T21:05:10.5804680Z     return (buf0, buf1, )
2023-01-11T21:05:10.5804687Z 
2023-01-11T21:05:10.5804691Z 
2023-01-11T21:05:10.5804767Z if __name__ == "__main__":
2023-01-11T21:05:10.5804881Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5805005Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5805183Z     arg0_1 = rand_strided((201, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5805290Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5805562Z [2023-01-11 20:55:48,739] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 328
2023-01-11T21:05:10.5805567Z 
2023-01-11T21:05:10.5805635Z ok (26.687s)
2023-01-11T21:05:10.5806069Z   test_log2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5806198Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5806456Z [2023-01-11 20:55:48,785] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 329
2023-01-11T21:05:10.5806723Z [2023-01-11 20:55:51,402] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 329
2023-01-11T21:05:10.5806729Z 
2023-01-11T21:05:10.5806827Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5806898Z import torch
2023-01-11T21:05:10.5806955Z import random
2023-01-11T21:05:10.5807070Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5807193Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5807198Z 
2023-01-11T21:05:10.5807275Z aten = torch.ops.aten
2023-01-11T21:05:10.5807409Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5807500Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5807538Z 
2023-01-11T21:05:10.5807542Z 
2023-01-11T21:05:10.5807677Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5807872Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5807988Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5808092Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5808191Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5808252Z {
2023-01-11T21:05:10.5808350Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5808411Z     {
2023-01-11T21:05:10.5808475Z         #pragma omp for 
2023-01-11T21:05:10.5808557Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5808619Z         {
2023-01-11T21:05:10.5808752Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5808837Z             auto tmp1 = tmp0.log();
2023-01-11T21:05:10.5808981Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1.4426950408889634));
2023-01-11T21:05:10.5809069Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5809201Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.5809301Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.5809386Z             auto tmp6 = tmp5.log();
2023-01-11T21:05:10.5809469Z             auto tmp7 = tmp6 * tmp2;
2023-01-11T21:05:10.5809600Z             auto tmp8 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.5809724Z             auto tmp9 = tmp7 - tmp8;
2023-01-11T21:05:10.5809816Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5809908Z             tmp9.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.5809956Z         }
2023-01-11T21:05:10.5810050Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5810132Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.5810195Z         {
2023-01-11T21:05:10.5810280Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5810371Z             auto tmp1 = std::log(tmp0);
2023-01-11T21:05:10.5810480Z             auto tmp2 = static_cast<float>(1.4426950408889634);
2023-01-11T21:05:10.5810550Z             auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5810652Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.5810735Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.5810821Z             auto tmp6 = std::log(tmp5);
2023-01-11T21:05:10.5810900Z             auto tmp7 = tmp6 * tmp2;
2023-01-11T21:05:10.5810994Z             auto tmp8 = static_cast<float>(2);
2023-01-11T21:05:10.5811114Z             auto tmp9 = tmp7 - tmp8;
2023-01-11T21:05:10.5811181Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.5811259Z             out_ptr1[i0] = tmp9;
2023-01-11T21:05:10.5811319Z         }
2023-01-11T21:05:10.5811378Z     }
2023-01-11T21:05:10.5811435Z }
2023-01-11T21:05:10.5811512Z ''')
2023-01-11T21:05:10.5811517Z 
2023-01-11T21:05:10.5811521Z 
2023-01-11T21:05:10.5811612Z async_compile.wait(globals())
2023-01-11T21:05:10.5811673Z del async_compile
2023-01-11T21:05:10.5811689Z 
2023-01-11T21:05:10.5811746Z def call(args):
2023-01-11T21:05:10.5811813Z     arg0_1, = args
2023-01-11T21:05:10.5811882Z     args.clear()
2023-01-11T21:05:10.5812076Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5812266Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5812428Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5812494Z     del arg0_1
2023-01-11T21:05:10.5812557Z     return (buf0, buf1, )
2023-01-11T21:05:10.5812562Z 
2023-01-11T21:05:10.5812567Z 
2023-01-11T21:05:10.5812640Z if __name__ == "__main__":
2023-01-11T21:05:10.5812751Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5812872Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5813064Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5813201Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5813206Z 
2023-01-11T21:05:10.5813271Z ok (2.664s)
2023-01-11T21:05:10.5813709Z   test_log_fp64_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5813834Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5814081Z [2023-01-11 20:55:51,438] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 330
2023-01-11T21:05:10.5814340Z [2023-01-11 20:55:54,031] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 330
2023-01-11T21:05:10.5814345Z 
2023-01-11T21:05:10.5814436Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5814507Z import torch
2023-01-11T21:05:10.5814578Z import random
2023-01-11T21:05:10.5814691Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5814838Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5814844Z 
2023-01-11T21:05:10.5814920Z aten = torch.ops.aten
2023-01-11T21:05:10.5815040Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5815128Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5815133Z 
2023-01-11T21:05:10.5815137Z 
2023-01-11T21:05:10.5815268Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5815471Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5815591Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.5815693Z                        double* __restrict__ out_ptr0,
2023-01-11T21:05:10.5815788Z                        double* __restrict__ out_ptr1)
2023-01-11T21:05:10.5815848Z {
2023-01-11T21:05:10.5815931Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5815990Z     {
2023-01-11T21:05:10.5816066Z         #pragma omp for 
2023-01-11T21:05:10.5816147Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.5816211Z         {
2023-01-11T21:05:10.5816272Z             {
2023-01-11T21:05:10.5816323Z                 {
2023-01-11T21:05:10.5816413Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5816510Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:05:10.5816625Z                     auto tmp2 = static_cast<double>(1.4426950408889634);
2023-01-11T21:05:10.5816714Z                     auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.5816797Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5816879Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5816929Z                 }
2023-01-11T21:05:10.5816989Z             }
2023-01-11T21:05:10.5817049Z         }
2023-01-11T21:05:10.5817111Z     }
2023-01-11T21:05:10.5817171Z }
2023-01-11T21:05:10.5817247Z ''')
2023-01-11T21:05:10.5817252Z 
2023-01-11T21:05:10.5817256Z 
2023-01-11T21:05:10.5817343Z async_compile.wait(globals())
2023-01-11T21:05:10.5817401Z del async_compile
2023-01-11T21:05:10.5817418Z 
2023-01-11T21:05:10.5817475Z def call(args):
2023-01-11T21:05:10.5817542Z     arg0_1, = args
2023-01-11T21:05:10.5817611Z     args.clear()
2023-01-11T21:05:10.5817805Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5817998Z     buf1 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5818158Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5818226Z     del arg0_1
2023-01-11T21:05:10.5818289Z     return (buf0, buf1, )
2023-01-11T21:05:10.5818294Z 
2023-01-11T21:05:10.5818298Z 
2023-01-11T21:05:10.5818372Z if __name__ == "__main__":
2023-01-11T21:05:10.5818581Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5818782Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5818983Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.5819092Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5819098Z 
2023-01-11T21:05:10.5819164Z ok (2.630s)
2023-01-11T21:05:10.5819607Z   test_log_softmax_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5819731Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5819975Z [2023-01-11 20:55:54,136] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 331
2023-01-11T21:05:10.5820235Z [2023-01-11 20:55:57,039] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 331
2023-01-11T21:05:10.5820242Z 
2023-01-11T21:05:10.5820335Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5820404Z import torch
2023-01-11T21:05:10.5820503Z import random
2023-01-11T21:05:10.5820621Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5820741Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5820746Z 
2023-01-11T21:05:10.5820826Z aten = torch.ops.aten
2023-01-11T21:05:10.5820945Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5821037Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5821042Z 
2023-01-11T21:05:10.5821046Z 
2023-01-11T21:05:10.5821179Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5821382Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5821502Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5821605Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5821702Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5821797Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.5821880Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.5821972Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.5822063Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.5822154Z                        float* __restrict__ out_ptr5,
2023-01-11T21:05:10.5822245Z                        float* __restrict__ out_ptr6,
2023-01-11T21:05:10.5822334Z                        float* __restrict__ out_ptr7,
2023-01-11T21:05:10.5822424Z                        float* __restrict__ out_ptr8)
2023-01-11T21:05:10.5822470Z {
2023-01-11T21:05:10.5822565Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5822625Z     {
2023-01-11T21:05:10.5822698Z         #pragma omp for 
2023-01-11T21:05:10.5822777Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5822840Z         {
2023-01-11T21:05:10.5822901Z             {
2023-01-11T21:05:10.5822951Z                 {
2023-01-11T21:05:10.5823189Z                     float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5823279Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5823344Z                     {
2023-01-11T21:05:10.5823410Z                         {
2023-01-11T21:05:10.5823513Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.5823613Z                             auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.5823694Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5823794Z                             tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:05:10.5823860Z                         }
2023-01-11T21:05:10.5823925Z                     }
2023-01-11T21:05:10.5824007Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.5824068Z                 }
2023-01-11T21:05:10.5824162Z             }
2023-01-11T21:05:10.5824211Z         }
2023-01-11T21:05:10.5824285Z         #pragma omp for 
2023-01-11T21:05:10.5824364Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5824425Z         {
2023-01-11T21:05:10.5824491Z             {
2023-01-11T21:05:10.5824554Z                 {
2023-01-11T21:05:10.5824633Z                     float tmp6 = 0;
2023-01-11T21:05:10.5824833Z                     float tmp7 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5824923Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5824987Z                     {
2023-01-11T21:05:10.5825053Z                         {
2023-01-11T21:05:10.5825153Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.5825254Z                             auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.5825352Z                             auto tmp3 = out_ptr0[i0];
2023-01-11T21:05:10.5825435Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5825578Z                             auto tmp4 = tmp2 - tmp3;
2023-01-11T21:05:10.5825680Z                             auto tmp5 = std::exp(tmp4);
2023-01-11T21:05:10.5825763Z                             tmp6 += tmp5;
2023-01-11T21:05:10.5825909Z                             tmp7 = std::max(tmp7, tmp1);
2023-01-11T21:05:10.5825977Z                         }
2023-01-11T21:05:10.5826041Z                     }
2023-01-11T21:05:10.5826112Z                     out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.5826192Z                     out_ptr2[i0] = tmp7;
2023-01-11T21:05:10.5826254Z                 }
2023-01-11T21:05:10.5826316Z             }
2023-01-11T21:05:10.5826376Z         }
2023-01-11T21:05:10.5826449Z         #pragma omp for 
2023-01-11T21:05:10.5826528Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5826576Z         {
2023-01-11T21:05:10.5826637Z             {
2023-01-11T21:05:10.5826698Z                 {
2023-01-11T21:05:10.5826909Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5827001Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5827069Z                     {
2023-01-11T21:05:10.5827160Z                         {
2023-01-11T21:05:10.5827293Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:05:10.5827442Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:05:10.5827514Z                         }
2023-01-11T21:05:10.5827578Z                     }
2023-01-11T21:05:10.5827659Z                     out_ptr3[i0] = tmp1;
2023-01-11T21:05:10.5827721Z                 }
2023-01-11T21:05:10.5827781Z             }
2023-01-11T21:05:10.5827828Z         }
2023-01-11T21:05:10.5827902Z         #pragma omp for 
2023-01-11T21:05:10.5827980Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5828040Z         {
2023-01-11T21:05:10.5828101Z             {
2023-01-11T21:05:10.5828163Z                 {
2023-01-11T21:05:10.5828229Z                     float tmp4 = 0;
2023-01-11T21:05:10.5828318Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5828383Z                     {
2023-01-11T21:05:10.5828450Z                         {
2023-01-11T21:05:10.5828550Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:05:10.5828647Z                             auto tmp1 = out_ptr3[i0];
2023-01-11T21:05:10.5828792Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5828881Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:05:10.5828960Z                             tmp4 += tmp3;
2023-01-11T21:05:10.5829025Z                         }
2023-01-11T21:05:10.5829090Z                     }
2023-01-11T21:05:10.5829172Z                     out_ptr4[i0] = tmp4;
2023-01-11T21:05:10.5829234Z                 }
2023-01-11T21:05:10.5829296Z             }
2023-01-11T21:05:10.5829343Z         }
2023-01-11T21:05:10.5829419Z         #pragma omp for 
2023-01-11T21:05:10.5829496Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5829594Z         {
2023-01-11T21:05:10.5829656Z             {
2023-01-11T21:05:10.5829716Z                 {
2023-01-11T21:05:10.5829793Z                     float tmp4 = 0;
2023-01-11T21:05:10.5829869Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5829937Z                     {
2023-01-11T21:05:10.5830001Z                         {
2023-01-11T21:05:10.5830102Z                             auto tmp0 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.5830198Z                             auto tmp1 = out_ptr2[i0];
2023-01-11T21:05:10.5830340Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5830442Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:05:10.5830510Z                             tmp4 += tmp3;
2023-01-11T21:05:10.5830576Z                         }
2023-01-11T21:05:10.5830638Z                     }
2023-01-11T21:05:10.5830720Z                     out_ptr5[i0] = tmp4;
2023-01-11T21:05:10.5830782Z                 }
2023-01-11T21:05:10.5830841Z             }
2023-01-11T21:05:10.5830903Z         }
2023-01-11T21:05:10.5830964Z         #pragma omp for 
2023-01-11T21:05:10.5831041Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5831101Z         {
2023-01-11T21:05:10.5831206Z             #pragma GCC ivdep
2023-01-11T21:05:10.5831288Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5831351Z             {
2023-01-11T21:05:10.5831401Z                 {
2023-01-11T21:05:10.5831464Z                     {
2023-01-11T21:05:10.5831563Z                         auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.5831662Z                         auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.5831756Z                         auto tmp3 = out_ptr0[i0];
2023-01-11T21:05:10.5831847Z                         auto tmp5 = out_ptr1[i0];
2023-01-11T21:05:10.5831940Z                         auto tmp8 = out_ptr3[i1];
2023-01-11T21:05:10.5832032Z                         auto tmp10 = out_ptr4[i1];
2023-01-11T21:05:10.5832110Z                         auto tmp13 = out_ptr2[i0];
2023-01-11T21:05:10.5832200Z                         auto tmp15 = out_ptr5[i0];
2023-01-11T21:05:10.5832293Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.5832434Z                         auto tmp4 = tmp2 - tmp3;
2023-01-11T21:05:10.5832533Z                         auto tmp6 = std::log(tmp5);
2023-01-11T21:05:10.5832671Z                         auto tmp7 = tmp4 - tmp6;
2023-01-11T21:05:10.5832807Z                         auto tmp9 = tmp0 - tmp8;
2023-01-11T21:05:10.5832896Z                         auto tmp11 = std::log(tmp10);
2023-01-11T21:05:10.5833035Z                         auto tmp12 = tmp9 - tmp11;
2023-01-11T21:05:10.5833173Z                         auto tmp14 = tmp1 - tmp13;
2023-01-11T21:05:10.5833269Z                         auto tmp16 = std::log(tmp15);
2023-01-11T21:05:10.5833410Z                         auto tmp17 = tmp14 - tmp16;
2023-01-11T21:05:10.5833499Z                         out_ptr6[i1 + (8*i0)] = tmp7;
2023-01-11T21:05:10.5833592Z                         out_ptr7[i1 + (8*i0)] = tmp12;
2023-01-11T21:05:10.5833674Z                         out_ptr8[i1 + (8*i0)] = tmp17;
2023-01-11T21:05:10.5833738Z                     }
2023-01-11T21:05:10.5833799Z                 }
2023-01-11T21:05:10.5833864Z             }
2023-01-11T21:05:10.5833925Z         }
2023-01-11T21:05:10.5833984Z     }
2023-01-11T21:05:10.5834044Z }
2023-01-11T21:05:10.5834107Z ''')
2023-01-11T21:05:10.5834114Z 
2023-01-11T21:05:10.5834118Z 
2023-01-11T21:05:10.5834213Z async_compile.wait(globals())
2023-01-11T21:05:10.5834285Z del async_compile
2023-01-11T21:05:10.5834290Z 
2023-01-11T21:05:10.5834360Z def call(args):
2023-01-11T21:05:10.5834436Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5834506Z     args.clear()
2023-01-11T21:05:10.5834701Z     buf0 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5834881Z     buf1 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5835070Z     buf6 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5835287Z     buf3 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5835473Z     buf4 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5835660Z     buf7 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5835844Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5836027Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5836210Z     buf8 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5836563Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf8.data_ptr()))
2023-01-11T21:05:10.5836621Z     del arg0_1
2023-01-11T21:05:10.5836688Z     del arg1_1
2023-01-11T21:05:10.5836770Z     return (buf2, buf5, buf8, )
2023-01-11T21:05:10.5836774Z 
2023-01-11T21:05:10.5836779Z 
2023-01-11T21:05:10.5836882Z if __name__ == "__main__":
2023-01-11T21:05:10.5836998Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5837119Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5837314Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5837509Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5837610Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5837615Z 
2023-01-11T21:05:10.5837681Z ok (3.008s)
2023-01-11T21:05:10.5838256Z   test_logsumexp_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5838390Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5838725Z [2023-01-11 20:55:57,158] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 332
2023-01-11T21:05:10.5838993Z [2023-01-11 20:55:59,930] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 332
2023-01-11T21:05:10.5838999Z 
2023-01-11T21:05:10.5839092Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5839162Z import torch
2023-01-11T21:05:10.5839233Z import random
2023-01-11T21:05:10.5839334Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5839455Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5839460Z 
2023-01-11T21:05:10.5839537Z aten = torch.ops.aten
2023-01-11T21:05:10.5839671Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5839766Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5839771Z 
2023-01-11T21:05:10.5839775Z 
2023-01-11T21:05:10.5839909Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5840116Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5840234Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5840323Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.5840428Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5840527Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.5840751Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.5840813Z {
2023-01-11T21:05:10.5840900Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.5840981Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:05:10.5841067Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5841129Z     {
2023-01-11T21:05:10.5841280Z         #pragma omp for 
2023-01-11T21:05:10.5841363Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5841426Z         {
2023-01-11T21:05:10.5841491Z             {
2023-01-11T21:05:10.5841555Z                 {
2023-01-11T21:05:10.5841772Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5841866Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5841931Z                     {
2023-01-11T21:05:10.5841999Z                         {
2023-01-11T21:05:10.5842106Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.5842210Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:05:10.5842278Z                         }
2023-01-11T21:05:10.5842330Z                     }
2023-01-11T21:05:10.5842414Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.5842478Z                 }
2023-01-11T21:05:10.5842541Z             }
2023-01-11T21:05:10.5842601Z         }
2023-01-11T21:05:10.5842678Z         #pragma omp for 
2023-01-11T21:05:10.5842757Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5842805Z         {
2023-01-11T21:05:10.5842866Z             {
2023-01-11T21:05:10.5842928Z                 {
2023-01-11T21:05:10.5843041Z                     float tmp4 = 0;
2023-01-11T21:05:10.5843134Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5843199Z                     {
2023-01-11T21:05:10.5843264Z                         {
2023-01-11T21:05:10.5843353Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.5843448Z                             auto tmp1 = out_ptr0[i0];
2023-01-11T21:05:10.5843595Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5843697Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:05:10.5843776Z                             tmp4 += tmp3;
2023-01-11T21:05:10.5843842Z                         }
2023-01-11T21:05:10.5843905Z                     }
2023-01-11T21:05:10.5843978Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.5844040Z                 }
2023-01-11T21:05:10.5844102Z             }
2023-01-11T21:05:10.5844161Z         }
2023-01-11T21:05:10.5844235Z         #pragma omp for 
2023-01-11T21:05:10.5844314Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5844363Z         {
2023-01-11T21:05:10.5844423Z             {
2023-01-11T21:05:10.5844485Z                 {
2023-01-11T21:05:10.5844574Z                     auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.5844663Z                     auto tmp2 = out_ptr0[i0];
2023-01-11T21:05:10.5844756Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:05:10.5844850Z                     auto tmp3 = std::abs(tmp2);
2023-01-11T21:05:10.5844973Z                     auto tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5845050Z                     auto tmp5 = tmp3 == tmp4;
2023-01-11T21:05:10.5845153Z                     auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:05:10.5845253Z                     auto tmp7 = tmp5 ? tmp6 : tmp2;
2023-01-11T21:05:10.5845340Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:05:10.5845427Z                     in_out_ptr0[i0] = tmp8;
2023-01-11T21:05:10.5845489Z                 }
2023-01-11T21:05:10.5845552Z             }
2023-01-11T21:05:10.5845600Z         }
2023-01-11T21:05:10.5845675Z         #pragma omp for 
2023-01-11T21:05:10.5845753Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5845813Z         {
2023-01-11T21:05:10.5845873Z             {
2023-01-11T21:05:10.5845934Z                 {
2023-01-11T21:05:10.5846133Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5846221Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5846285Z                     {
2023-01-11T21:05:10.5846351Z                         {
2023-01-11T21:05:10.5846453Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:05:10.5846554Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:05:10.5846651Z                         }
2023-01-11T21:05:10.5846718Z                     }
2023-01-11T21:05:10.5846789Z                     out_ptr2[i0] = tmp1;
2023-01-11T21:05:10.5846852Z                 }
2023-01-11T21:05:10.5846914Z             }
2023-01-11T21:05:10.5846974Z         }
2023-01-11T21:05:10.5847047Z         #pragma omp for 
2023-01-11T21:05:10.5847127Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5847175Z         {
2023-01-11T21:05:10.5847235Z             {
2023-01-11T21:05:10.5847297Z                 {
2023-01-11T21:05:10.5847374Z                     float tmp4 = 0;
2023-01-11T21:05:10.5847462Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.5847526Z                     {
2023-01-11T21:05:10.5847590Z                         {
2023-01-11T21:05:10.5847678Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:05:10.5847774Z                             auto tmp1 = out_ptr2[i0];
2023-01-11T21:05:10.5847919Z                             auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5848021Z                             auto tmp3 = std::exp(tmp2);
2023-01-11T21:05:10.5848100Z                             tmp4 += tmp3;
2023-01-11T21:05:10.5848196Z                         }
2023-01-11T21:05:10.5848264Z                     }
2023-01-11T21:05:10.5848335Z                     out_ptr3[i0] = tmp4;
2023-01-11T21:05:10.5848400Z                 }
2023-01-11T21:05:10.5848461Z             }
2023-01-11T21:05:10.5848522Z         }
2023-01-11T21:05:10.5848596Z         #pragma omp for 
2023-01-11T21:05:10.5848675Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5848735Z         {
2023-01-11T21:05:10.5848784Z             {
2023-01-11T21:05:10.5848848Z                 {
2023-01-11T21:05:10.5848939Z                     auto tmp0 = out_ptr3[i0];
2023-01-11T21:05:10.5849029Z                     auto tmp2 = out_ptr2[i0];
2023-01-11T21:05:10.5849124Z                     auto tmp1 = std::log(tmp0);
2023-01-11T21:05:10.5849217Z                     auto tmp3 = std::abs(tmp2);
2023-01-11T21:05:10.5849345Z                     auto tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5849423Z                     auto tmp5 = tmp3 == tmp4;
2023-01-11T21:05:10.5849528Z                     auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:05:10.5849625Z                     auto tmp7 = tmp5 ? tmp6 : tmp2;
2023-01-11T21:05:10.5849714Z                     auto tmp8 = tmp1 + tmp7;
2023-01-11T21:05:10.5849817Z                     auto tmp9 = static_cast<float>(2);
2023-01-11T21:05:10.5849952Z                     auto tmp10 = tmp8 - tmp9;
2023-01-11T21:05:10.5850039Z                     in_out_ptr1[i0] = tmp10;
2023-01-11T21:05:10.5850090Z                 }
2023-01-11T21:05:10.5850150Z             }
2023-01-11T21:05:10.5850210Z         }
2023-01-11T21:05:10.5850270Z     }
2023-01-11T21:05:10.5850328Z }
2023-01-11T21:05:10.5850403Z ''')
2023-01-11T21:05:10.5850410Z 
2023-01-11T21:05:10.5850414Z 
2023-01-11T21:05:10.5850503Z async_compile.wait(globals())
2023-01-11T21:05:10.5850564Z del async_compile
2023-01-11T21:05:10.5850569Z 
2023-01-11T21:05:10.5850637Z def call(args):
2023-01-11T21:05:10.5850705Z     arg0_1, = args
2023-01-11T21:05:10.5850774Z     args.clear()
2023-01-11T21:05:10.5850971Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5851159Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5851268Z     buf2 = as_strided(buf0, (8, ), (1, )); del buf0  # reuse
2023-01-11T21:05:10.5851445Z     buf3 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5851630Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5851737Z     buf5 = as_strided(buf3, (8, ), (1, )); del buf3  # reuse
2023-01-11T21:05:10.5851947Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.5852049Z     del arg0_1
2023-01-11T21:05:10.5852124Z     return (buf2, buf5, )
2023-01-11T21:05:10.5852130Z 
2023-01-11T21:05:10.5852134Z 
2023-01-11T21:05:10.5852207Z if __name__ == "__main__":
2023-01-11T21:05:10.5852323Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5852446Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5852626Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5852732Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5852737Z 
2023-01-11T21:05:10.5852801Z ok (2.888s)
2023-01-11T21:05:10.5853239Z   test_long_tensor_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5853365Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5853624Z [2023-01-11 20:55:59,981] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 333
2023-01-11T21:05:10.5853914Z [2023-01-11 20:56:02,713] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 333
2023-01-11T21:05:10.5853921Z 
2023-01-11T21:05:10.5854013Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5854081Z import torch
2023-01-11T21:05:10.5854137Z import random
2023-01-11T21:05:10.5854249Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5854369Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5854374Z 
2023-01-11T21:05:10.5854449Z aten = torch.ops.aten
2023-01-11T21:05:10.5854582Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5854671Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5854676Z 
2023-01-11T21:05:10.5854680Z 
2023-01-11T21:05:10.5854816Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5855017Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5855124Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5855219Z                        long* __restrict__ out_ptr0,
2023-01-11T21:05:10.5855312Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.5855370Z {
2023-01-11T21:05:10.5855467Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5855527Z     {
2023-01-11T21:05:10.5855601Z         #pragma omp for 
2023-01-11T21:05:10.5855670Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.5855731Z         {
2023-01-11T21:05:10.5855793Z             {
2023-01-11T21:05:10.5855855Z                 {
2023-01-11T21:05:10.5855946Z                     auto tmp1 = in_ptr0[i0];
2023-01-11T21:05:10.5856051Z                     auto tmp0 = static_cast<long>(294);
2023-01-11T21:05:10.5856186Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.5856278Z                     auto tmp3 = static_cast<long>(295);
2023-01-11T21:05:10.5856365Z                     auto tmp4 = tmp3 + tmp1;
2023-01-11T21:05:10.5856447Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5856532Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.5856595Z                 }
2023-01-11T21:05:10.5856655Z             }
2023-01-11T21:05:10.5856703Z         }
2023-01-11T21:05:10.5856762Z     }
2023-01-11T21:05:10.5856819Z }
2023-01-11T21:05:10.5856895Z ''')
2023-01-11T21:05:10.5856900Z 
2023-01-11T21:05:10.5856904Z 
2023-01-11T21:05:10.5856992Z async_compile.wait(globals())
2023-01-11T21:05:10.5857063Z del async_compile
2023-01-11T21:05:10.5857068Z 
2023-01-11T21:05:10.5857135Z def call(args):
2023-01-11T21:05:10.5857202Z     arg0_1, = args
2023-01-11T21:05:10.5857258Z     args.clear()
2023-01-11T21:05:10.5857447Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5857634Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5857833Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5857902Z     del arg0_1
2023-01-11T21:05:10.5857979Z     return (buf0, buf1, )
2023-01-11T21:05:10.5857983Z 
2023-01-11T21:05:10.5857988Z 
2023-01-11T21:05:10.5858060Z if __name__ == "__main__":
2023-01-11T21:05:10.5858173Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5858281Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5858549Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5858662Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5858666Z 
2023-01-11T21:05:10.5858733Z ok (2.782s)
2023-01-11T21:05:10.5859074Z   test_lowmem_dropout1_cpu (__main__.CpuTests) ... [2023-01-11 20:56:02,766] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 334
2023-01-11T21:05:10.5859343Z [2023-01-11 20:56:05,389] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 334
2023-01-11T21:05:10.5859635Z [2023-01-11 20:56:05,396] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 334
2023-01-11T21:05:10.5859901Z [2023-01-11 20:56:05,408] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 334
2023-01-11T21:05:10.5860141Z [2023-01-11 20:56:05,671] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 335
2023-01-11T21:05:10.5860393Z [2023-01-11 20:56:05,673] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.5860651Z [2023-01-11 20:56:08,286] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 335
2023-01-11T21:05:10.5860900Z [2023-01-11 20:56:08,293] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 335
2023-01-11T21:05:10.5860905Z 
2023-01-11T21:05:10.5860998Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5861065Z import torch
2023-01-11T21:05:10.5861133Z import random
2023-01-11T21:05:10.5861245Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5861353Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5861370Z 
2023-01-11T21:05:10.5861434Z aten = torch.ops.aten
2023-01-11T21:05:10.5861566Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5861656Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5861661Z 
2023-01-11T21:05:10.5861665Z 
2023-01-11T21:05:10.5861798Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5862000Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5862116Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5862217Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5862314Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5862363Z {
2023-01-11T21:05:10.5862458Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5862518Z     {
2023-01-11T21:05:10.5862592Z         #pragma omp for 
2023-01-11T21:05:10.5862676Z         for(long i0=0; i0<6250; i0+=1)
2023-01-11T21:05:10.5862737Z         {
2023-01-11T21:05:10.5862863Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5862992Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.5863079Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5863169Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5863230Z         }
2023-01-11T21:05:10.5863324Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5863410Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:05:10.5863471Z         {
2023-01-11T21:05:10.5863542Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5863621Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5863734Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5863816Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5863875Z         }
2023-01-11T21:05:10.5863934Z     }
2023-01-11T21:05:10.5863980Z }
2023-01-11T21:05:10.5864065Z ''')
2023-01-11T21:05:10.5864069Z 
2023-01-11T21:05:10.5864073Z 
2023-01-11T21:05:10.5864159Z async_compile.wait(globals())
2023-01-11T21:05:10.5864230Z del async_compile
2023-01-11T21:05:10.5864235Z 
2023-01-11T21:05:10.5864303Z def call(args):
2023-01-11T21:05:10.5864387Z     primals_1, primals_2 = args
2023-01-11T21:05:10.5864455Z     args.clear()
2023-01-11T21:05:10.5864654Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5864814Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5864885Z     del primals_2
2023-01-11T21:05:10.5864968Z     return (buf0, primals_1, )
2023-01-11T21:05:10.5864972Z 
2023-01-11T21:05:10.5864979Z 
2023-01-11T21:05:10.5865052Z if __name__ == "__main__":
2023-01-11T21:05:10.5865164Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5865283Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5865533Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5865736Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5865848Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:05:10.5865853Z 
2023-01-11T21:05:10.5865857Z 
2023-01-11T21:05:10.5865947Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5866018Z import torch
2023-01-11T21:05:10.5866087Z import random
2023-01-11T21:05:10.5866203Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5866322Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5866327Z 
2023-01-11T21:05:10.5866405Z aten = torch.ops.aten
2023-01-11T21:05:10.5866538Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5866620Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5866624Z 
2023-01-11T21:05:10.5866628Z 
2023-01-11T21:05:10.5866761Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5866966Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5867086Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5867190Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5867290Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5867351Z {
2023-01-11T21:05:10.5867434Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5867496Z     {
2023-01-11T21:05:10.5867572Z         #pragma omp for 
2023-01-11T21:05:10.5867656Z         for(long i0=0; i0<6250; i0+=1)
2023-01-11T21:05:10.5867718Z         {
2023-01-11T21:05:10.5867851Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.5867984Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.5868070Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5868148Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.5868212Z         }
2023-01-11T21:05:10.5868307Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.5868399Z         for(long i0=100000; i0<100000; i0+=1)
2023-01-11T21:05:10.5868460Z         {
2023-01-11T21:05:10.5868546Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5868613Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5868694Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.5868773Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5868834Z         }
2023-01-11T21:05:10.5868895Z     }
2023-01-11T21:05:10.5868954Z }
2023-01-11T21:05:10.5869031Z ''')
2023-01-11T21:05:10.5869036Z 
2023-01-11T21:05:10.5869040Z 
2023-01-11T21:05:10.5869128Z async_compile.wait(globals())
2023-01-11T21:05:10.5869187Z del async_compile
2023-01-11T21:05:10.5869229Z 
2023-01-11T21:05:10.5869301Z def call(args):
2023-01-11T21:05:10.5869387Z     primals_1, tangents_1 = args
2023-01-11T21:05:10.5869457Z     args.clear()
2023-01-11T21:05:10.5869660Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5869834Z     kernel_cpp_0(c_void_p(tangents_1.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5869906Z     del primals_1
2023-01-11T21:05:10.5869964Z     del tangents_1
2023-01-11T21:05:10.5870041Z     return (None, buf0, )
2023-01-11T21:05:10.5870045Z 
2023-01-11T21:05:10.5870049Z 
2023-01-11T21:05:10.5870126Z if __name__ == "__main__":
2023-01-11T21:05:10.5870239Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5870361Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5870564Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5870770Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5870898Z     print_performance(lambda: call([primals_1, tangents_1]))
2023-01-11T21:05:10.5870903Z 
2023-01-11T21:05:10.5870908Z 
2023-01-11T21:05:10.5871016Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5871088Z import torch
2023-01-11T21:05:10.5871155Z import random
2023-01-11T21:05:10.5871268Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5871385Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5871390Z 
2023-01-11T21:05:10.5871466Z aten = torch.ops.aten
2023-01-11T21:05:10.5871596Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5871685Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5871833Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.5871838Z 
2023-01-11T21:05:10.5871842Z 
2023-01-11T21:05:10.5871973Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5872179Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5872293Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:05:10.5872398Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5872500Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.5872596Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5872655Z {
2023-01-11T21:05:10.5872738Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5872798Z     {
2023-01-11T21:05:10.5872872Z         #pragma omp for 
2023-01-11T21:05:10.5872956Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:05:10.5873017Z         {
2023-01-11T21:05:10.5873077Z             {
2023-01-11T21:05:10.5873127Z                 {
2023-01-11T21:05:10.5873211Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.5873302Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:05:10.5873390Z                     auto tmp7 = in_ptr2[i0];
2023-01-11T21:05:10.5873491Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.5873626Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.5873731Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:05:10.5873808Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.5873911Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.5873999Z                     auto tmp8 = tmp6 * tmp7;
2023-01-11T21:05:10.5874086Z                     auto tmp9 = tmp5 * tmp8;
2023-01-11T21:05:10.5874199Z                     auto tmp10 = static_cast<float>(1.492537313432836);
2023-01-11T21:05:10.5874288Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:05:10.5874372Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.5874436Z                 }
2023-01-11T21:05:10.5874486Z             }
2023-01-11T21:05:10.5874546Z         }
2023-01-11T21:05:10.5874637Z     }
2023-01-11T21:05:10.5874697Z }
2023-01-11T21:05:10.5874772Z ''')
2023-01-11T21:05:10.5874778Z 
2023-01-11T21:05:10.5874782Z 
2023-01-11T21:05:10.5874869Z async_compile.wait(globals())
2023-01-11T21:05:10.5874942Z del async_compile
2023-01-11T21:05:10.5874949Z 
2023-01-11T21:05:10.5875005Z def call(args):
2023-01-11T21:05:10.5875089Z     primals_1, primals_2 = args
2023-01-11T21:05:10.5875157Z     args.clear()
2023-01-11T21:05:10.5875286Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.5875485Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5875694Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(primals_2.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5875764Z     del primals_2
2023-01-11T21:05:10.5875865Z     return (buf0, primals_1, seed_cpu_None.clone(), )
2023-01-11T21:05:10.5875881Z 
2023-01-11T21:05:10.5875885Z 
2023-01-11T21:05:10.5875950Z if __name__ == "__main__":
2023-01-11T21:05:10.5876061Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5876182Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5876411Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5876619Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5876817Z     primals_2 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5876943Z     print_performance(lambda: call([primals_1, primals_2]))
2023-01-11T21:05:10.5876949Z 
2023-01-11T21:05:10.5876953Z 
2023-01-11T21:05:10.5877045Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5877100Z import torch
2023-01-11T21:05:10.5877171Z import random
2023-01-11T21:05:10.5877287Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5877404Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5877409Z 
2023-01-11T21:05:10.5877487Z aten = torch.ops.aten
2023-01-11T21:05:10.5877617Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5877704Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5877709Z 
2023-01-11T21:05:10.5877715Z 
2023-01-11T21:05:10.5883427Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5883691Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5883801Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5883908Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5884010Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.5884110Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5884171Z {
2023-01-11T21:05:10.5884269Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5884329Z     {
2023-01-11T21:05:10.5884392Z         #pragma omp for 
2023-01-11T21:05:10.5884478Z         for(long i0=0; i0<100000; i0+=1)
2023-01-11T21:05:10.5884545Z         {
2023-01-11T21:05:10.5884607Z             {
2023-01-11T21:05:10.5884673Z                 {
2023-01-11T21:05:10.5884762Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.5884854Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:05:10.5884934Z                     auto tmp10 = in_ptr2[i0];
2023-01-11T21:05:10.5885035Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.5885172Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.5885276Z                     auto tmp3 = static_cast<float>(0.33);
2023-01-11T21:05:10.5885365Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.5885468Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.5885558Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5885658Z                     auto tmp8 = static_cast<float>(1.492537313432836);
2023-01-11T21:05:10.5885747Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.5885963Z                     auto tmp11 = tmp9 * tmp10;
2023-01-11T21:05:10.5886047Z                     out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.5886111Z                 }
2023-01-11T21:05:10.5886176Z             }
2023-01-11T21:05:10.5886240Z         }
2023-01-11T21:05:10.5886287Z     }
2023-01-11T21:05:10.5886346Z }
2023-01-11T21:05:10.5886453Z ''')
2023-01-11T21:05:10.5886460Z 
2023-01-11T21:05:10.5886464Z 
2023-01-11T21:05:10.5886554Z async_compile.wait(globals())
2023-01-11T21:05:10.5886623Z del async_compile
2023-01-11T21:05:10.5886629Z 
2023-01-11T21:05:10.5886697Z def call(args):
2023-01-11T21:05:10.5886807Z     primals_1, philox_seed_like, tangents_1 = args
2023-01-11T21:05:10.5886878Z     args.clear()
2023-01-11T21:05:10.5887068Z     buf0 = empty_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5887284Z     kernel_cpp_0(c_void_p(philox_seed_like.data_ptr()), c_void_p(tangents_1.data_ptr()), c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5887367Z     del philox_seed_like
2023-01-11T21:05:10.5887436Z     del primals_1
2023-01-11T21:05:10.5887505Z     del tangents_1
2023-01-11T21:05:10.5887626Z     return (None, buf0, )
2023-01-11T21:05:10.5887632Z 
2023-01-11T21:05:10.5887636Z 
2023-01-11T21:05:10.5887710Z if __name__ == "__main__":
2023-01-11T21:05:10.5887811Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5887932Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5888140Z     primals_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5888338Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5888541Z     tangents_1 = rand_strided((100000, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5888678Z     print_performance(lambda: call([primals_1, philox_seed_like, tangents_1]))
2023-01-11T21:05:10.5888951Z [2023-01-11 20:56:10,979] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 335
2023-01-11T21:05:10.5888959Z 
2023-01-11T21:05:10.5889027Z ok (8.361s)
2023-01-11T21:05:10.5889366Z   test_lowmem_dropout2_cpu (__main__.CpuTests) ... [2023-01-11 20:56:11,543] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 336
2023-01-11T21:05:10.5889625Z [2023-01-11 20:56:11,545] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.5889892Z [2023-01-11 20:56:14,249] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 336
2023-01-11T21:05:10.5890133Z [2023-01-11 20:56:14,256] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling BACKWARDS graph 336
2023-01-11T21:05:10.5890393Z [2023-01-11 20:56:17,005] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling BACKWARDS graph 336
2023-01-11T21:05:10.5890399Z 
2023-01-11T21:05:10.5890493Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5890564Z import torch
2023-01-11T21:05:10.5890632Z import random
2023-01-11T21:05:10.5890747Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5890866Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5890873Z 
2023-01-11T21:05:10.5890952Z aten = torch.ops.aten
2023-01-11T21:05:10.5891073Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5891163Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5891324Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.5891329Z 
2023-01-11T21:05:10.5891333Z 
2023-01-11T21:05:10.5891465Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5891670Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5891784Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5891885Z                        const long* __restrict__ seed0)
2023-01-11T21:05:10.5891977Z {
2023-01-11T21:05:10.5892062Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5892122Z     {
2023-01-11T21:05:10.5892197Z         #pragma omp for 
2023-01-11T21:05:10.5892282Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.5892343Z         {
2023-01-11T21:05:10.5892406Z             {
2023-01-11T21:05:10.5892469Z                 {
2023-01-11T21:05:10.5892542Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.5892638Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:05:10.5892740Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.5892878Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.5892985Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:05:10.5893076Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.5893180Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.5893257Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5893362Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:05:10.5893451Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.5893574Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.5893640Z                 }
2023-01-11T21:05:10.5893701Z             }
2023-01-11T21:05:10.5893763Z         }
2023-01-11T21:05:10.5893810Z     }
2023-01-11T21:05:10.5893868Z }
2023-01-11T21:05:10.5893946Z ''')
2023-01-11T21:05:10.5893951Z 
2023-01-11T21:05:10.5893955Z 
2023-01-11T21:05:10.5894087Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:05:10.5894291Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5894405Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5894505Z                        const long* __restrict__ seed0)
2023-01-11T21:05:10.5894552Z {
2023-01-11T21:05:10.5894647Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5894710Z     {
2023-01-11T21:05:10.5894786Z         #pragma omp for 
2023-01-11T21:05:10.5894868Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.5894929Z         {
2023-01-11T21:05:10.5894991Z             {
2023-01-11T21:05:10.5895043Z                 {
2023-01-11T21:05:10.5895127Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.5895221Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:05:10.5895329Z                     auto tmp1 = static_cast<int>(256 + i0);
2023-01-11T21:05:10.5895465Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.5895569Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:05:10.5895659Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.5895751Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.5895841Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5895944Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:05:10.5896035Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.5896124Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.5896188Z                 }
2023-01-11T21:05:10.5896249Z             }
2023-01-11T21:05:10.5896299Z         }
2023-01-11T21:05:10.5896359Z     }
2023-01-11T21:05:10.5896418Z }
2023-01-11T21:05:10.5896495Z ''')
2023-01-11T21:05:10.5896500Z 
2023-01-11T21:05:10.5896504Z 
2023-01-11T21:05:10.5896594Z async_compile.wait(globals())
2023-01-11T21:05:10.5896664Z del async_compile
2023-01-11T21:05:10.5896670Z 
2023-01-11T21:05:10.5896738Z def call(args):
2023-01-11T21:05:10.5896825Z     primals_1, primals_2, primals_3 = args
2023-01-11T21:05:10.5896894Z     args.clear()
2023-01-11T21:05:10.5897026Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.5897224Z     buf0 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5897355Z     aten.mm.out(primals_3, as_strided(primals_1, (32, 32), (1, 32)), out=buf0)
2023-01-11T21:05:10.5897458Z     del primals_1
2023-01-11T21:05:10.5897543Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.5897689Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(seed_cpu_None.data_ptr()))
2023-01-11T21:05:10.5897872Z     buf2 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5897995Z     aten.mm.out(buf1, as_strided(primals_2, (32, 32), (1, 32)), out=buf2)
2023-01-11T21:05:10.5898078Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:05:10.5898218Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(seed_cpu_None.data_ptr()))
2023-01-11T21:05:10.5898373Z     return (buf3, primals_3, seed_cpu_None.clone(), buf1, as_strided(primals_2, (32, 32), (32, 1)), )
2023-01-11T21:05:10.5898379Z 
2023-01-11T21:05:10.5898384Z 
2023-01-11T21:05:10.5898458Z if __name__ == "__main__":
2023-01-11T21:05:10.5898673Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5898798Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5898982Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5899192Z     primals_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5899423Z     primals_2 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5899627Z     primals_3 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5899765Z     print_performance(lambda: call([primals_1, primals_2, primals_3]))
2023-01-11T21:05:10.5899771Z 
2023-01-11T21:05:10.5899775Z 
2023-01-11T21:05:10.5899868Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5899937Z import torch
2023-01-11T21:05:10.5900009Z import random
2023-01-11T21:05:10.5900111Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5900233Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5900238Z 
2023-01-11T21:05:10.5900317Z aten = torch.ops.aten
2023-01-11T21:05:10.5900451Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5900541Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5900546Z 
2023-01-11T21:05:10.5900551Z 
2023-01-11T21:05:10.5900688Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5900891Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5901010Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.5901101Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5901201Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5901262Z {
2023-01-11T21:05:10.5901358Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5901419Z     {
2023-01-11T21:05:10.5901496Z         #pragma omp for 
2023-01-11T21:05:10.5901579Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.5901627Z         {
2023-01-11T21:05:10.5901693Z             {
2023-01-11T21:05:10.5901756Z                 {
2023-01-11T21:05:10.5901849Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.5901940Z                     auto tmp6 = in_ptr1[i0];
2023-01-11T21:05:10.5902047Z                     auto tmp1 = static_cast<int>(256 + i0);
2023-01-11T21:05:10.5902184Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.5902276Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:05:10.5902366Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.5902472Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.5902562Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5902666Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:05:10.5902755Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.5902840Z                     out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.5902891Z                 }
2023-01-11T21:05:10.5902952Z             }
2023-01-11T21:05:10.5903047Z         }
2023-01-11T21:05:10.5903107Z     }
2023-01-11T21:05:10.5903166Z }
2023-01-11T21:05:10.5903246Z ''')
2023-01-11T21:05:10.5903251Z 
2023-01-11T21:05:10.5903256Z 
2023-01-11T21:05:10.5903389Z kernel_cpp_1 = async_compile.cpp('''
2023-01-11T21:05:10.5903582Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5903698Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.5903801Z                        const long* __restrict__ in_ptr0)
2023-01-11T21:05:10.5903861Z {
2023-01-11T21:05:10.5903957Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5904018Z     {
2023-01-11T21:05:10.5904093Z         #pragma omp for 
2023-01-11T21:05:10.5904162Z         for(long i0=0; i0<256; i0+=1)
2023-01-11T21:05:10.5904223Z         {
2023-01-11T21:05:10.5904288Z             {
2023-01-11T21:05:10.5904350Z                 {
2023-01-11T21:05:10.5904441Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.5904539Z                     auto tmp6 = in_out_ptr0[i0];
2023-01-11T21:05:10.5904641Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.5904763Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.5904898Z                     auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:05:10.5904989Z                     auto tmp4 = tmp2 > tmp3;
2023-01-11T21:05:10.5905096Z                     auto tmp5 = static_cast<float>(tmp4);
2023-01-11T21:05:10.5905185Z                     auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.5905290Z                     auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:05:10.5905379Z                     auto tmp9 = tmp7 * tmp8;
2023-01-11T21:05:10.5905469Z                     in_out_ptr0[i0] = tmp9;
2023-01-11T21:05:10.5905519Z                 }
2023-01-11T21:05:10.5905580Z             }
2023-01-11T21:05:10.5905641Z         }
2023-01-11T21:05:10.5905700Z     }
2023-01-11T21:05:10.5905757Z }
2023-01-11T21:05:10.5905836Z ''')
2023-01-11T21:05:10.5905841Z 
2023-01-11T21:05:10.5905845Z 
2023-01-11T21:05:10.5905933Z async_compile.wait(globals())
2023-01-11T21:05:10.5905990Z del async_compile
2023-01-11T21:05:10.5905995Z 
2023-01-11T21:05:10.5906063Z def call(args):
2023-01-11T21:05:10.5906195Z     primals_3, philox_seed_like, mul_1, permute_4, tangents_1 = args
2023-01-11T21:05:10.5906267Z     args.clear()
2023-01-11T21:05:10.5906464Z     buf0 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5906646Z     kernel_cpp_0(c_void_p(philox_seed_like.data_ptr()), c_void_p(tangents_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5906718Z     del tangents_1
2023-01-11T21:05:10.5906902Z     buf1 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5907019Z     aten.mm.out(as_strided(buf0, (32, 8), (1, 32)), mul_1, out=buf1)
2023-01-11T21:05:10.5907085Z     del mul_1
2023-01-11T21:05:10.5907279Z     buf2 = empty_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5907379Z     aten.mm.out(buf0, permute_4, out=buf2)
2023-01-11T21:05:10.5907443Z     del buf0
2023-01-11T21:05:10.5907512Z     del permute_4
2023-01-11T21:05:10.5907586Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:05:10.5907734Z     kernel_cpp_1(c_void_p(buf3.data_ptr()), c_void_p(philox_seed_like.data_ptr()))
2023-01-11T21:05:10.5907812Z     del philox_seed_like
2023-01-11T21:05:10.5908008Z     buf4 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5908131Z     aten.mm.out(as_strided(buf3, (32, 8), (1, 32)), primals_3, out=buf4)
2023-01-11T21:05:10.5908195Z     del buf3
2023-01-11T21:05:10.5908264Z     del primals_3
2023-01-11T21:05:10.5908382Z     return (as_strided(buf4, (32, 32), (32, 1)), as_strided(buf1, (32, 32), (32, 1)), None, )
2023-01-11T21:05:10.5908399Z 
2023-01-11T21:05:10.5908404Z 
2023-01-11T21:05:10.5908465Z if __name__ == "__main__":
2023-01-11T21:05:10.5908576Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5908728Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5908930Z     primals_3 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5909129Z     philox_seed_like = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5909321Z     mul_1 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5909524Z     permute_4 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5909722Z     tangents_1 = rand_strided((8, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5909874Z     print_performance(lambda: call([primals_3, philox_seed_like, mul_1, permute_4, tangents_1]))
2023-01-11T21:05:10.5909879Z 
2023-01-11T21:05:10.5909945Z ok (5.927s)
2023-01-11T21:05:10.5910385Z   test_masked_fill_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5910582Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5910845Z [2023-01-11 20:56:17,078] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 337
2023-01-11T21:05:10.5911111Z [2023-01-11 20:56:19,735] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 337
2023-01-11T21:05:10.5911116Z 
2023-01-11T21:05:10.5911212Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5911281Z import torch
2023-01-11T21:05:10.5911351Z import random
2023-01-11T21:05:10.5911452Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5911574Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5911579Z 
2023-01-11T21:05:10.5911656Z aten = torch.ops.aten
2023-01-11T21:05:10.5911794Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5911885Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5911890Z 
2023-01-11T21:05:10.5911894Z 
2023-01-11T21:05:10.5912030Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5912236Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5912353Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.5912445Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5912544Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5912640Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5912700Z {
2023-01-11T21:05:10.5912794Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5912854Z     {
2023-01-11T21:05:10.5912929Z         #pragma omp for 
2023-01-11T21:05:10.5912997Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5913061Z         {
2023-01-11T21:05:10.5913139Z             #pragma GCC ivdep
2023-01-11T21:05:10.5913223Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.5913286Z             {
2023-01-11T21:05:10.5913349Z                 {
2023-01-11T21:05:10.5913417Z                     {
2023-01-11T21:05:10.5913499Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5913599Z                         auto tmp2 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:05:10.5913766Z                         auto tmp1 = static_cast<float>(-10000.0);
2023-01-11T21:05:10.5913865Z                         auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:05:10.5913969Z                         auto tmp4 = static_cast<float>(2);
2023-01-11T21:05:10.5914062Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.5914152Z                         auto tmp6 = tmp0 == 0;
2023-01-11T21:05:10.5914247Z                         auto tmp7 = static_cast<float>(667.0);
2023-01-11T21:05:10.5914353Z                         auto tmp8 = static_cast<float>(2.0);
2023-01-11T21:05:10.5914473Z                         auto tmp9 = tmp2 / tmp8;
2023-01-11T21:05:10.5914575Z                         auto tmp10 = tmp6 ? tmp7 : tmp9;
2023-01-11T21:05:10.5914671Z                         out_ptr0[i1 + (16*i0)] = tmp5;
2023-01-11T21:05:10.5914767Z                         out_ptr1[i1 + (16*i0)] = tmp10;
2023-01-11T21:05:10.5914832Z                     }
2023-01-11T21:05:10.5914883Z                 }
2023-01-11T21:05:10.5914945Z             }
2023-01-11T21:05:10.5915005Z         }
2023-01-11T21:05:10.5915065Z     }
2023-01-11T21:05:10.5915125Z }
2023-01-11T21:05:10.5915202Z ''')
2023-01-11T21:05:10.5915207Z 
2023-01-11T21:05:10.5915211Z 
2023-01-11T21:05:10.5915302Z async_compile.wait(globals())
2023-01-11T21:05:10.5915360Z del async_compile
2023-01-11T21:05:10.5915379Z 
2023-01-11T21:05:10.5915434Z def call(args):
2023-01-11T21:05:10.5915507Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5915576Z     args.clear()
2023-01-11T21:05:10.5915777Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5915976Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5916191Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5916262Z     del arg0_1
2023-01-11T21:05:10.5916312Z     del arg1_1
2023-01-11T21:05:10.5916389Z     return (buf0, buf1, )
2023-01-11T21:05:10.5916394Z 
2023-01-11T21:05:10.5916398Z 
2023-01-11T21:05:10.5916472Z if __name__ == "__main__":
2023-01-11T21:05:10.5916584Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5916707Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5916898Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5917097Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5917211Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5917221Z 
2023-01-11T21:05:10.5917273Z ok (2.736s)
2023-01-11T21:05:10.5917618Z   test_masked_fill_promotion_cpu (__main__.CpuTests) ... [2023-01-11 20:56:19,788] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 338
2023-01-11T21:05:10.5917884Z [2023-01-11 20:56:22,468] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 338
2023-01-11T21:05:10.5918138Z [2023-01-11 20:56:22,555] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 339
2023-01-11T21:05:10.5918400Z [2023-01-11 20:56:25,229] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 339
2023-01-11T21:05:10.5918405Z 
2023-01-11T21:05:10.5918497Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5918566Z import torch
2023-01-11T21:05:10.5918635Z import random
2023-01-11T21:05:10.5918735Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5918854Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5918861Z 
2023-01-11T21:05:10.5918938Z aten = torch.ops.aten
2023-01-11T21:05:10.5919068Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5919161Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5919168Z 
2023-01-11T21:05:10.5919172Z 
2023-01-11T21:05:10.5919304Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5919507Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5919624Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.5919715Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5919812Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.5919871Z {
2023-01-11T21:05:10.5919966Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5920027Z     {
2023-01-11T21:05:10.5920103Z         #pragma omp for 
2023-01-11T21:05:10.5920183Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5920263Z         {
2023-01-11T21:05:10.5920346Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.5920409Z             {
2023-01-11T21:05:10.5920514Z                 float g_tmp_buffer_in_ptr0[16] = {0};
2023-01-11T21:05:10.5920773Z                 flag_to_float(in_ptr0 + 16*i1, g_tmp_buffer_in_ptr0, 16);
2023-01-11T21:05:10.5920931Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(g_tmp_buffer_in_ptr0);
2023-01-11T21:05:10.5921071Z                 auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i0) + (16*i1));
2023-01-11T21:05:10.5921208Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3.5));
2023-01-11T21:05:10.5921321Z                 auto tmp3 = decltype(tmp1)::blendv(tmp2, tmp1, tmp0);
2023-01-11T21:05:10.5921425Z                 tmp3.store(out_ptr0 + (16*i0) + (16*i1));
2023-01-11T21:05:10.5921490Z             }
2023-01-11T21:05:10.5921584Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.5921674Z             for(long i1=16; i1<16; i1+=1)
2023-01-11T21:05:10.5921737Z             {
2023-01-11T21:05:10.5921823Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5921906Z                 auto tmp2 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:05:10.5922060Z                 auto tmp1 = static_cast<float>(3.5);
2023-01-11T21:05:10.5922158Z                 auto tmp3 = tmp0 ? tmp1 : tmp2;
2023-01-11T21:05:10.5922250Z                 out_ptr0[i1 + (16*i0)] = tmp3;
2023-01-11T21:05:10.5922314Z             }
2023-01-11T21:05:10.5922378Z         }
2023-01-11T21:05:10.5922439Z     }
2023-01-11T21:05:10.5922485Z }
2023-01-11T21:05:10.5922568Z ''')
2023-01-11T21:05:10.5922573Z 
2023-01-11T21:05:10.5922577Z 
2023-01-11T21:05:10.5922666Z async_compile.wait(globals())
2023-01-11T21:05:10.5922739Z del async_compile
2023-01-11T21:05:10.5922744Z 
2023-01-11T21:05:10.5922814Z def call(args):
2023-01-11T21:05:10.5922889Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5922960Z     args.clear()
2023-01-11T21:05:10.5923147Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5923309Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5923379Z     del arg0_1
2023-01-11T21:05:10.5923445Z     del arg1_1
2023-01-11T21:05:10.5923515Z     return (buf0, )
2023-01-11T21:05:10.5923520Z 
2023-01-11T21:05:10.5923523Z 
2023-01-11T21:05:10.5923597Z if __name__ == "__main__":
2023-01-11T21:05:10.5923709Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5923830Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5924007Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5924206Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5924319Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5924324Z 
2023-01-11T21:05:10.5924328Z 
2023-01-11T21:05:10.5924420Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5924490Z import torch
2023-01-11T21:05:10.5924559Z import random
2023-01-11T21:05:10.5924672Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5924794Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5924799Z 
2023-01-11T21:05:10.5924868Z aten = torch.ops.aten
2023-01-11T21:05:10.5925000Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5925090Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5925095Z 
2023-01-11T21:05:10.5925099Z 
2023-01-11T21:05:10.5925232Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5925434Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5925550Z extern "C" void kernel(const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.5925651Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.5925746Z                        long* __restrict__ out_ptr0)
2023-01-11T21:05:10.5925833Z {
2023-01-11T21:05:10.5925929Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5925989Z     {
2023-01-11T21:05:10.5926065Z         #pragma omp for 
2023-01-11T21:05:10.5926145Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5926209Z         {
2023-01-11T21:05:10.5926288Z             #pragma GCC ivdep
2023-01-11T21:05:10.5926359Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.5926420Z             {
2023-01-11T21:05:10.5926482Z                 {
2023-01-11T21:05:10.5926546Z                     {
2023-01-11T21:05:10.5926639Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.5926739Z                         auto tmp3 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:05:10.5926846Z                         auto tmp1 = static_cast<float>(3.5);
2023-01-11T21:05:10.5926941Z                         auto tmp2 = static_cast<long>(tmp1);
2023-01-11T21:05:10.5927039Z                         auto tmp4 = tmp0 ? tmp2 : tmp3;
2023-01-11T21:05:10.5927134Z                         out_ptr0[i1 + (16*i0)] = tmp4;
2023-01-11T21:05:10.5927204Z                     }
2023-01-11T21:05:10.5927269Z                 }
2023-01-11T21:05:10.5927332Z             }
2023-01-11T21:05:10.5927394Z         }
2023-01-11T21:05:10.5927468Z     }
2023-01-11T21:05:10.5927528Z }
2023-01-11T21:05:10.5927605Z ''')
2023-01-11T21:05:10.5927611Z 
2023-01-11T21:05:10.5927614Z 
2023-01-11T21:05:10.5927704Z async_compile.wait(globals())
2023-01-11T21:05:10.5927774Z del async_compile
2023-01-11T21:05:10.5927779Z 
2023-01-11T21:05:10.5927846Z def call(args):
2023-01-11T21:05:10.5927919Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5927976Z     args.clear()
2023-01-11T21:05:10.5928168Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5928331Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.5928397Z     del arg0_1
2023-01-11T21:05:10.5928462Z     del arg1_1
2023-01-11T21:05:10.5928532Z     return (buf0, )
2023-01-11T21:05:10.5928539Z 
2023-01-11T21:05:10.5928543Z 
2023-01-11T21:05:10.5928615Z if __name__ == "__main__":
2023-01-11T21:05:10.5928716Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5928840Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5929029Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.5929224Z     arg1_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5929336Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5929341Z 
2023-01-11T21:05:10.5929407Z ok (5.489s)
2023-01-11T21:05:10.5929841Z   test_max_min_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5929969Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5930231Z [2023-01-11 20:56:25,266] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 340
2023-01-11T21:05:10.5930494Z [2023-01-11 20:56:27,940] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 340
2023-01-11T21:05:10.5930878Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5931004Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5931256Z [2023-01-11 20:56:27,983] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 341
2023-01-11T21:05:10.5931553Z [2023-01-11 20:56:28,000] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 341
2023-01-11T21:05:10.5931558Z 
2023-01-11T21:05:10.5931651Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5931722Z import torch
2023-01-11T21:05:10.5931791Z import random
2023-01-11T21:05:10.5931904Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5932009Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5932026Z 
2023-01-11T21:05:10.5932089Z aten = torch.ops.aten
2023-01-11T21:05:10.5932223Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5932314Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5932319Z 
2023-01-11T21:05:10.5932323Z 
2023-01-11T21:05:10.5932455Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5932659Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5932776Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5932882Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5932982Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5933092Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5933154Z {
2023-01-11T21:05:10.5933250Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5933311Z     {
2023-01-11T21:05:10.5933388Z         #pragma omp for 
2023-01-11T21:05:10.5933469Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5933517Z         {
2023-01-11T21:05:10.5933581Z             {
2023-01-11T21:05:10.5933645Z                 {
2023-01-11T21:05:10.5933737Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5933828Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5933960Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:05:10.5934085Z                     auto tmp3 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:05:10.5934172Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5934241Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5934304Z                 }
2023-01-11T21:05:10.5934368Z             }
2023-01-11T21:05:10.5934431Z         }
2023-01-11T21:05:10.5934493Z     }
2023-01-11T21:05:10.5934551Z }
2023-01-11T21:05:10.5934616Z ''')
2023-01-11T21:05:10.5934621Z 
2023-01-11T21:05:10.5934640Z 
2023-01-11T21:05:10.5934713Z async_compile.wait(globals())
2023-01-11T21:05:10.5934784Z del async_compile
2023-01-11T21:05:10.5934789Z 
2023-01-11T21:05:10.5934858Z def call(args):
2023-01-11T21:05:10.5934932Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5935004Z     args.clear()
2023-01-11T21:05:10.5935193Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5935382Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5935559Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5935629Z     del arg0_1
2023-01-11T21:05:10.5935694Z     del arg1_1
2023-01-11T21:05:10.5935771Z     return (buf0, buf1, )
2023-01-11T21:05:10.5935775Z 
2023-01-11T21:05:10.5935779Z 
2023-01-11T21:05:10.5935857Z if __name__ == "__main__":
2023-01-11T21:05:10.5935970Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5936093Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5936282Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5936458Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5936576Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5936581Z 
2023-01-11T21:05:10.5936585Z 
2023-01-11T21:05:10.5936677Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5936746Z import torch
2023-01-11T21:05:10.5936817Z import random
2023-01-11T21:05:10.5936930Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5937081Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5937086Z 
2023-01-11T21:05:10.5937162Z aten = torch.ops.aten
2023-01-11T21:05:10.5937283Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5937374Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5937379Z 
2023-01-11T21:05:10.5937383Z 
2023-01-11T21:05:10.5937516Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5937720Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5937838Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5937941Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.5938040Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5938137Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.5938187Z {
2023-01-11T21:05:10.5938283Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5938346Z     {
2023-01-11T21:05:10.5938420Z         #pragma omp for 
2023-01-11T21:05:10.5938579Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5938646Z         {
2023-01-11T21:05:10.5938696Z             {
2023-01-11T21:05:10.5938803Z                 {
2023-01-11T21:05:10.5938899Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.5938991Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.5939121Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:05:10.5939246Z                     auto tmp3 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:05:10.5939330Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.5939413Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.5939463Z                 }
2023-01-11T21:05:10.5939525Z             }
2023-01-11T21:05:10.5939585Z         }
2023-01-11T21:05:10.5939644Z     }
2023-01-11T21:05:10.5939702Z }
2023-01-11T21:05:10.5939784Z ''')
2023-01-11T21:05:10.5939791Z 
2023-01-11T21:05:10.5939795Z 
2023-01-11T21:05:10.5939885Z async_compile.wait(globals())
2023-01-11T21:05:10.5939942Z del async_compile
2023-01-11T21:05:10.5939947Z 
2023-01-11T21:05:10.5940017Z def call(args):
2023-01-11T21:05:10.5940093Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.5940166Z     args.clear()
2023-01-11T21:05:10.5940356Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5940544Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5940728Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5940782Z     del arg0_1
2023-01-11T21:05:10.5940847Z     del arg1_1
2023-01-11T21:05:10.5940921Z     return (buf0, buf1, )
2023-01-11T21:05:10.5940925Z 
2023-01-11T21:05:10.5940929Z 
2023-01-11T21:05:10.5941003Z if __name__ == "__main__":
2023-01-11T21:05:10.5941115Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5941241Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5941431Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5941619Z     arg1_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5941720Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.5941725Z 
2023-01-11T21:05:10.5941790Z ok (2.771s)
2023-01-11T21:05:10.5942229Z   test_max_pool2d1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5942354Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5942614Z [2023-01-11 20:56:28,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 342
2023-01-11T21:05:10.5942910Z [2023-01-11 20:56:30,778] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 342
2023-01-11T21:05:10.5942917Z 
2023-01-11T21:05:10.5943011Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5943079Z import torch
2023-01-11T21:05:10.5943149Z import random
2023-01-11T21:05:10.5943250Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5943368Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5943373Z 
2023-01-11T21:05:10.5943450Z aten = torch.ops.aten
2023-01-11T21:05:10.5943580Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5943669Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5943674Z 
2023-01-11T21:05:10.5943678Z 
2023-01-11T21:05:10.5943809Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5944011Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5944131Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5944217Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5944344Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.5944405Z {
2023-01-11T21:05:10.5944501Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5944566Z     {
2023-01-11T21:05:10.5944645Z         #pragma omp for 
2023-01-11T21:05:10.5944724Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.5944773Z         {
2023-01-11T21:05:10.5944853Z             #pragma GCC ivdep
2023-01-11T21:05:10.5944933Z             for(long i1=0; i1<7; i1+=1)
2023-01-11T21:05:10.5944994Z             {
2023-01-11T21:05:10.5945074Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5945160Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:05:10.5945224Z                 {
2023-01-11T21:05:10.5945276Z                     {
2023-01-11T21:05:10.5945342Z                         {
2023-01-11T21:05:10.5945456Z                             auto tmp0 = in_ptr0[(2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5945570Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5945684Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5945796Z                             auto tmp5 = in_ptr0[16 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5945901Z                             auto tmp7 = in_ptr0[17 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5945993Z                             auto tmp9 = in_ptr0[18 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5946104Z                             auto tmp11 = in_ptr0[32 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5946211Z                             auto tmp13 = in_ptr0[33 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5946315Z                             auto tmp15 = in_ptr0[34 + (2*i2) + (32*i1) + (256*i0)];
2023-01-11T21:05:10.5946445Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:05:10.5946567Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:05:10.5946690Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:05:10.5946808Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:05:10.5946932Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:05:10.5947062Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:05:10.5947179Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:05:10.5947305Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:05:10.5947420Z                             auto tmp17 = static_cast<long>((2*i2) + (32*i1));
2023-01-11T21:05:10.5947566Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5947663Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:05:10.5947772Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:05:10.5947890Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5947983Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:05:10.5948074Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:05:10.5948192Z                             auto tmp24 = static_cast<long>(16 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5948285Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:05:10.5948387Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:05:10.5948504Z                             auto tmp27 = static_cast<long>(17 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5948598Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:05:10.5948702Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:05:10.5948820Z                             auto tmp30 = static_cast<long>(18 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5948941Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:05:10.5949045Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:05:10.5949159Z                             auto tmp33 = static_cast<long>(32 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5949254Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:05:10.5949354Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:05:10.5949469Z                             auto tmp36 = static_cast<long>(33 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5949562Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:05:10.5949650Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:05:10.5949765Z                             auto tmp39 = static_cast<long>(34 + (2*i2) + (32*i1));
2023-01-11T21:05:10.5949858Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:05:10.5949963Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:05:10.5950066Z                             out_ptr0[i2 + (7*i1) + (49*i0)] = tmp16;
2023-01-11T21:05:10.5950165Z                             out_ptr1[i2 + (7*i1) + (49*i0)] = tmp41;
2023-01-11T21:05:10.5950234Z                         }
2023-01-11T21:05:10.5950300Z                     }
2023-01-11T21:05:10.5950351Z                 }
2023-01-11T21:05:10.5950417Z             }
2023-01-11T21:05:10.5950480Z         }
2023-01-11T21:05:10.5950540Z     }
2023-01-11T21:05:10.5950600Z }
2023-01-11T21:05:10.5950679Z ''')
2023-01-11T21:05:10.5950684Z 
2023-01-11T21:05:10.5950689Z 
2023-01-11T21:05:10.5950777Z async_compile.wait(globals())
2023-01-11T21:05:10.5950835Z del async_compile
2023-01-11T21:05:10.5950840Z 
2023-01-11T21:05:10.5950910Z def call(args):
2023-01-11T21:05:10.5950979Z     arg0_1, = args
2023-01-11T21:05:10.5951048Z     args.clear()
2023-01-11T21:05:10.5951262Z     buf0 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5951472Z     buf1 = empty_strided((2, 4, 7, 7), (196, 49, 7, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5951637Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5951691Z     del arg0_1
2023-01-11T21:05:10.5951768Z     return (buf0, buf1, )
2023-01-11T21:05:10.5951773Z 
2023-01-11T21:05:10.5951777Z 
2023-01-11T21:05:10.5951851Z if __name__ == "__main__":
2023-01-11T21:05:10.5951963Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5952084Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5952302Z     arg0_1 = rand_strided((2, 4, 16, 16), (1024, 256, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5952408Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5952445Z 
2023-01-11T21:05:10.5952512Z ok (2.783s)
2023-01-11T21:05:10.5952954Z   test_max_pool2d2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5953067Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5953324Z [2023-01-11 20:56:30,946] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 343
2023-01-11T21:05:10.5953587Z [2023-01-11 20:56:33,713] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 343
2023-01-11T21:05:10.5953592Z 
2023-01-11T21:05:10.5953685Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5953753Z import torch
2023-01-11T21:05:10.5953824Z import random
2023-01-11T21:05:10.5953937Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5954055Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5954060Z 
2023-01-11T21:05:10.5954151Z aten = torch.ops.aten
2023-01-11T21:05:10.5954289Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5954378Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5954383Z 
2023-01-11T21:05:10.5954387Z 
2023-01-11T21:05:10.5954518Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5954720Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5954837Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5954936Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5955032Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.5955079Z {
2023-01-11T21:05:10.5955175Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5955238Z     {
2023-01-11T21:05:10.5955313Z         #pragma omp for 
2023-01-11T21:05:10.5955395Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.5955456Z         {
2023-01-11T21:05:10.5955537Z             #pragma GCC ivdep
2023-01-11T21:05:10.5955609Z             for(long i1=0; i1<27; i1+=1)
2023-01-11T21:05:10.5955671Z             {
2023-01-11T21:05:10.5955750Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5955837Z                 for(long i2=0; i2<27; i2+=1)
2023-01-11T21:05:10.5955900Z                 {
2023-01-11T21:05:10.5955964Z                     {
2023-01-11T21:05:10.5956031Z                         {
2023-01-11T21:05:10.5956130Z                             auto tmp0 = in_ptr0[(2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5956246Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5956355Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5956470Z                             auto tmp5 = in_ptr0[55 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5956579Z                             auto tmp7 = in_ptr0[56 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5956687Z                             auto tmp9 = in_ptr0[57 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5956801Z                             auto tmp11 = in_ptr0[110 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5956913Z                             auto tmp13 = in_ptr0[111 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5957012Z                             auto tmp15 = in_ptr0[112 + (2*i2) + (110*i1) + (3025*i0)];
2023-01-11T21:05:10.5957139Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:05:10.5957261Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:05:10.5957381Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:05:10.5957531Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:05:10.5957657Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:05:10.5957792Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:05:10.5957921Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:05:10.5958044Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:05:10.5958148Z                             auto tmp17 = static_cast<long>((2*i2) + (110*i1));
2023-01-11T21:05:10.5958266Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5958361Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:05:10.5958464Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:05:10.5958582Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5958678Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:05:10.5958781Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:05:10.5958924Z                             auto tmp24 = static_cast<long>(55 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5959007Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:05:10.5959110Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:05:10.5959228Z                             auto tmp27 = static_cast<long>(56 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5959321Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:05:10.5959422Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:05:10.5959537Z                             auto tmp30 = static_cast<long>(57 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5959631Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:05:10.5959734Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:05:10.5959840Z                             auto tmp33 = static_cast<long>(110 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5959938Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:05:10.5960040Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:05:10.5960156Z                             auto tmp36 = static_cast<long>(111 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5960251Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:05:10.5960350Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:05:10.5960469Z                             auto tmp39 = static_cast<long>(112 + (2*i2) + (110*i1));
2023-01-11T21:05:10.5960562Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:05:10.5960772Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:05:10.5960878Z                             out_ptr0[i2 + (27*i1) + (729*i0)] = tmp16;
2023-01-11T21:05:10.5960988Z                             out_ptr1[i2 + (27*i1) + (729*i0)] = tmp41;
2023-01-11T21:05:10.5961057Z                         }
2023-01-11T21:05:10.5961125Z                     }
2023-01-11T21:05:10.5961190Z                 }
2023-01-11T21:05:10.5961254Z             }
2023-01-11T21:05:10.5961302Z         }
2023-01-11T21:05:10.5961364Z     }
2023-01-11T21:05:10.5961424Z }
2023-01-11T21:05:10.5961509Z ''')
2023-01-11T21:05:10.5961514Z 
2023-01-11T21:05:10.5961518Z 
2023-01-11T21:05:10.5961611Z async_compile.wait(globals())
2023-01-11T21:05:10.5961683Z del async_compile
2023-01-11T21:05:10.5961688Z 
2023-01-11T21:05:10.5961757Z def call(args):
2023-01-11T21:05:10.5961811Z     arg0_1, = args
2023-01-11T21:05:10.5961881Z     args.clear()
2023-01-11T21:05:10.5962104Z     buf0 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5962320Z     buf1 = empty_strided((16, 64, 27, 27), (46656, 729, 27, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5962546Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5962616Z     del arg0_1
2023-01-11T21:05:10.5962695Z     return (buf0, buf1, )
2023-01-11T21:05:10.5962701Z 
2023-01-11T21:05:10.5962705Z 
2023-01-11T21:05:10.5962781Z if __name__ == "__main__":
2023-01-11T21:05:10.5962882Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5963004Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5963233Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5963340Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5963345Z 
2023-01-11T21:05:10.5963413Z ok (3.600s)
2023-01-11T21:05:10.5963852Z   test_max_pool2d3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5964019Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5964281Z [2023-01-11 20:56:34,421] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 344
2023-01-11T21:05:10.5964287Z 
2023-01-11T21:05:10.5964378Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5964434Z import torch
2023-01-11T21:05:10.5964504Z import random
2023-01-11T21:05:10.5964617Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5964738Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5964742Z 
2023-01-11T21:05:10.5964820Z aten = torch.ops.aten
2023-01-11T21:05:10.5964951Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5965042Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5965048Z 
2023-01-11T21:05:10.5965052Z 
2023-01-11T21:05:10.5965185Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5965374Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5965495Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5965597Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5965694Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.5965755Z {
2023-01-11T21:05:10.5965850Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5965914Z     {
2023-01-11T21:05:10.5965976Z         #pragma omp for 
2023-01-11T21:05:10.5966057Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.5966119Z         {
2023-01-11T21:05:10.5966199Z             #pragma GCC ivdep
2023-01-11T21:05:10.5966280Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.5966344Z             {
2023-01-11T21:05:10.5966408Z                 {
2023-01-11T21:05:10.5966463Z                     {
2023-01-11T21:05:10.5966637Z                         auto tmp0 = static_cast<long>((-1) + (2*i0));
2023-01-11T21:05:10.5966740Z                         auto tmp1 = static_cast<long>(0);
2023-01-11T21:05:10.5966836Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.5966939Z                         auto tmp3 = static_cast<long>(8);
2023-01-11T21:05:10.5967032Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.5967123Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:05:10.5967279Z                         auto tmp6 = static_cast<long>((-1) + (2*i1));
2023-01-11T21:05:10.5967372Z                         auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:05:10.5967463Z                         auto tmp8 = tmp6 < tmp3;
2023-01-11T21:05:10.5967554Z                         auto tmp9 = tmp7 & tmp8;
2023-01-11T21:05:10.5967649Z                         auto tmp10 = tmp5 & tmp9;
2023-01-11T21:05:10.5967892Z                         float tmp11 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5968001Z                         if(tmp10)
2023-01-11T21:05:10.5968056Z                         {
2023-01-11T21:05:10.5968233Z                             auto tmp12 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5968322Z                             tmp11 = tmp12;
2023-01-11T21:05:10.5968390Z                         }
2023-01-11T21:05:10.5968498Z                         auto tmp13 = static_cast<long>(2*i1);
2023-01-11T21:05:10.5968594Z                         auto tmp14 = tmp13 >= tmp1;
2023-01-11T21:05:10.5968688Z                         auto tmp15 = tmp13 < tmp3;
2023-01-11T21:05:10.5968784Z                         auto tmp16 = tmp14 & tmp15;
2023-01-11T21:05:10.5968862Z                         auto tmp17 = tmp5 & tmp16;
2023-01-11T21:05:10.5969080Z                         float tmp18 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5969155Z                         if(tmp17)
2023-01-11T21:05:10.5969222Z                         {
2023-01-11T21:05:10.5969399Z                             auto tmp19 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5969482Z                             tmp18 = tmp19;
2023-01-11T21:05:10.5969550Z                         }
2023-01-11T21:05:10.5969706Z                         auto tmp20 = (tmp11 != tmp11) ? tmp11 : std::max(tmp18, tmp11);
2023-01-11T21:05:10.5969819Z                         auto tmp21 = static_cast<long>(1 + (2*i1));
2023-01-11T21:05:10.5969912Z                         auto tmp22 = tmp21 >= tmp1;
2023-01-11T21:05:10.5970003Z                         auto tmp23 = tmp21 < tmp3;
2023-01-11T21:05:10.5970095Z                         auto tmp24 = tmp22 & tmp23;
2023-01-11T21:05:10.5970186Z                         auto tmp25 = tmp5 & tmp24;
2023-01-11T21:05:10.5970403Z                         float tmp26 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5970477Z                         if(tmp25)
2023-01-11T21:05:10.5970531Z                         {
2023-01-11T21:05:10.5970706Z                             auto tmp27 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5970787Z                             tmp26 = tmp27;
2023-01-11T21:05:10.5970854Z                         }
2023-01-11T21:05:10.5970987Z                         auto tmp28 = (tmp20 != tmp20) ? tmp20 : std::max(tmp26, tmp20);
2023-01-11T21:05:10.5971097Z                         auto tmp29 = static_cast<long>(2*i0);
2023-01-11T21:05:10.5971189Z                         auto tmp30 = tmp29 >= tmp1;
2023-01-11T21:05:10.5971268Z                         auto tmp31 = tmp29 < tmp3;
2023-01-11T21:05:10.5971358Z                         auto tmp32 = tmp30 & tmp31;
2023-01-11T21:05:10.5971448Z                         auto tmp33 = tmp32 & tmp9;
2023-01-11T21:05:10.5971663Z                         float tmp34 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5971739Z                         if(tmp33)
2023-01-11T21:05:10.5971807Z                         {
2023-01-11T21:05:10.5971982Z                             auto tmp35 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5972066Z                             tmp34 = tmp35;
2023-01-11T21:05:10.5972120Z                         }
2023-01-11T21:05:10.5972248Z                         auto tmp36 = (tmp28 != tmp28) ? tmp28 : std::max(tmp34, tmp28);
2023-01-11T21:05:10.5972341Z                         auto tmp37 = tmp32 & tmp16;
2023-01-11T21:05:10.5972554Z                         float tmp38 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5972628Z                         if(tmp37)
2023-01-11T21:05:10.5972694Z                         {
2023-01-11T21:05:10.5972800Z                             auto tmp39 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:05:10.5972869Z                             tmp38 = tmp39;
2023-01-11T21:05:10.5972935Z                         }
2023-01-11T21:05:10.5973063Z                         auto tmp40 = (tmp36 != tmp36) ? tmp36 : std::max(tmp38, tmp36);
2023-01-11T21:05:10.5973156Z                         auto tmp41 = tmp32 & tmp24;
2023-01-11T21:05:10.5973404Z                         float tmp42 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5973478Z                         if(tmp41)
2023-01-11T21:05:10.5973544Z                         {
2023-01-11T21:05:10.5973652Z                             auto tmp43 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5973721Z                             tmp42 = tmp43;
2023-01-11T21:05:10.5973790Z                         }
2023-01-11T21:05:10.5973915Z                         auto tmp44 = (tmp40 != tmp40) ? tmp40 : std::max(tmp42, tmp40);
2023-01-11T21:05:10.5974024Z                         auto tmp45 = static_cast<long>(1 + (2*i0));
2023-01-11T21:05:10.5974117Z                         auto tmp46 = tmp45 >= tmp1;
2023-01-11T21:05:10.5974211Z                         auto tmp47 = tmp45 < tmp3;
2023-01-11T21:05:10.5974302Z                         auto tmp48 = tmp46 & tmp47;
2023-01-11T21:05:10.5974379Z                         auto tmp49 = tmp48 & tmp9;
2023-01-11T21:05:10.5974593Z                         float tmp50 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5974670Z                         if(tmp49)
2023-01-11T21:05:10.5974735Z                         {
2023-01-11T21:05:10.5974870Z                             auto tmp51 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5974953Z                             tmp50 = tmp51;
2023-01-11T21:05:10.5975018Z                         }
2023-01-11T21:05:10.5975143Z                         auto tmp52 = (tmp44 != tmp44) ? tmp44 : std::max(tmp50, tmp44);
2023-01-11T21:05:10.5975222Z                         auto tmp53 = tmp48 & tmp16;
2023-01-11T21:05:10.5975435Z                         float tmp54 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5975508Z                         if(tmp53)
2023-01-11T21:05:10.5975573Z                         {
2023-01-11T21:05:10.5975679Z                             auto tmp55 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5975759Z                             tmp54 = tmp55;
2023-01-11T21:05:10.5975830Z                         }
2023-01-11T21:05:10.5975941Z                         auto tmp56 = (tmp52 != tmp52) ? tmp52 : std::max(tmp54, tmp52);
2023-01-11T21:05:10.5976033Z                         auto tmp57 = tmp48 & tmp24;
2023-01-11T21:05:10.5976246Z                         float tmp58 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5976319Z                         if(tmp57)
2023-01-11T21:05:10.5976385Z                         {
2023-01-11T21:05:10.5976489Z                             auto tmp59 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5976569Z                             tmp58 = tmp59;
2023-01-11T21:05:10.5976635Z                         }
2023-01-11T21:05:10.5976747Z                         auto tmp60 = (tmp56 != tmp56) ? tmp56 : std::max(tmp58, tmp56);
2023-01-11T21:05:10.5976956Z                         float tmp61 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5977029Z                         if(tmp10)
2023-01-11T21:05:10.5977095Z                         {
2023-01-11T21:05:10.5977271Z                             auto tmp62 = in_ptr0[(-9) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5977351Z                             tmp61 = tmp62;
2023-01-11T21:05:10.5977417Z                         }
2023-01-11T21:05:10.5977601Z                         auto tmp63 = static_cast<long>((-9) + (2*i1) + (16*i0));
2023-01-11T21:05:10.5977803Z                         float tmp64 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5977877Z                         if(tmp17)
2023-01-11T21:05:10.5977943Z                         {
2023-01-11T21:05:10.5978113Z                             auto tmp65 = in_ptr0[(-8) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5978193Z                             tmp64 = tmp65;
2023-01-11T21:05:10.5978259Z                         }
2023-01-11T21:05:10.5978441Z                         auto tmp66 = static_cast<long>((-8) + (2*i1) + (16*i0));
2023-01-11T21:05:10.5978605Z                         auto tmp67 = tmp64 > tmp61;
2023-01-11T21:05:10.5978754Z                         auto tmp68 = tmp67 ? tmp66 : tmp63;
2023-01-11T21:05:10.5978882Z                         auto tmp69 = (tmp61 != tmp61) ? tmp61 : std::max(tmp64, tmp61);
2023-01-11T21:05:10.5979104Z                         float tmp70 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5979181Z                         if(tmp25)
2023-01-11T21:05:10.5979250Z                         {
2023-01-11T21:05:10.5979422Z                             auto tmp71 = in_ptr0[(-7) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5979505Z                             tmp70 = tmp71;
2023-01-11T21:05:10.5979560Z                         }
2023-01-11T21:05:10.5979745Z                         auto tmp72 = static_cast<long>((-7) + (2*i1) + (16*i0));
2023-01-11T21:05:10.5979840Z                         auto tmp73 = tmp70 > tmp69;
2023-01-11T21:05:10.5979944Z                         auto tmp74 = tmp73 ? tmp72 : tmp68;
2023-01-11T21:05:10.5980075Z                         auto tmp75 = (tmp69 != tmp69) ? tmp69 : std::max(tmp70, tmp69);
2023-01-11T21:05:10.5980296Z                         float tmp76 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5980372Z                         if(tmp33)
2023-01-11T21:05:10.5980426Z                         {
2023-01-11T21:05:10.5980629Z                             auto tmp77 = in_ptr0[(-1) + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5980712Z                             tmp76 = tmp77;
2023-01-11T21:05:10.5980779Z                         }
2023-01-11T21:05:10.5980962Z                         auto tmp78 = static_cast<long>((-1) + (2*i1) + (16*i0));
2023-01-11T21:05:10.5981056Z                         auto tmp79 = tmp76 > tmp75;
2023-01-11T21:05:10.5981157Z                         auto tmp80 = tmp79 ? tmp78 : tmp74;
2023-01-11T21:05:10.5981284Z                         auto tmp81 = (tmp75 != tmp75) ? tmp75 : std::max(tmp76, tmp75);
2023-01-11T21:05:10.5981486Z                         float tmp82 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5981559Z                         if(tmp37)
2023-01-11T21:05:10.5981628Z                         {
2023-01-11T21:05:10.5981732Z                             auto tmp83 = in_ptr0[(2*i1) + (16*i0)];
2023-01-11T21:05:10.5981813Z                             tmp82 = tmp83;
2023-01-11T21:05:10.5981882Z                         }
2023-01-11T21:05:10.5981996Z                         auto tmp84 = static_cast<long>((2*i1) + (16*i0));
2023-01-11T21:05:10.5982076Z                         auto tmp85 = tmp82 > tmp81;
2023-01-11T21:05:10.5982177Z                         auto tmp86 = tmp85 ? tmp84 : tmp80;
2023-01-11T21:05:10.5982303Z                         auto tmp87 = (tmp81 != tmp81) ? tmp81 : std::max(tmp82, tmp81);
2023-01-11T21:05:10.5982518Z                         float tmp88 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5982592Z                         if(tmp41)
2023-01-11T21:05:10.5982658Z                         {
2023-01-11T21:05:10.5982763Z                             auto tmp89 = in_ptr0[1 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5982849Z                             tmp88 = tmp89;
2023-01-11T21:05:10.5982902Z                         }
2023-01-11T21:05:10.5983017Z                         auto tmp90 = static_cast<long>(1 + (2*i1) + (16*i0));
2023-01-11T21:05:10.5983111Z                         auto tmp91 = tmp88 > tmp87;
2023-01-11T21:05:10.5983213Z                         auto tmp92 = tmp91 ? tmp90 : tmp86;
2023-01-11T21:05:10.5983343Z                         auto tmp93 = (tmp87 != tmp87) ? tmp87 : std::max(tmp88, tmp87);
2023-01-11T21:05:10.5983556Z                         float tmp94 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5983629Z                         if(tmp49)
2023-01-11T21:05:10.5983695Z                         {
2023-01-11T21:05:10.5983787Z                             auto tmp95 = in_ptr0[7 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5983869Z                             tmp94 = tmp95;
2023-01-11T21:05:10.5983935Z                         }
2023-01-11T21:05:10.5984047Z                         auto tmp96 = static_cast<long>(7 + (2*i1) + (16*i0));
2023-01-11T21:05:10.5984182Z                         auto tmp97 = tmp94 > tmp93;
2023-01-11T21:05:10.5984284Z                         auto tmp98 = tmp97 ? tmp96 : tmp92;
2023-01-11T21:05:10.5984411Z                         auto tmp99 = (tmp93 != tmp93) ? tmp93 : std::max(tmp94, tmp93);
2023-01-11T21:05:10.5984628Z                         float tmp100 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5984689Z                         if(tmp53)
2023-01-11T21:05:10.5984757Z                         {
2023-01-11T21:05:10.5984864Z                             auto tmp101 = in_ptr0[8 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5984950Z                             tmp100 = tmp101;
2023-01-11T21:05:10.5985018Z                         }
2023-01-11T21:05:10.5985137Z                         auto tmp102 = static_cast<long>(8 + (2*i1) + (16*i0));
2023-01-11T21:05:10.5985236Z                         auto tmp103 = tmp100 > tmp99;
2023-01-11T21:05:10.5985327Z                         auto tmp104 = tmp103 ? tmp102 : tmp98;
2023-01-11T21:05:10.5985465Z                         auto tmp105 = (tmp99 != tmp99) ? tmp99 : std::max(tmp100, tmp99);
2023-01-11T21:05:10.5985728Z                         float tmp106 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.5985806Z                         if(tmp57)
2023-01-11T21:05:10.5985872Z                         {
2023-01-11T21:05:10.5985979Z                             auto tmp107 = in_ptr0[9 + (2*i1) + (16*i0)];
2023-01-11T21:05:10.5986064Z                             tmp106 = tmp107;
2023-01-11T21:05:10.5986131Z                         }
2023-01-11T21:05:10.5986232Z                         auto tmp108 = static_cast<long>(9 + (2*i1) + (16*i0));
2023-01-11T21:05:10.5986328Z                         auto tmp109 = tmp106 > tmp105;
2023-01-11T21:05:10.5986431Z                         auto tmp110 = tmp109 ? tmp108 : tmp104;
2023-01-11T21:05:10.5986565Z                         auto tmp111 = (tmp105 != tmp105) ? tmp105 : std::max(tmp106, tmp105);
2023-01-11T21:05:10.5986664Z                         out_ptr0[i1 + (4*i0)] = tmp60;
2023-01-11T21:05:10.5986759Z                         out_ptr1[i1 + (4*i0)] = tmp110;
2023-01-11T21:05:10.5986825Z                     }
2023-01-11T21:05:10.5986878Z                 }
2023-01-11T21:05:10.5986941Z             }
2023-01-11T21:05:10.5987002Z         }
2023-01-11T21:05:10.5987063Z     }
2023-01-11T21:05:10.5987123Z }
2023-01-11T21:05:10.5987201Z ''')
2023-01-11T21:05:10.5987207Z 
2023-01-11T21:05:10.5987212Z 
2023-01-11T21:05:10.5987303Z async_compile.wait(globals())
2023-01-11T21:05:10.5987360Z del async_compile
2023-01-11T21:05:10.5987366Z 
2023-01-11T21:05:10.5987436Z def call(args):
2023-01-11T21:05:10.5987503Z     arg0_1, = args
2023-01-11T21:05:10.5987573Z     args.clear()
2023-01-11T21:05:10.5987788Z     buf0 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5987991Z     buf1 = empty_strided((1, 1, 4, 4), (16, 16, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5988158Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5988226Z     del arg0_1
2023-01-11T21:05:10.5988289Z     return (buf0, buf1, )
2023-01-11T21:05:10.5988296Z 
2023-01-11T21:05:10.5988301Z 
2023-01-11T21:05:10.5988375Z if __name__ == "__main__":
2023-01-11T21:05:10.5988487Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5988609Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5988817Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5988923Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.5989192Z [2023-01-11 20:56:37,284] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 344
2023-01-11T21:05:10.5989198Z 
2023-01-11T21:05:10.5989263Z ok (2.903s)
2023-01-11T21:05:10.5989688Z   test_max_pool2d4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.5989846Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.5990104Z [2023-01-11 20:56:37,335] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 345
2023-01-11T21:05:10.5990366Z [2023-01-11 20:56:40,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 345
2023-01-11T21:05:10.5990371Z 
2023-01-11T21:05:10.5990464Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.5990532Z import torch
2023-01-11T21:05:10.5990601Z import random
2023-01-11T21:05:10.5990713Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.5990831Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.5990839Z 
2023-01-11T21:05:10.5990904Z aten = torch.ops.aten
2023-01-11T21:05:10.5991035Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.5991125Z async_compile = AsyncCompile()
2023-01-11T21:05:10.5991162Z 
2023-01-11T21:05:10.5991167Z 
2023-01-11T21:05:10.5991301Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.5991504Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.5991621Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.5991719Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.5991814Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.5991861Z {
2023-01-11T21:05:10.5991956Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.5992016Z     {
2023-01-11T21:05:10.5992090Z         #pragma omp for 
2023-01-11T21:05:10.5992171Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.5992232Z         {
2023-01-11T21:05:10.5992312Z             #pragma GCC ivdep
2023-01-11T21:05:10.5992384Z             for(long i1=0; i1<55; i1+=1)
2023-01-11T21:05:10.5992446Z             {
2023-01-11T21:05:10.5992524Z                 #pragma GCC ivdep
2023-01-11T21:05:10.5992614Z                 for(long i2=0; i2<55; i2+=1)
2023-01-11T21:05:10.5992678Z                 {
2023-01-11T21:05:10.5992743Z                     {
2023-01-11T21:05:10.5992797Z                         {
2023-01-11T21:05:10.5992909Z                             auto tmp0 = in_ptr0[(2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993023Z                             auto tmp1 = in_ptr0[1 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993133Z                             auto tmp3 = in_ptr0[2 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993245Z                             auto tmp5 = in_ptr0[111 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993359Z                             auto tmp7 = in_ptr0[112 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993474Z                             auto tmp9 = in_ptr0[113 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993588Z                             auto tmp11 = in_ptr0[222 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993704Z                             auto tmp13 = in_ptr0[223 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993804Z                             auto tmp15 = in_ptr0[224 + (2*i2) + (222*i1) + (12321*i0)];
2023-01-11T21:05:10.5993932Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:05:10.5994054Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:05:10.5994173Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:05:10.5994291Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:05:10.5994414Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:05:10.5994602Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:05:10.5994733Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:05:10.5994863Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:05:10.5994969Z                             auto tmp17 = static_cast<long>((2*i2) + (222*i1));
2023-01-11T21:05:10.5995089Z                             auto tmp18 = static_cast<long>(1 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5995186Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:05:10.5995291Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:05:10.5995412Z                             auto tmp21 = static_cast<long>(2 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5995507Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:05:10.5995612Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:05:10.5995734Z                             auto tmp24 = static_cast<long>(111 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5995817Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:05:10.5995949Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:05:10.5996071Z                             auto tmp27 = static_cast<long>(112 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5996168Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:05:10.5996271Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:05:10.5996387Z                             auto tmp30 = static_cast<long>(113 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5996481Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:05:10.5996582Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:05:10.5996688Z                             auto tmp33 = static_cast<long>(222 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5996788Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:05:10.5996891Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:05:10.5997009Z                             auto tmp36 = static_cast<long>(223 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5997106Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:05:10.5997206Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:05:10.5997325Z                             auto tmp39 = static_cast<long>(224 + (2*i2) + (222*i1));
2023-01-11T21:05:10.5997406Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:05:10.5997504Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:05:10.5997610Z                             out_ptr0[i2 + (55*i1) + (3025*i0)] = tmp16;
2023-01-11T21:05:10.5997714Z                             out_ptr1[i2 + (55*i1) + (3025*i0)] = tmp41;
2023-01-11T21:05:10.5997784Z                         }
2023-01-11T21:05:10.5997852Z                     }
2023-01-11T21:05:10.5997916Z                 }
2023-01-11T21:05:10.5997979Z             }
2023-01-11T21:05:10.5998028Z         }
2023-01-11T21:05:10.5998088Z     }
2023-01-11T21:05:10.5998150Z }
2023-01-11T21:05:10.5998229Z ''')
2023-01-11T21:05:10.5998234Z 
2023-01-11T21:05:10.5998238Z 
2023-01-11T21:05:10.5998329Z async_compile.wait(globals())
2023-01-11T21:05:10.5998402Z del async_compile
2023-01-11T21:05:10.5998407Z 
2023-01-11T21:05:10.5998476Z def call(args):
2023-01-11T21:05:10.5998531Z     arg0_1, = args
2023-01-11T21:05:10.5998601Z     args.clear()
2023-01-11T21:05:10.5998825Z     buf0 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.5999042Z     buf1 = empty_strided((2, 8, 55, 55), (24200, 3025, 55, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.5999204Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.5999298Z     del arg0_1
2023-01-11T21:05:10.5999374Z     return (buf0, buf1, )
2023-01-11T21:05:10.5999378Z 
2023-01-11T21:05:10.5999382Z 
2023-01-11T21:05:10.5999462Z if __name__ == "__main__":
2023-01-11T21:05:10.5999565Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.5999687Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.5999914Z     arg0_1 = rand_strided((2, 8, 111, 111), (98568, 12321, 111, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6000022Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6000028Z 
2023-01-11T21:05:10.6000095Z ok (2.937s)
2023-01-11T21:05:10.6000530Z   test_max_pool2d5_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6000781Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6001094Z [2023-01-11 20:56:40,404] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 346
2023-01-11T21:05:10.6001363Z [2023-01-11 20:56:43,152] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 346
2023-01-11T21:05:10.6001368Z 
2023-01-11T21:05:10.6001448Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6001518Z import torch
2023-01-11T21:05:10.6001588Z import random
2023-01-11T21:05:10.6001704Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6001827Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6001833Z 
2023-01-11T21:05:10.6001911Z aten = torch.ops.aten
2023-01-11T21:05:10.6002044Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6002122Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6002141Z 
2023-01-11T21:05:10.6002148Z 
2023-01-11T21:05:10.6002266Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6002470Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6002591Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6002692Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6002788Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.6002850Z {
2023-01-11T21:05:10.6002948Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6002995Z     {
2023-01-11T21:05:10.6003071Z         #pragma omp for 
2023-01-11T21:05:10.6003153Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.6003214Z         {
2023-01-11T21:05:10.6003294Z             #pragma GCC ivdep
2023-01-11T21:05:10.6003379Z             for(long i1=0; i1<18; i1+=1)
2023-01-11T21:05:10.6003443Z             {
2023-01-11T21:05:10.6003510Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6003598Z                 for(long i2=0; i2<18; i2+=1)
2023-01-11T21:05:10.6003664Z                 {
2023-01-11T21:05:10.6003729Z                     {
2023-01-11T21:05:10.6003796Z                         {
2023-01-11T21:05:10.6003910Z                             auto tmp0 = in_ptr0[(3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004025Z                             auto tmp1 = in_ptr0[1 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004123Z                             auto tmp3 = in_ptr0[2 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004235Z                             auto tmp5 = in_ptr0[55 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004344Z                             auto tmp7 = in_ptr0[56 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004449Z                             auto tmp9 = in_ptr0[57 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004563Z                             auto tmp11 = in_ptr0[110 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004676Z                             auto tmp13 = in_ptr0[111 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004827Z                             auto tmp15 = in_ptr0[112 + (3*i2) + (165*i1) + (3025*i0)];
2023-01-11T21:05:10.6004959Z                             auto tmp2 = (tmp0 != tmp0) ? tmp0 : std::max(tmp1, tmp0);
2023-01-11T21:05:10.6005069Z                             auto tmp4 = (tmp2 != tmp2) ? tmp2 : std::max(tmp3, tmp2);
2023-01-11T21:05:10.6005187Z                             auto tmp6 = (tmp4 != tmp4) ? tmp4 : std::max(tmp5, tmp4);
2023-01-11T21:05:10.6005308Z                             auto tmp8 = (tmp6 != tmp6) ? tmp6 : std::max(tmp7, tmp6);
2023-01-11T21:05:10.6005433Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::max(tmp9, tmp8);
2023-01-11T21:05:10.6005564Z                             auto tmp12 = (tmp10 != tmp10) ? tmp10 : std::max(tmp11, tmp10);
2023-01-11T21:05:10.6005693Z                             auto tmp14 = (tmp12 != tmp12) ? tmp12 : std::max(tmp13, tmp12);
2023-01-11T21:05:10.6005821Z                             auto tmp16 = (tmp14 != tmp14) ? tmp14 : std::max(tmp15, tmp14);
2023-01-11T21:05:10.6005938Z                             auto tmp17 = static_cast<long>((3*i2) + (165*i1));
2023-01-11T21:05:10.6006085Z                             auto tmp18 = static_cast<long>(1 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6006183Z                             auto tmp19 = tmp1 > tmp0;
2023-01-11T21:05:10.6006274Z                             auto tmp20 = tmp19 ? tmp18 : tmp17;
2023-01-11T21:05:10.6006395Z                             auto tmp21 = static_cast<long>(2 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6006494Z                             auto tmp22 = tmp3 > tmp2;
2023-01-11T21:05:10.6006597Z                             auto tmp23 = tmp22 ? tmp21 : tmp20;
2023-01-11T21:05:10.6006715Z                             auto tmp24 = static_cast<long>(55 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6006812Z                             auto tmp25 = tmp5 > tmp4;
2023-01-11T21:05:10.6006914Z                             auto tmp26 = tmp25 ? tmp24 : tmp23;
2023-01-11T21:05:10.6007021Z                             auto tmp27 = static_cast<long>(56 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6007116Z                             auto tmp28 = tmp7 > tmp6;
2023-01-11T21:05:10.6007220Z                             auto tmp29 = tmp28 ? tmp27 : tmp26;
2023-01-11T21:05:10.6007337Z                             auto tmp30 = static_cast<long>(57 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6007432Z                             auto tmp31 = tmp9 > tmp8;
2023-01-11T21:05:10.6007532Z                             auto tmp32 = tmp31 ? tmp30 : tmp29;
2023-01-11T21:05:10.6007650Z                             auto tmp33 = static_cast<long>(110 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6007745Z                             auto tmp34 = tmp11 > tmp10;
2023-01-11T21:05:10.6007834Z                             auto tmp35 = tmp34 ? tmp33 : tmp32;
2023-01-11T21:05:10.6007950Z                             auto tmp36 = static_cast<long>(111 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6008046Z                             auto tmp37 = tmp13 > tmp12;
2023-01-11T21:05:10.6008146Z                             auto tmp38 = tmp37 ? tmp36 : tmp35;
2023-01-11T21:05:10.6008263Z                             auto tmp39 = static_cast<long>(112 + (3*i2) + (165*i1));
2023-01-11T21:05:10.6008357Z                             auto tmp40 = tmp15 > tmp14;
2023-01-11T21:05:10.6008455Z                             auto tmp41 = tmp40 ? tmp39 : tmp38;
2023-01-11T21:05:10.6008561Z                             out_ptr0[i2 + (18*i1) + (324*i0)] = tmp16;
2023-01-11T21:05:10.6008651Z                             out_ptr1[i2 + (18*i1) + (324*i0)] = tmp41;
2023-01-11T21:05:10.6008718Z                         }
2023-01-11T21:05:10.6008784Z                     }
2023-01-11T21:05:10.6008847Z                 }
2023-01-11T21:05:10.6008909Z             }
2023-01-11T21:05:10.6008970Z         }
2023-01-11T21:05:10.6009028Z     }
2023-01-11T21:05:10.6009074Z }
2023-01-11T21:05:10.6009153Z ''')
2023-01-11T21:05:10.6009186Z 
2023-01-11T21:05:10.6009190Z 
2023-01-11T21:05:10.6009282Z async_compile.wait(globals())
2023-01-11T21:05:10.6009353Z del async_compile
2023-01-11T21:05:10.6009358Z 
2023-01-11T21:05:10.6009427Z def call(args):
2023-01-11T21:05:10.6009497Z     arg0_1, = args
2023-01-11T21:05:10.6009567Z     args.clear()
2023-01-11T21:05:10.6009778Z     buf0 = empty_strided((16, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6009994Z     buf1 = empty_strided((16, 64, 18, 18), (20736, 324, 18, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6010156Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6010224Z     del arg0_1
2023-01-11T21:05:10.6010300Z     return (buf0, buf1, )
2023-01-11T21:05:10.6010305Z 
2023-01-11T21:05:10.6010309Z 
2023-01-11T21:05:10.6010383Z if __name__ == "__main__":
2023-01-11T21:05:10.6010496Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6010619Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6010830Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6010968Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6010973Z 
2023-01-11T21:05:10.6011040Z ok (3.543s)
2023-01-11T21:05:10.6011477Z   test_max_pool2d6_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6011604Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6011862Z [2023-01-11 20:56:43,902] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 347
2023-01-11T21:05:10.6012109Z [2023-01-11 20:56:43,913] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.max_pool2d_with_indices
2023-01-11T21:05:10.6012373Z [2023-01-11 20:56:43,916] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 347
2023-01-11T21:05:10.6012381Z 
2023-01-11T21:05:10.6012475Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6012544Z import torch
2023-01-11T21:05:10.6012601Z import random
2023-01-11T21:05:10.6012715Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6012836Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6012841Z 
2023-01-11T21:05:10.6012921Z aten = torch.ops.aten
2023-01-11T21:05:10.6013056Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6013146Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6013151Z 
2023-01-11T21:05:10.6013155Z 
2023-01-11T21:05:10.6013241Z async_compile.wait(globals())
2023-01-11T21:05:10.6013299Z del async_compile
2023-01-11T21:05:10.6013316Z 
2023-01-11T21:05:10.6013376Z def call(args):
2023-01-11T21:05:10.6013442Z     arg0_1, = args
2023-01-11T21:05:10.6013512Z     args.clear()
2023-01-11T21:05:10.6013641Z     buf0 = aten.max_pool2d_with_indices(arg0_1, [13, 13], [13, 13], [0, 0], 1, False)
2023-01-11T21:05:10.6013709Z     del arg0_1
2023-01-11T21:05:10.6013775Z     buf1 = buf0[0]
2023-01-11T21:05:10.6013870Z     assert_size_stride(buf1, (16, 64, 4, 4), (1024, 16, 4, 1))
2023-01-11T21:05:10.6013935Z     buf2 = buf0[1]
2023-01-11T21:05:10.6014040Z     assert_size_stride(buf2, (16, 64, 4, 4), (1024, 16, 4, 1))
2023-01-11T21:05:10.6014103Z     del buf0
2023-01-11T21:05:10.6014179Z     return (buf1, buf2, )
2023-01-11T21:05:10.6014184Z 
2023-01-11T21:05:10.6014188Z 
2023-01-11T21:05:10.6014262Z if __name__ == "__main__":
2023-01-11T21:05:10.6014371Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6014491Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6014704Z     arg0_1 = rand_strided((16, 64, 55, 55), (193600, 3025, 55, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6014842Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6014847Z 
2023-01-11T21:05:10.6014912Z ok (0.590s)
2023-01-11T21:05:10.6015376Z   test_max_pool2d_with_indices_backward2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6015501Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6015758Z [2023-01-11 20:56:44,396] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 348
2023-01-11T21:05:10.6016020Z [2023-01-11 20:56:47,161] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 348
2023-01-11T21:05:10.6016028Z 
2023-01-11T21:05:10.6016120Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6016189Z import torch
2023-01-11T21:05:10.6016245Z import random
2023-01-11T21:05:10.6016360Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6016507Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6016513Z 
2023-01-11T21:05:10.6016592Z aten = torch.ops.aten
2023-01-11T21:05:10.6016723Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6016813Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6016818Z 
2023-01-11T21:05:10.6016822Z 
2023-01-11T21:05:10.6016955Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6017160Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6017263Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.6017366Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6017464Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6017526Z {
2023-01-11T21:05:10.6017622Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6017684Z     {
2023-01-11T21:05:10.6017758Z         #pragma omp for 
2023-01-11T21:05:10.6017828Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6017889Z         {
2023-01-11T21:05:10.6017968Z             #pragma GCC ivdep
2023-01-11T21:05:10.6018051Z             for(long i1=0; i1<40; i1+=1)
2023-01-11T21:05:10.6018114Z             {
2023-01-11T21:05:10.6018192Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6018280Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:05:10.6018331Z                 {
2023-01-11T21:05:10.6018400Z                     {
2023-01-11T21:05:10.6018540Z                         {
2023-01-11T21:05:10.6018659Z                             auto tmp0 = static_cast<int>(i2 + (56*i1));
2023-01-11T21:05:10.6018771Z                             auto tmp1 = static_cast<int>((i1 / 2));
2023-01-11T21:05:10.6018878Z                             auto tmp2 = static_cast<int>((i2 / 2));
2023-01-11T21:05:10.6018998Z                             auto tmp3 = static_cast<int>(1 + (((1 + i1) / 2)));
2023-01-11T21:05:10.6019101Z                             auto tmp4 = static_cast<int>(1 + (((1 + i2) / 2)));
2023-01-11T21:05:10.6019207Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:05:10.6019340Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:05:10.6019466Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:05:10.6019573Z                             auto tmp8 = static_cast<int>(21);
2023-01-11T21:05:10.6019701Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:05:10.6019810Z                             auto tmp10 = static_cast<int>(29);
2023-01-11T21:05:10.6019940Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:05:10.6020075Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:05:10.6020157Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:05:10.6020260Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:05:10.6020415Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:05:10.6020545Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:05:10.6020694Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:05:10.6020823Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:05:10.6020939Z                             auto tmp19 = in_ptr0[tmp18 + (29*tmp16) + (609*i0)];
2023-01-11T21:05:10.6021054Z                             auto tmp20 = in_ptr1[tmp18 + (29*tmp16) + (609*i0)];
2023-01-11T21:05:10.6021137Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:05:10.6021245Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:05:10.6021351Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:05:10.6021447Z                             auto tmp24 = tmp7 + tmp14;
2023-01-11T21:05:10.6021615Z                             auto tmp25 = (tmp17 != tmp17) ? tmp17 : std::min(tmp24, tmp17);
2023-01-11T21:05:10.6021734Z                             auto tmp26 = in_ptr0[tmp25 + (29*tmp16) + (609*i0)];
2023-01-11T21:05:10.6021847Z                             auto tmp27 = in_ptr1[tmp25 + (29*tmp16) + (609*i0)];
2023-01-11T21:05:10.6021942Z                             auto tmp28 = tmp26 == tmp0;
2023-01-11T21:05:10.6022026Z                             auto tmp29 = tmp12 < tmp9;
2023-01-11T21:05:10.6022121Z                             auto tmp30 = tmp24 < tmp11;
2023-01-11T21:05:10.6022215Z                             auto tmp31 = tmp29 & tmp30;
2023-01-11T21:05:10.6022305Z                             auto tmp32 = tmp31 & tmp28;
2023-01-11T21:05:10.6022399Z                             auto tmp33 = tmp23 + tmp27;
2023-01-11T21:05:10.6022504Z                             auto tmp34 = tmp32 ? tmp33 : tmp23;
2023-01-11T21:05:10.6022599Z                             auto tmp35 = tmp6 + tmp14;
2023-01-11T21:05:10.6022729Z                             auto tmp36 = (tmp15 != tmp15) ? tmp15 : std::min(tmp35, tmp15);
2023-01-11T21:05:10.6022833Z                             auto tmp37 = in_ptr0[tmp18 + (29*tmp36) + (609*i0)];
2023-01-11T21:05:10.6022951Z                             auto tmp38 = in_ptr1[tmp18 + (29*tmp36) + (609*i0)];
2023-01-11T21:05:10.6023047Z                             auto tmp39 = tmp37 == tmp0;
2023-01-11T21:05:10.6023144Z                             auto tmp40 = tmp35 < tmp9;
2023-01-11T21:05:10.6023239Z                             auto tmp41 = tmp13 < tmp11;
2023-01-11T21:05:10.6023335Z                             auto tmp42 = tmp40 & tmp41;
2023-01-11T21:05:10.6023430Z                             auto tmp43 = tmp42 & tmp39;
2023-01-11T21:05:10.6023512Z                             auto tmp44 = tmp34 + tmp38;
2023-01-11T21:05:10.6023617Z                             auto tmp45 = tmp43 ? tmp44 : tmp34;
2023-01-11T21:05:10.6023732Z                             auto tmp46 = in_ptr0[tmp25 + (29*tmp36) + (609*i0)];
2023-01-11T21:05:10.6023848Z                             auto tmp47 = in_ptr1[tmp25 + (29*tmp36) + (609*i0)];
2023-01-11T21:05:10.6023942Z                             auto tmp48 = tmp46 == tmp0;
2023-01-11T21:05:10.6024035Z                             auto tmp49 = tmp40 & tmp30;
2023-01-11T21:05:10.6024128Z                             auto tmp50 = tmp49 & tmp48;
2023-01-11T21:05:10.6024220Z                             auto tmp51 = tmp45 + tmp47;
2023-01-11T21:05:10.6024309Z                             auto tmp52 = tmp50 ? tmp51 : tmp45;
2023-01-11T21:05:10.6024412Z                             out_ptr0[i2 + (56*i1) + (2240*i0)] = tmp52;
2023-01-11T21:05:10.6024479Z                         }
2023-01-11T21:05:10.6024544Z                     }
2023-01-11T21:05:10.6024609Z                 }
2023-01-11T21:05:10.6024701Z             }
2023-01-11T21:05:10.6024763Z         }
2023-01-11T21:05:10.6024811Z     }
2023-01-11T21:05:10.6024869Z }
2023-01-11T21:05:10.6024948Z ''')
2023-01-11T21:05:10.6024954Z 
2023-01-11T21:05:10.6024961Z 
2023-01-11T21:05:10.6025051Z async_compile.wait(globals())
2023-01-11T21:05:10.6025121Z del async_compile
2023-01-11T21:05:10.6025126Z 
2023-01-11T21:05:10.6025194Z def call(args):
2023-01-11T21:05:10.6025275Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6025334Z     args.clear()
2023-01-11T21:05:10.6025555Z     buf0 = empty_strided((2, 4, 40, 56), (8960, 2240, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6025716Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6025784Z     del arg0_1
2023-01-11T21:05:10.6025849Z     del arg2_1
2023-01-11T21:05:10.6025919Z     return (buf0, )
2023-01-11T21:05:10.6025924Z 
2023-01-11T21:05:10.6025928Z 
2023-01-11T21:05:10.6026005Z if __name__ == "__main__":
2023-01-11T21:05:10.6026119Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6026228Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6026476Z     arg0_1 = rand_strided((2, 4, 21, 29), (2436, 609, 29, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6026695Z     arg1_1 = rand_strided((2, 4, 40, 56), (8960, 2240, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6026908Z     arg2_1 = rand_strided((2, 4, 21, 29), (2436, 609, 29, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6027029Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6027034Z 
2023-01-11T21:05:10.6027100Z ok (2.841s)
2023-01-11T21:05:10.6027560Z   test_max_pool2d_with_indices_backward3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6027688Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6027949Z [2023-01-11 20:56:47,758] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 349
2023-01-11T21:05:10.6028199Z [2023-01-11 20:56:50,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 349
2023-01-11T21:05:10.6028218Z 
2023-01-11T21:05:10.6028297Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6028366Z import torch
2023-01-11T21:05:10.6028435Z import random
2023-01-11T21:05:10.6028548Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6028667Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6028672Z 
2023-01-11T21:05:10.6028748Z aten = torch.ops.aten
2023-01-11T21:05:10.6028879Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6028959Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6028964Z 
2023-01-11T21:05:10.6028982Z 
2023-01-11T21:05:10.6029101Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6029304Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6029422Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.6029525Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6029623Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6029685Z {
2023-01-11T21:05:10.6029780Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6029828Z     {
2023-01-11T21:05:10.6029907Z         #pragma omp for 
2023-01-11T21:05:10.6029989Z         for(long i0=0; i0<8192; i0+=1)
2023-01-11T21:05:10.6030052Z         {
2023-01-11T21:05:10.6030131Z             #pragma GCC ivdep
2023-01-11T21:05:10.6030216Z             for(long i1=0; i1<37; i1+=1)
2023-01-11T21:05:10.6030295Z             {
2023-01-11T21:05:10.6030378Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6030468Z                 for(long i2=0; i2<38; i2+=1)
2023-01-11T21:05:10.6030533Z                 {
2023-01-11T21:05:10.6030602Z                     {
2023-01-11T21:05:10.6030670Z                         {
2023-01-11T21:05:10.6030783Z                             auto tmp0 = static_cast<int>(i2 + (38*i1));
2023-01-11T21:05:10.6030884Z                             auto tmp1 = static_cast<int>(((1 + i1) / 2));
2023-01-11T21:05:10.6030996Z                             auto tmp2 = static_cast<int>(((1 + i2) / 2));
2023-01-11T21:05:10.6031108Z                             auto tmp3 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:05:10.6031216Z                             auto tmp4 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:05:10.6031322Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:05:10.6031453Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:05:10.6031579Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:05:10.6031685Z                             auto tmp8 = static_cast<int>(19);
2023-01-11T21:05:10.6031839Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:05:10.6031954Z                             auto tmp10 = (tmp8 != tmp8) ? tmp8 : std::min(tmp4, tmp8);
2023-01-11T21:05:10.6032052Z                             auto tmp11 = tmp6 + tmp5;
2023-01-11T21:05:10.6032145Z                             auto tmp12 = tmp7 + tmp5;
2023-01-11T21:05:10.6032250Z                             auto tmp13 = static_cast<int>(1);
2023-01-11T21:05:10.6032401Z                             auto tmp14 = tmp9 - tmp13;
2023-01-11T21:05:10.6032533Z                             auto tmp15 = (tmp14 != tmp14) ? tmp14 : std::min(tmp11, tmp14);
2023-01-11T21:05:10.6032681Z                             auto tmp16 = tmp10 - tmp13;
2023-01-11T21:05:10.6032810Z                             auto tmp17 = (tmp16 != tmp16) ? tmp16 : std::min(tmp12, tmp16);
2023-01-11T21:05:10.6032914Z                             auto tmp18 = in_ptr0[tmp17 + (19*tmp15) + (361*i0)];
2023-01-11T21:05:10.6033034Z                             auto tmp19 = in_ptr1[tmp17 + (19*tmp15) + (361*i0)];
2023-01-11T21:05:10.6033133Z                             auto tmp20 = tmp18 == tmp0;
2023-01-11T21:05:10.6033243Z                             auto tmp21 = static_cast<float>(0.0);
2023-01-11T21:05:10.6033346Z                             auto tmp22 = tmp20 ? tmp19 : tmp21;
2023-01-11T21:05:10.6033452Z                             out_ptr0[i2 + (38*i1) + (1406*i0)] = tmp22;
2023-01-11T21:05:10.6033521Z                         }
2023-01-11T21:05:10.6033573Z                     }
2023-01-11T21:05:10.6033636Z                 }
2023-01-11T21:05:10.6033699Z             }
2023-01-11T21:05:10.6033761Z         }
2023-01-11T21:05:10.6033822Z     }
2023-01-11T21:05:10.6033881Z }
2023-01-11T21:05:10.6033958Z ''')
2023-01-11T21:05:10.6033965Z 
2023-01-11T21:05:10.6033969Z 
2023-01-11T21:05:10.6034046Z async_compile.wait(globals())
2023-01-11T21:05:10.6034121Z del async_compile
2023-01-11T21:05:10.6034126Z 
2023-01-11T21:05:10.6034196Z def call(args):
2023-01-11T21:05:10.6034279Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6034349Z     args.clear()
2023-01-11T21:05:10.6034577Z     buf0 = empty_strided((32, 256, 37, 38), (359936, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6034733Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6034799Z     del arg0_1
2023-01-11T21:05:10.6034851Z     del arg2_1
2023-01-11T21:05:10.6034922Z     return (buf0, )
2023-01-11T21:05:10.6034927Z 
2023-01-11T21:05:10.6034931Z 
2023-01-11T21:05:10.6035004Z if __name__ == "__main__":
2023-01-11T21:05:10.6035120Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6035241Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6035504Z     arg0_1 = rand_strided((32, 256, 19, 19), (92416, 361, 19, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6035729Z     arg1_1 = rand_strided((32, 256, 37, 38), (359936, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6035949Z     arg2_1 = rand_strided((32, 256, 19, 19), (92416, 361, 19, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6036056Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6036061Z 
2023-01-11T21:05:10.6036127Z ok (6.884s)
2023-01-11T21:05:10.6036586Z   test_max_pool2d_with_indices_backward4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6036711Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6036974Z [2023-01-11 20:56:54,123] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 350
2023-01-11T21:05:10.6036979Z 
2023-01-11T21:05:10.6037099Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6037173Z import torch
2023-01-11T21:05:10.6037241Z import random
2023-01-11T21:05:10.6037354Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6037461Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6037466Z 
2023-01-11T21:05:10.6037544Z aten = torch.ops.aten
2023-01-11T21:05:10.6037675Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6037765Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6037770Z 
2023-01-11T21:05:10.6037774Z 
2023-01-11T21:05:10.6037908Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6038112Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6038231Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.6038335Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6038421Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6038483Z {
2023-01-11T21:05:10.6038578Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6038638Z     {
2023-01-11T21:05:10.6038713Z         #pragma omp for 
2023-01-11T21:05:10.6038794Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.6038855Z         {
2023-01-11T21:05:10.6038921Z             #pragma GCC ivdep
2023-01-11T21:05:10.6039002Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6039063Z             {
2023-01-11T21:05:10.6039143Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6039231Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:05:10.6039294Z                 {
2023-01-11T21:05:10.6039358Z                     {
2023-01-11T21:05:10.6039411Z                         {
2023-01-11T21:05:10.6039524Z                             auto tmp0 = static_cast<int>(i2 + (4*i1));
2023-01-11T21:05:10.6039695Z                             auto tmp1 = static_cast<int>((-2) + i1);
2023-01-11T21:05:10.6039863Z                             auto tmp2 = static_cast<int>((-2) + i2);
2023-01-11T21:05:10.6039971Z                             auto tmp3 = static_cast<int>(3 + i1);
2023-01-11T21:05:10.6040077Z                             auto tmp4 = static_cast<int>(3 + i2);
2023-01-11T21:05:10.6040181Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:05:10.6040297Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:05:10.6040419Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:05:10.6040523Z                             auto tmp8 = static_cast<int>(3);
2023-01-11T21:05:10.6040757Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:05:10.6040865Z                             auto tmp10 = static_cast<int>(4);
2023-01-11T21:05:10.6041049Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:05:10.6041147Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:05:10.6041246Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:05:10.6041337Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:05:10.6041489Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:05:10.6041622Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:05:10.6041772Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:05:10.6041903Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:05:10.6042022Z                             auto tmp19 = in_ptr0[tmp18 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6042139Z                             auto tmp20 = in_ptr1[tmp18 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6042239Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:05:10.6042350Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:05:10.6042478Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:05:10.6042578Z                             auto tmp24 = tmp7 + tmp14;
2023-01-11T21:05:10.6042706Z                             auto tmp25 = (tmp17 != tmp17) ? tmp17 : std::min(tmp24, tmp17);
2023-01-11T21:05:10.6042821Z                             auto tmp26 = in_ptr0[tmp25 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6042935Z                             auto tmp27 = in_ptr1[tmp25 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6043030Z                             auto tmp28 = tmp26 == tmp0;
2023-01-11T21:05:10.6043124Z                             auto tmp29 = tmp12 < tmp9;
2023-01-11T21:05:10.6043207Z                             auto tmp30 = tmp24 < tmp11;
2023-01-11T21:05:10.6043300Z                             auto tmp31 = tmp29 & tmp30;
2023-01-11T21:05:10.6043394Z                             auto tmp32 = tmp31 & tmp28;
2023-01-11T21:05:10.6043487Z                             auto tmp33 = tmp23 + tmp27;
2023-01-11T21:05:10.6043590Z                             auto tmp34 = tmp32 ? tmp33 : tmp23;
2023-01-11T21:05:10.6043696Z                             auto tmp35 = static_cast<int>(2);
2023-01-11T21:05:10.6043790Z                             auto tmp36 = tmp7 + tmp35;
2023-01-11T21:05:10.6043916Z                             auto tmp37 = (tmp17 != tmp17) ? tmp17 : std::min(tmp36, tmp17);
2023-01-11T21:05:10.6044017Z                             auto tmp38 = in_ptr0[tmp37 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6044132Z                             auto tmp39 = in_ptr1[tmp37 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6044226Z                             auto tmp40 = tmp38 == tmp0;
2023-01-11T21:05:10.6044319Z                             auto tmp41 = tmp36 < tmp11;
2023-01-11T21:05:10.6044413Z                             auto tmp42 = tmp29 & tmp41;
2023-01-11T21:05:10.6044510Z                             auto tmp43 = tmp42 & tmp40;
2023-01-11T21:05:10.6044602Z                             auto tmp44 = tmp34 + tmp39;
2023-01-11T21:05:10.6044712Z                             auto tmp45 = tmp43 ? tmp44 : tmp34;
2023-01-11T21:05:10.6044795Z                             auto tmp46 = tmp7 + tmp8;
2023-01-11T21:05:10.6044922Z                             auto tmp47 = (tmp17 != tmp17) ? tmp17 : std::min(tmp46, tmp17);
2023-01-11T21:05:10.6045035Z                             auto tmp48 = in_ptr0[tmp47 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6045148Z                             auto tmp49 = in_ptr1[tmp47 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6045242Z                             auto tmp50 = tmp48 == tmp0;
2023-01-11T21:05:10.6045334Z                             auto tmp51 = tmp46 < tmp11;
2023-01-11T21:05:10.6045428Z                             auto tmp52 = tmp29 & tmp51;
2023-01-11T21:05:10.6045520Z                             auto tmp53 = tmp52 & tmp50;
2023-01-11T21:05:10.6045629Z                             auto tmp54 = tmp45 + tmp49;
2023-01-11T21:05:10.6045733Z                             auto tmp55 = tmp53 ? tmp54 : tmp45;
2023-01-11T21:05:10.6045829Z                             auto tmp56 = tmp7 + tmp10;
2023-01-11T21:05:10.6045959Z                             auto tmp57 = (tmp17 != tmp17) ? tmp17 : std::min(tmp56, tmp17);
2023-01-11T21:05:10.6046074Z                             auto tmp58 = in_ptr0[tmp57 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6046186Z                             auto tmp59 = in_ptr1[tmp57 + (4*tmp16) + (12*i0)];
2023-01-11T21:05:10.6046280Z                             auto tmp60 = tmp58 == tmp0;
2023-01-11T21:05:10.6046372Z                             auto tmp61 = tmp56 < tmp11;
2023-01-11T21:05:10.6046452Z                             auto tmp62 = tmp29 & tmp61;
2023-01-11T21:05:10.6046543Z                             auto tmp63 = tmp62 & tmp60;
2023-01-11T21:05:10.6046635Z                             auto tmp64 = tmp55 + tmp59;
2023-01-11T21:05:10.6046739Z                             auto tmp65 = tmp63 ? tmp64 : tmp55;
2023-01-11T21:05:10.6046835Z                             auto tmp66 = tmp6 + tmp14;
2023-01-11T21:05:10.6046993Z                             auto tmp67 = (tmp15 != tmp15) ? tmp15 : std::min(tmp66, tmp15);
2023-01-11T21:05:10.6047110Z                             auto tmp68 = in_ptr0[tmp18 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6047211Z                             auto tmp69 = in_ptr1[tmp18 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6047305Z                             auto tmp70 = tmp68 == tmp0;
2023-01-11T21:05:10.6047400Z                             auto tmp71 = tmp66 < tmp9;
2023-01-11T21:05:10.6047492Z                             auto tmp72 = tmp13 < tmp11;
2023-01-11T21:05:10.6047584Z                             auto tmp73 = tmp71 & tmp72;
2023-01-11T21:05:10.6047677Z                             auto tmp74 = tmp73 & tmp70;
2023-01-11T21:05:10.6047768Z                             auto tmp75 = tmp65 + tmp69;
2023-01-11T21:05:10.6047872Z                             auto tmp76 = tmp74 ? tmp75 : tmp65;
2023-01-11T21:05:10.6047972Z                             auto tmp77 = in_ptr0[tmp25 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6048085Z                             auto tmp78 = in_ptr1[tmp25 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6048178Z                             auto tmp79 = tmp77 == tmp0;
2023-01-11T21:05:10.6048271Z                             auto tmp80 = tmp71 & tmp30;
2023-01-11T21:05:10.6048363Z                             auto tmp81 = tmp80 & tmp79;
2023-01-11T21:05:10.6048454Z                             auto tmp82 = tmp76 + tmp78;
2023-01-11T21:05:10.6048557Z                             auto tmp83 = tmp81 ? tmp82 : tmp76;
2023-01-11T21:05:10.6048659Z                             auto tmp84 = in_ptr0[tmp37 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6048770Z                             auto tmp85 = in_ptr1[tmp37 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6048866Z                             auto tmp86 = tmp84 == tmp0;
2023-01-11T21:05:10.6048958Z                             auto tmp87 = tmp71 & tmp41;
2023-01-11T21:05:10.6049049Z                             auto tmp88 = tmp87 & tmp86;
2023-01-11T21:05:10.6049144Z                             auto tmp89 = tmp83 + tmp85;
2023-01-11T21:05:10.6049246Z                             auto tmp90 = tmp88 ? tmp89 : tmp83;
2023-01-11T21:05:10.6049361Z                             auto tmp91 = in_ptr0[tmp47 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6049460Z                             auto tmp92 = in_ptr1[tmp47 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6049551Z                             auto tmp93 = tmp91 == tmp0;
2023-01-11T21:05:10.6049644Z                             auto tmp94 = tmp71 & tmp51;
2023-01-11T21:05:10.6049736Z                             auto tmp95 = tmp94 & tmp93;
2023-01-11T21:05:10.6049827Z                             auto tmp96 = tmp90 + tmp92;
2023-01-11T21:05:10.6049930Z                             auto tmp97 = tmp95 ? tmp96 : tmp90;
2023-01-11T21:05:10.6050072Z                             auto tmp98 = in_ptr0[tmp57 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6050186Z                             auto tmp99 = in_ptr1[tmp57 + (4*tmp67) + (12*i0)];
2023-01-11T21:05:10.6050274Z                             auto tmp100 = tmp98 == tmp0;
2023-01-11T21:05:10.6050373Z                             auto tmp101 = tmp71 & tmp61;
2023-01-11T21:05:10.6050472Z                             auto tmp102 = tmp101 & tmp100;
2023-01-11T21:05:10.6050567Z                             auto tmp103 = tmp97 + tmp99;
2023-01-11T21:05:10.6050672Z                             auto tmp104 = tmp102 ? tmp103 : tmp97;
2023-01-11T21:05:10.6050767Z                             auto tmp105 = tmp6 + tmp35;
2023-01-11T21:05:10.6050901Z                             auto tmp106 = (tmp15 != tmp15) ? tmp15 : std::min(tmp105, tmp15);
2023-01-11T21:05:10.6051018Z                             auto tmp107 = in_ptr0[tmp18 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6051124Z                             auto tmp108 = in_ptr1[tmp18 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6051220Z                             auto tmp109 = tmp107 == tmp0;
2023-01-11T21:05:10.6051352Z                             auto tmp110 = tmp105 < tmp9;
2023-01-11T21:05:10.6051451Z                             auto tmp111 = tmp110 & tmp72;
2023-01-11T21:05:10.6051548Z                             auto tmp112 = tmp111 & tmp109;
2023-01-11T21:05:10.6051644Z                             auto tmp113 = tmp104 + tmp108;
2023-01-11T21:05:10.6051750Z                             auto tmp114 = tmp112 ? tmp113 : tmp104;
2023-01-11T21:05:10.6051854Z                             auto tmp115 = in_ptr0[tmp25 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6051968Z                             auto tmp116 = in_ptr1[tmp25 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6052065Z                             auto tmp117 = tmp115 == tmp0;
2023-01-11T21:05:10.6052159Z                             auto tmp118 = tmp110 & tmp30;
2023-01-11T21:05:10.6052257Z                             auto tmp119 = tmp118 & tmp117;
2023-01-11T21:05:10.6052353Z                             auto tmp120 = tmp114 + tmp116;
2023-01-11T21:05:10.6052459Z                             auto tmp121 = tmp119 ? tmp120 : tmp114;
2023-01-11T21:05:10.6052574Z                             auto tmp122 = in_ptr0[tmp37 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6052674Z                             auto tmp123 = in_ptr1[tmp37 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6052769Z                             auto tmp124 = tmp122 == tmp0;
2023-01-11T21:05:10.6052862Z                             auto tmp125 = tmp110 & tmp41;
2023-01-11T21:05:10.6052959Z                             auto tmp126 = tmp125 & tmp124;
2023-01-11T21:05:10.6053054Z                             auto tmp127 = tmp121 + tmp123;
2023-01-11T21:05:10.6053158Z                             auto tmp128 = tmp126 ? tmp127 : tmp121;
2023-01-11T21:05:10.6053272Z                             auto tmp129 = in_ptr0[tmp47 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6053386Z                             auto tmp130 = in_ptr1[tmp47 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6053470Z                             auto tmp131 = tmp129 == tmp0;
2023-01-11T21:05:10.6053566Z                             auto tmp132 = tmp110 & tmp51;
2023-01-11T21:05:10.6053662Z                             auto tmp133 = tmp132 & tmp131;
2023-01-11T21:05:10.6053758Z                             auto tmp134 = tmp128 + tmp130;
2023-01-11T21:05:10.6053861Z                             auto tmp135 = tmp133 ? tmp134 : tmp128;
2023-01-11T21:05:10.6053974Z                             auto tmp136 = in_ptr0[tmp57 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6054086Z                             auto tmp137 = in_ptr1[tmp57 + (4*tmp106) + (12*i0)];
2023-01-11T21:05:10.6054168Z                             auto tmp138 = tmp136 == tmp0;
2023-01-11T21:05:10.6054262Z                             auto tmp139 = tmp110 & tmp61;
2023-01-11T21:05:10.6054359Z                             auto tmp140 = tmp139 & tmp138;
2023-01-11T21:05:10.6054482Z                             auto tmp141 = tmp135 + tmp137;
2023-01-11T21:05:10.6054585Z                             auto tmp142 = tmp140 ? tmp141 : tmp135;
2023-01-11T21:05:10.6054683Z                             auto tmp143 = tmp6 + tmp8;
2023-01-11T21:05:10.6054815Z                             auto tmp144 = (tmp15 != tmp15) ? tmp15 : std::min(tmp143, tmp15);
2023-01-11T21:05:10.6054928Z                             auto tmp145 = in_ptr0[tmp18 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6055032Z                             auto tmp146 = in_ptr1[tmp18 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6055128Z                             auto tmp147 = tmp145 == tmp0;
2023-01-11T21:05:10.6055223Z                             auto tmp148 = tmp143 < tmp9;
2023-01-11T21:05:10.6055317Z                             auto tmp149 = tmp148 & tmp72;
2023-01-11T21:05:10.6055414Z                             auto tmp150 = tmp149 & tmp147;
2023-01-11T21:05:10.6055510Z                             auto tmp151 = tmp142 + tmp146;
2023-01-11T21:05:10.6055615Z                             auto tmp152 = tmp150 ? tmp151 : tmp142;
2023-01-11T21:05:10.6055769Z                             auto tmp153 = in_ptr0[tmp25 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6055869Z                             auto tmp154 = in_ptr1[tmp25 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6055963Z                             auto tmp155 = tmp153 == tmp0;
2023-01-11T21:05:10.6056055Z                             auto tmp156 = tmp148 & tmp30;
2023-01-11T21:05:10.6056151Z                             auto tmp157 = tmp156 & tmp155;
2023-01-11T21:05:10.6056245Z                             auto tmp158 = tmp152 + tmp154;
2023-01-11T21:05:10.6056348Z                             auto tmp159 = tmp157 ? tmp158 : tmp152;
2023-01-11T21:05:10.6056462Z                             auto tmp160 = in_ptr0[tmp37 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6056572Z                             auto tmp161 = in_ptr1[tmp37 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6056660Z                             auto tmp162 = tmp160 == tmp0;
2023-01-11T21:05:10.6056753Z                             auto tmp163 = tmp148 & tmp41;
2023-01-11T21:05:10.6056850Z                             auto tmp164 = tmp163 & tmp162;
2023-01-11T21:05:10.6056945Z                             auto tmp165 = tmp159 + tmp161;
2023-01-11T21:05:10.6057049Z                             auto tmp166 = tmp164 ? tmp165 : tmp159;
2023-01-11T21:05:10.6057162Z                             auto tmp167 = in_ptr0[tmp47 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6057274Z                             auto tmp168 = in_ptr1[tmp47 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6057356Z                             auto tmp169 = tmp167 == tmp0;
2023-01-11T21:05:10.6057453Z                             auto tmp170 = tmp148 & tmp51;
2023-01-11T21:05:10.6057551Z                             auto tmp171 = tmp170 & tmp169;
2023-01-11T21:05:10.6057648Z                             auto tmp172 = tmp166 + tmp168;
2023-01-11T21:05:10.6057754Z                             auto tmp173 = tmp171 ? tmp172 : tmp166;
2023-01-11T21:05:10.6057868Z                             auto tmp174 = in_ptr0[tmp57 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6057983Z                             auto tmp175 = in_ptr1[tmp57 + (4*tmp144) + (12*i0)];
2023-01-11T21:05:10.6058079Z                             auto tmp176 = tmp174 == tmp0;
2023-01-11T21:05:10.6058160Z                             auto tmp177 = tmp148 & tmp61;
2023-01-11T21:05:10.6058255Z                             auto tmp178 = tmp177 & tmp176;
2023-01-11T21:05:10.6058349Z                             auto tmp179 = tmp173 + tmp175;
2023-01-11T21:05:10.6058452Z                             auto tmp180 = tmp178 ? tmp179 : tmp173;
2023-01-11T21:05:10.6058643Z                             auto tmp181 = tmp6 + tmp10;
2023-01-11T21:05:10.6058778Z                             auto tmp182 = (tmp15 != tmp15) ? tmp15 : std::min(tmp181, tmp15);
2023-01-11T21:05:10.6058894Z                             auto tmp183 = in_ptr0[tmp18 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6059043Z                             auto tmp184 = in_ptr1[tmp18 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6059128Z                             auto tmp185 = tmp183 == tmp0;
2023-01-11T21:05:10.6059227Z                             auto tmp186 = tmp181 < tmp9;
2023-01-11T21:05:10.6059321Z                             auto tmp187 = tmp186 & tmp72;
2023-01-11T21:05:10.6059422Z                             auto tmp188 = tmp187 & tmp185;
2023-01-11T21:05:10.6059520Z                             auto tmp189 = tmp180 + tmp184;
2023-01-11T21:05:10.6059627Z                             auto tmp190 = tmp188 ? tmp189 : tmp180;
2023-01-11T21:05:10.6059747Z                             auto tmp191 = in_ptr0[tmp25 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6059860Z                             auto tmp192 = in_ptr1[tmp25 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6059942Z                             auto tmp193 = tmp191 == tmp0;
2023-01-11T21:05:10.6060040Z                             auto tmp194 = tmp186 & tmp30;
2023-01-11T21:05:10.6060137Z                             auto tmp195 = tmp194 & tmp193;
2023-01-11T21:05:10.6060267Z                             auto tmp196 = tmp190 + tmp192;
2023-01-11T21:05:10.6060375Z                             auto tmp197 = tmp195 ? tmp196 : tmp190;
2023-01-11T21:05:10.6060488Z                             auto tmp198 = in_ptr0[tmp37 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6060601Z                             auto tmp199 = in_ptr1[tmp37 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6060684Z                             auto tmp200 = tmp198 == tmp0;
2023-01-11T21:05:10.6060778Z                             auto tmp201 = tmp186 & tmp41;
2023-01-11T21:05:10.6060875Z                             auto tmp202 = tmp201 & tmp200;
2023-01-11T21:05:10.6060972Z                             auto tmp203 = tmp197 + tmp199;
2023-01-11T21:05:10.6061076Z                             auto tmp204 = tmp202 ? tmp203 : tmp197;
2023-01-11T21:05:10.6061191Z                             auto tmp205 = in_ptr0[tmp47 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6061303Z                             auto tmp206 = in_ptr1[tmp47 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6061401Z                             auto tmp207 = tmp205 == tmp0;
2023-01-11T21:05:10.6061482Z                             auto tmp208 = tmp186 & tmp51;
2023-01-11T21:05:10.6061578Z                             auto tmp209 = tmp208 & tmp207;
2023-01-11T21:05:10.6061674Z                             auto tmp210 = tmp204 + tmp206;
2023-01-11T21:05:10.6061777Z                             auto tmp211 = tmp209 ? tmp210 : tmp204;
2023-01-11T21:05:10.6061890Z                             auto tmp212 = in_ptr0[tmp57 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6062001Z                             auto tmp213 = in_ptr1[tmp57 + (4*tmp182) + (12*i0)];
2023-01-11T21:05:10.6062097Z                             auto tmp214 = tmp212 == tmp0;
2023-01-11T21:05:10.6062191Z                             auto tmp215 = tmp186 & tmp61;
2023-01-11T21:05:10.6062278Z                             auto tmp216 = tmp215 & tmp214;
2023-01-11T21:05:10.6062372Z                             auto tmp217 = tmp211 + tmp213;
2023-01-11T21:05:10.6062478Z                             auto tmp218 = tmp216 ? tmp217 : tmp211;
2023-01-11T21:05:10.6062582Z                             out_ptr0[i2 + (4*i1) + (12*i0)] = tmp218;
2023-01-11T21:05:10.6062651Z                         }
2023-01-11T21:05:10.6062717Z                     }
2023-01-11T21:05:10.6062781Z                 }
2023-01-11T21:05:10.6062830Z             }
2023-01-11T21:05:10.6062891Z         }
2023-01-11T21:05:10.6062952Z     }
2023-01-11T21:05:10.6063012Z }
2023-01-11T21:05:10.6063106Z ''')
2023-01-11T21:05:10.6063112Z 
2023-01-11T21:05:10.6063116Z 
2023-01-11T21:05:10.6063206Z async_compile.wait(globals())
2023-01-11T21:05:10.6063278Z del async_compile
2023-01-11T21:05:10.6063283Z 
2023-01-11T21:05:10.6063339Z def call(args):
2023-01-11T21:05:10.6063421Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6063524Z     args.clear()
2023-01-11T21:05:10.6063744Z     buf0 = empty_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6063909Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6063978Z     del arg0_1
2023-01-11T21:05:10.6064044Z     del arg2_1
2023-01-11T21:05:10.6064101Z     return (buf0, )
2023-01-11T21:05:10.6064120Z 
2023-01-11T21:05:10.6064124Z 
2023-01-11T21:05:10.6064186Z if __name__ == "__main__":
2023-01-11T21:05:10.6064301Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6064424Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6064638Z     arg0_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6064850Z     arg1_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6065058Z     arg2_1 = rand_strided((2, 64, 3, 4), (768, 12, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6065183Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6065479Z [2023-01-11 20:56:57,100] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 350
2023-01-11T21:05:10.6065486Z 
2023-01-11T21:05:10.6065540Z ok (3.025s)
2023-01-11T21:05:10.6065999Z   test_max_pool2d_with_indices_backward5_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6066124Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6066383Z [2023-01-11 20:56:57,159] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 351
2023-01-11T21:05:10.6066641Z [2023-01-11 20:56:57,186] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.max_pool2d_with_indices_backward
2023-01-11T21:05:10.6066902Z [2023-01-11 20:56:57,190] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 351
2023-01-11T21:05:10.6066908Z 
2023-01-11T21:05:10.6067003Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6067073Z import torch
2023-01-11T21:05:10.6067142Z import random
2023-01-11T21:05:10.6067244Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6067363Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6067368Z 
2023-01-11T21:05:10.6067445Z aten = torch.ops.aten
2023-01-11T21:05:10.6067578Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6067669Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6067675Z 
2023-01-11T21:05:10.6067679Z 
2023-01-11T21:05:10.6067764Z async_compile.wait(globals())
2023-01-11T21:05:10.6067838Z del async_compile
2023-01-11T21:05:10.6067843Z 
2023-01-11T21:05:10.6067913Z def call(args):
2023-01-11T21:05:10.6067980Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6068049Z     args.clear()
2023-01-11T21:05:10.6068203Z     buf0 = aten.max_pool2d_with_indices_backward(arg0_1, arg1_1, [13, 13], [1, 1], [2, 2], [1, 1], False, arg2_1)
2023-01-11T21:05:10.6068270Z     del arg0_1
2023-01-11T21:05:10.6068337Z     del arg1_1
2023-01-11T21:05:10.6068401Z     del arg2_1
2023-01-11T21:05:10.6068466Z     buf1 = buf0
2023-01-11T21:05:10.6068563Z     assert_size_stride(buf1, (2, 64, 20, 20), (25600, 400, 20, 1))
2023-01-11T21:05:10.6068627Z     del buf0
2023-01-11T21:05:10.6068696Z     return (buf1, )
2023-01-11T21:05:10.6068702Z 
2023-01-11T21:05:10.6068706Z 
2023-01-11T21:05:10.6068780Z if __name__ == "__main__":
2023-01-11T21:05:10.6068892Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6069012Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6069262Z     arg0_1 = rand_strided((2, 64, 12, 12), (9216, 144, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6069484Z     arg1_1 = rand_strided((2, 64, 20, 20), (25600, 400, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6069688Z     arg2_1 = rand_strided((2, 64, 12, 12), (9216, 144, 12, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6069809Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6069814Z 
2023-01-11T21:05:10.6069879Z ok (0.133s)
2023-01-11T21:05:10.6070338Z   test_max_pool2d_with_indices_backward_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6070463Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6070727Z [2023-01-11 20:56:57,279] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 352
2023-01-11T21:05:10.6071049Z [2023-01-11 20:56:59,954] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 352
2023-01-11T21:05:10.6071055Z 
2023-01-11T21:05:10.6071149Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6071216Z import torch
2023-01-11T21:05:10.6071272Z import random
2023-01-11T21:05:10.6071385Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6071503Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6071508Z 
2023-01-11T21:05:10.6071585Z aten = torch.ops.aten
2023-01-11T21:05:10.6071717Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6071812Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6071817Z 
2023-01-11T21:05:10.6071821Z 
2023-01-11T21:05:10.6071955Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6072160Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6072265Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.6072374Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6072473Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6072534Z {
2023-01-11T21:05:10.6072631Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6072694Z     {
2023-01-11T21:05:10.6072771Z         #pragma omp for 
2023-01-11T21:05:10.6072839Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6072901Z         {
2023-01-11T21:05:10.6072980Z             #pragma GCC ivdep
2023-01-11T21:05:10.6073063Z             for(long i1=0; i1<18; i1+=1)
2023-01-11T21:05:10.6073126Z             {
2023-01-11T21:05:10.6073204Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6073292Z                 for(long i2=0; i2<14; i2+=1)
2023-01-11T21:05:10.6073343Z                 {
2023-01-11T21:05:10.6073410Z                     {
2023-01-11T21:05:10.6073476Z                         {
2023-01-11T21:05:10.6073588Z                             auto tmp0 = static_cast<int>(i2 + (14*i1));
2023-01-11T21:05:10.6073699Z                             auto tmp1 = static_cast<int>((i1 / 2));
2023-01-11T21:05:10.6073805Z                             auto tmp2 = static_cast<int>((i2 / 2));
2023-01-11T21:05:10.6073916Z                             auto tmp3 = static_cast<int>(1 + (i1 / 2));
2023-01-11T21:05:10.6074013Z                             auto tmp4 = static_cast<int>(1 + (i2 / 2));
2023-01-11T21:05:10.6074118Z                             auto tmp5 = static_cast<int>(0);
2023-01-11T21:05:10.6074248Z                             auto tmp6 = (tmp5 != tmp5) ? tmp5 : std::max(tmp1, tmp5);
2023-01-11T21:05:10.6074372Z                             auto tmp7 = (tmp5 != tmp5) ? tmp5 : std::max(tmp2, tmp5);
2023-01-11T21:05:10.6074477Z                             auto tmp8 = static_cast<int>(9);
2023-01-11T21:05:10.6074633Z                             auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::min(tmp3, tmp8);
2023-01-11T21:05:10.6074737Z                             auto tmp10 = static_cast<int>(7);
2023-01-11T21:05:10.6074868Z                             auto tmp11 = (tmp10 != tmp10) ? tmp10 : std::min(tmp4, tmp10);
2023-01-11T21:05:10.6074965Z                             auto tmp12 = tmp6 + tmp5;
2023-01-11T21:05:10.6075045Z                             auto tmp13 = tmp7 + tmp5;
2023-01-11T21:05:10.6075147Z                             auto tmp14 = static_cast<int>(1);
2023-01-11T21:05:10.6075294Z                             auto tmp15 = tmp9 - tmp14;
2023-01-11T21:05:10.6075423Z                             auto tmp16 = (tmp15 != tmp15) ? tmp15 : std::min(tmp12, tmp15);
2023-01-11T21:05:10.6075572Z                             auto tmp17 = tmp11 - tmp14;
2023-01-11T21:05:10.6075700Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp13, tmp17);
2023-01-11T21:05:10.6075817Z                             auto tmp19 = in_ptr0[tmp18 + (7*tmp16) + (63*i0)];
2023-01-11T21:05:10.6075934Z                             auto tmp20 = in_ptr1[tmp18 + (7*tmp16) + (63*i0)];
2023-01-11T21:05:10.6076018Z                             auto tmp21 = tmp19 == tmp0;
2023-01-11T21:05:10.6076154Z                             auto tmp22 = static_cast<float>(0.0);
2023-01-11T21:05:10.6076263Z                             auto tmp23 = tmp21 ? tmp20 : tmp22;
2023-01-11T21:05:10.6076366Z                             out_ptr0[i2 + (14*i1) + (252*i0)] = tmp23;
2023-01-11T21:05:10.6076434Z                         }
2023-01-11T21:05:10.6076500Z                     }
2023-01-11T21:05:10.6076563Z                 }
2023-01-11T21:05:10.6076613Z             }
2023-01-11T21:05:10.6076673Z         }
2023-01-11T21:05:10.6076732Z     }
2023-01-11T21:05:10.6076790Z }
2023-01-11T21:05:10.6076869Z ''')
2023-01-11T21:05:10.6076874Z 
2023-01-11T21:05:10.6076878Z 
2023-01-11T21:05:10.6076968Z async_compile.wait(globals())
2023-01-11T21:05:10.6077038Z del async_compile
2023-01-11T21:05:10.6077045Z 
2023-01-11T21:05:10.6077101Z def call(args):
2023-01-11T21:05:10.6077180Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6077250Z     args.clear()
2023-01-11T21:05:10.6077470Z     buf0 = empty_strided((2, 4, 18, 14), (1008, 252, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6077633Z     kernel_cpp_0(c_void_p(arg2_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6077700Z     del arg0_1
2023-01-11T21:05:10.6077764Z     del arg2_1
2023-01-11T21:05:10.6077820Z     return (buf0, )
2023-01-11T21:05:10.6077837Z 
2023-01-11T21:05:10.6077841Z 
2023-01-11T21:05:10.6077902Z if __name__ == "__main__":
2023-01-11T21:05:10.6078016Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6078140Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6078351Z     arg0_1 = rand_strided((2, 4, 9, 7), (252, 63, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6078568Z     arg1_1 = rand_strided((2, 4, 18, 14), (1008, 252, 14, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6078775Z     arg2_1 = rand_strided((2, 4, 9, 7), (252, 63, 7, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6078897Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6078903Z 
2023-01-11T21:05:10.6078968Z ok (2.720s)
2023-01-11T21:05:10.6079388Z   test_mean_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6079512Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6079771Z [2023-01-11 20:57:00,007] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 353
2023-01-11T21:05:10.6080061Z [2023-01-11 20:57:02,798] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 353
2023-01-11T21:05:10.6080066Z 
2023-01-11T21:05:10.6080158Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6080227Z import torch
2023-01-11T21:05:10.6080295Z import random
2023-01-11T21:05:10.6080408Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6080526Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6080532Z 
2023-01-11T21:05:10.6080747Z aten = torch.ops.aten
2023-01-11T21:05:10.6080882Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6080976Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6080981Z 
2023-01-11T21:05:10.6080985Z 
2023-01-11T21:05:10.6081120Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6081329Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6081448Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6081556Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.6081660Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6081800Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6081902Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.6081962Z {
2023-01-11T21:05:10.6082046Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.6082127Z     auto out_ptr0 = in_out_ptr1;
2023-01-11T21:05:10.6082187Z     {
2023-01-11T21:05:10.6082373Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6082434Z         float tmp1 = 0;
2023-01-11T21:05:10.6082549Z         auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:05:10.6082651Z         #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6082716Z         {
2023-01-11T21:05:10.6082824Z             #pragma omp for reduction(+:tmp1_vec) 
2023-01-11T21:05:10.6082910Z             for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6082974Z             {
2023-01-11T21:05:10.6083094Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6083176Z                 tmp1_vec += tmp0;
2023-01-11T21:05:10.6083240Z             }
2023-01-11T21:05:10.6083437Z             tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:05:10.6083560Z             #pragma omp for simd simdlen(8) reduction(+:tmp1) 
2023-01-11T21:05:10.6083647Z             for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6083711Z             {
2023-01-11T21:05:10.6083798Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6083860Z                 tmp1 += tmp0;
2023-01-11T21:05:10.6083922Z             }
2023-01-11T21:05:10.6083982Z         }
2023-01-11T21:05:10.6084057Z         out_ptr0[0] = tmp1;
2023-01-11T21:05:10.6084118Z     }
2023-01-11T21:05:10.6084215Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6084275Z     {
2023-01-11T21:05:10.6084335Z         #pragma omp for 
2023-01-11T21:05:10.6084415Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6084476Z         {
2023-01-11T21:05:10.6084539Z             {
2023-01-11T21:05:10.6084602Z                 {
2023-01-11T21:05:10.6084679Z                     float tmp1 = 0;
2023-01-11T21:05:10.6084757Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6084821Z                     {
2023-01-11T21:05:10.6084887Z                         {
2023-01-11T21:05:10.6084991Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6085074Z                             tmp1 += tmp0;
2023-01-11T21:05:10.6085141Z                         }
2023-01-11T21:05:10.6085205Z                     }
2023-01-11T21:05:10.6085275Z                     out_ptr1[i0] = tmp1;
2023-01-11T21:05:10.6085338Z                 }
2023-01-11T21:05:10.6085398Z             }
2023-01-11T21:05:10.6085497Z         }
2023-01-11T21:05:10.6085572Z         #pragma omp for 
2023-01-11T21:05:10.6085650Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6085716Z         {
2023-01-11T21:05:10.6085790Z             {
2023-01-11T21:05:10.6085886Z                 {
2023-01-11T21:05:10.6086018Z                     auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.6086151Z                     auto tmp1 = static_cast<float>(8);
2023-01-11T21:05:10.6086243Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6086331Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6086396Z                 }
2023-01-11T21:05:10.6086444Z             }
2023-01-11T21:05:10.6086504Z         }
2023-01-11T21:05:10.6086593Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6086671Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6086731Z         {
2023-01-11T21:05:10.6086810Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6086859Z             {
2023-01-11T21:05:10.6086921Z                 {
2023-01-11T21:05:10.6086988Z                     {
2023-01-11T21:05:10.6087090Z                         auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.6087193Z                         auto tmp1 = in_ptr0[8 + i1 + (32*i0)];
2023-01-11T21:05:10.6087331Z                         auto tmp3 = in_ptr0[16 + i1 + (32*i0)];
2023-01-11T21:05:10.6087434Z                         auto tmp5 = in_ptr0[24 + i1 + (32*i0)];
2023-01-11T21:05:10.6087514Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6087609Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6087700Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6087804Z                         auto tmp7 = static_cast<float>(4);
2023-01-11T21:05:10.6087894Z                         auto tmp8 = tmp6 / tmp7;
2023-01-11T21:05:10.6087988Z                         out_ptr2[i1 + (8*i0)] = tmp8;
2023-01-11T21:05:10.6088053Z                     }
2023-01-11T21:05:10.6088115Z                 }
2023-01-11T21:05:10.6088167Z             }
2023-01-11T21:05:10.6088226Z         }
2023-01-11T21:05:10.6088300Z         #pragma omp for 
2023-01-11T21:05:10.6088381Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6088441Z         {
2023-01-11T21:05:10.6088576Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6088714Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 32 + (16*i0));
2023-01-11T21:05:10.6088785Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6088916Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6088998Z             auto tmp4 = tmp2 / tmp3;
2023-01-11T21:05:10.6089088Z             tmp4.store(out_ptr3 + 16*i0);
2023-01-11T21:05:10.6089148Z         }
2023-01-11T21:05:10.6089243Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6089323Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.6089371Z         {
2023-01-11T21:05:10.6089451Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6089539Z             auto tmp1 = in_ptr0[32 + i0];
2023-01-11T21:05:10.6089621Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6089719Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.6089802Z             auto tmp4 = tmp2 / tmp3;
2023-01-11T21:05:10.6089879Z             out_ptr3[i0] = tmp4;
2023-01-11T21:05:10.6089928Z         }
2023-01-11T21:05:10.6090004Z         #pragma omp single
2023-01-11T21:05:10.6090065Z         {
2023-01-11T21:05:10.6090125Z             {
2023-01-11T21:05:10.6090189Z                 {
2023-01-11T21:05:10.6090279Z                     auto tmp0 = out_ptr0[0];
2023-01-11T21:05:10.6090369Z                     auto tmp1 = static_cast<float>(64);
2023-01-11T21:05:10.6090459Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6090544Z                     in_out_ptr1[0] = tmp2;
2023-01-11T21:05:10.6090607Z                 }
2023-01-11T21:05:10.6090668Z             }
2023-01-11T21:05:10.6090728Z         }
2023-01-11T21:05:10.6090820Z     }
2023-01-11T21:05:10.6090865Z }
2023-01-11T21:05:10.6090958Z ''')
2023-01-11T21:05:10.6090963Z 
2023-01-11T21:05:10.6090968Z 
2023-01-11T21:05:10.6091057Z async_compile.wait(globals())
2023-01-11T21:05:10.6091129Z del async_compile
2023-01-11T21:05:10.6091137Z 
2023-01-11T21:05:10.6091206Z def call(args):
2023-01-11T21:05:10.6091274Z     arg0_1, = args
2023-01-11T21:05:10.6091345Z     args.clear()
2023-01-11T21:05:10.6091520Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6091723Z     buf1 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6091807Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6092014Z     buf3 = empty_strided((1, 2, 1, 8), (16, 8, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6092203Z     buf4 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6092286Z     buf5 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6092498Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.6092569Z     del arg0_1
2023-01-11T21:05:10.6092642Z     return (buf5, buf2, buf3, buf4, )
2023-01-11T21:05:10.6092692Z 
2023-01-11T21:05:10.6092711Z 
2023-01-11T21:05:10.6092775Z if __name__ == "__main__":
2023-01-11T21:05:10.6092887Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6093009Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6093219Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6093326Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6093331Z 
2023-01-11T21:05:10.6093401Z ok (2.843s)
2023-01-11T21:05:10.6093847Z   test_min_max_reduction_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6093978Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6094240Z [2023-01-11 20:57:02,857] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 354
2023-01-11T21:05:10.6094490Z [2023-01-11 20:57:05,576] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 354
2023-01-11T21:05:10.6094495Z 
2023-01-11T21:05:10.6094586Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6094659Z import torch
2023-01-11T21:05:10.6094730Z import random
2023-01-11T21:05:10.6094843Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6094962Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6094967Z 
2023-01-11T21:05:10.6095044Z aten = torch.ops.aten
2023-01-11T21:05:10.6095162Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6095255Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6095260Z 
2023-01-11T21:05:10.6095265Z 
2023-01-11T21:05:10.6095397Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6095605Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6095723Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6095827Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6095926Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6096022Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6096102Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6096162Z {
2023-01-11T21:05:10.6096222Z     {
2023-01-11T21:05:10.6096639Z         #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:05:10.6096982Z         float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6097158Z         auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:05:10.6097405Z         #pragma omp declare reduction(min:at::vec::Vectorized<float>:omp_out = at::vec::minimum(omp_out, omp_in)) initializer(omp_priv={{std::numeric_limits<float>::infinity()}})
2023-01-11T21:05:10.6097527Z         float tmp4 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6097642Z         auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:05:10.6097838Z         float tmp7 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6097938Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:05:10.6098040Z         #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6098103Z         {
2023-01-11T21:05:10.6098267Z             #pragma omp for reduction(max:tmp3_vec) reduction(min:tmp4_vec) reduction(max:tmp7_vec) 
2023-01-11T21:05:10.6098354Z             for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6098417Z             {
2023-01-11T21:05:10.6098615Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6098777Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6098869Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6099003Z                 auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6099089Z                 auto tmp6 = tmp0 + tmp5;
2023-01-11T21:05:10.6099208Z                 tmp3_vec = at::vec::maximum(tmp3_vec, tmp2);
2023-01-11T21:05:10.6099321Z                 tmp4_vec = at::vec::minimum(tmp4_vec, tmp2);
2023-01-11T21:05:10.6099432Z                 tmp7_vec = at::vec::maximum(tmp7_vec, tmp6);
2023-01-11T21:05:10.6099496Z             }
2023-01-11T21:05:10.6099706Z             tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp3_vec);
2023-01-11T21:05:10.6099899Z             tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::minimum(x, y);}, tmp4_vec);
2023-01-11T21:05:10.6100107Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp7_vec);
2023-01-11T21:05:10.6100276Z             #pragma omp for simd simdlen(8) reduction(max:tmp3) reduction(min:tmp4) reduction(max:tmp7) 
2023-01-11T21:05:10.6100361Z             for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6100425Z             {
2023-01-11T21:05:10.6100511Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6100595Z                 auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.6100680Z                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6100768Z                 auto tmp5 = static_cast<float>(1);
2023-01-11T21:05:10.6100852Z                 auto tmp6 = tmp0 + tmp5;
2023-01-11T21:05:10.6100948Z                 tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:05:10.6101040Z                 tmp4 = std::min(tmp4, tmp2);
2023-01-11T21:05:10.6101130Z                 tmp7 = std::max(tmp7, tmp6);
2023-01-11T21:05:10.6101195Z             }
2023-01-11T21:05:10.6101255Z         }
2023-01-11T21:05:10.6101319Z         out_ptr0[0] = tmp3;
2023-01-11T21:05:10.6101393Z         out_ptr1[0] = tmp4;
2023-01-11T21:05:10.6101467Z         out_ptr2[0] = tmp7;
2023-01-11T21:05:10.6101527Z     }
2023-01-11T21:05:10.6101585Z }
2023-01-11T21:05:10.6101664Z ''')
2023-01-11T21:05:10.6101669Z 
2023-01-11T21:05:10.6101673Z 
2023-01-11T21:05:10.6101762Z async_compile.wait(globals())
2023-01-11T21:05:10.6101821Z del async_compile
2023-01-11T21:05:10.6101838Z 
2023-01-11T21:05:10.6101893Z def call(args):
2023-01-11T21:05:10.6101966Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6102037Z     args.clear()
2023-01-11T21:05:10.6102222Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6102430Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6102622Z     buf2 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6102836Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6102892Z     del arg0_1
2023-01-11T21:05:10.6102956Z     del arg1_1
2023-01-11T21:05:10.6103036Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6103041Z 
2023-01-11T21:05:10.6103045Z 
2023-01-11T21:05:10.6103119Z if __name__ == "__main__":
2023-01-11T21:05:10.6103232Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6103354Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6103549Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6103741Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6103845Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6103850Z 
2023-01-11T21:05:10.6103914Z ok (2.778s)
2023-01-11T21:05:10.6104396Z   test_misaligned_address_issue1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6104527Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6104789Z [2023-01-11 20:57:05,635] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 355
2023-01-11T21:05:10.6105052Z [2023-01-11 20:57:08,260] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 355
2023-01-11T21:05:10.6105057Z 
2023-01-11T21:05:10.6105150Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6105221Z import torch
2023-01-11T21:05:10.6105288Z import random
2023-01-11T21:05:10.6105389Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6105511Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6105516Z 
2023-01-11T21:05:10.6105596Z aten = torch.ops.aten
2023-01-11T21:05:10.6105729Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6105819Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6105824Z 
2023-01-11T21:05:10.6105828Z 
2023-01-11T21:05:10.6105960Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6106162Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6106279Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.6106370Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6106468Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6106530Z {
2023-01-11T21:05:10.6106590Z     {
2023-01-11T21:05:10.6106652Z         {
2023-01-11T21:05:10.6106738Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.6106823Z             auto tmp1 = in_ptr1[tmp0];
2023-01-11T21:05:10.6106889Z             out_ptr0[0] = tmp1;
2023-01-11T21:05:10.6106949Z         }
2023-01-11T21:05:10.6107007Z     }
2023-01-11T21:05:10.6107065Z }
2023-01-11T21:05:10.6107141Z ''')
2023-01-11T21:05:10.6107146Z 
2023-01-11T21:05:10.6107150Z 
2023-01-11T21:05:10.6107235Z async_compile.wait(globals())
2023-01-11T21:05:10.6107306Z del async_compile
2023-01-11T21:05:10.6107311Z 
2023-01-11T21:05:10.6107367Z def call(args):
2023-01-11T21:05:10.6107438Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6107506Z     args.clear()
2023-01-11T21:05:10.6107698Z     buf0 = empty_strided((1, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6107856Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6107954Z     del arg0_1
2023-01-11T21:05:10.6108018Z     del arg1_1
2023-01-11T21:05:10.6108076Z     return (buf0, )
2023-01-11T21:05:10.6108093Z 
2023-01-11T21:05:10.6108097Z 
2023-01-11T21:05:10.6108158Z if __name__ == "__main__":
2023-01-11T21:05:10.6108272Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6108392Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6108594Z     arg0_1 = rand_strided((1, 1000), (1000, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6108786Z     arg1_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6108899Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6108904Z 
2023-01-11T21:05:10.6108969Z ok (2.682s)
2023-01-11T21:05:10.6109404Z   test_mm_views_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6109532Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6109806Z [2023-01-11 20:57:08,305] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 356
2023-01-11T21:05:10.6110070Z [2023-01-11 20:57:08,309] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 356
2023-01-11T21:05:10.6110075Z 
2023-01-11T21:05:10.6110167Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6110235Z import torch
2023-01-11T21:05:10.6110305Z import random
2023-01-11T21:05:10.6110418Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6110536Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6110540Z 
2023-01-11T21:05:10.6110617Z aten = torch.ops.aten
2023-01-11T21:05:10.6110735Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6110827Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6110832Z 
2023-01-11T21:05:10.6110836Z 
2023-01-11T21:05:10.6110923Z async_compile.wait(globals())
2023-01-11T21:05:10.6110992Z del async_compile
2023-01-11T21:05:10.6110999Z 
2023-01-11T21:05:10.6111069Z def call(args):
2023-01-11T21:05:10.6111142Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6111211Z     args.clear()
2023-01-11T21:05:10.6111396Z     buf0 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6111514Z     aten.mm.out(arg0_1, as_strided(arg1_1, (32, 32), (32, 1)), out=buf0)
2023-01-11T21:05:10.6111580Z     del arg0_1
2023-01-11T21:05:10.6111644Z     del arg1_1
2023-01-11T21:05:10.6111713Z     return (buf0, )
2023-01-11T21:05:10.6111718Z 
2023-01-11T21:05:10.6111722Z 
2023-01-11T21:05:10.6111795Z if __name__ == "__main__":
2023-01-11T21:05:10.6111907Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6112025Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6112214Z     arg0_1 = rand_strided((32, 32), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6112423Z     arg1_1 = rand_strided((32, 1, 32), (32, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6112537Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6112542Z 
2023-01-11T21:05:10.6112608Z ok (0.047s)
2023-01-11T21:05:10.6113047Z   test_move_arange_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6113169Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6113424Z [2023-01-11 20:57:08,366] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 357
2023-01-11T21:05:10.6113714Z [2023-01-11 20:57:11,047] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 357
2023-01-11T21:05:10.6113718Z 
2023-01-11T21:05:10.6113810Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6113867Z import torch
2023-01-11T21:05:10.6113935Z import random
2023-01-11T21:05:10.6114047Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6114164Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6114169Z 
2023-01-11T21:05:10.6114246Z aten = torch.ops.aten
2023-01-11T21:05:10.6114378Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6114469Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6114474Z 
2023-01-11T21:05:10.6114478Z 
2023-01-11T21:05:10.6114615Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6114806Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6114926Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6115029Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6115090Z {
2023-01-11T21:05:10.6115185Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6115275Z     {
2023-01-11T21:05:10.6115351Z         #pragma omp for 
2023-01-11T21:05:10.6115419Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6115480Z         {
2023-01-11T21:05:10.6115541Z             {
2023-01-11T21:05:10.6115605Z                 {
2023-01-11T21:05:10.6115695Z                     auto tmp2 = in_ptr0[i0];
2023-01-11T21:05:10.6115797Z                     auto tmp0 = static_cast<long>(i0);
2023-01-11T21:05:10.6115903Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.6115980Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6116062Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6116126Z                 }
2023-01-11T21:05:10.6116187Z             }
2023-01-11T21:05:10.6116248Z         }
2023-01-11T21:05:10.6116310Z     }
2023-01-11T21:05:10.6116368Z }
2023-01-11T21:05:10.6116432Z ''')
2023-01-11T21:05:10.6116437Z 
2023-01-11T21:05:10.6116441Z 
2023-01-11T21:05:10.6116528Z async_compile.wait(globals())
2023-01-11T21:05:10.6116600Z del async_compile
2023-01-11T21:05:10.6116604Z 
2023-01-11T21:05:10.6116673Z def call(args):
2023-01-11T21:05:10.6116740Z     arg0_1, = args
2023-01-11T21:05:10.6116809Z     args.clear()
2023-01-11T21:05:10.6117001Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6117121Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6117187Z     del arg0_1
2023-01-11T21:05:10.6117255Z     return (buf0, )
2023-01-11T21:05:10.6117260Z 
2023-01-11T21:05:10.6117264Z 
2023-01-11T21:05:10.6117338Z if __name__ == "__main__":
2023-01-11T21:05:10.6117449Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6117571Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6117765Z     arg0_1 = rand_strided((32, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6117870Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6117875Z 
2023-01-11T21:05:10.6117927Z ok (2.739s)
2023-01-11T21:05:10.6118065Z   test_multi_device_cpu (__main__.CpuTests) ... skip: requires cuda (0.002s)
2023-01-11T21:05:10.6118224Z   test_multi_gpu_device_cpu (__main__.CpuTests) ... skip: requires multiple cuda devices (0.001s)
2023-01-11T21:05:10.6118369Z   test_multilayer_low_prec_cpu (__main__.CpuTests) ... skip: requires CUDA (0.001s)
2023-01-11T21:05:10.6118512Z   test_nan_to_num_cpu (__main__.CpuTests) ... skip: Skipping due to op bugs (0.001s)
2023-01-11T21:05:10.6118944Z   test_narrow_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6119098Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6119360Z [2023-01-11 20:57:11,118] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 358
2023-01-11T21:05:10.6119626Z [2023-01-11 20:57:13,784] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 358
2023-01-11T21:05:10.6119631Z 
2023-01-11T21:05:10.6119723Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6119778Z import torch
2023-01-11T21:05:10.6119848Z import random
2023-01-11T21:05:10.6119962Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6120080Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6120085Z 
2023-01-11T21:05:10.6120162Z aten = torch.ops.aten
2023-01-11T21:05:10.6120293Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6120383Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6120390Z 
2023-01-11T21:05:10.6120394Z 
2023-01-11T21:05:10.6120511Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6120908Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6121031Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6121131Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6121192Z {
2023-01-11T21:05:10.6121289Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6121352Z     {
2023-01-11T21:05:10.6121414Z         #pragma omp for 
2023-01-11T21:05:10.6121498Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.6121561Z         {
2023-01-11T21:05:10.6121702Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 640 + (16*i0));
2023-01-11T21:05:10.6121834Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6121920Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6122054Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6122141Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6122220Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6122285Z         }
2023-01-11T21:05:10.6122382Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6122466Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:05:10.6122530Z         {
2023-01-11T21:05:10.6122620Z             auto tmp0 = in_ptr0[640 + i0];
2023-01-11T21:05:10.6122717Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.6122788Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6122884Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:05:10.6122965Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6123043Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6123104Z         }
2023-01-11T21:05:10.6123164Z     }
2023-01-11T21:05:10.6123222Z }
2023-01-11T21:05:10.6123290Z ''')
2023-01-11T21:05:10.6123297Z 
2023-01-11T21:05:10.6123301Z 
2023-01-11T21:05:10.6123387Z async_compile.wait(globals())
2023-01-11T21:05:10.6123460Z del async_compile
2023-01-11T21:05:10.6123465Z 
2023-01-11T21:05:10.6123533Z def call(args):
2023-01-11T21:05:10.6123604Z     arg0_1, = args
2023-01-11T21:05:10.6123675Z     args.clear()
2023-01-11T21:05:10.6123875Z     buf0 = empty_strided((16, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6123993Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6124099Z     return (as_strided(arg0_1, (64, 16), (64, 1), 10), buf0, )
2023-01-11T21:05:10.6124104Z 
2023-01-11T21:05:10.6124109Z 
2023-01-11T21:05:10.6124183Z if __name__ == "__main__":
2023-01-11T21:05:10.6124294Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6124415Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6124612Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6124758Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6124763Z 
2023-01-11T21:05:10.6124827Z ok (2.742s)
2023-01-11T21:05:10.6125274Z   test_new_empty_strided_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6125401Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6125647Z [2023-01-11 20:57:13,887] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 359
2023-01-11T21:05:10.6125910Z [2023-01-11 20:57:16,528] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 359
2023-01-11T21:05:10.6125915Z 
2023-01-11T21:05:10.6126008Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6126079Z import torch
2023-01-11T21:05:10.6126147Z import random
2023-01-11T21:05:10.6126260Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6126379Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6126412Z 
2023-01-11T21:05:10.6126493Z aten = torch.ops.aten
2023-01-11T21:05:10.6126613Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6126702Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6126707Z 
2023-01-11T21:05:10.6126711Z 
2023-01-11T21:05:10.6126843Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6127045Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6127159Z extern "C" void kernel(float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6127220Z {
2023-01-11T21:05:10.6127316Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6127364Z     {
2023-01-11T21:05:10.6127440Z         #pragma omp for 
2023-01-11T21:05:10.6127522Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.6127586Z         {
2023-01-11T21:05:10.6127723Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(123));
2023-01-11T21:05:10.6127815Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6127880Z         }
2023-01-11T21:05:10.6127961Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6128049Z         for(long i0=16384; i0<16384; i0+=1)
2023-01-11T21:05:10.6128111Z         {
2023-01-11T21:05:10.6128212Z             auto tmp0 = static_cast<float>(123);
2023-01-11T21:05:10.6128292Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6128353Z         }
2023-01-11T21:05:10.6128413Z     }
2023-01-11T21:05:10.6128458Z }
2023-01-11T21:05:10.6128536Z ''')
2023-01-11T21:05:10.6128541Z 
2023-01-11T21:05:10.6128546Z 
2023-01-11T21:05:10.6128633Z async_compile.wait(globals())
2023-01-11T21:05:10.6128705Z del async_compile
2023-01-11T21:05:10.6128711Z 
2023-01-11T21:05:10.6128781Z def call(args):
2023-01-11T21:05:10.6128849Z     arg0_1, = args
2023-01-11T21:05:10.6128920Z     args.clear()
2023-01-11T21:05:10.6129122Z     buf0 = empty_strided((1, 128, 128), (16384, 128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6129226Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6129297Z     return (buf0, )
2023-01-11T21:05:10.6129301Z 
2023-01-11T21:05:10.6129306Z 
2023-01-11T21:05:10.6129379Z if __name__ == "__main__":
2023-01-11T21:05:10.6129491Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6129611Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6129804Z     arg0_1 = rand_strided((55, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6129909Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6129914Z 
2023-01-11T21:05:10.6129966Z ok (2.742s)
2023-01-11T21:05:10.6130407Z   test_new_ones_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6130567Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6130825Z [2023-01-11 20:57:16,678] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 360
2023-01-11T21:05:10.6131086Z [2023-01-11 20:57:19,297] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 360
2023-01-11T21:05:10.6131091Z 
2023-01-11T21:05:10.6131183Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6131253Z import torch
2023-01-11T21:05:10.6131322Z import random
2023-01-11T21:05:10.6131438Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6131544Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6131562Z 
2023-01-11T21:05:10.6131625Z aten = torch.ops.aten
2023-01-11T21:05:10.6131758Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6131848Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6131853Z 
2023-01-11T21:05:10.6131857Z 
2023-01-11T21:05:10.6132035Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6132241Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6132351Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6132447Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6132494Z {
2023-01-11T21:05:10.6132553Z     {
2023-01-11T21:05:10.6132614Z         {
2023-01-11T21:05:10.6132712Z             auto tmp0 = static_cast<float>(1);
2023-01-11T21:05:10.6132789Z             out_ptr0[0] = tmp0;
2023-01-11T21:05:10.6132849Z         }
2023-01-11T21:05:10.6132907Z     }
2023-01-11T21:05:10.6132955Z     {
2023-01-11T21:05:10.6133014Z         {
2023-01-11T21:05:10.6133110Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:05:10.6133188Z             out_ptr1[0] = tmp0;
2023-01-11T21:05:10.6133248Z         }
2023-01-11T21:05:10.6133312Z     }
2023-01-11T21:05:10.6133357Z }
2023-01-11T21:05:10.6133434Z ''')
2023-01-11T21:05:10.6133438Z 
2023-01-11T21:05:10.6133444Z 
2023-01-11T21:05:10.6133533Z async_compile.wait(globals())
2023-01-11T21:05:10.6133605Z del async_compile
2023-01-11T21:05:10.6133610Z 
2023-01-11T21:05:10.6133681Z def call(args):
2023-01-11T21:05:10.6133749Z     arg0_1, = args
2023-01-11T21:05:10.6133819Z     args.clear()
2023-01-11T21:05:10.6134004Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6134170Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6134304Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6134382Z     return (buf0, buf1, )
2023-01-11T21:05:10.6134386Z 
2023-01-11T21:05:10.6134391Z 
2023-01-11T21:05:10.6134464Z if __name__ == "__main__":
2023-01-11T21:05:10.6134576Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6134698Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6134887Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6134994Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6134999Z 
2023-01-11T21:05:10.6135051Z ok (2.762s)
2023-01-11T21:05:10.6135492Z   test_nll_loss_forward_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6135616Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6135873Z [2023-01-11 20:57:19,403] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 361
2023-01-11T21:05:10.6136169Z [2023-01-11 20:57:22,077] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 361
2023-01-11T21:05:10.6136175Z 
2023-01-11T21:05:10.6136268Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6136339Z import torch
2023-01-11T21:05:10.6136407Z import random
2023-01-11T21:05:10.6136521Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6136627Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6136632Z 
2023-01-11T21:05:10.6136708Z aten = torch.ops.aten
2023-01-11T21:05:10.6136837Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6136927Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6136932Z 
2023-01-11T21:05:10.6136937Z 
2023-01-11T21:05:10.6137067Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6137269Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6137383Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6137488Z                        const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.6137579Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6137710Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6137772Z {
2023-01-11T21:05:10.6137856Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.6137916Z     {
2023-01-11T21:05:10.6137978Z         {
2023-01-11T21:05:10.6138052Z             float tmp3 = 0;
2023-01-11T21:05:10.6138143Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6138204Z             {
2023-01-11T21:05:10.6138306Z                 #pragma omp for reduction(+:tmp3) 
2023-01-11T21:05:10.6138394Z                 for(long i0=0; i0<5; i0+=1)
2023-01-11T21:05:10.6138458Z                 {
2023-01-11T21:05:10.6138614Z                     {
2023-01-11T21:05:10.6138712Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6138803Z                         auto tmp1 = in_ptr1[tmp0 + (5*i0)];
2023-01-11T21:05:10.6138938Z                         auto tmp2 = -tmp1;
2023-01-11T21:05:10.6139018Z                         tmp3 += tmp2;
2023-01-11T21:05:10.6139085Z                     }
2023-01-11T21:05:10.6139152Z                 }
2023-01-11T21:05:10.6139218Z             }
2023-01-11T21:05:10.6139281Z             out_ptr0[0] = tmp3;
2023-01-11T21:05:10.6139345Z         }
2023-01-11T21:05:10.6139406Z     }
2023-01-11T21:05:10.6139466Z     {
2023-01-11T21:05:10.6139527Z         {
2023-01-11T21:05:10.6139611Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:05:10.6139708Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:05:10.6139777Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6139856Z             in_out_ptr0[0] = tmp2;
2023-01-11T21:05:10.6139916Z         }
2023-01-11T21:05:10.6139974Z     }
2023-01-11T21:05:10.6140034Z     {
2023-01-11T21:05:10.6140093Z         {
2023-01-11T21:05:10.6140193Z             auto tmp0 = static_cast<float>(5.0);
2023-01-11T21:05:10.6140257Z             out_ptr1[0] = tmp0;
2023-01-11T21:05:10.6140317Z         }
2023-01-11T21:05:10.6140378Z     }
2023-01-11T21:05:10.6140437Z }
2023-01-11T21:05:10.6140513Z ''')
2023-01-11T21:05:10.6140520Z 
2023-01-11T21:05:10.6140527Z 
2023-01-11T21:05:10.6140614Z async_compile.wait(globals())
2023-01-11T21:05:10.6140684Z del async_compile
2023-01-11T21:05:10.6140690Z 
2023-01-11T21:05:10.6140745Z def call(args):
2023-01-11T21:05:10.6140818Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6140887Z     args.clear()
2023-01-11T21:05:10.6141074Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6141157Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6141337Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6141526Z     kernel_cpp_0(c_void_p(buf1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6141580Z     del arg0_1
2023-01-11T21:05:10.6141679Z     del arg1_1
2023-01-11T21:05:10.6141754Z     return (buf1, buf2, )
2023-01-11T21:05:10.6141759Z 
2023-01-11T21:05:10.6141763Z 
2023-01-11T21:05:10.6141838Z if __name__ == "__main__":
2023-01-11T21:05:10.6141953Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6142075Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6142271Z     arg0_1 = rand_strided((5, 5), (5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6142455Z     arg1_1 = rand_strided((5, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6142555Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6142560Z 
2023-01-11T21:05:10.6142625Z ok (2.780s)
2023-01-11T21:05:10.6143086Z   test_no_mega_fusion_during_lowering_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6143214Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6143503Z [2023-01-11 20:57:22,609] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 362
2023-01-11T21:05:10.6143509Z 
2023-01-11T21:05:10.6143602Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6143671Z import torch
2023-01-11T21:05:10.6143739Z import random
2023-01-11T21:05:10.6143851Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6143958Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6143963Z 
2023-01-11T21:05:10.6144040Z aten = torch.ops.aten
2023-01-11T21:05:10.6144171Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6144261Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6144266Z 
2023-01-11T21:05:10.6144270Z 
2023-01-11T21:05:10.6144402Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6144607Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6144720Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6144827Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6144918Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6145018Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6145117Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:05:10.6145216Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:05:10.6145313Z                        const float* __restrict__ in_ptr5,
2023-01-11T21:05:10.6145411Z                        const float* __restrict__ in_ptr6,
2023-01-11T21:05:10.6145509Z                        const float* __restrict__ in_ptr7,
2023-01-11T21:05:10.6145608Z                        const float* __restrict__ in_ptr8,
2023-01-11T21:05:10.6145695Z                        const float* __restrict__ in_ptr9,
2023-01-11T21:05:10.6145799Z                        const float* __restrict__ in_ptr10,
2023-01-11T21:05:10.6145904Z                        const float* __restrict__ in_ptr11,
2023-01-11T21:05:10.6146005Z                        const float* __restrict__ in_ptr12,
2023-01-11T21:05:10.6146106Z                        const float* __restrict__ in_ptr13,
2023-01-11T21:05:10.6146205Z                        const float* __restrict__ in_ptr14,
2023-01-11T21:05:10.6146304Z                        const float* __restrict__ in_ptr15,
2023-01-11T21:05:10.6146391Z                        const float* __restrict__ in_ptr16,
2023-01-11T21:05:10.6146490Z                        const float* __restrict__ in_ptr17,
2023-01-11T21:05:10.6146588Z                        const float* __restrict__ in_ptr18,
2023-01-11T21:05:10.6146687Z                        const float* __restrict__ in_ptr19,
2023-01-11T21:05:10.6146785Z                        const float* __restrict__ in_ptr20,
2023-01-11T21:05:10.6146916Z                        const float* __restrict__ in_ptr21,
2023-01-11T21:05:10.6147014Z                        const float* __restrict__ in_ptr22,
2023-01-11T21:05:10.6147114Z                        const float* __restrict__ in_ptr23,
2023-01-11T21:05:10.6147199Z                        const float* __restrict__ in_ptr24,
2023-01-11T21:05:10.6147297Z                        const float* __restrict__ in_ptr25,
2023-01-11T21:05:10.6147395Z                        const float* __restrict__ in_ptr26,
2023-01-11T21:05:10.6147493Z                        const float* __restrict__ in_ptr27,
2023-01-11T21:05:10.6147591Z                        const float* __restrict__ in_ptr28,
2023-01-11T21:05:10.6147688Z                        const float* __restrict__ in_ptr29,
2023-01-11T21:05:10.6147786Z                        const float* __restrict__ in_ptr30,
2023-01-11T21:05:10.6147871Z                        const float* __restrict__ in_ptr31,
2023-01-11T21:05:10.6147975Z                        const float* __restrict__ in_ptr32,
2023-01-11T21:05:10.6148073Z                        const float* __restrict__ in_ptr33,
2023-01-11T21:05:10.6148171Z                        const float* __restrict__ in_ptr34,
2023-01-11T21:05:10.6148297Z                        const float* __restrict__ in_ptr35,
2023-01-11T21:05:10.6148397Z                        const float* __restrict__ in_ptr36,
2023-01-11T21:05:10.6148494Z                        const float* __restrict__ in_ptr37,
2023-01-11T21:05:10.6148593Z                        const float* __restrict__ in_ptr38,
2023-01-11T21:05:10.6148679Z                        const float* __restrict__ in_ptr39,
2023-01-11T21:05:10.6148777Z                        const float* __restrict__ in_ptr40,
2023-01-11T21:05:10.6148874Z                        const float* __restrict__ in_ptr41,
2023-01-11T21:05:10.6148972Z                        const float* __restrict__ in_ptr42,
2023-01-11T21:05:10.6149070Z                        const float* __restrict__ in_ptr43,
2023-01-11T21:05:10.6149168Z                        const float* __restrict__ in_ptr44,
2023-01-11T21:05:10.6149265Z                        const float* __restrict__ in_ptr45,
2023-01-11T21:05:10.6149352Z                        const float* __restrict__ in_ptr46,
2023-01-11T21:05:10.6149449Z                        const float* __restrict__ in_ptr47,
2023-01-11T21:05:10.6149547Z                        const float* __restrict__ in_ptr48,
2023-01-11T21:05:10.6149648Z                        const float* __restrict__ in_ptr49)
2023-01-11T21:05:10.6149711Z {
2023-01-11T21:05:10.6149797Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.6149893Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6149940Z     {
2023-01-11T21:05:10.6150015Z         #pragma omp for 
2023-01-11T21:05:10.6150096Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6150158Z         {
2023-01-11T21:05:10.6150294Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6150425Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6150552Z             auto tmp4 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i0);
2023-01-11T21:05:10.6150678Z             auto tmp6 = at::vec::Vectorized<float>::loadu(in_ptr3 + 16*i0);
2023-01-11T21:05:10.6150788Z             auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr4 + 16*i0);
2023-01-11T21:05:10.6150918Z             auto tmp10 = at::vec::Vectorized<float>::loadu(in_ptr5 + 16*i0);
2023-01-11T21:05:10.6151046Z             auto tmp12 = at::vec::Vectorized<float>::loadu(in_ptr6 + 16*i0);
2023-01-11T21:05:10.6151171Z             auto tmp14 = at::vec::Vectorized<float>::loadu(in_ptr7 + 16*i0);
2023-01-11T21:05:10.6151296Z             auto tmp16 = at::vec::Vectorized<float>::loadu(in_ptr8 + 16*i0);
2023-01-11T21:05:10.6151379Z             auto tmp1 = tmp0 + tmp0;
2023-01-11T21:05:10.6151462Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6151541Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.6151636Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.6151718Z             auto tmp9 = tmp7 + tmp8;
2023-01-11T21:05:10.6151800Z             auto tmp11 = tmp9 + tmp10;
2023-01-11T21:05:10.6151888Z             auto tmp13 = tmp11 + tmp12;
2023-01-11T21:05:10.6151970Z             auto tmp15 = tmp13 + tmp14;
2023-01-11T21:05:10.6152052Z             auto tmp17 = tmp15 + tmp16;
2023-01-11T21:05:10.6152144Z             tmp17.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6152192Z         }
2023-01-11T21:05:10.6152286Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6152367Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6152429Z         {
2023-01-11T21:05:10.6152511Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6152592Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.6152670Z             auto tmp4 = in_ptr2[i0];
2023-01-11T21:05:10.6152736Z             auto tmp6 = in_ptr3[i0];
2023-01-11T21:05:10.6152815Z             auto tmp8 = in_ptr4[i0];
2023-01-11T21:05:10.6152899Z             auto tmp10 = in_ptr5[i0];
2023-01-11T21:05:10.6152979Z             auto tmp12 = in_ptr6[i0];
2023-01-11T21:05:10.6153058Z             auto tmp14 = in_ptr7[i0];
2023-01-11T21:05:10.6153163Z             auto tmp16 = in_ptr8[i0];
2023-01-11T21:05:10.6153244Z             auto tmp1 = tmp0 + tmp0;
2023-01-11T21:05:10.6153311Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6153388Z             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.6153467Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.6153545Z             auto tmp9 = tmp7 + tmp8;
2023-01-11T21:05:10.6153628Z             auto tmp11 = tmp9 + tmp10;
2023-01-11T21:05:10.6153712Z             auto tmp13 = tmp11 + tmp12;
2023-01-11T21:05:10.6153793Z             auto tmp15 = tmp13 + tmp14;
2023-01-11T21:05:10.6153863Z             auto tmp17 = tmp15 + tmp16;
2023-01-11T21:05:10.6153941Z             out_ptr0[i0] = tmp17;
2023-01-11T21:05:10.6154002Z         }
2023-01-11T21:05:10.6154076Z         #pragma omp for 
2023-01-11T21:05:10.6154158Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6154218Z         {
2023-01-11T21:05:10.6154353Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6154471Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr9 + 16*i0);
2023-01-11T21:05:10.6154600Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr10 + 16*i0);
2023-01-11T21:05:10.6154730Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr11 + 16*i0);
2023-01-11T21:05:10.6154855Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr12 + 16*i0);
2023-01-11T21:05:10.6154981Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr13 + 16*i0);
2023-01-11T21:05:10.6155110Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr14 + 16*i0);
2023-01-11T21:05:10.6155238Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr15 + 16*i0);
2023-01-11T21:05:10.6155364Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr16 + 16*i0);
2023-01-11T21:05:10.6155450Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6155518Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6155597Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6155678Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6155760Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6155845Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6155927Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6156009Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6156093Z             tmp16.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6156153Z         }
2023-01-11T21:05:10.6156247Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6156328Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6156390Z         {
2023-01-11T21:05:10.6156474Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.6156555Z             auto tmp1 = in_ptr9[i0];
2023-01-11T21:05:10.6156624Z             auto tmp3 = in_ptr10[i0];
2023-01-11T21:05:10.6156766Z             auto tmp5 = in_ptr11[i0];
2023-01-11T21:05:10.6156846Z             auto tmp7 = in_ptr12[i0];
2023-01-11T21:05:10.6156926Z             auto tmp9 = in_ptr13[i0];
2023-01-11T21:05:10.6157015Z             auto tmp11 = in_ptr14[i0];
2023-01-11T21:05:10.6157100Z             auto tmp13 = in_ptr15[i0];
2023-01-11T21:05:10.6157185Z             auto tmp15 = in_ptr16[i0];
2023-01-11T21:05:10.6157254Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6157335Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6157415Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6157496Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6157578Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6157662Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6157732Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6157818Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6157903Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:05:10.6157966Z         }
2023-01-11T21:05:10.6158042Z         #pragma omp for 
2023-01-11T21:05:10.6158123Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6158184Z         {
2023-01-11T21:05:10.6158332Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6158465Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr17 + 16*i0);
2023-01-11T21:05:10.6158595Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr18 + 16*i0);
2023-01-11T21:05:10.6158721Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr19 + 16*i0);
2023-01-11T21:05:10.6158847Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr20 + 16*i0);
2023-01-11T21:05:10.6158971Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr21 + 16*i0);
2023-01-11T21:05:10.6159101Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr22 + 16*i0);
2023-01-11T21:05:10.6159228Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr23 + 16*i0);
2023-01-11T21:05:10.6159356Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr24 + 16*i0);
2023-01-11T21:05:10.6159440Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6159511Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6159592Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6159673Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6159755Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6159839Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6159922Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6159991Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6160088Z             tmp16.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6160148Z         }
2023-01-11T21:05:10.6160242Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6160322Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6160385Z         {
2023-01-11T21:05:10.6160475Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.6160547Z             auto tmp1 = in_ptr17[i0];
2023-01-11T21:05:10.6160742Z             auto tmp3 = in_ptr18[i0];
2023-01-11T21:05:10.6160826Z             auto tmp5 = in_ptr19[i0];
2023-01-11T21:05:10.6160909Z             auto tmp7 = in_ptr20[i0];
2023-01-11T21:05:10.6160989Z             auto tmp9 = in_ptr21[i0];
2023-01-11T21:05:10.6161075Z             auto tmp11 = in_ptr22[i0];
2023-01-11T21:05:10.6161158Z             auto tmp13 = in_ptr23[i0];
2023-01-11T21:05:10.6161228Z             auto tmp15 = in_ptr24[i0];
2023-01-11T21:05:10.6161312Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6161394Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6161474Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6161555Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6161638Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6161723Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6161793Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6161929Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6162009Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:05:10.6162070Z         }
2023-01-11T21:05:10.6162144Z         #pragma omp for 
2023-01-11T21:05:10.6162226Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6162288Z         {
2023-01-11T21:05:10.6162410Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6162541Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr25 + 16*i0);
2023-01-11T21:05:10.6162669Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr26 + 16*i0);
2023-01-11T21:05:10.6162795Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr27 + 16*i0);
2023-01-11T21:05:10.6162920Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr28 + 16*i0);
2023-01-11T21:05:10.6163045Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr29 + 16*i0);
2023-01-11T21:05:10.6163175Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr30 + 16*i0);
2023-01-11T21:05:10.6163308Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr31 + 16*i0);
2023-01-11T21:05:10.6163471Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr32 + 16*i0);
2023-01-11T21:05:10.6163544Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6163626Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6163706Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6163786Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6163869Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6163951Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6164033Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6164102Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6164199Z             tmp16.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6164260Z         }
2023-01-11T21:05:10.6164352Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6164434Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6164495Z         {
2023-01-11T21:05:10.6164581Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.6164652Z             auto tmp1 = in_ptr25[i0];
2023-01-11T21:05:10.6164735Z             auto tmp3 = in_ptr26[i0];
2023-01-11T21:05:10.6164814Z             auto tmp5 = in_ptr27[i0];
2023-01-11T21:05:10.6164894Z             auto tmp7 = in_ptr28[i0];
2023-01-11T21:05:10.6164974Z             auto tmp9 = in_ptr29[i0];
2023-01-11T21:05:10.6165057Z             auto tmp11 = in_ptr30[i0];
2023-01-11T21:05:10.6165139Z             auto tmp13 = in_ptr31[i0];
2023-01-11T21:05:10.6165208Z             auto tmp15 = in_ptr32[i0];
2023-01-11T21:05:10.6165289Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6165370Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6165448Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6165526Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6165606Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6165691Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6165761Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6165842Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6165923Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:05:10.6165984Z         }
2023-01-11T21:05:10.6166057Z         #pragma omp for 
2023-01-11T21:05:10.6166136Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6166196Z         {
2023-01-11T21:05:10.6166317Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6166447Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr33 + 16*i0);
2023-01-11T21:05:10.6166579Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr34 + 16*i0);
2023-01-11T21:05:10.6166704Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr35 + 16*i0);
2023-01-11T21:05:10.6166829Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr36 + 16*i0);
2023-01-11T21:05:10.6166981Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr37 + 16*i0);
2023-01-11T21:05:10.6167110Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr38 + 16*i0);
2023-01-11T21:05:10.6167240Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr39 + 16*i0);
2023-01-11T21:05:10.6167357Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr40 + 16*i0);
2023-01-11T21:05:10.6172492Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6172616Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6172700Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6172780Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6172865Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6172939Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6173023Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6173107Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6173205Z             tmp16.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6173274Z         }
2023-01-11T21:05:10.6173372Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6173453Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6173503Z         {
2023-01-11T21:05:10.6173691Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.6173778Z             auto tmp1 = in_ptr33[i0];
2023-01-11T21:05:10.6173859Z             auto tmp3 = in_ptr34[i0];
2023-01-11T21:05:10.6173938Z             auto tmp5 = in_ptr35[i0];
2023-01-11T21:05:10.6174017Z             auto tmp7 = in_ptr36[i0];
2023-01-11T21:05:10.6174097Z             auto tmp9 = in_ptr37[i0];
2023-01-11T21:05:10.6174168Z             auto tmp11 = in_ptr38[i0];
2023-01-11T21:05:10.6174249Z             auto tmp13 = in_ptr39[i0];
2023-01-11T21:05:10.6174330Z             auto tmp15 = in_ptr40[i0];
2023-01-11T21:05:10.6174414Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6174493Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6174572Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6174655Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6174723Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6174805Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6174891Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6174972Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6175053Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:05:10.6175115Z         }
2023-01-11T21:05:10.6175190Z         #pragma omp for 
2023-01-11T21:05:10.6175258Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6175318Z         {
2023-01-11T21:05:10.6175465Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6175599Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr41 + 16*i0);
2023-01-11T21:05:10.6175729Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr42 + 16*i0);
2023-01-11T21:05:10.6175854Z             auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr43 + 16*i0);
2023-01-11T21:05:10.6175983Z             auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr44 + 16*i0);
2023-01-11T21:05:10.6176106Z             auto tmp9 = at::vec::Vectorized<float>::loadu(in_ptr45 + 16*i0);
2023-01-11T21:05:10.6176226Z             auto tmp11 = at::vec::Vectorized<float>::loadu(in_ptr46 + 16*i0);
2023-01-11T21:05:10.6176355Z             auto tmp13 = at::vec::Vectorized<float>::loadu(in_ptr47 + 16*i0);
2023-01-11T21:05:10.6176481Z             auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr48 + 16*i0);
2023-01-11T21:05:10.6176564Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6176644Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6176722Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6176802Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6176882Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6176952Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6177040Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6177153Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6177252Z             tmp16.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6177313Z         }
2023-01-11T21:05:10.6177407Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6177489Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6177549Z         {
2023-01-11T21:05:10.6177637Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.6177708Z             auto tmp1 = in_ptr41[i0];
2023-01-11T21:05:10.6177790Z             auto tmp3 = in_ptr42[i0];
2023-01-11T21:05:10.6177870Z             auto tmp5 = in_ptr43[i0];
2023-01-11T21:05:10.6177950Z             auto tmp7 = in_ptr44[i0];
2023-01-11T21:05:10.6178028Z             auto tmp9 = in_ptr45[i0];
2023-01-11T21:05:10.6178111Z             auto tmp11 = in_ptr46[i0];
2023-01-11T21:05:10.6178195Z             auto tmp13 = in_ptr47[i0];
2023-01-11T21:05:10.6178265Z             auto tmp15 = in_ptr48[i0];
2023-01-11T21:05:10.6178345Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6178428Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6178603Z             auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6178686Z             auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6178768Z             auto tmp10 = tmp8 + tmp9;
2023-01-11T21:05:10.6178882Z             auto tmp12 = tmp10 + tmp11;
2023-01-11T21:05:10.6178954Z             auto tmp14 = tmp12 + tmp13;
2023-01-11T21:05:10.6179037Z             auto tmp16 = tmp14 + tmp15;
2023-01-11T21:05:10.6179121Z             in_out_ptr0[i0] = tmp16;
2023-01-11T21:05:10.6179183Z         }
2023-01-11T21:05:10.6179259Z         #pragma omp for 
2023-01-11T21:05:10.6179341Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6179389Z         {
2023-01-11T21:05:10.6179525Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6179657Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr49 + 16*i0);
2023-01-11T21:05:10.6179741Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6179878Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6179971Z         }
2023-01-11T21:05:10.6180101Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6180217Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6180277Z         {
2023-01-11T21:05:10.6180366Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.6180449Z             auto tmp1 = in_ptr49[i0];
2023-01-11T21:05:10.6180568Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6180678Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6180738Z         }
2023-01-11T21:05:10.6180784Z     }
2023-01-11T21:05:10.6180844Z }
2023-01-11T21:05:10.6180959Z ''')
2023-01-11T21:05:10.6180966Z 
2023-01-11T21:05:10.6180970Z 
2023-01-11T21:05:10.6181059Z async_compile.wait(globals())
2023-01-11T21:05:10.6181131Z del async_compile
2023-01-11T21:05:10.6181136Z 
2023-01-11T21:05:10.6181206Z def call(args):
2023-01-11T21:05:10.6181540Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1 = args
2023-01-11T21:05:10.6181615Z     args.clear()
2023-01-11T21:05:10.6181817Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6181887Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6181968Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6182048Z     buf3 = buf2; del buf2  # reuse
2023-01-11T21:05:10.6182127Z     buf4 = buf3; del buf3  # reuse
2023-01-11T21:05:10.6182205Z     buf5 = buf4; del buf4  # reuse
2023-01-11T21:05:10.6182284Z     buf6 = buf5; del buf5  # reuse
2023-01-11T21:05:10.6183552Z     kernel_cpp_0(c_void_p(buf6.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg5_1.data_ptr()), c_void_p(arg6_1.data_ptr()), c_void_p(arg7_1.data_ptr()), c_void_p(arg8_1.data_ptr()), c_void_p(arg9_1.data_ptr()), c_void_p(arg10_1.data_ptr()), c_void_p(arg11_1.data_ptr()), c_void_p(arg12_1.data_ptr()), c_void_p(arg13_1.data_ptr()), c_void_p(arg14_1.data_ptr()), c_void_p(arg15_1.data_ptr()), c_void_p(arg16_1.data_ptr()), c_void_p(arg17_1.data_ptr()), c_void_p(arg18_1.data_ptr()), c_void_p(arg19_1.data_ptr()), c_void_p(arg20_1.data_ptr()), c_void_p(arg21_1.data_ptr()), c_void_p(arg22_1.data_ptr()), c_void_p(arg23_1.data_ptr()), c_void_p(arg24_1.data_ptr()), c_void_p(arg25_1.data_ptr()), c_void_p(arg26_1.data_ptr()), c_void_p(arg27_1.data_ptr()), c_void_p(arg28_1.data_ptr()), c_void_p(arg29_1.data_ptr()), c_void_p(arg30_1.data_ptr()), c_void_p(arg31_1.data_ptr()), c_void_p(arg32_1.data_ptr()), c_void_p(arg33_1.data_ptr()), c_void_p(arg34_1.data_ptr()), c_void_p(arg35_1.data_ptr()), c_void_p(arg36_1.data_ptr()), c_void_p(arg37_1.data_ptr()), c_void_p(arg38_1.data_ptr()), c_void_p(arg39_1.data_ptr()), c_void_p(arg40_1.data_ptr()), c_void_p(arg41_1.data_ptr()), c_void_p(arg42_1.data_ptr()), c_void_p(arg43_1.data_ptr()), c_void_p(arg44_1.data_ptr()), c_void_p(arg45_1.data_ptr()), c_void_p(arg46_1.data_ptr()), c_void_p(arg47_1.data_ptr()), c_void_p(arg48_1.data_ptr()), c_void_p(arg49_1.data_ptr()))
2023-01-11T21:05:10.6183657Z     del arg0_1
2023-01-11T21:05:10.6183723Z     del arg10_1
2023-01-11T21:05:10.6183788Z     del arg11_1
2023-01-11T21:05:10.6183851Z     del arg12_1
2023-01-11T21:05:10.6183916Z     del arg13_1
2023-01-11T21:05:10.6183966Z     del arg14_1
2023-01-11T21:05:10.6184029Z     del arg15_1
2023-01-11T21:05:10.6184092Z     del arg16_1
2023-01-11T21:05:10.6184156Z     del arg17_1
2023-01-11T21:05:10.6184222Z     del arg18_1
2023-01-11T21:05:10.6184287Z     del arg19_1
2023-01-11T21:05:10.6184338Z     del arg1_1
2023-01-11T21:05:10.6184402Z     del arg20_1
2023-01-11T21:05:10.6184470Z     del arg21_1
2023-01-11T21:05:10.6184535Z     del arg22_1
2023-01-11T21:05:10.6184599Z     del arg23_1
2023-01-11T21:05:10.6184663Z     del arg24_1
2023-01-11T21:05:10.6184727Z     del arg25_1
2023-01-11T21:05:10.6184779Z     del arg26_1
2023-01-11T21:05:10.6184844Z     del arg27_1
2023-01-11T21:05:10.6184907Z     del arg28_1
2023-01-11T21:05:10.6184971Z     del arg29_1
2023-01-11T21:05:10.6185035Z     del arg2_1
2023-01-11T21:05:10.6185097Z     del arg30_1
2023-01-11T21:05:10.6185147Z     del arg31_1
2023-01-11T21:05:10.6185210Z     del arg32_1
2023-01-11T21:05:10.6185272Z     del arg33_1
2023-01-11T21:05:10.6185335Z     del arg34_1
2023-01-11T21:05:10.6185399Z     del arg35_1
2023-01-11T21:05:10.6185461Z     del arg36_1
2023-01-11T21:05:10.6185524Z     del arg37_1
2023-01-11T21:05:10.6185576Z     del arg38_1
2023-01-11T21:05:10.6185639Z     del arg39_1
2023-01-11T21:05:10.6185703Z     del arg3_1
2023-01-11T21:05:10.6185766Z     del arg40_1
2023-01-11T21:05:10.6185828Z     del arg41_1
2023-01-11T21:05:10.6185894Z     del arg42_1
2023-01-11T21:05:10.6185944Z     del arg43_1
2023-01-11T21:05:10.6186007Z     del arg44_1
2023-01-11T21:05:10.6186070Z     del arg45_1
2023-01-11T21:05:10.6186132Z     del arg46_1
2023-01-11T21:05:10.6186198Z     del arg47_1
2023-01-11T21:05:10.6186260Z     del arg48_1
2023-01-11T21:05:10.6186322Z     del arg49_1
2023-01-11T21:05:10.6186374Z     del arg4_1
2023-01-11T21:05:10.6186437Z     del arg5_1
2023-01-11T21:05:10.6186501Z     del arg6_1
2023-01-11T21:05:10.6186564Z     del arg7_1
2023-01-11T21:05:10.6186627Z     del arg8_1
2023-01-11T21:05:10.6186690Z     del arg9_1
2023-01-11T21:05:10.6186747Z     return (buf6, )
2023-01-11T21:05:10.6186752Z 
2023-01-11T21:05:10.6186771Z 
2023-01-11T21:05:10.6186832Z if __name__ == "__main__":
2023-01-11T21:05:10.6186949Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6187074Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6187274Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6187499Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6187689Z     arg2_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6187880Z     arg3_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6188050Z     arg4_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6188233Z     arg5_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6188415Z     arg6_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6188598Z     arg7_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6188781Z     arg8_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6188962Z     arg9_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6189152Z     arg10_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6189345Z     arg11_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6189548Z     arg12_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6189737Z     arg13_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6189919Z     arg14_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6190102Z     arg15_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6190285Z     arg16_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6190469Z     arg17_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6190651Z     arg18_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6190834Z     arg19_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6191006Z     arg20_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6191189Z     arg21_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6191374Z     arg22_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6191557Z     arg23_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6191740Z     arg24_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6191924Z     arg25_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6192105Z     arg26_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6192287Z     arg27_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6192456Z     arg28_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6192640Z     arg29_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6192826Z     arg30_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6193010Z     arg31_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6193195Z     arg32_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6193380Z     arg33_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6193564Z     arg34_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6193746Z     arg35_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6193914Z     arg36_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6194097Z     arg37_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6194279Z     arg38_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6194462Z     arg39_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6194672Z     arg40_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6194856Z     arg41_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6195039Z     arg42_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6195223Z     arg43_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6195391Z     arg44_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6195573Z     arg45_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6195754Z     arg46_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6195937Z     arg47_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6196117Z     arg48_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6196301Z     arg49_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6196694Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1]))
2023-01-11T21:05:10.6196969Z [2023-01-11 20:57:25,629] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 362
2023-01-11T21:05:10.6196975Z 
2023-01-11T21:05:10.6197054Z --> 7
2023-01-11T21:05:10.6197119Z ok (3.581s)
2023-01-11T21:05:10.6197550Z   test_no_op_reduction_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6197680Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6197941Z [2023-01-11 20:57:25,697] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 363
2023-01-11T21:05:10.6198206Z [2023-01-11 20:57:28,332] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 363
2023-01-11T21:05:10.6198211Z 
2023-01-11T21:05:10.6198303Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6198371Z import torch
2023-01-11T21:05:10.6198441Z import random
2023-01-11T21:05:10.6198557Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6198677Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6198682Z 
2023-01-11T21:05:10.6198745Z aten = torch.ops.aten
2023-01-11T21:05:10.6198881Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6198970Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6198974Z 
2023-01-11T21:05:10.6198979Z 
2023-01-11T21:05:10.6199115Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6199316Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6199435Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6199533Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6199629Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6199676Z {
2023-01-11T21:05:10.6199774Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6199834Z     {
2023-01-11T21:05:10.6199908Z         #pragma omp for 
2023-01-11T21:05:10.6199988Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6200049Z         {
2023-01-11T21:05:10.6200098Z             {
2023-01-11T21:05:10.6200195Z                 {
2023-01-11T21:05:10.6200287Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6200391Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6200480Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6200568Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6200779Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.6200829Z                 }
2023-01-11T21:05:10.6200892Z             }
2023-01-11T21:05:10.6200953Z         }
2023-01-11T21:05:10.6201015Z     }
2023-01-11T21:05:10.6201074Z }
2023-01-11T21:05:10.6201155Z ''')
2023-01-11T21:05:10.6201161Z 
2023-01-11T21:05:10.6201165Z 
2023-01-11T21:05:10.6201254Z async_compile.wait(globals())
2023-01-11T21:05:10.6201313Z del async_compile
2023-01-11T21:05:10.6201331Z 
2023-01-11T21:05:10.6201388Z def call(args):
2023-01-11T21:05:10.6201455Z     arg0_1, = args
2023-01-11T21:05:10.6201525Z     args.clear()
2023-01-11T21:05:10.6201720Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6201925Z     buf1 = empty_strided((8, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6202154Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6202223Z     del arg0_1
2023-01-11T21:05:10.6202286Z     return (buf0, buf1, )
2023-01-11T21:05:10.6202291Z 
2023-01-11T21:05:10.6202295Z 
2023-01-11T21:05:10.6202369Z if __name__ == "__main__":
2023-01-11T21:05:10.6202486Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6202609Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6202815Z     arg0_1 = rand_strided((8, 1, 1), (1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6202923Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6202928Z 
2023-01-11T21:05:10.6202993Z ok (2.673s)
2023-01-11T21:05:10.6203326Z   test_output_strides_cpu (__main__.CpuTests) ... [2023-01-11 20:57:28,366] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 364
2023-01-11T21:05:10.6203588Z [2023-01-11 20:57:31,043] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 364
2023-01-11T21:05:10.6203846Z [2023-01-11 20:57:31,064] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 365
2023-01-11T21:05:10.6204106Z [2023-01-11 20:57:31,068] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 365
2023-01-11T21:05:10.6204358Z [2023-01-11 20:57:31,099] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 366
2023-01-11T21:05:10.6204621Z [2023-01-11 20:57:31,105] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 366
2023-01-11T21:05:10.6204626Z 
2023-01-11T21:05:10.6204718Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6204787Z import torch
2023-01-11T21:05:10.6204855Z import random
2023-01-11T21:05:10.6204957Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6205079Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6205083Z 
2023-01-11T21:05:10.6205165Z aten = torch.ops.aten
2023-01-11T21:05:10.6205300Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6205390Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6205395Z 
2023-01-11T21:05:10.6205399Z 
2023-01-11T21:05:10.6205531Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6205735Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6205852Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6205950Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6205997Z {
2023-01-11T21:05:10.6206093Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6206153Z     {
2023-01-11T21:05:10.6206227Z         #pragma omp for 
2023-01-11T21:05:10.6206308Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6206411Z         {
2023-01-11T21:05:10.6206478Z             #pragma GCC ivdep
2023-01-11T21:05:10.6206561Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.6206624Z             {
2023-01-11T21:05:10.6206706Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6206793Z                 for(long i2=0; i2<4; i2+=1)
2023-01-11T21:05:10.6206857Z                 {
2023-01-11T21:05:10.6206922Z                     {
2023-01-11T21:05:10.6206976Z                         {
2023-01-11T21:05:10.6207086Z                             auto tmp0 = in_ptr0[i1 + (16*i2) + (64*i0)];
2023-01-11T21:05:10.6207189Z                             out_ptr0[i2 + (4*i1) + (64*i0)] = tmp0;
2023-01-11T21:05:10.6207254Z                         }
2023-01-11T21:05:10.6207319Z                     }
2023-01-11T21:05:10.6207381Z                 }
2023-01-11T21:05:10.6207442Z             }
2023-01-11T21:05:10.6207490Z         }
2023-01-11T21:05:10.6207549Z     }
2023-01-11T21:05:10.6207607Z }
2023-01-11T21:05:10.6207688Z ''')
2023-01-11T21:05:10.6207693Z 
2023-01-11T21:05:10.6207697Z 
2023-01-11T21:05:10.6207785Z async_compile.wait(globals())
2023-01-11T21:05:10.6207856Z del async_compile
2023-01-11T21:05:10.6207860Z 
2023-01-11T21:05:10.6207957Z def call(args):
2023-01-11T21:05:10.6208014Z     arg0_1, = args
2023-01-11T21:05:10.6208083Z     args.clear()
2023-01-11T21:05:10.6208294Z     buf0 = empty_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6208427Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6208493Z     del arg0_1
2023-01-11T21:05:10.6208561Z     return (buf0, )
2023-01-11T21:05:10.6208566Z 
2023-01-11T21:05:10.6208570Z 
2023-01-11T21:05:10.6208643Z if __name__ == "__main__":
2023-01-11T21:05:10.6208756Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6208866Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6209077Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6209186Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6209191Z 
2023-01-11T21:05:10.6209195Z 
2023-01-11T21:05:10.6209286Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6209357Z import torch
2023-01-11T21:05:10.6209426Z import random
2023-01-11T21:05:10.6209537Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6209644Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6209661Z 
2023-01-11T21:05:10.6209724Z aten = torch.ops.aten
2023-01-11T21:05:10.6209855Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6209945Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6209950Z 
2023-01-11T21:05:10.6209954Z 
2023-01-11T21:05:10.6210040Z async_compile.wait(globals())
2023-01-11T21:05:10.6210110Z del async_compile
2023-01-11T21:05:10.6210115Z 
2023-01-11T21:05:10.6210184Z def call(args):
2023-01-11T21:05:10.6210250Z     arg0_1, = args
2023-01-11T21:05:10.6210306Z     args.clear()
2023-01-11T21:05:10.6210406Z     return (as_strided(arg0_1, (64, 4), (4, 1)), )
2023-01-11T21:05:10.6210411Z 
2023-01-11T21:05:10.6210415Z 
2023-01-11T21:05:10.6210489Z if __name__ == "__main__":
2023-01-11T21:05:10.6210600Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6210720Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6210930Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6211036Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6211041Z 
2023-01-11T21:05:10.6211045Z 
2023-01-11T21:05:10.6211134Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6211201Z import torch
2023-01-11T21:05:10.6211257Z import random
2023-01-11T21:05:10.6211368Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6211486Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6211491Z 
2023-01-11T21:05:10.6211567Z aten = torch.ops.aten
2023-01-11T21:05:10.6211735Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6211824Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6211829Z 
2023-01-11T21:05:10.6211832Z 
2023-01-11T21:05:10.6211919Z async_compile.wait(globals())
2023-01-11T21:05:10.6211977Z del async_compile
2023-01-11T21:05:10.6211994Z 
2023-01-11T21:05:10.6212050Z def call(args):
2023-01-11T21:05:10.6212117Z     arg0_1, = args
2023-01-11T21:05:10.6212185Z     args.clear()
2023-01-11T21:05:10.6212290Z     return (as_strided(arg0_1, (4, 4, 1), (4, 16, 0), 3), )
2023-01-11T21:05:10.6212294Z 
2023-01-11T21:05:10.6212299Z 
2023-01-11T21:05:10.6212372Z if __name__ == "__main__":
2023-01-11T21:05:10.6212482Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6212602Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6212801Z     arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6212909Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6212916Z 
2023-01-11T21:05:10.6213381Z inductor/test_torchinductor.py:3148: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6213499Z   self.assertEqual(inp.storage(), out.storage())
2023-01-11T21:05:10.6214167Z /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6214256Z   device=typed_storage.device,
2023-01-11T21:05:10.6214320Z ok (2.769s)
2023-01-11T21:05:10.6214758Z   test_permute_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6214885Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6215144Z [2023-01-11 20:57:31,147] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 367
2023-01-11T21:05:10.6215413Z [2023-01-11 20:57:33,819] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 367
2023-01-11T21:05:10.6215419Z 
2023-01-11T21:05:10.6215514Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6215569Z import torch
2023-01-11T21:05:10.6215639Z import random
2023-01-11T21:05:10.6215753Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6215876Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6215881Z 
2023-01-11T21:05:10.6215959Z aten = torch.ops.aten
2023-01-11T21:05:10.6216092Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6216184Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6216189Z 
2023-01-11T21:05:10.6216193Z 
2023-01-11T21:05:10.6216324Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6216516Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6216637Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6216735Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6216830Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6216890Z {
2023-01-11T21:05:10.6216987Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6217046Z     {
2023-01-11T21:05:10.6217109Z         #pragma omp for 
2023-01-11T21:05:10.6217223Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6217287Z         {
2023-01-11T21:05:10.6217425Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6217560Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6217645Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6217773Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6217842Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6217922Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:05:10.6218012Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6218101Z             tmp5.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6218162Z         }
2023-01-11T21:05:10.6218256Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6218336Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.6218384Z         {
2023-01-11T21:05:10.6218572Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6218677Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6218761Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6218858Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.6218975Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6219059Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:05:10.6219124Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6219202Z             out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.6219263Z         }
2023-01-11T21:05:10.6219325Z     }
2023-01-11T21:05:10.6219385Z }
2023-01-11T21:05:10.6219469Z ''')
2023-01-11T21:05:10.6219474Z 
2023-01-11T21:05:10.6219478Z 
2023-01-11T21:05:10.6219568Z async_compile.wait(globals())
2023-01-11T21:05:10.6219626Z del async_compile
2023-01-11T21:05:10.6219631Z 
2023-01-11T21:05:10.6219700Z def call(args):
2023-01-11T21:05:10.6219766Z     arg0_1, = args
2023-01-11T21:05:10.6219835Z     args.clear()
2023-01-11T21:05:10.6220053Z     buf0 = empty_strided((2, 2, 2, 2, 2), (4, 8, 1, 16, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6220266Z     buf1 = empty_strided((2, 2, 2, 2, 2), (4, 8, 1, 16, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6220434Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6220502Z     del arg0_1
2023-01-11T21:05:10.6220565Z     return (buf0, buf1, )
2023-01-11T21:05:10.6220570Z 
2023-01-11T21:05:10.6220574Z 
2023-01-11T21:05:10.6220648Z if __name__ == "__main__":
2023-01-11T21:05:10.6220759Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6220882Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6221096Z     arg0_1 = rand_strided((2, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6221202Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6221207Z 
2023-01-11T21:05:10.6221274Z ok (2.719s)
2023-01-11T21:05:10.6221711Z   test_pow1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6221838Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6222083Z [2023-01-11 20:57:34,116] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 368
2023-01-11T21:05:10.6222344Z [2023-01-11 20:57:36,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 368
2023-01-11T21:05:10.6222349Z 
2023-01-11T21:05:10.6222441Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6222510Z import torch
2023-01-11T21:05:10.6222578Z import random
2023-01-11T21:05:10.6222691Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6222811Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6222849Z 
2023-01-11T21:05:10.6222927Z aten = torch.ops.aten
2023-01-11T21:05:10.6223047Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6223136Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6223143Z 
2023-01-11T21:05:10.6223148Z 
2023-01-11T21:05:10.6223281Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6223481Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6223602Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6223701Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6223798Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6223891Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6223968Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.6224059Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.6224149Z                        float* __restrict__ out_ptr5,
2023-01-11T21:05:10.6224243Z                        float* __restrict__ out_ptr6,
2023-01-11T21:05:10.6224333Z                        float* __restrict__ out_ptr7,
2023-01-11T21:05:10.6224455Z                        float* __restrict__ out_ptr8,
2023-01-11T21:05:10.6224548Z                        float* __restrict__ out_ptr9,
2023-01-11T21:05:10.6224633Z                        float* __restrict__ out_ptr10,
2023-01-11T21:05:10.6224732Z                        float* __restrict__ out_ptr11,
2023-01-11T21:05:10.6224828Z                        float* __restrict__ out_ptr12,
2023-01-11T21:05:10.6224922Z                        float* __restrict__ out_ptr13,
2023-01-11T21:05:10.6225016Z                        float* __restrict__ out_ptr14,
2023-01-11T21:05:10.6225109Z                        float* __restrict__ out_ptr15)
2023-01-11T21:05:10.6225169Z {
2023-01-11T21:05:10.6225253Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6225315Z     {
2023-01-11T21:05:10.6225394Z         #pragma omp for 
2023-01-11T21:05:10.6225474Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6225538Z         {
2023-01-11T21:05:10.6225675Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6225773Z             auto tmp1 = tmp0.reciprocal();
2023-01-11T21:05:10.6225844Z             auto tmp2 = tmp1 * tmp1;
2023-01-11T21:05:10.6225926Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:05:10.6226006Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.6226085Z             auto tmp5 = tmp2 * tmp1;
2023-01-11T21:05:10.6226168Z             auto tmp6 = tmp5 * tmp5;
2023-01-11T21:05:10.6226247Z             auto tmp7 = tmp6 * tmp1;
2023-01-11T21:05:10.6226327Z             auto tmp8 = tmp3 * tmp1;
2023-01-11T21:05:10.6226446Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6226529Z             auto tmp10 = tmp0 * tmp0;
2023-01-11T21:05:10.6226613Z             auto tmp11 = tmp10 * tmp0;
2023-01-11T21:05:10.6226700Z             auto tmp12 = tmp10 * tmp10;
2023-01-11T21:05:10.6226783Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:05:10.6226866Z             auto tmp14 = tmp11 * tmp11;
2023-01-11T21:05:10.6226950Z             auto tmp15 = tmp14 * tmp0;
2023-01-11T21:05:10.6227019Z             auto tmp16 = tmp12 * tmp12;
2023-01-11T21:05:10.6227111Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6227201Z             tmp7.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6227289Z             tmp6.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.6227377Z             tmp8.store(out_ptr3 + 16*i0);
2023-01-11T21:05:10.6227464Z             tmp3.store(out_ptr4 + 16*i0);
2023-01-11T21:05:10.6227551Z             tmp5.store(out_ptr5 + 16*i0);
2023-01-11T21:05:10.6227624Z             tmp2.store(out_ptr6 + 16*i0);
2023-01-11T21:05:10.6227711Z             tmp1.store(out_ptr7 + 16*i0);
2023-01-11T21:05:10.6227797Z             tmp9.store(out_ptr8 + 16*i0);
2023-01-11T21:05:10.6227888Z             tmp10.store(out_ptr9 + 16*i0);
2023-01-11T21:05:10.6228017Z             tmp11.store(out_ptr10 + 16*i0);
2023-01-11T21:05:10.6228111Z             tmp12.store(out_ptr11 + 16*i0);
2023-01-11T21:05:10.6228202Z             tmp13.store(out_ptr12 + 16*i0);
2023-01-11T21:05:10.6228281Z             tmp14.store(out_ptr13 + 16*i0);
2023-01-11T21:05:10.6228371Z             tmp15.store(out_ptr14 + 16*i0);
2023-01-11T21:05:10.6228461Z             tmp16.store(out_ptr15 + 16*i0);
2023-01-11T21:05:10.6228524Z         }
2023-01-11T21:05:10.6228619Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6228701Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6228764Z         {
2023-01-11T21:05:10.6228835Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6228913Z             auto tmp1 = 1 / tmp0;
2023-01-11T21:05:10.6228995Z             auto tmp2 = tmp1 * tmp1;
2023-01-11T21:05:10.6229076Z             auto tmp3 = tmp2 * tmp2;
2023-01-11T21:05:10.6229155Z             auto tmp4 = tmp3 * tmp3;
2023-01-11T21:05:10.6229238Z             auto tmp5 = tmp2 * tmp1;
2023-01-11T21:05:10.6229316Z             auto tmp6 = tmp5 * tmp5;
2023-01-11T21:05:10.6229382Z             auto tmp7 = tmp6 * tmp1;
2023-01-11T21:05:10.6229495Z             auto tmp8 = tmp3 * tmp1;
2023-01-11T21:05:10.6229598Z             auto tmp9 = static_cast<float>(1);
2023-01-11T21:05:10.6229685Z             auto tmp10 = tmp0 * tmp0;
2023-01-11T21:05:10.6229770Z             auto tmp11 = tmp10 * tmp0;
2023-01-11T21:05:10.6229853Z             auto tmp12 = tmp10 * tmp10;
2023-01-11T21:05:10.6229935Z             auto tmp13 = tmp12 * tmp0;
2023-01-11T21:05:10.6230006Z             auto tmp14 = tmp11 * tmp11;
2023-01-11T21:05:10.6230088Z             auto tmp15 = tmp14 * tmp0;
2023-01-11T21:05:10.6230169Z             auto tmp16 = tmp12 * tmp12;
2023-01-11T21:05:10.6230248Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6230323Z             out_ptr1[i0] = tmp7;
2023-01-11T21:05:10.6230398Z             out_ptr2[i0] = tmp6;
2023-01-11T21:05:10.6230472Z             out_ptr3[i0] = tmp8;
2023-01-11T21:05:10.6230537Z             out_ptr4[i0] = tmp3;
2023-01-11T21:05:10.6230610Z             out_ptr5[i0] = tmp5;
2023-01-11T21:05:10.6230684Z             out_ptr6[i0] = tmp2;
2023-01-11T21:05:10.6230761Z             out_ptr7[i0] = tmp1;
2023-01-11T21:05:10.6230835Z             out_ptr8[i0] = tmp9;
2023-01-11T21:05:10.6230912Z             out_ptr9[i0] = tmp10;
2023-01-11T21:05:10.6230989Z             out_ptr10[i0] = tmp11;
2023-01-11T21:05:10.6231052Z             out_ptr11[i0] = tmp12;
2023-01-11T21:05:10.6231128Z             out_ptr12[i0] = tmp13;
2023-01-11T21:05:10.6231202Z             out_ptr13[i0] = tmp14;
2023-01-11T21:05:10.6231277Z             out_ptr14[i0] = tmp15;
2023-01-11T21:05:10.6231353Z             out_ptr15[i0] = tmp16;
2023-01-11T21:05:10.6231413Z         }
2023-01-11T21:05:10.6231472Z     }
2023-01-11T21:05:10.6231518Z }
2023-01-11T21:05:10.6231596Z ''')
2023-01-11T21:05:10.6231601Z 
2023-01-11T21:05:10.6231606Z 
2023-01-11T21:05:10.6231693Z async_compile.wait(globals())
2023-01-11T21:05:10.6231767Z del async_compile
2023-01-11T21:05:10.6231772Z 
2023-01-11T21:05:10.6231841Z def call(args):
2023-01-11T21:05:10.6231909Z     arg0_1, = args
2023-01-11T21:05:10.6231979Z     args.clear()
2023-01-11T21:05:10.6232171Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6232368Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6232562Z     buf2 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6232752Z     buf3 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6232943Z     buf4 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6233132Z     buf5 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6233321Z     buf6 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6233512Z     buf7 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6233721Z     buf8 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6233913Z     buf9 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6234110Z     buf10 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6234304Z     buf11 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6234497Z     buf12 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6234689Z     buf13 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6234877Z     buf14 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6235067Z     buf15 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6235583Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf13.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf15.data_ptr()))
2023-01-11T21:05:10.6235751Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, arg0_1, buf9, buf10, buf11, buf12, buf13, buf14, buf15, )
2023-01-11T21:05:10.6235757Z 
2023-01-11T21:05:10.6235762Z 
2023-01-11T21:05:10.6235826Z if __name__ == "__main__":
2023-01-11T21:05:10.6235940Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6236062Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6236259Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6236364Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6236371Z 
2023-01-11T21:05:10.6236437Z ok (3.101s)
2023-01-11T21:05:10.6236872Z   test_pow2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6237000Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6237262Z [2023-01-11 20:57:36,979] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 369
2023-01-11T21:05:10.6237514Z [2023-01-11 20:57:39,640] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 369
2023-01-11T21:05:10.6237531Z 
2023-01-11T21:05:10.6237611Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6237679Z import torch
2023-01-11T21:05:10.6237750Z import random
2023-01-11T21:05:10.6237864Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6237984Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6237989Z 
2023-01-11T21:05:10.6238067Z aten = torch.ops.aten
2023-01-11T21:05:10.6238200Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6238277Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6238282Z 
2023-01-11T21:05:10.6238299Z 
2023-01-11T21:05:10.6238418Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6238622Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6238741Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6238842Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6238940Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6238999Z {
2023-01-11T21:05:10.6239095Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6239173Z     {
2023-01-11T21:05:10.6239248Z         #pragma omp for 
2023-01-11T21:05:10.6239329Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6239390Z         {
2023-01-11T21:05:10.6239526Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6239662Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(1000));
2023-01-11T21:05:10.6239749Z             auto tmp2 = tmp0.pow(tmp1);
2023-01-11T21:05:10.6239823Z             auto tmp3 = tmp1.pow(tmp0);
2023-01-11T21:05:10.6239914Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6240003Z             tmp3.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6240065Z         }
2023-01-11T21:05:10.6240158Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6240238Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6240300Z         {
2023-01-11T21:05:10.6240369Z             auto tmp1 = in_ptr0[i0];
2023-01-11T21:05:10.6240469Z             auto tmp0 = static_cast<float>(1000);
2023-01-11T21:05:10.6240571Z             auto tmp2 = std::pow(tmp0, tmp1);
2023-01-11T21:05:10.6240783Z             auto tmp3 = std::pow(tmp1, tmp0);
2023-01-11T21:05:10.6240864Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6240996Z             out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.6241060Z         }
2023-01-11T21:05:10.6241107Z     }
2023-01-11T21:05:10.6241166Z }
2023-01-11T21:05:10.6241247Z ''')
2023-01-11T21:05:10.6241252Z 
2023-01-11T21:05:10.6241257Z 
2023-01-11T21:05:10.6241345Z async_compile.wait(globals())
2023-01-11T21:05:10.6241422Z del async_compile
2023-01-11T21:05:10.6241427Z 
2023-01-11T21:05:10.6241498Z def call(args):
2023-01-11T21:05:10.6241566Z     arg0_1, = args
2023-01-11T21:05:10.6241623Z     args.clear()
2023-01-11T21:05:10.6241827Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6242023Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6242191Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6242258Z     del arg0_1
2023-01-11T21:05:10.6242334Z     return (buf0, buf1, )
2023-01-11T21:05:10.6242339Z 
2023-01-11T21:05:10.6242345Z 
2023-01-11T21:05:10.6242420Z if __name__ == "__main__":
2023-01-11T21:05:10.6242533Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6242641Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6242836Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6242943Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6242948Z 
2023-01-11T21:05:10.6243012Z ok (2.720s)
2023-01-11T21:05:10.6243328Z   test_pow3_cpu (__main__.CpuTests) ... [2023-01-11 20:57:39,684] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 370
2023-01-11T21:05:10.6243593Z [2023-01-11 20:57:42,363] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 370
2023-01-11T21:05:10.6243601Z 
2023-01-11T21:05:10.6243693Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6243760Z import torch
2023-01-11T21:05:10.6243817Z import random
2023-01-11T21:05:10.6243933Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6244052Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6244056Z 
2023-01-11T21:05:10.6244132Z aten = torch.ops.aten
2023-01-11T21:05:10.6244264Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6244354Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6244359Z 
2023-01-11T21:05:10.6244363Z 
2023-01-11T21:05:10.6244493Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6244694Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6244799Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6244896Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6245007Z {
2023-01-11T21:05:10.6245068Z     {
2023-01-11T21:05:10.6245130Z         {
2023-01-11T21:05:10.6245213Z             auto tmp1 = in_ptr0[0];
2023-01-11T21:05:10.6245323Z             auto tmp0 = static_cast<float>(0.12300000339746475);
2023-01-11T21:05:10.6245394Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6245485Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:05:10.6245561Z             out_ptr0[0] = tmp3;
2023-01-11T21:05:10.6245623Z         }
2023-01-11T21:05:10.6245683Z     }
2023-01-11T21:05:10.6245740Z }
2023-01-11T21:05:10.6245818Z ''')
2023-01-11T21:05:10.6245823Z 
2023-01-11T21:05:10.6245827Z 
2023-01-11T21:05:10.6245902Z async_compile.wait(globals())
2023-01-11T21:05:10.6245972Z del async_compile
2023-01-11T21:05:10.6245976Z 
2023-01-11T21:05:10.6246044Z def call(args):
2023-01-11T21:05:10.6246112Z     arg0_1, = args
2023-01-11T21:05:10.6246182Z     args.clear()
2023-01-11T21:05:10.6246367Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6246503Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6246570Z     del arg0_1
2023-01-11T21:05:10.6246627Z     return (buf0, )
2023-01-11T21:05:10.6246631Z 
2023-01-11T21:05:10.6246702Z 
2023-01-11T21:05:10.6246779Z if __name__ == "__main__":
2023-01-11T21:05:10.6246891Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6247012Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6247199Z     arg0_1 = rand_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6247307Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6247312Z 
2023-01-11T21:05:10.6247378Z ok (2.720s)
2023-01-11T21:05:10.6247719Z   test_profiler_mark_wrapper_call_cpu (__main__.CpuTests) ... STAGE:2023-01-11 20:57:42 744:744 ActivityProfilerController.cpp:300] Completed Stage: Warm Up
2023-01-11T21:05:10.6247965Z [2023-01-11 20:57:42,390] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 371
2023-01-11T21:05:10.6248231Z [2023-01-11 20:57:45,124] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 371
2023-01-11T21:05:10.6248481Z STAGE:2023-01-11 20:57:45 744:744 ActivityProfilerController.cpp:306] Completed Stage: Collection
2023-01-11T21:05:10.6248740Z STAGE:2023-01-11 20:57:45 744:744 ActivityProfilerController.cpp:310] Completed Stage: Post Processing
2023-01-11T21:05:10.6248745Z 
2023-01-11T21:05:10.6248840Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6248910Z import torch
2023-01-11T21:05:10.6248981Z import random
2023-01-11T21:05:10.6249096Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6249202Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6249207Z 
2023-01-11T21:05:10.6249284Z aten = torch.ops.aten
2023-01-11T21:05:10.6249416Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6249506Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6249514Z 
2023-01-11T21:05:10.6249518Z 
2023-01-11T21:05:10.6249648Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6249851Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6249970Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6250073Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6250158Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6250218Z {
2023-01-11T21:05:10.6250313Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6250373Z     {
2023-01-11T21:05:10.6250447Z         #pragma omp for 
2023-01-11T21:05:10.6250527Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.6250588Z         {
2023-01-11T21:05:10.6250709Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6250838Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6250953Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6251043Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6251105Z         }
2023-01-11T21:05:10.6251201Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6251284Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.6251333Z         {
2023-01-11T21:05:10.6251414Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6251494Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.6251574Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6251652Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6251712Z         }
2023-01-11T21:05:10.6251771Z     }
2023-01-11T21:05:10.6251817Z }
2023-01-11T21:05:10.6251893Z ''')
2023-01-11T21:05:10.6251898Z 
2023-01-11T21:05:10.6251902Z 
2023-01-11T21:05:10.6251988Z async_compile.wait(globals())
2023-01-11T21:05:10.6252060Z del async_compile
2023-01-11T21:05:10.6252065Z 
2023-01-11T21:05:10.6252135Z def call(args):
2023-01-11T21:05:10.6252208Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6252279Z     args.clear()
2023-01-11T21:05:10.6252378Z     from torch.profiler import record_function
2023-01-11T21:05:10.6252535Z     with record_function('inductor_wrapper_call'):
2023-01-11T21:05:10.6252774Z         buf0 = empty_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6252940Z         kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6253008Z         del arg0_1
2023-01-11T21:05:10.6253073Z         del arg1_1
2023-01-11T21:05:10.6253144Z         return (buf0, )
2023-01-11T21:05:10.6253148Z 
2023-01-11T21:05:10.6253152Z 
2023-01-11T21:05:10.6253226Z if __name__ == "__main__":
2023-01-11T21:05:10.6253325Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6253447Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6253641Z     arg0_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6253835Z     arg1_1 = rand_strided((100, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6253951Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6253956Z 
2023-01-11T21:05:10.6254020Z ok (2.773s)
2023-01-11T21:05:10.6254372Z   test_rand_like_deterministic_cpu (__main__.CpuTests) ... [2023-01-11 20:57:45,272] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 372
2023-01-11T21:05:10.6254628Z [2023-01-11 20:57:45,273] torch._inductor.lowering: [WARNING] using triton random, expect difference from eager
2023-01-11T21:05:10.6254878Z [2023-01-11 20:57:47,982] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 372
2023-01-11T21:05:10.6254895Z 
2023-01-11T21:05:10.6254976Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6255046Z import torch
2023-01-11T21:05:10.6255117Z import random
2023-01-11T21:05:10.6255232Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6255352Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6255359Z 
2023-01-11T21:05:10.6255436Z aten = torch.ops.aten
2023-01-11T21:05:10.6255574Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6255654Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6255814Z seed_cpu_None = None  # 9130db9322feaa41c28986790b86d7dd047e77339ff46fce775dbaa5929b26ce
2023-01-11T21:05:10.6255820Z 
2023-01-11T21:05:10.6255824Z 
2023-01-11T21:05:10.6255955Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6256158Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6256272Z extern "C" void kernel(const long* __restrict__ seed0,
2023-01-11T21:05:10.6256369Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6256465Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6256526Z {
2023-01-11T21:05:10.6256608Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6256667Z     {
2023-01-11T21:05:10.6256775Z         #pragma omp for 
2023-01-11T21:05:10.6256856Z         for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.6256919Z         {
2023-01-11T21:05:10.6256983Z             {
2023-01-11T21:05:10.6257034Z                 {
2023-01-11T21:05:10.6257123Z                     auto tmp0 = seed0[0];
2023-01-11T21:05:10.6257225Z                     auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.6257362Z                     auto tmp2 = static_cast<float>(normalized_rand_cpu(tmp0, tmp1));;
2023-01-11T21:05:10.6257468Z                     auto tmp3 = static_cast<int>(1024 + i0);
2023-01-11T21:05:10.6257603Z                     auto tmp4 = static_cast<float>(normalized_rand_cpu(tmp0, tmp3));;
2023-01-11T21:05:10.6257688Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6257773Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.6257824Z                 }
2023-01-11T21:05:10.6257888Z             }
2023-01-11T21:05:10.6257949Z         }
2023-01-11T21:05:10.6258010Z     }
2023-01-11T21:05:10.6258072Z }
2023-01-11T21:05:10.6258149Z ''')
2023-01-11T21:05:10.6258154Z 
2023-01-11T21:05:10.6258157Z 
2023-01-11T21:05:10.6258247Z async_compile.wait(globals())
2023-01-11T21:05:10.6258303Z del async_compile
2023-01-11T21:05:10.6258336Z 
2023-01-11T21:05:10.6258407Z def call(args):
2023-01-11T21:05:10.6258554Z     arg0_1, = args
2023-01-11T21:05:10.6258631Z     args.clear()
2023-01-11T21:05:10.6258765Z     torch.randint(2**31, size=(), dtype=torch.int64, out=seed_cpu_None)
2023-01-11T21:05:10.6258967Z     buf0 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6259162Z     buf1 = empty_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6259320Z     kernel_cpp_0(c_void_p(seed_cpu_None.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6259399Z     return (buf0, buf1, )
2023-01-11T21:05:10.6259403Z 
2023-01-11T21:05:10.6259407Z 
2023-01-11T21:05:10.6259483Z if __name__ == "__main__":
2023-01-11T21:05:10.6259599Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6259725Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6259921Z     seed_cpu_None = rand_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6260118Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6260225Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6260230Z 
2023-01-11T21:05:10.6260295Z ok (2.853s)
2023-01-11T21:05:10.6260723Z   test_reduction1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6260850Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6261112Z [2023-01-11 20:57:48,031] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 373
2023-01-11T21:05:10.6261376Z [2023-01-11 20:57:50,773] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 373
2023-01-11T21:05:10.6261382Z 
2023-01-11T21:05:10.6261475Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6261546Z import torch
2023-01-11T21:05:10.6261616Z import random
2023-01-11T21:05:10.6261730Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6261835Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6261853Z 
2023-01-11T21:05:10.6261918Z aten = torch.ops.aten
2023-01-11T21:05:10.6262050Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6262141Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6262146Z 
2023-01-11T21:05:10.6262150Z 
2023-01-11T21:05:10.6262282Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6262485Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6262638Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6262736Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6262835Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6262915Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6263008Z                        long* __restrict__ out_ptr3,
2023-01-11T21:05:10.6263101Z                        long* __restrict__ out_ptr4)
2023-01-11T21:05:10.6263160Z {
2023-01-11T21:05:10.6263221Z     {
2023-01-11T21:05:10.6263281Z         {
2023-01-11T21:05:10.6263344Z             float tmp1 = 0;
2023-01-11T21:05:10.6263568Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6263688Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6263805Z             struct IndexValue_7 {size_t index; float value;};
2023-01-11T21:05:10.6264024Z             IndexValue_7 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6264160Z             #pragma omp declare reduction(argmax : struct IndexValue_7 :\
2023-01-11T21:05:10.6264338Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6264485Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6264715Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6264818Z             struct IndexValue_8 {size_t index; float value;};
2023-01-11T21:05:10.6264948Z             IndexValue_8 tmp5{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6265081Z             #pragma omp declare reduction(argmin : struct IndexValue_8 :\
2023-01-11T21:05:10.6265223Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6265365Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6265506Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6265589Z             for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.6265653Z             {
2023-01-11T21:05:10.6265704Z                 {
2023-01-11T21:05:10.6265796Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6265873Z                     tmp1 += tmp0;
2023-01-11T21:05:10.6265972Z                     tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:05:10.6266068Z                     tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:05:10.6266159Z                     if (tmp4.value < tmp0) {
2023-01-11T21:05:10.6266265Z                         tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:05:10.6266318Z                     }
2023-01-11T21:05:10.6266405Z                     if (tmp5.value > tmp0) {
2023-01-11T21:05:10.6266507Z                         tmp5.index = i0; tmp5.value = tmp0;
2023-01-11T21:05:10.6266573Z                     }
2023-01-11T21:05:10.6266636Z                 }
2023-01-11T21:05:10.6266697Z             }
2023-01-11T21:05:10.6266775Z             out_ptr0[0] = tmp1;
2023-01-11T21:05:10.6266837Z             out_ptr1[0] = tmp2;
2023-01-11T21:05:10.6266913Z             out_ptr2[0] = tmp3;
2023-01-11T21:05:10.6266997Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:05:10.6267078Z             out_ptr4[0] = tmp5.index;
2023-01-11T21:05:10.6267139Z         }
2023-01-11T21:05:10.6267199Z     }
2023-01-11T21:05:10.6267257Z }
2023-01-11T21:05:10.6267321Z ''')
2023-01-11T21:05:10.6267326Z 
2023-01-11T21:05:10.6267331Z 
2023-01-11T21:05:10.6267419Z async_compile.wait(globals())
2023-01-11T21:05:10.6267489Z del async_compile
2023-01-11T21:05:10.6267494Z 
2023-01-11T21:05:10.6267562Z def call(args):
2023-01-11T21:05:10.6267629Z     arg0_1, = args
2023-01-11T21:05:10.6267697Z     args.clear()
2023-01-11T21:05:10.6267882Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6268081Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6268256Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6268434Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6268607Z     buf4 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6268845Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.6268913Z     del arg0_1
2023-01-11T21:05:10.6269008Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.6269013Z 
2023-01-11T21:05:10.6269018Z 
2023-01-11T21:05:10.6269092Z if __name__ == "__main__":
2023-01-11T21:05:10.6269192Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6269314Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6269503Z     arg0_1 = rand_strided((3, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6269611Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6269616Z 
2023-01-11T21:05:10.6269682Z ok (2.786s)
2023-01-11T21:05:10.6270151Z   test_reduction2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6270280Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6270540Z [2023-01-11 20:57:50,813] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 374
2023-01-11T21:05:10.6270804Z [2023-01-11 20:57:53,521] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 374
2023-01-11T21:05:10.6270810Z 
2023-01-11T21:05:10.6270903Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6270958Z import torch
2023-01-11T21:05:10.6271028Z import random
2023-01-11T21:05:10.6271144Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6271265Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6271270Z 
2023-01-11T21:05:10.6271346Z aten = torch.ops.aten
2023-01-11T21:05:10.6271477Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6271566Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6271571Z 
2023-01-11T21:05:10.6271575Z 
2023-01-11T21:05:10.6271706Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6271895Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6272013Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6272110Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6272204Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6272297Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6272390Z                        long* __restrict__ out_ptr3)
2023-01-11T21:05:10.6272450Z {
2023-01-11T21:05:10.6272497Z     {
2023-01-11T21:05:10.6272560Z         {
2023-01-11T21:05:10.6272636Z             float tmp1 = 0;
2023-01-11T21:05:10.6272839Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6272961Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6273077Z             struct IndexValue_9 {size_t index; float value;};
2023-01-11T21:05:10.6273208Z             IndexValue_9 tmp4{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6273329Z             #pragma omp declare reduction(argmin : struct IndexValue_9 :\
2023-01-11T21:05:10.6273477Z                 omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6273620Z                 omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6273790Z             	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6273895Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6273960Z             {
2023-01-11T21:05:10.6274132Z                 #pragma omp for reduction(+:tmp1) reduction(max:tmp2) reduction(min:tmp3) reduction(argmin:tmp4) 
2023-01-11T21:05:10.6274221Z                 for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6274272Z                 {
2023-01-11T21:05:10.6274336Z                     {
2023-01-11T21:05:10.6274430Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6274507Z                         tmp1 += tmp0;
2023-01-11T21:05:10.6274607Z                         tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:05:10.6274705Z                         tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:05:10.6274799Z                         if (tmp4.value > tmp0) {
2023-01-11T21:05:10.6274906Z                             tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:05:10.6274963Z                         }
2023-01-11T21:05:10.6275029Z                     }
2023-01-11T21:05:10.6275092Z                 }
2023-01-11T21:05:10.6275153Z             }
2023-01-11T21:05:10.6275258Z             out_ptr0[0] = tmp1;
2023-01-11T21:05:10.6275335Z             out_ptr1[0] = tmp2;
2023-01-11T21:05:10.6275397Z             out_ptr2[0] = tmp3;
2023-01-11T21:05:10.6275479Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:05:10.6275540Z         }
2023-01-11T21:05:10.6275600Z     }
2023-01-11T21:05:10.6275658Z }
2023-01-11T21:05:10.6275736Z ''')
2023-01-11T21:05:10.6275741Z 
2023-01-11T21:05:10.6275746Z 
2023-01-11T21:05:10.6275833Z async_compile.wait(globals())
2023-01-11T21:05:10.6275890Z del async_compile
2023-01-11T21:05:10.6275907Z 
2023-01-11T21:05:10.6275963Z def call(args):
2023-01-11T21:05:10.6276029Z     arg0_1, = args
2023-01-11T21:05:10.6276098Z     args.clear()
2023-01-11T21:05:10.6276284Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6276465Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6276641Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6276818Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6277016Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.6277083Z     del arg0_1
2023-01-11T21:05:10.6277168Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:05:10.6277173Z 
2023-01-11T21:05:10.6277177Z 
2023-01-11T21:05:10.6277252Z if __name__ == "__main__":
2023-01-11T21:05:10.6277365Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6277487Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6277678Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6277786Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6277792Z 
2023-01-11T21:05:10.6277844Z ok (2.748s)
2023-01-11T21:05:10.6278282Z   test_reduction3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6278406Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6278667Z [2023-01-11 20:57:53,563] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 375
2023-01-11T21:05:10.6278930Z [2023-01-11 20:57:56,289] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 375
2023-01-11T21:05:10.6278935Z 
2023-01-11T21:05:10.6279028Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6279124Z import torch
2023-01-11T21:05:10.6279192Z import random
2023-01-11T21:05:10.6279306Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6279413Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6279421Z 
2023-01-11T21:05:10.6279497Z aten = torch.ops.aten
2023-01-11T21:05:10.6279628Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6279719Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6279724Z 
2023-01-11T21:05:10.6279728Z 
2023-01-11T21:05:10.6279860Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6280062Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6280179Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6280278Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6280360Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6280451Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6280548Z                        long* __restrict__ out_ptr3)
2023-01-11T21:05:10.6280722Z {
2023-01-11T21:05:10.6280783Z     {
2023-01-11T21:05:10.6280845Z         {
2023-01-11T21:05:10.6280985Z             float tmp1 = 0;
2023-01-11T21:05:10.6281182Z             float tmp2 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6281306Z             float tmp3 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6281423Z             struct IndexValue_10 {size_t index; float value;};
2023-01-11T21:05:10.6281645Z             IndexValue_10 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6281786Z             #pragma omp declare reduction(argmax : struct IndexValue_10 :\
2023-01-11T21:05:10.6281936Z                 omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6282081Z                 omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6282316Z             	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6282407Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6282472Z             {
2023-01-11T21:05:10.6282648Z                 #pragma omp for reduction(+:tmp1) reduction(max:tmp2) reduction(min:tmp3) reduction(argmax:tmp4) 
2023-01-11T21:05:10.6282737Z                 for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6282806Z                 {
2023-01-11T21:05:10.6282871Z                     {
2023-01-11T21:05:10.6282966Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6283031Z                         tmp1 += tmp0;
2023-01-11T21:05:10.6283130Z                         tmp2 = std::max(tmp2, tmp0);
2023-01-11T21:05:10.6283227Z                         tmp3 = std::min(tmp3, tmp0);
2023-01-11T21:05:10.6283318Z                         if (tmp4.value < tmp0) {
2023-01-11T21:05:10.6283428Z                             tmp4.index = i0; tmp4.value = tmp0;
2023-01-11T21:05:10.6283499Z                         }
2023-01-11T21:05:10.6283565Z                     }
2023-01-11T21:05:10.6283628Z                 }
2023-01-11T21:05:10.6283676Z             }
2023-01-11T21:05:10.6283753Z             out_ptr0[0] = tmp1;
2023-01-11T21:05:10.6283829Z             out_ptr1[0] = tmp2;
2023-01-11T21:05:10.6283903Z             out_ptr2[0] = tmp3;
2023-01-11T21:05:10.6283986Z             out_ptr3[0] = tmp4.index;
2023-01-11T21:05:10.6284047Z         }
2023-01-11T21:05:10.6284094Z     }
2023-01-11T21:05:10.6284152Z }
2023-01-11T21:05:10.6284228Z ''')
2023-01-11T21:05:10.6284234Z 
2023-01-11T21:05:10.6284238Z 
2023-01-11T21:05:10.6284325Z async_compile.wait(globals())
2023-01-11T21:05:10.6284395Z del async_compile
2023-01-11T21:05:10.6284400Z 
2023-01-11T21:05:10.6284468Z def call(args):
2023-01-11T21:05:10.6284534Z     arg0_1, = args
2023-01-11T21:05:10.6284602Z     args.clear()
2023-01-11T21:05:10.6284773Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6284992Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6285167Z     buf2 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6285345Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6285557Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.6285624Z     del arg0_1
2023-01-11T21:05:10.6285711Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:05:10.6285716Z 
2023-01-11T21:05:10.6285720Z 
2023-01-11T21:05:10.6285794Z if __name__ == "__main__":
2023-01-11T21:05:10.6285894Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6286016Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6286205Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6286310Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6286318Z 
2023-01-11T21:05:10.6286383Z ok (2.768s)
2023-01-11T21:05:10.6286615Z   test_reduction4_cpu (__main__.CpuTests) ... skip: Non-deterministic CPU results (0.002s)
2023-01-11T21:05:10.6287107Z   test_reflection_pad2d_backward_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6287234Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6287494Z [2023-01-11 20:57:56,339] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 376
2023-01-11T21:05:10.6287744Z [2023-01-11 20:57:59,100] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 376
2023-01-11T21:05:10.6288143Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6288269Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6288524Z [2023-01-11 20:57:59,143] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 377
2023-01-11T21:05:10.6288785Z [2023-01-11 20:58:01,985] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 377
2023-01-11T21:05:10.6289178Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6289305Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6289560Z [2023-01-11 20:58:02,028] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 378
2023-01-11T21:05:10.6289566Z 
2023-01-11T21:05:10.6289658Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6289726Z import torch
2023-01-11T21:05:10.6289782Z import random
2023-01-11T21:05:10.6289894Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6290013Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6290018Z 
2023-01-11T21:05:10.6290094Z aten = torch.ops.aten
2023-01-11T21:05:10.6290225Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6290315Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6290320Z 
2023-01-11T21:05:10.6290323Z 
2023-01-11T21:05:10.6290455Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6290656Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6290792Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6290890Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6290952Z {
2023-01-11T21:05:10.6291048Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6291108Z     {
2023-01-11T21:05:10.6291183Z         #pragma omp for 
2023-01-11T21:05:10.6291263Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6291311Z         {
2023-01-11T21:05:10.6291389Z             #pragma GCC ivdep
2023-01-11T21:05:10.6291471Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6291535Z             {
2023-01-11T21:05:10.6291599Z                 {
2023-01-11T21:05:10.6291665Z                     {
2023-01-11T21:05:10.6291769Z                         auto tmp0 = static_cast<int>(i0);
2023-01-11T21:05:10.6291857Z                         auto tmp1 = static_cast<int>(i1);
2023-01-11T21:05:10.6291966Z                         auto tmp2 = in_ptr0[tmp1 + (8*tmp0)];
2023-01-11T21:05:10.6292063Z                         out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:05:10.6292128Z                     }
2023-01-11T21:05:10.6292193Z                 }
2023-01-11T21:05:10.6292297Z             }
2023-01-11T21:05:10.6292359Z         }
2023-01-11T21:05:10.6292405Z     }
2023-01-11T21:05:10.6292466Z }
2023-01-11T21:05:10.6292544Z ''')
2023-01-11T21:05:10.6292549Z 
2023-01-11T21:05:10.6292553Z 
2023-01-11T21:05:10.6292642Z async_compile.wait(globals())
2023-01-11T21:05:10.6292712Z del async_compile
2023-01-11T21:05:10.6292717Z 
2023-01-11T21:05:10.6292786Z def call(args):
2023-01-11T21:05:10.6292861Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6292919Z     args.clear()
2023-01-11T21:05:10.6293133Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6293266Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6293333Z     del arg0_1
2023-01-11T21:05:10.6293406Z     return (buf0, )
2023-01-11T21:05:10.6293411Z 
2023-01-11T21:05:10.6293414Z 
2023-01-11T21:05:10.6293489Z if __name__ == "__main__":
2023-01-11T21:05:10.6293606Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6293731Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6293930Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6294140Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6294253Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6294258Z 
2023-01-11T21:05:10.6294262Z 
2023-01-11T21:05:10.6294356Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6294425Z import torch
2023-01-11T21:05:10.6294495Z import random
2023-01-11T21:05:10.6294613Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6294733Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6294739Z 
2023-01-11T21:05:10.6294803Z aten = torch.ops.aten
2023-01-11T21:05:10.6294939Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6295029Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6295034Z 
2023-01-11T21:05:10.6295040Z 
2023-01-11T21:05:10.6295171Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6295375Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6295493Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6295592Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6295653Z {
2023-01-11T21:05:10.6295738Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6295798Z     {
2023-01-11T21:05:10.6295873Z         #pragma omp for 
2023-01-11T21:05:10.6295955Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6296016Z         {
2023-01-11T21:05:10.6296096Z             #pragma GCC ivdep
2023-01-11T21:05:10.6296164Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6296259Z             {
2023-01-11T21:05:10.6296322Z                 {
2023-01-11T21:05:10.6296388Z                     {
2023-01-11T21:05:10.6296499Z                         auto tmp0 = static_cast<int>(1 + i0);
2023-01-11T21:05:10.6296605Z                         auto tmp1 = static_cast<int>(1 + i1);
2023-01-11T21:05:10.6296711Z                         auto tmp2 = in_ptr0[tmp1 + (10*tmp0)];
2023-01-11T21:05:10.6296799Z                         auto tmp3 = static_cast<int>(i1);
2023-01-11T21:05:10.6296891Z                         auto tmp4 = tmp3 >= 1;
2023-01-11T21:05:10.6296981Z                         auto tmp5 = tmp3 <= 1;
2023-01-11T21:05:10.6297073Z                         auto tmp6 = tmp4 & tmp5;
2023-01-11T21:05:10.6297157Z                         float tmp7 = 0.0;
2023-01-11T21:05:10.6297229Z                         if(tmp6)
2023-01-11T21:05:10.6297296Z                         {
2023-01-11T21:05:10.6297403Z                             auto tmp8 = static_cast<int>(1 + i0);
2023-01-11T21:05:10.6297567Z                             auto tmp9 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:05:10.6297675Z                             auto tmp10 = in_ptr0[tmp9 + (10*tmp8)];
2023-01-11T21:05:10.6297782Z                             tmp7 = tmp10;
2023-01-11T21:05:10.6297850Z                         }
2023-01-11T21:05:10.6297943Z                         auto tmp11 = tmp2 + tmp7;
2023-01-11T21:05:10.6298035Z                         auto tmp12 = tmp3 >= 6;
2023-01-11T21:05:10.6298125Z                         auto tmp13 = tmp3 <= 6;
2023-01-11T21:05:10.6298207Z                         auto tmp14 = tmp12 & tmp13;
2023-01-11T21:05:10.6298290Z                         float tmp15 = 0.0;
2023-01-11T21:05:10.6298364Z                         if(tmp14)
2023-01-11T21:05:10.6298431Z                         {
2023-01-11T21:05:10.6298622Z                             auto tmp16 = static_cast<int>(1 + i0);
2023-01-11T21:05:10.6298809Z                             auto tmp17 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:05:10.6298924Z                             auto tmp18 = in_ptr0[tmp17 + (10*tmp16)];
2023-01-11T21:05:10.6298993Z                             tmp15 = tmp18;
2023-01-11T21:05:10.6299066Z                         }
2023-01-11T21:05:10.6299162Z                         auto tmp19 = tmp11 + tmp15;
2023-01-11T21:05:10.6299266Z                         auto tmp20 = static_cast<int>(i0);
2023-01-11T21:05:10.6299358Z                         auto tmp21 = tmp20 >= 1;
2023-01-11T21:05:10.6299450Z                         auto tmp22 = tmp20 <= 1;
2023-01-11T21:05:10.6299542Z                         auto tmp23 = tmp21 & tmp22;
2023-01-11T21:05:10.6299615Z                         float tmp24 = 0.0;
2023-01-11T21:05:10.6299690Z                         if(tmp23)
2023-01-11T21:05:10.6299757Z                         {
2023-01-11T21:05:10.6299935Z                             auto tmp25 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:05:10.6300045Z                             auto tmp26 = static_cast<int>(1 + i1);
2023-01-11T21:05:10.6300159Z                             auto tmp27 = in_ptr0[tmp26 + (10*tmp25)];
2023-01-11T21:05:10.6300241Z                             tmp24 = tmp27;
2023-01-11T21:05:10.6300310Z                         }
2023-01-11T21:05:10.6300392Z                         auto tmp28 = tmp19 + tmp24;
2023-01-11T21:05:10.6300484Z                         auto tmp29 = tmp20 >= 6;
2023-01-11T21:05:10.6300574Z                         auto tmp30 = tmp20 <= 6;
2023-01-11T21:05:10.6300671Z                         auto tmp31 = tmp29 & tmp30;
2023-01-11T21:05:10.6300754Z                         float tmp32 = 0.0;
2023-01-11T21:05:10.6300828Z                         if(tmp31)
2023-01-11T21:05:10.6300893Z                         {
2023-01-11T21:05:10.6301054Z                             auto tmp33 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:05:10.6301161Z                             auto tmp34 = static_cast<int>(1 + i1);
2023-01-11T21:05:10.6301270Z                             auto tmp35 = in_ptr0[tmp34 + (10*tmp33)];
2023-01-11T21:05:10.6301389Z                             tmp32 = tmp35;
2023-01-11T21:05:10.6301455Z                         }
2023-01-11T21:05:10.6301548Z                         auto tmp36 = tmp28 + tmp32;
2023-01-11T21:05:10.6301643Z                         auto tmp37 = tmp23 & tmp6;
2023-01-11T21:05:10.6301714Z                         float tmp38 = 0.0;
2023-01-11T21:05:10.6301787Z                         if(tmp37)
2023-01-11T21:05:10.6301851Z                         {
2023-01-11T21:05:10.6302025Z                             auto tmp39 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:05:10.6302197Z                             auto tmp40 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:05:10.6302305Z                             auto tmp41 = in_ptr0[tmp40 + (10*tmp39)];
2023-01-11T21:05:10.6302384Z                             tmp38 = tmp41;
2023-01-11T21:05:10.6302437Z                         }
2023-01-11T21:05:10.6302529Z                         auto tmp42 = tmp36 + tmp38;
2023-01-11T21:05:10.6302622Z                         auto tmp43 = tmp23 & tmp14;
2023-01-11T21:05:10.6302706Z                         float tmp44 = 0.0;
2023-01-11T21:05:10.6302779Z                         if(tmp43)
2023-01-11T21:05:10.6302873Z                         {
2023-01-11T21:05:10.6303049Z                             auto tmp45 = static_cast<int>(1 + ((-1)*i0));
2023-01-11T21:05:10.6303227Z                             auto tmp46 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:05:10.6303321Z                             auto tmp47 = in_ptr0[tmp46 + (10*tmp45)];
2023-01-11T21:05:10.6303401Z                             tmp44 = tmp47;
2023-01-11T21:05:10.6303466Z                         }
2023-01-11T21:05:10.6303558Z                         auto tmp48 = tmp42 + tmp44;
2023-01-11T21:05:10.6303651Z                         auto tmp49 = tmp31 & tmp6;
2023-01-11T21:05:10.6303733Z                         float tmp50 = 0.0;
2023-01-11T21:05:10.6303806Z                         if(tmp49)
2023-01-11T21:05:10.6303862Z                         {
2023-01-11T21:05:10.6304038Z                             auto tmp51 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:05:10.6304211Z                             auto tmp52 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:05:10.6304319Z                             auto tmp53 = in_ptr0[tmp52 + (10*tmp51)];
2023-01-11T21:05:10.6304401Z                             tmp50 = tmp53;
2023-01-11T21:05:10.6304467Z                         }
2023-01-11T21:05:10.6304558Z                         auto tmp54 = tmp48 + tmp50;
2023-01-11T21:05:10.6304636Z                         auto tmp55 = tmp31 & tmp14;
2023-01-11T21:05:10.6304718Z                         float tmp56 = 0.0;
2023-01-11T21:05:10.6304791Z                         if(tmp55)
2023-01-11T21:05:10.6304855Z                         {
2023-01-11T21:05:10.6305029Z                             auto tmp57 = static_cast<int>(15 + ((-1)*i0));
2023-01-11T21:05:10.6305202Z                             auto tmp58 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:05:10.6305311Z                             auto tmp59 = in_ptr0[tmp58 + (10*tmp57)];
2023-01-11T21:05:10.6305380Z                             tmp56 = tmp59;
2023-01-11T21:05:10.6305445Z                         }
2023-01-11T21:05:10.6305540Z                         auto tmp60 = tmp54 + tmp56;
2023-01-11T21:05:10.6305634Z                         out_ptr0[i1 + (8*i0)] = tmp60;
2023-01-11T21:05:10.6305699Z                     }
2023-01-11T21:05:10.6305764Z                 }
2023-01-11T21:05:10.6305826Z             }
2023-01-11T21:05:10.6305874Z         }
2023-01-11T21:05:10.6305934Z     }
2023-01-11T21:05:10.6305992Z }
2023-01-11T21:05:10.6306068Z ''')
2023-01-11T21:05:10.6306074Z 
2023-01-11T21:05:10.6306078Z 
2023-01-11T21:05:10.6306167Z async_compile.wait(globals())
2023-01-11T21:05:10.6306237Z del async_compile
2023-01-11T21:05:10.6306242Z 
2023-01-11T21:05:10.6306310Z def call(args):
2023-01-11T21:05:10.6306382Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6306439Z     args.clear()
2023-01-11T21:05:10.6306685Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6306817Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6306885Z     del arg0_1
2023-01-11T21:05:10.6306958Z     return (buf0, )
2023-01-11T21:05:10.6306963Z 
2023-01-11T21:05:10.6306967Z 
2023-01-11T21:05:10.6307040Z if __name__ == "__main__":
2023-01-11T21:05:10.6307153Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6307263Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6307480Z     arg0_1 = rand_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6307689Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6307802Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6307808Z 
2023-01-11T21:05:10.6307811Z 
2023-01-11T21:05:10.6307904Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6307974Z import torch
2023-01-11T21:05:10.6308043Z import random
2023-01-11T21:05:10.6308155Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6308289Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6308295Z 
2023-01-11T21:05:10.6308372Z aten = torch.ops.aten
2023-01-11T21:05:10.6308505Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6308595Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6308600Z 
2023-01-11T21:05:10.6308604Z 
2023-01-11T21:05:10.6308736Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6308938Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6309058Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6309156Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6309203Z {
2023-01-11T21:05:10.6309298Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6309360Z     {
2023-01-11T21:05:10.6309435Z         #pragma omp for 
2023-01-11T21:05:10.6309516Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6309576Z         {
2023-01-11T21:05:10.6309653Z             #pragma GCC ivdep
2023-01-11T21:05:10.6309724Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6309785Z             {
2023-01-11T21:05:10.6309848Z                 {
2023-01-11T21:05:10.6309914Z                     {
2023-01-11T21:05:10.6310022Z                         auto tmp0 = static_cast<int>(3 + i0);
2023-01-11T21:05:10.6310127Z                         auto tmp1 = static_cast<int>(1 + i1);
2023-01-11T21:05:10.6310232Z                         auto tmp2 = in_ptr0[tmp1 + (11*tmp0)];
2023-01-11T21:05:10.6310320Z                         auto tmp3 = static_cast<int>(i1);
2023-01-11T21:05:10.6310409Z                         auto tmp4 = tmp3 >= 1;
2023-01-11T21:05:10.6310501Z                         auto tmp5 = tmp3 <= 1;
2023-01-11T21:05:10.6310592Z                         auto tmp6 = tmp4 & tmp5;
2023-01-11T21:05:10.6310678Z                         float tmp7 = 0.0;
2023-01-11T21:05:10.6310750Z                         if(tmp6)
2023-01-11T21:05:10.6310817Z                         {
2023-01-11T21:05:10.6310913Z                             auto tmp8 = static_cast<int>(3 + i0);
2023-01-11T21:05:10.6311089Z                             auto tmp9 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:05:10.6311195Z                             auto tmp10 = in_ptr0[tmp9 + (11*tmp8)];
2023-01-11T21:05:10.6311276Z                             tmp7 = tmp10;
2023-01-11T21:05:10.6311343Z                         }
2023-01-11T21:05:10.6311438Z                         auto tmp11 = tmp2 + tmp7;
2023-01-11T21:05:10.6311529Z                         auto tmp12 = tmp3 >= 5;
2023-01-11T21:05:10.6311606Z                         auto tmp13 = tmp3 <= 6;
2023-01-11T21:05:10.6311699Z                         auto tmp14 = tmp12 & tmp13;
2023-01-11T21:05:10.6311782Z                         float tmp15 = 0.0;
2023-01-11T21:05:10.6311888Z                         if(tmp14)
2023-01-11T21:05:10.6311956Z                         {
2023-01-11T21:05:10.6312064Z                             auto tmp16 = static_cast<int>(3 + i0);
2023-01-11T21:05:10.6312243Z                             auto tmp17 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:05:10.6312351Z                             auto tmp18 = in_ptr0[tmp17 + (11*tmp16)];
2023-01-11T21:05:10.6312420Z                             tmp15 = tmp18;
2023-01-11T21:05:10.6312489Z                         }
2023-01-11T21:05:10.6312580Z                         auto tmp19 = tmp11 + tmp15;
2023-01-11T21:05:10.6312683Z                         auto tmp20 = static_cast<int>(i0);
2023-01-11T21:05:10.6312776Z                         auto tmp21 = tmp20 >= 1;
2023-01-11T21:05:10.6312865Z                         auto tmp22 = tmp20 <= 3;
2023-01-11T21:05:10.6312957Z                         auto tmp23 = tmp21 & tmp22;
2023-01-11T21:05:10.6313028Z                         float tmp24 = 0.0;
2023-01-11T21:05:10.6313104Z                         if(tmp23)
2023-01-11T21:05:10.6313170Z                         {
2023-01-11T21:05:10.6313345Z                             auto tmp25 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:05:10.6313481Z                             auto tmp26 = static_cast<int>(1 + i1);
2023-01-11T21:05:10.6313590Z                             auto tmp27 = in_ptr0[tmp26 + (11*tmp25)];
2023-01-11T21:05:10.6313672Z                             tmp24 = tmp27;
2023-01-11T21:05:10.6313726Z                         }
2023-01-11T21:05:10.6313818Z                         auto tmp28 = tmp19 + tmp24;
2023-01-11T21:05:10.6313909Z                         auto tmp29 = tmp20 >= 3;
2023-01-11T21:05:10.6313998Z                         auto tmp30 = tmp20 <= 6;
2023-01-11T21:05:10.6314090Z                         auto tmp31 = tmp29 & tmp30;
2023-01-11T21:05:10.6314173Z                         float tmp32 = 0.0;
2023-01-11T21:05:10.6314247Z                         if(tmp31)
2023-01-11T21:05:10.6314301Z                         {
2023-01-11T21:05:10.6314480Z                             auto tmp33 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:05:10.6314587Z                             auto tmp34 = static_cast<int>(1 + i1);
2023-01-11T21:05:10.6314699Z                             auto tmp35 = in_ptr0[tmp34 + (11*tmp33)];
2023-01-11T21:05:10.6314781Z                             tmp32 = tmp35;
2023-01-11T21:05:10.6314847Z                         }
2023-01-11T21:05:10.6314939Z                         auto tmp36 = tmp28 + tmp32;
2023-01-11T21:05:10.6315031Z                         auto tmp37 = tmp23 & tmp6;
2023-01-11T21:05:10.6315102Z                         float tmp38 = 0.0;
2023-01-11T21:05:10.6315175Z                         if(tmp37)
2023-01-11T21:05:10.6315240Z                         {
2023-01-11T21:05:10.6315414Z                             auto tmp39 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:05:10.6315589Z                             auto tmp40 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:05:10.6315700Z                             auto tmp41 = in_ptr0[tmp40 + (11*tmp39)];
2023-01-11T21:05:10.6315780Z                             tmp38 = tmp41;
2023-01-11T21:05:10.6315833Z                         }
2023-01-11T21:05:10.6315929Z                         auto tmp42 = tmp36 + tmp38;
2023-01-11T21:05:10.6316019Z                         auto tmp43 = tmp23 & tmp14;
2023-01-11T21:05:10.6316103Z                         float tmp44 = 0.0;
2023-01-11T21:05:10.6316176Z                         if(tmp43)
2023-01-11T21:05:10.6316246Z                         {
2023-01-11T21:05:10.6316418Z                             auto tmp45 = static_cast<int>(3 + ((-1)*i0));
2023-01-11T21:05:10.6316582Z                             auto tmp46 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:05:10.6316690Z                             auto tmp47 = in_ptr0[tmp46 + (11*tmp45)];
2023-01-11T21:05:10.6316770Z                             tmp44 = tmp47;
2023-01-11T21:05:10.6316836Z                         }
2023-01-11T21:05:10.6316928Z                         auto tmp48 = tmp42 + tmp44;
2023-01-11T21:05:10.6317054Z                         auto tmp49 = tmp31 & tmp6;
2023-01-11T21:05:10.6317138Z                         float tmp50 = 0.0;
2023-01-11T21:05:10.6317201Z                         if(tmp49)
2023-01-11T21:05:10.6317267Z                         {
2023-01-11T21:05:10.6317443Z                             auto tmp51 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:05:10.6317617Z                             auto tmp52 = static_cast<int>(1 + ((-1)*i1));
2023-01-11T21:05:10.6317721Z                             auto tmp53 = in_ptr0[tmp52 + (11*tmp51)];
2023-01-11T21:05:10.6317801Z                             tmp50 = tmp53;
2023-01-11T21:05:10.6317869Z                         }
2023-01-11T21:05:10.6317961Z                         auto tmp54 = tmp48 + tmp50;
2023-01-11T21:05:10.6318039Z                         auto tmp55 = tmp31 & tmp14;
2023-01-11T21:05:10.6318120Z                         float tmp56 = 0.0;
2023-01-11T21:05:10.6318193Z                         if(tmp55)
2023-01-11T21:05:10.6318262Z                         {
2023-01-11T21:05:10.6318436Z                             auto tmp57 = static_cast<int>(17 + ((-1)*i0));
2023-01-11T21:05:10.6318645Z                             auto tmp58 = static_cast<int>(15 + ((-1)*i1));
2023-01-11T21:05:10.6318753Z                             auto tmp59 = in_ptr0[tmp58 + (11*tmp57)];
2023-01-11T21:05:10.6318822Z                             tmp56 = tmp59;
2023-01-11T21:05:10.6318887Z                         }
2023-01-11T21:05:10.6318979Z                         auto tmp60 = tmp54 + tmp56;
2023-01-11T21:05:10.6319076Z                         out_ptr0[i1 + (8*i0)] = tmp60;
2023-01-11T21:05:10.6319142Z                     }
2023-01-11T21:05:10.6319206Z                 }
2023-01-11T21:05:10.6319269Z             }
2023-01-11T21:05:10.6319318Z         }
2023-01-11T21:05:10.6319378Z     }
2023-01-11T21:05:10.6319437Z }
2023-01-11T21:05:10.6319513Z ''')
2023-01-11T21:05:10.6319519Z 
2023-01-11T21:05:10.6319524Z 
2023-01-11T21:05:10.6319614Z async_compile.wait(globals())
2023-01-11T21:05:10.6319683Z del async_compile
2023-01-11T21:05:10.6319689Z 
2023-01-11T21:05:10.6319758Z def call(args):
2023-01-11T21:05:10.6319818Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6319891Z     args.clear()
2023-01-11T21:05:10.6320102Z     buf0 = empty_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6320236Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6320304Z     del arg0_1
2023-01-11T21:05:10.6320374Z     return (buf0, )
2023-01-11T21:05:10.6320379Z 
2023-01-11T21:05:10.6320383Z 
2023-01-11T21:05:10.6320458Z if __name__ == "__main__":
2023-01-11T21:05:10.6320570Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6320803Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6321027Z     arg0_1 = rand_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6321241Z     arg1_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6321357Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6321627Z [2023-01-11 20:58:04,891] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 378
2023-01-11T21:05:10.6321633Z 
2023-01-11T21:05:10.6321701Z ok (8.600s)
2023-01-11T21:05:10.6322149Z   test_reflection_pad2d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6322275Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6322534Z [2023-01-11 20:58:04,950] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 379
2023-01-11T21:05:10.6322843Z [2023-01-11 20:58:07,737] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 379
2023-01-11T21:05:10.6322862Z 
2023-01-11T21:05:10.6322941Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6323013Z import torch
2023-01-11T21:05:10.6323084Z import random
2023-01-11T21:05:10.6323198Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6323317Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6323322Z 
2023-01-11T21:05:10.6323400Z aten = torch.ops.aten
2023-01-11T21:05:10.6323533Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6323611Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6323616Z 
2023-01-11T21:05:10.6323621Z 
2023-01-11T21:05:10.6323755Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6323962Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6324083Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6324185Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6324286Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6324347Z {
2023-01-11T21:05:10.6324481Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6324531Z     {
2023-01-11T21:05:10.6324607Z         #pragma omp for 
2023-01-11T21:05:10.6324690Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.6324752Z         {
2023-01-11T21:05:10.6324832Z             #pragma GCC ivdep
2023-01-11T21:05:10.6324917Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.6324966Z             {
2023-01-11T21:05:10.6325030Z                 {
2023-01-11T21:05:10.6325095Z                     {
2023-01-11T21:05:10.6325200Z                         auto tmp0 = static_cast<int>(7);
2023-01-11T21:05:10.6325302Z                         auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.6325403Z                         auto tmp2 = static_cast<int>(1);
2023-01-11T21:05:10.6325547Z                         auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.6325641Z                         auto tmp4 = std::abs(tmp3);
2023-01-11T21:05:10.6325780Z                         auto tmp5 = tmp0 - tmp4;
2023-01-11T21:05:10.6325882Z                         auto tmp6 = std::abs(tmp5);
2023-01-11T21:05:10.6326023Z                         auto tmp7 = tmp0 - tmp6;
2023-01-11T21:05:10.6326126Z                         auto tmp8 = static_cast<int>(i1);
2023-01-11T21:05:10.6326263Z                         auto tmp9 = tmp8 - tmp2;
2023-01-11T21:05:10.6326364Z                         auto tmp10 = std::abs(tmp9);
2023-01-11T21:05:10.6326506Z                         auto tmp11 = tmp0 - tmp10;
2023-01-11T21:05:10.6326594Z                         auto tmp12 = std::abs(tmp11);
2023-01-11T21:05:10.6326733Z                         auto tmp13 = tmp0 - tmp12;
2023-01-11T21:05:10.6326838Z                         auto tmp14 = in_ptr0[tmp13 + (8*tmp7)];
2023-01-11T21:05:10.6326935Z                         out_ptr0[i1 + (10*i0)] = tmp14;
2023-01-11T21:05:10.6327000Z                     }
2023-01-11T21:05:10.6327063Z                 }
2023-01-11T21:05:10.6327123Z             }
2023-01-11T21:05:10.6327171Z         }
2023-01-11T21:05:10.6327248Z         #pragma omp for 
2023-01-11T21:05:10.6327330Z         for(long i0=0; i0<15; i0+=1)
2023-01-11T21:05:10.6327393Z         {
2023-01-11T21:05:10.6327470Z             #pragma GCC ivdep
2023-01-11T21:05:10.6327554Z             for(long i1=0; i1<11; i1+=1)
2023-01-11T21:05:10.6327617Z             {
2023-01-11T21:05:10.6327667Z                 {
2023-01-11T21:05:10.6327730Z                     {
2023-01-11T21:05:10.6327830Z                         auto tmp0 = static_cast<int>(7);
2023-01-11T21:05:10.6327932Z                         auto tmp1 = static_cast<int>(i0);
2023-01-11T21:05:10.6328032Z                         auto tmp2 = static_cast<int>(3);
2023-01-11T21:05:10.6328170Z                         auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.6328267Z                         auto tmp4 = std::abs(tmp3);
2023-01-11T21:05:10.6328429Z                         auto tmp5 = tmp0 - tmp4;
2023-01-11T21:05:10.6328527Z                         auto tmp6 = std::abs(tmp5);
2023-01-11T21:05:10.6328665Z                         auto tmp7 = tmp0 - tmp6;
2023-01-11T21:05:10.6328766Z                         auto tmp8 = static_cast<int>(i1);
2023-01-11T21:05:10.6328866Z                         auto tmp9 = static_cast<int>(1);
2023-01-11T21:05:10.6329006Z                         auto tmp10 = tmp8 - tmp9;
2023-01-11T21:05:10.6329106Z                         auto tmp11 = std::abs(tmp10);
2023-01-11T21:05:10.6329233Z                         auto tmp12 = tmp0 - tmp11;
2023-01-11T21:05:10.6329335Z                         auto tmp13 = std::abs(tmp12);
2023-01-11T21:05:10.6329474Z                         auto tmp14 = tmp0 - tmp13;
2023-01-11T21:05:10.6329579Z                         auto tmp15 = in_ptr0[tmp14 + (8*tmp7)];
2023-01-11T21:05:10.6329675Z                         out_ptr1[i1 + (11*i0)] = tmp15;
2023-01-11T21:05:10.6329742Z                     }
2023-01-11T21:05:10.6329807Z                 }
2023-01-11T21:05:10.6329857Z             }
2023-01-11T21:05:10.6329917Z         }
2023-01-11T21:05:10.6329977Z     }
2023-01-11T21:05:10.6330079Z }
2023-01-11T21:05:10.6330158Z ''')
2023-01-11T21:05:10.6330163Z 
2023-01-11T21:05:10.6330167Z 
2023-01-11T21:05:10.6330256Z async_compile.wait(globals())
2023-01-11T21:05:10.6330326Z del async_compile
2023-01-11T21:05:10.6330331Z 
2023-01-11T21:05:10.6330399Z def call(args):
2023-01-11T21:05:10.6330454Z     arg0_1, = args
2023-01-11T21:05:10.6330523Z     args.clear()
2023-01-11T21:05:10.6330742Z     buf0 = empty_strided((1, 1, 10, 10), (100, 100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6330956Z     buf1 = empty_strided((1, 1, 15, 11), (165, 165, 11, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6331145Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6331245Z     del arg0_1
2023-01-11T21:05:10.6331353Z     return (buf0, buf1, )
2023-01-11T21:05:10.6331361Z 
2023-01-11T21:05:10.6331367Z 
2023-01-11T21:05:10.6331443Z if __name__ == "__main__":
2023-01-11T21:05:10.6331562Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6331686Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6331901Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6332011Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6332016Z 
2023-01-11T21:05:10.6332082Z ok (2.845s)
2023-01-11T21:05:10.6332520Z   test_relu_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6332650Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6332913Z [2023-01-11 20:58:07,789] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 380
2023-01-11T21:05:10.6333166Z [2023-01-11 20:58:10,526] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 380
2023-01-11T21:05:10.6333186Z 
2023-01-11T21:05:10.6333266Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6333335Z import torch
2023-01-11T21:05:10.6333406Z import random
2023-01-11T21:05:10.6333521Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6333640Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6333645Z 
2023-01-11T21:05:10.6333721Z aten = torch.ops.aten
2023-01-11T21:05:10.6333853Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6333932Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6333937Z 
2023-01-11T21:05:10.6333955Z 
2023-01-11T21:05:10.6334157Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6334359Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6334480Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6334584Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6334685Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6334780Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6334840Z {
2023-01-11T21:05:10.6334925Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6334986Z     {
2023-01-11T21:05:10.6335063Z         #pragma omp for 
2023-01-11T21:05:10.6335144Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6335208Z         {
2023-01-11T21:05:10.6335341Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6335475Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6335590Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:05:10.6335677Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.6335829Z             auto tmp4 = at::vec::clamp_min(tmp3, decltype(tmp3)(0));
2023-01-11T21:05:10.6335963Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:05:10.6336047Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:05:10.6336138Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6336232Z             tmp6.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6336294Z         }
2023-01-11T21:05:10.6336375Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6336454Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6336517Z         {
2023-01-11T21:05:10.6336598Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6336676Z             auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.6336762Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.6336844Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.6336918Z             auto tmp4 = tmp3 * (tmp3>0);
2023-01-11T21:05:10.6337017Z             auto tmp5 = static_cast<float>(10);
2023-01-11T21:05:10.6337101Z             auto tmp6 = tmp4 / tmp5;
2023-01-11T21:05:10.6337177Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6337254Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.6337315Z         }
2023-01-11T21:05:10.6337363Z     }
2023-01-11T21:05:10.6337421Z }
2023-01-11T21:05:10.6337497Z ''')
2023-01-11T21:05:10.6337502Z 
2023-01-11T21:05:10.6337507Z 
2023-01-11T21:05:10.6337594Z async_compile.wait(globals())
2023-01-11T21:05:10.6337667Z del async_compile
2023-01-11T21:05:10.6337672Z 
2023-01-11T21:05:10.6337741Z def call(args):
2023-01-11T21:05:10.6337815Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6337884Z     args.clear()
2023-01-11T21:05:10.6338064Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6338253Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6338442Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6338587Z     del arg0_1
2023-01-11T21:05:10.6338657Z     del arg1_1
2023-01-11T21:05:10.6338735Z     return (buf0, buf1, )
2023-01-11T21:05:10.6338740Z 
2023-01-11T21:05:10.6338744Z 
2023-01-11T21:05:10.6338819Z if __name__ == "__main__":
2023-01-11T21:05:10.6338933Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6339042Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6339242Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6339433Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6339547Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6339552Z 
2023-01-11T21:05:10.6339621Z ok (2.789s)
2023-01-11T21:05:10.6340065Z   test_remainder_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6340229Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6340488Z [2023-01-11 20:58:10,590] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 381
2023-01-11T21:05:10.6340750Z [2023-01-11 20:58:13,276] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 381
2023-01-11T21:05:10.6340756Z 
2023-01-11T21:05:10.6340835Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6340904Z import torch
2023-01-11T21:05:10.6340973Z import random
2023-01-11T21:05:10.6341086Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6341207Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6341212Z 
2023-01-11T21:05:10.6341288Z aten = torch.ops.aten
2023-01-11T21:05:10.6341421Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6341524Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6341542Z 
2023-01-11T21:05:10.6341546Z 
2023-01-11T21:05:10.6341667Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6341904Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6342073Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6342213Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6342312Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6342409Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6342502Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6342548Z {
2023-01-11T21:05:10.6342647Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6342707Z     {
2023-01-11T21:05:10.6342781Z         #pragma omp for 
2023-01-11T21:05:10.6342861Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.6342922Z         {
2023-01-11T21:05:10.6342988Z             {
2023-01-11T21:05:10.6343038Z                 {
2023-01-11T21:05:10.6343132Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6343222Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.6343320Z                     auto tmp2 = mod(tmp0, tmp1);
2023-01-11T21:05:10.6343409Z                     auto tmp3 = tmp2 + tmp1;
2023-01-11T21:05:10.6343532Z                     auto tmp4 = ((tmp2 != 0) & ((tmp2 < 0) != (tmp1 < 0))) ? tmp3 : tmp2;
2023-01-11T21:05:10.6343637Z                     auto tmp5 = static_cast<float>(1);
2023-01-11T21:05:10.6343714Z                     auto tmp6 = tmp0 + tmp5;
2023-01-11T21:05:10.6343853Z                     auto tmp7 = tmp1 - tmp5;
2023-01-11T21:05:10.6343950Z                     auto tmp8 = mod(tmp6, tmp7);
2023-01-11T21:05:10.6344041Z                     auto tmp9 = tmp8 + tmp7;
2023-01-11T21:05:10.6344162Z                     auto tmp10 = ((tmp8 != 0) & ((tmp8 < 0) != (tmp7 < 0))) ? tmp9 : tmp8;
2023-01-11T21:05:10.6344300Z                     auto tmp11 = tmp0 - tmp5;
2023-01-11T21:05:10.6344390Z                     auto tmp12 = tmp1 + tmp5;
2023-01-11T21:05:10.6344474Z                     auto tmp13 = mod(tmp11, tmp12);
2023-01-11T21:05:10.6344566Z                     auto tmp14 = tmp13 + tmp12;
2023-01-11T21:05:10.6344692Z                     auto tmp15 = ((tmp13 != 0) & ((tmp13 < 0) != (tmp12 < 0))) ? tmp14 : tmp13;
2023-01-11T21:05:10.6344776Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6344859Z                     out_ptr1[i0] = tmp10;
2023-01-11T21:05:10.6344941Z                     out_ptr2[i0] = tmp15;
2023-01-11T21:05:10.6345005Z                 }
2023-01-11T21:05:10.6345067Z             }
2023-01-11T21:05:10.6345116Z         }
2023-01-11T21:05:10.6345218Z     }
2023-01-11T21:05:10.6345275Z }
2023-01-11T21:05:10.6345354Z ''')
2023-01-11T21:05:10.6345359Z 
2023-01-11T21:05:10.6345363Z 
2023-01-11T21:05:10.6345452Z async_compile.wait(globals())
2023-01-11T21:05:10.6345523Z del async_compile
2023-01-11T21:05:10.6345530Z 
2023-01-11T21:05:10.6345598Z def call(args):
2023-01-11T21:05:10.6345658Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6345728Z     args.clear()
2023-01-11T21:05:10.6345922Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6346111Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6346297Z     buf2 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6346509Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6346578Z     del arg0_1
2023-01-11T21:05:10.6346629Z     del arg1_1
2023-01-11T21:05:10.6346712Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6346717Z 
2023-01-11T21:05:10.6346721Z 
2023-01-11T21:05:10.6346795Z if __name__ == "__main__":
2023-01-11T21:05:10.6346908Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6347059Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6347255Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6347447Z     arg1_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6347561Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6347566Z 
2023-01-11T21:05:10.6347618Z ok (2.751s)
2023-01-11T21:05:10.6348055Z   test_repeat_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6348184Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6348443Z [2023-01-11 20:58:13,320] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 382
2023-01-11T21:05:10.6348707Z [2023-01-11 20:58:16,139] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 382
2023-01-11T21:05:10.6348712Z 
2023-01-11T21:05:10.6348805Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6348875Z import torch
2023-01-11T21:05:10.6348944Z import random
2023-01-11T21:05:10.6349056Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6349162Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6349180Z 
2023-01-11T21:05:10.6349244Z aten = torch.ops.aten
2023-01-11T21:05:10.6349376Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6349466Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6349473Z 
2023-01-11T21:05:10.6349478Z 
2023-01-11T21:05:10.6349612Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6349813Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6349933Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6350032Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6350115Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6350207Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6350266Z {
2023-01-11T21:05:10.6350363Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6350424Z     {
2023-01-11T21:05:10.6350514Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6350593Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6350642Z         {
2023-01-11T21:05:10.6350723Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.6350785Z             {
2023-01-11T21:05:10.6350899Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6350986Z                 for(long i2=0; i2<12; i2+=1)
2023-01-11T21:05:10.6351050Z                 {
2023-01-11T21:05:10.6351133Z                     #pragma GCC ivdep
2023-01-11T21:05:10.6351212Z                     for(long i3=0; i3<8; i3+=1)
2023-01-11T21:05:10.6351277Z                     {
2023-01-11T21:05:10.6351344Z                         {
2023-01-11T21:05:10.6351414Z                             {
2023-01-11T21:05:10.6351532Z                                 auto tmp0 = in_ptr0[i3 + (8*(i2 % 4)) + (32*(i1 % 2))];
2023-01-11T21:05:10.6351643Z                                 out_ptr0[i3 + (8*i2) + (96*i1) + (384*i0)] = tmp0;
2023-01-11T21:05:10.6351710Z                             }
2023-01-11T21:05:10.6351763Z                         }
2023-01-11T21:05:10.6351828Z                     }
2023-01-11T21:05:10.6351889Z                 }
2023-01-11T21:05:10.6351951Z             }
2023-01-11T21:05:10.6352011Z         }
2023-01-11T21:05:10.6352085Z         #pragma omp for 
2023-01-11T21:05:10.6352166Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6352215Z         {
2023-01-11T21:05:10.6352297Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.6352359Z             {
2023-01-11T21:05:10.6352525Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i1);
2023-01-11T21:05:10.6352632Z                 tmp0.store(out_ptr1 + (16*i1) + (64*i0));
2023-01-11T21:05:10.6352694Z             }
2023-01-11T21:05:10.6352786Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.6352857Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:05:10.6352920Z             {
2023-01-11T21:05:10.6353005Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.6353096Z                 out_ptr1[i1 + (64*i0)] = tmp0;
2023-01-11T21:05:10.6353158Z             }
2023-01-11T21:05:10.6353220Z         }
2023-01-11T21:05:10.6353295Z         #pragma omp for 
2023-01-11T21:05:10.6353362Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6353426Z         {
2023-01-11T21:05:10.6353505Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.6353568Z             {
2023-01-11T21:05:10.6353703Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i1);
2023-01-11T21:05:10.6353806Z                 tmp0.store(out_ptr2 + (16*i1) + (64*i0));
2023-01-11T21:05:10.6353869Z             }
2023-01-11T21:05:10.6353948Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.6354031Z             for(long i1=64; i1<64; i1+=1)
2023-01-11T21:05:10.6354093Z             {
2023-01-11T21:05:10.6354176Z                 auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.6354266Z                 out_ptr2[i1 + (64*i0)] = tmp0;
2023-01-11T21:05:10.6354329Z             }
2023-01-11T21:05:10.6354377Z         }
2023-01-11T21:05:10.6354438Z     }
2023-01-11T21:05:10.6354497Z }
2023-01-11T21:05:10.6354575Z ''')
2023-01-11T21:05:10.6354581Z 
2023-01-11T21:05:10.6354585Z 
2023-01-11T21:05:10.6354673Z async_compile.wait(globals())
2023-01-11T21:05:10.6354749Z del async_compile
2023-01-11T21:05:10.6354754Z 
2023-01-11T21:05:10.6354824Z def call(args):
2023-01-11T21:05:10.6354892Z     arg0_1, = args
2023-01-11T21:05:10.6354949Z     args.clear()
2023-01-11T21:05:10.6355166Z     buf0 = empty_strided((2, 4, 12, 8), (384, 96, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6355376Z     buf1 = empty_strided((8, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6355599Z     buf2 = empty_strided((2, 1, 1, 2, 4, 8), (64, 64, 64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6355788Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6355855Z     del arg0_1
2023-01-11T21:05:10.6355938Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6355943Z 
2023-01-11T21:05:10.6355947Z 
2023-01-11T21:05:10.6356022Z if __name__ == "__main__":
2023-01-11T21:05:10.6356123Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6356279Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6356488Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6356598Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6356603Z 
2023-01-11T21:05:10.6356670Z ok (2.865s)
2023-01-11T21:05:10.6356813Z   test_roi_align_cpu (__main__.CpuTests) ... skip: requires torchvision (0.001s)
2023-01-11T21:05:10.6357248Z   test_roll_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6357373Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6357637Z [2023-01-11 20:58:16,235] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 383
2023-01-11T21:05:10.6357892Z [2023-01-11 20:58:19,215] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 383
2023-01-11T21:05:10.6357911Z 
2023-01-11T21:05:10.6358026Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6358097Z import torch
2023-01-11T21:05:10.6358169Z import random
2023-01-11T21:05:10.6358282Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6358402Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6358407Z 
2023-01-11T21:05:10.6358484Z aten = torch.ops.aten
2023-01-11T21:05:10.6358617Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6358695Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6358700Z 
2023-01-11T21:05:10.6358705Z 
2023-01-11T21:05:10.6358839Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6359043Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6359166Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6359265Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6359361Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6359421Z {
2023-01-11T21:05:10.6359504Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6359564Z     {
2023-01-11T21:05:10.6359653Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6359733Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6359794Z         {
2023-01-11T21:05:10.6359878Z             for(long i1=0; i1<56; i1+=1)
2023-01-11T21:05:10.6359940Z             {
2023-01-11T21:05:10.6360007Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6360096Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:05:10.6360158Z                 {
2023-01-11T21:05:10.6360249Z                     for(long i3=0; i3<1; i3+=1)
2023-01-11T21:05:10.6360314Z                     {
2023-01-11T21:05:10.6360484Z                         auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i3) + (16*((46 + i2) % 56)) + (896*((3 + i1) % 56)) + (50176*i0));
2023-01-11T21:05:10.6360732Z                         tmp0.store(out_ptr0 + (16*i2) + (16*i3) + (896*i1) + (50176*i0));
2023-01-11T21:05:10.6360802Z                     }
2023-01-11T21:05:10.6360889Z                     #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.6360981Z                     for(long i3=16; i3<16; i3+=1)
2023-01-11T21:05:10.6361049Z                     {
2023-01-11T21:05:10.6361175Z                         auto tmp0 = in_ptr0[i3 + (16*((46 + i2) % 56)) + (896*((3 + i1) % 56)) + (50176*i0)];
2023-01-11T21:05:10.6361287Z                         out_ptr0[i3 + (16*i2) + (896*i1) + (50176*i0)] = tmp0;
2023-01-11T21:05:10.6361353Z                     }
2023-01-11T21:05:10.6361418Z                 }
2023-01-11T21:05:10.6361468Z             }
2023-01-11T21:05:10.6361529Z         }
2023-01-11T21:05:10.6361620Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6361760Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6361822Z         {
2023-01-11T21:05:10.6361906Z             for(long i1=0; i1<56; i1+=1)
2023-01-11T21:05:10.6361968Z             {
2023-01-11T21:05:10.6362037Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6362126Z                 for(long i2=0; i2<56; i2+=1)
2023-01-11T21:05:10.6362190Z                 {
2023-01-11T21:05:10.6362271Z                     #pragma GCC ivdep
2023-01-11T21:05:10.6362363Z                     for(long i3=0; i3<16; i3+=1)
2023-01-11T21:05:10.6362427Z                     {
2023-01-11T21:05:10.6362496Z                         {
2023-01-11T21:05:10.6362553Z                             {
2023-01-11T21:05:10.6362680Z                                 auto tmp0 = in_ptr0[(100347 + i3 + (16*i2) + (896*i1) + (50176*i0)) % 100352];
2023-01-11T21:05:10.6362793Z                                 out_ptr1[i3 + (16*i2) + (896*i1) + (50176*i0)] = tmp0;
2023-01-11T21:05:10.6362863Z                             }
2023-01-11T21:05:10.6362935Z                         }
2023-01-11T21:05:10.6363001Z                     }
2023-01-11T21:05:10.6363066Z                 }
2023-01-11T21:05:10.6363114Z             }
2023-01-11T21:05:10.6363174Z         }
2023-01-11T21:05:10.6363267Z     }
2023-01-11T21:05:10.6363327Z }
2023-01-11T21:05:10.6363408Z ''')
2023-01-11T21:05:10.6363414Z 
2023-01-11T21:05:10.6363418Z 
2023-01-11T21:05:10.6363505Z async_compile.wait(globals())
2023-01-11T21:05:10.6363575Z del async_compile
2023-01-11T21:05:10.6363580Z 
2023-01-11T21:05:10.6363636Z def call(args):
2023-01-11T21:05:10.6363703Z     arg0_1, = args
2023-01-11T21:05:10.6363771Z     args.clear()
2023-01-11T21:05:10.6363992Z     buf0 = empty_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6364211Z     buf1 = empty_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6364373Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6364441Z     del arg0_1
2023-01-11T21:05:10.6364504Z     return (buf0, buf1, )
2023-01-11T21:05:10.6364521Z 
2023-01-11T21:05:10.6364526Z 
2023-01-11T21:05:10.6364587Z if __name__ == "__main__":
2023-01-11T21:05:10.6364702Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6364824Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6365043Z     arg0_1 = rand_strided((2, 56, 56, 16), (50176, 896, 16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6365149Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6365154Z 
2023-01-11T21:05:10.6365221Z ok (3.161s)
2023-01-11T21:05:10.6365669Z   test_round_correctness_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6365794Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6366055Z [2023-01-11 20:58:19,335] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 384
2023-01-11T21:05:10.6366304Z [2023-01-11 20:58:21,971] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 384
2023-01-11T21:05:10.6366309Z 
2023-01-11T21:05:10.6366401Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6366469Z import torch
2023-01-11T21:05:10.6366538Z import random
2023-01-11T21:05:10.6366651Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6366770Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6366775Z 
2023-01-11T21:05:10.6366850Z aten = torch.ops.aten
2023-01-11T21:05:10.6366973Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6367062Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6367095Z 
2023-01-11T21:05:10.6367099Z 
2023-01-11T21:05:10.6367235Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6367440Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6367560Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.6367659Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.6367720Z {
2023-01-11T21:05:10.6367816Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6367863Z     {
2023-01-11T21:05:10.6367939Z         #pragma omp for 
2023-01-11T21:05:10.6368020Z         for(long i0=0; i0<200; i0+=1)
2023-01-11T21:05:10.6368082Z         {
2023-01-11T21:05:10.6368143Z             {
2023-01-11T21:05:10.6368206Z                 {
2023-01-11T21:05:10.6368297Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6368392Z                     auto tmp1 = std::nearbyint(tmp0);
2023-01-11T21:05:10.6368475Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6368541Z                 }
2023-01-11T21:05:10.6368603Z             }
2023-01-11T21:05:10.6368662Z         }
2023-01-11T21:05:10.6368720Z     }
2023-01-11T21:05:10.6368777Z }
2023-01-11T21:05:10.6368841Z ''')
2023-01-11T21:05:10.6368885Z 
2023-01-11T21:05:10.6368890Z 
2023-01-11T21:05:10.6368980Z async_compile.wait(globals())
2023-01-11T21:05:10.6369053Z del async_compile
2023-01-11T21:05:10.6369058Z 
2023-01-11T21:05:10.6369129Z def call(args):
2023-01-11T21:05:10.6369195Z     arg0_1, = args
2023-01-11T21:05:10.6369264Z     args.clear()
2023-01-11T21:05:10.6369459Z     buf0 = empty_strided((200, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.6369577Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6369644Z     del arg0_1
2023-01-11T21:05:10.6369713Z     return (buf0, )
2023-01-11T21:05:10.6369718Z 
2023-01-11T21:05:10.6369722Z 
2023-01-11T21:05:10.6369795Z if __name__ == "__main__":
2023-01-11T21:05:10.6369909Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6370034Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6370232Z     arg0_1 = rand_strided((200, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.6370341Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6370346Z 
2023-01-11T21:05:10.6370398Z ok (2.664s)
2023-01-11T21:05:10.6370828Z   test_round_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6370955Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6371215Z [2023-01-11 20:58:22,020] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 385
2023-01-11T21:05:10.6371482Z [2023-01-11 20:58:24,711] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 385
2023-01-11T21:05:10.6371487Z 
2023-01-11T21:05:10.6371580Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6371650Z import torch
2023-01-11T21:05:10.6371720Z import random
2023-01-11T21:05:10.6371832Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6371939Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6371944Z 
2023-01-11T21:05:10.6372019Z aten = torch.ops.aten
2023-01-11T21:05:10.6372151Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6372240Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6372245Z 
2023-01-11T21:05:10.6372250Z 
2023-01-11T21:05:10.6372381Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6372583Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6372702Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6372832Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6372918Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6373014Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6373106Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6373165Z {
2023-01-11T21:05:10.6373261Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6373320Z     {
2023-01-11T21:05:10.6373394Z         #pragma omp for 
2023-01-11T21:05:10.6373461Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6373524Z         {
2023-01-11T21:05:10.6373655Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6373739Z             auto tmp1 = tmp0.round();
2023-01-11T21:05:10.6373873Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(100.0));
2023-01-11T21:05:10.6373955Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:05:10.6374044Z             auto tmp4 = tmp3.round();
2023-01-11T21:05:10.6374177Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(0.01));
2023-01-11T21:05:10.6374247Z             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.6374365Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6374458Z             tmp6.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6374518Z         }
2023-01-11T21:05:10.6374613Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6374694Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6374742Z         {
2023-01-11T21:05:10.6374823Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6374921Z             auto tmp1 = std::nearbyint(tmp0);
2023-01-11T21:05:10.6375021Z             auto tmp2 = static_cast<float>(100.0);
2023-01-11T21:05:10.6375102Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:05:10.6375201Z             auto tmp4 = std::nearbyint(tmp3);
2023-01-11T21:05:10.6375302Z             auto tmp5 = static_cast<float>(0.01);
2023-01-11T21:05:10.6375387Z             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.6375453Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6375531Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.6375593Z         }
2023-01-11T21:05:10.6375668Z         #pragma omp for 
2023-01-11T21:05:10.6375750Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6375811Z         {
2023-01-11T21:05:10.6375928Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6376060Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6376142Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6376225Z             auto tmp3 = tmp2.round();
2023-01-11T21:05:10.6376313Z             tmp3.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.6376374Z         }
2023-01-11T21:05:10.6376467Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6376547Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6376595Z         {
2023-01-11T21:05:10.6376678Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6376774Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6376856Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6376958Z             auto tmp3 = std::nearbyint(tmp2);
2023-01-11T21:05:10.6377036Z             out_ptr2[i0] = tmp3;
2023-01-11T21:05:10.6377096Z         }
2023-01-11T21:05:10.6377143Z     }
2023-01-11T21:05:10.6377201Z }
2023-01-11T21:05:10.6377280Z ''')
2023-01-11T21:05:10.6377285Z 
2023-01-11T21:05:10.6377290Z 
2023-01-11T21:05:10.6377377Z async_compile.wait(globals())
2023-01-11T21:05:10.6377448Z del async_compile
2023-01-11T21:05:10.6377453Z 
2023-01-11T21:05:10.6377519Z def call(args):
2023-01-11T21:05:10.6377592Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6377649Z     args.clear()
2023-01-11T21:05:10.6377840Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6378029Z     buf2 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6378247Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6378459Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6378617Z     del arg0_1
2023-01-11T21:05:10.6378684Z     del arg1_1
2023-01-11T21:05:10.6378751Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6378771Z 
2023-01-11T21:05:10.6378775Z 
2023-01-11T21:05:10.6378837Z if __name__ == "__main__":
2023-01-11T21:05:10.6378953Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6379077Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6379275Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6379469Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6379584Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6379592Z 
2023-01-11T21:05:10.6379658Z ok (2.743s)
2023-01-11T21:05:10.6380127Z   test_rsqrt_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6380254Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6380501Z [2023-01-11 20:58:24,756] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 386
2023-01-11T21:05:10.6380762Z [2023-01-11 20:58:27,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 386
2023-01-11T21:05:10.6380767Z 
2023-01-11T21:05:10.6380859Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6380928Z import torch
2023-01-11T21:05:10.6380996Z import random
2023-01-11T21:05:10.6381113Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6381232Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6381237Z 
2023-01-11T21:05:10.6381313Z aten = torch.ops.aten
2023-01-11T21:05:10.6381435Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6381525Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6381530Z 
2023-01-11T21:05:10.6381535Z 
2023-01-11T21:05:10.6381666Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6381868Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6381984Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6382082Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6382177Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6382236Z {
2023-01-11T21:05:10.6382319Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6382379Z     {
2023-01-11T21:05:10.6382456Z         #pragma omp for 
2023-01-11T21:05:10.6382536Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6382597Z         {
2023-01-11T21:05:10.6382733Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6382805Z             auto tmp1 = tmp0.rsqrt();
2023-01-11T21:05:10.6382936Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6383020Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.6383104Z             auto tmp4 = tmp3.rsqrt();
2023-01-11T21:05:10.6383234Z             auto tmp5 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6383356Z             auto tmp6 = tmp4 - tmp5;
2023-01-11T21:05:10.6383445Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6383536Z             tmp6.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6383585Z         }
2023-01-11T21:05:10.6383679Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6383759Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6383853Z         {
2023-01-11T21:05:10.6383937Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6384034Z             auto tmp1 = 1 / std::sqrt(tmp0);
2023-01-11T21:05:10.6384136Z             auto tmp2 = static_cast<float>(1);
2023-01-11T21:05:10.6384206Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.6384300Z             auto tmp4 = 1 / std::sqrt(tmp3);
2023-01-11T21:05:10.6384398Z             auto tmp5 = static_cast<float>(2);
2023-01-11T21:05:10.6384520Z             auto tmp6 = tmp4 - tmp5;
2023-01-11T21:05:10.6384598Z             out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6384674Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.6384737Z         }
2023-01-11T21:05:10.6384785Z     }
2023-01-11T21:05:10.6384844Z }
2023-01-11T21:05:10.6384924Z ''')
2023-01-11T21:05:10.6384929Z 
2023-01-11T21:05:10.6384933Z 
2023-01-11T21:05:10.6385021Z async_compile.wait(globals())
2023-01-11T21:05:10.6385093Z del async_compile
2023-01-11T21:05:10.6385098Z 
2023-01-11T21:05:10.6385168Z def call(args):
2023-01-11T21:05:10.6385236Z     arg0_1, = args
2023-01-11T21:05:10.6385293Z     args.clear()
2023-01-11T21:05:10.6385486Z     buf0 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6385704Z     buf1 = empty_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6385870Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6385938Z     del arg0_1
2023-01-11T21:05:10.6386014Z     return (buf0, buf1, )
2023-01-11T21:05:10.6386019Z 
2023-01-11T21:05:10.6386024Z 
2023-01-11T21:05:10.6386098Z if __name__ == "__main__":
2023-01-11T21:05:10.6386212Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6386322Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6386514Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6386620Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6386626Z 
2023-01-11T21:05:10.6386692Z ok (2.719s)
2023-01-11T21:05:10.6387132Z   test_scatter1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6387261Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6387520Z [2023-01-11 20:58:27,488] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 387
2023-01-11T21:05:10.6387784Z [2023-01-11 20:58:30,131] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 387
2023-01-11T21:05:10.6387790Z 
2023-01-11T21:05:10.6387883Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6387938Z import torch
2023-01-11T21:05:10.6388007Z import random
2023-01-11T21:05:10.6388122Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6388242Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6388247Z 
2023-01-11T21:05:10.6388327Z aten = torch.ops.aten
2023-01-11T21:05:10.6388458Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6388548Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6388554Z 
2023-01-11T21:05:10.6388558Z 
2023-01-11T21:05:10.6388692Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6388882Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6389001Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6389105Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6389210Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6389307Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6389401Z {
2023-01-11T21:05:10.6389497Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6389545Z     {
2023-01-11T21:05:10.6389620Z         #pragma omp for 
2023-01-11T21:05:10.6389699Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.6389765Z         {
2023-01-11T21:05:10.6389827Z             {
2023-01-11T21:05:10.6389889Z                 {
2023-01-11T21:05:10.6389982Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6390054Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6390119Z                 }
2023-01-11T21:05:10.6390180Z             }
2023-01-11T21:05:10.6390240Z         }
2023-01-11T21:05:10.6390316Z         #pragma omp single
2023-01-11T21:05:10.6390377Z         {
2023-01-11T21:05:10.6390438Z             {
2023-01-11T21:05:10.6390487Z                 {
2023-01-11T21:05:10.6390577Z                     auto tmp0 = in_ptr1[0];
2023-01-11T21:05:10.6390664Z                     auto tmp1 = in_ptr2[0];
2023-01-11T21:05:10.6390752Z                     out_ptr0[tmp0] = tmp1;
2023-01-11T21:05:10.6390817Z                 }
2023-01-11T21:05:10.6390878Z             }
2023-01-11T21:05:10.6390925Z         }
2023-01-11T21:05:10.6390984Z     }
2023-01-11T21:05:10.6391043Z }
2023-01-11T21:05:10.6391121Z ''')
2023-01-11T21:05:10.6391161Z 
2023-01-11T21:05:10.6391165Z 
2023-01-11T21:05:10.6391254Z async_compile.wait(globals())
2023-01-11T21:05:10.6391324Z del async_compile
2023-01-11T21:05:10.6391329Z 
2023-01-11T21:05:10.6391397Z def call(args):
2023-01-11T21:05:10.6391474Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6391531Z     args.clear()
2023-01-11T21:05:10.6391725Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6391909Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6391978Z     del arg0_1
2023-01-11T21:05:10.6392043Z     del arg1_1
2023-01-11T21:05:10.6392106Z     del arg2_1
2023-01-11T21:05:10.6392179Z     return (buf0, )
2023-01-11T21:05:10.6392184Z 
2023-01-11T21:05:10.6392188Z 
2023-01-11T21:05:10.6392248Z if __name__ == "__main__":
2023-01-11T21:05:10.6392360Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6392482Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6392677Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6392870Z     arg1_1 = rand_strided((1, 1), (1, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6393062Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6393189Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6393194Z 
2023-01-11T21:05:10.6393260Z ok (2.699s)
2023-01-11T21:05:10.6393689Z   test_scatter2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6393817Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6394078Z [2023-01-11 20:58:30,178] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 388
2023-01-11T21:05:10.6394340Z [2023-01-11 20:58:32,856] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 388
2023-01-11T21:05:10.6394345Z 
2023-01-11T21:05:10.6394437Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6394506Z import torch
2023-01-11T21:05:10.6394575Z import random
2023-01-11T21:05:10.6394689Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6394807Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6394812Z 
2023-01-11T21:05:10.6394876Z aten = torch.ops.aten
2023-01-11T21:05:10.6395007Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6395128Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6395134Z 
2023-01-11T21:05:10.6395138Z 
2023-01-11T21:05:10.6395270Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6395474Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6395592Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6395696Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6395798Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6395882Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6395941Z {
2023-01-11T21:05:10.6396035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6396095Z     {
2023-01-11T21:05:10.6396170Z         #pragma omp for 
2023-01-11T21:05:10.6396251Z         for(long i0=0; i0<2048; i0+=1)
2023-01-11T21:05:10.6396312Z         {
2023-01-11T21:05:10.6396431Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6396524Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6396584Z         }
2023-01-11T21:05:10.6396678Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6396795Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:05:10.6396859Z         {
2023-01-11T21:05:10.6396941Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6397009Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6397068Z         }
2023-01-11T21:05:10.6397141Z         #pragma omp for 
2023-01-11T21:05:10.6397219Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.6397280Z         {
2023-01-11T21:05:10.6397357Z             #pragma GCC ivdep
2023-01-11T21:05:10.6397429Z             for(long i1=0; i1<512; i1+=1)
2023-01-11T21:05:10.6397494Z             {
2023-01-11T21:05:10.6397556Z                 {
2023-01-11T21:05:10.6397621Z                     {
2023-01-11T21:05:10.6397722Z                         auto tmp0 = in_ptr1[i1 + (512*i0)];
2023-01-11T21:05:10.6397825Z                         auto tmp1 = in_ptr2[i1 + (512*i0)];
2023-01-11T21:05:10.6397939Z                         atomic_add(&out_ptr0[i1 + (512*tmp0)], tmp1);
2023-01-11T21:05:10.6397992Z                     }
2023-01-11T21:05:10.6398057Z                 }
2023-01-11T21:05:10.6398118Z             }
2023-01-11T21:05:10.6398178Z         }
2023-01-11T21:05:10.6398237Z     }
2023-01-11T21:05:10.6398296Z }
2023-01-11T21:05:10.6398372Z ''')
2023-01-11T21:05:10.6398377Z 
2023-01-11T21:05:10.6398381Z 
2023-01-11T21:05:10.6398455Z async_compile.wait(globals())
2023-01-11T21:05:10.6398525Z del async_compile
2023-01-11T21:05:10.6398531Z 
2023-01-11T21:05:10.6398599Z def call(args):
2023-01-11T21:05:10.6398680Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6398749Z     args.clear()
2023-01-11T21:05:10.6398952Z     buf0 = empty_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6399139Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6399208Z     del arg0_1
2023-01-11T21:05:10.6399259Z     del arg1_1
2023-01-11T21:05:10.6399323Z     del arg2_1
2023-01-11T21:05:10.6399394Z     return (buf0, )
2023-01-11T21:05:10.6399399Z 
2023-01-11T21:05:10.6399403Z 
2023-01-11T21:05:10.6399481Z if __name__ == "__main__":
2023-01-11T21:05:10.6399594Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6399715Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6399917Z     arg0_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6400102Z     arg1_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6400301Z     arg2_1 = rand_strided((64, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6400421Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6400426Z 
2023-01-11T21:05:10.6400490Z ok (2.772s)
2023-01-11T21:05:10.6401182Z   test_scatter3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6401313Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6401574Z [2023-01-11 20:58:32,949] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 389
2023-01-11T21:05:10.6401842Z [2023-01-11 20:58:35,609] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 389
2023-01-11T21:05:10.6401847Z 
2023-01-11T21:05:10.6401939Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6402010Z import torch
2023-01-11T21:05:10.6402067Z import random
2023-01-11T21:05:10.6402182Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6402305Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6402310Z 
2023-01-11T21:05:10.6402389Z aten = torch.ops.aten
2023-01-11T21:05:10.6402572Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6402663Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6402668Z 
2023-01-11T21:05:10.6402672Z 
2023-01-11T21:05:10.6402807Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6403010Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6403116Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6403220Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6403319Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6403380Z {
2023-01-11T21:05:10.6403476Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6403536Z     {
2023-01-11T21:05:10.6403613Z         #pragma omp for 
2023-01-11T21:05:10.6403684Z         for(long i0=0; i0<117; i0+=1)
2023-01-11T21:05:10.6403746Z         {
2023-01-11T21:05:10.6403882Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6403978Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6404039Z         }
2023-01-11T21:05:10.6404132Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6404217Z         for(long i0=1872; i0<1885; i0+=1)
2023-01-11T21:05:10.6404266Z         {
2023-01-11T21:05:10.6404351Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6404429Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6404489Z         }
2023-01-11T21:05:10.6404563Z         #pragma omp for 
2023-01-11T21:05:10.6404643Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6404690Z         {
2023-01-11T21:05:10.6404752Z             {
2023-01-11T21:05:10.6404815Z                 {
2023-01-11T21:05:10.6404904Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6405011Z                     auto tmp1 = static_cast<float>(0.8);
2023-01-11T21:05:10.6405113Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:05:10.6405175Z                 }
2023-01-11T21:05:10.6405224Z             }
2023-01-11T21:05:10.6405287Z         }
2023-01-11T21:05:10.6405346Z     }
2023-01-11T21:05:10.6405405Z }
2023-01-11T21:05:10.6405483Z ''')
2023-01-11T21:05:10.6405488Z 
2023-01-11T21:05:10.6405492Z 
2023-01-11T21:05:10.6405579Z async_compile.wait(globals())
2023-01-11T21:05:10.6405650Z del async_compile
2023-01-11T21:05:10.6405655Z 
2023-01-11T21:05:10.6405711Z def call(args):
2023-01-11T21:05:10.6405784Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6405854Z     args.clear()
2023-01-11T21:05:10.6406063Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6406224Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6406291Z     del arg0_1
2023-01-11T21:05:10.6406398Z     del arg1_1
2023-01-11T21:05:10.6406467Z     return (buf0, )
2023-01-11T21:05:10.6406472Z 
2023-01-11T21:05:10.6406476Z 
2023-01-11T21:05:10.6406537Z if __name__ == "__main__":
2023-01-11T21:05:10.6406653Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6406775Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6406985Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6407182Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6407296Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6407300Z 
2023-01-11T21:05:10.6407365Z ok (2.709s)
2023-01-11T21:05:10.6407802Z   test_scatter4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6407929Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6408221Z [2023-01-11 20:58:35,684] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 390
2023-01-11T21:05:10.6408483Z [2023-01-11 20:58:38,341] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 390
2023-01-11T21:05:10.6408487Z 
2023-01-11T21:05:10.6408579Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6408647Z import torch
2023-01-11T21:05:10.6408717Z import random
2023-01-11T21:05:10.6408830Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6408947Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6408952Z 
2023-01-11T21:05:10.6409029Z aten = torch.ops.aten
2023-01-11T21:05:10.6409147Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6409238Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6409242Z 
2023-01-11T21:05:10.6409246Z 
2023-01-11T21:05:10.6409377Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6409582Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6409700Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6409803Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6409906Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6410006Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6410054Z {
2023-01-11T21:05:10.6410150Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6410212Z     {
2023-01-11T21:05:10.6410290Z         #pragma omp for 
2023-01-11T21:05:10.6410373Z         for(long i0=0; i0<12152; i0+=1)
2023-01-11T21:05:10.6410435Z         {
2023-01-11T21:05:10.6410570Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6410651Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6410713Z         }
2023-01-11T21:05:10.6410806Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6410896Z         for(long i0=194432; i0<194432; i0+=1)
2023-01-11T21:05:10.6410957Z         {
2023-01-11T21:05:10.6411039Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6411118Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6411166Z         }
2023-01-11T21:05:10.6411238Z         #pragma omp for 
2023-01-11T21:05:10.6411316Z         for(long i0=0; i0<992; i0+=1)
2023-01-11T21:05:10.6411377Z         {
2023-01-11T21:05:10.6411439Z             {
2023-01-11T21:05:10.6411502Z                 {
2023-01-11T21:05:10.6411580Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6411667Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:05:10.6411762Z                     out_ptr0[i0 + (992*tmp0)] = tmp1;
2023-01-11T21:05:10.6411825Z                 }
2023-01-11T21:05:10.6411917Z             }
2023-01-11T21:05:10.6411983Z         }
2023-01-11T21:05:10.6412042Z     }
2023-01-11T21:05:10.6412087Z }
2023-01-11T21:05:10.6412165Z ''')
2023-01-11T21:05:10.6412170Z 
2023-01-11T21:05:10.6412174Z 
2023-01-11T21:05:10.6412264Z async_compile.wait(globals())
2023-01-11T21:05:10.6412335Z del async_compile
2023-01-11T21:05:10.6412339Z 
2023-01-11T21:05:10.6412408Z def call(args):
2023-01-11T21:05:10.6412487Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6412557Z     args.clear()
2023-01-11T21:05:10.6412747Z     buf0 = empty_strided((196, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6412934Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6413001Z     del arg0_1
2023-01-11T21:05:10.6413066Z     del arg1_1
2023-01-11T21:05:10.6413129Z     del arg2_1
2023-01-11T21:05:10.6413200Z     return (buf0, )
2023-01-11T21:05:10.6413204Z 
2023-01-11T21:05:10.6413211Z 
2023-01-11T21:05:10.6413283Z if __name__ == "__main__":
2023-01-11T21:05:10.6413397Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6413507Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6413739Z     arg0_1 = rand_strided((196, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6413937Z     arg1_1 = rand_strided((1, 992), (992, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6414135Z     arg2_1 = rand_strided((1, 992), (992, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6414257Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6414262Z 
2023-01-11T21:05:10.6414327Z ok (2.856s)
2023-01-11T21:05:10.6414484Z   test_scatter_add1_cpu (__main__.CpuTests) ... skip: Flaky test, needs debugging (0.001s)
2023-01-11T21:05:10.6414926Z   test_scatter_add2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6415057Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6415304Z [2023-01-11 20:58:38,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 391
2023-01-11T21:05:10.6415567Z [2023-01-11 20:58:41,157] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 391
2023-01-11T21:05:10.6415572Z 
2023-01-11T21:05:10.6415666Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6415736Z import torch
2023-01-11T21:05:10.6415807Z import random
2023-01-11T21:05:10.6415924Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6416044Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6416049Z 
2023-01-11T21:05:10.6416128Z aten = torch.ops.aten
2023-01-11T21:05:10.6416249Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6416341Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6416346Z 
2023-01-11T21:05:10.6416350Z 
2023-01-11T21:05:10.6416486Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6416691Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6416809Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6416913Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6417016Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6417115Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6417162Z {
2023-01-11T21:05:10.6417258Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6417319Z     {
2023-01-11T21:05:10.6417395Z         #pragma omp for 
2023-01-11T21:05:10.6417478Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.6417540Z         {
2023-01-11T21:05:10.6417635Z             {
2023-01-11T21:05:10.6417686Z                 {
2023-01-11T21:05:10.6417776Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6417861Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6417928Z                 }
2023-01-11T21:05:10.6417992Z             }
2023-01-11T21:05:10.6418052Z         }
2023-01-11T21:05:10.6418113Z         #pragma omp for 
2023-01-11T21:05:10.6418192Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6418253Z         {
2023-01-11T21:05:10.6418331Z             #pragma GCC ivdep
2023-01-11T21:05:10.6418416Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6418563Z             {
2023-01-11T21:05:10.6418632Z                 {
2023-01-11T21:05:10.6418684Z                     {
2023-01-11T21:05:10.6418788Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:05:10.6418887Z                         auto tmp1 = in_ptr2[i1 + (3*i0)];
2023-01-11T21:05:10.6419000Z                         atomic_add(&out_ptr0[i1 + (3*tmp0)], tmp1);
2023-01-11T21:05:10.6419069Z                     }
2023-01-11T21:05:10.6419131Z                 }
2023-01-11T21:05:10.6419193Z             }
2023-01-11T21:05:10.6419241Z         }
2023-01-11T21:05:10.6419358Z     }
2023-01-11T21:05:10.6419419Z }
2023-01-11T21:05:10.6419502Z ''')
2023-01-11T21:05:10.6419507Z 
2023-01-11T21:05:10.6419511Z 
2023-01-11T21:05:10.6419601Z async_compile.wait(globals())
2023-01-11T21:05:10.6419673Z del async_compile
2023-01-11T21:05:10.6419677Z 
2023-01-11T21:05:10.6419750Z def call(args):
2023-01-11T21:05:10.6419818Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6419889Z     args.clear()
2023-01-11T21:05:10.6420085Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6420273Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6420343Z     del arg0_1
2023-01-11T21:05:10.6420409Z     del arg1_1
2023-01-11T21:05:10.6420477Z     del arg2_1
2023-01-11T21:05:10.6420535Z     return (buf0, )
2023-01-11T21:05:10.6420554Z 
2023-01-11T21:05:10.6420558Z 
2023-01-11T21:05:10.6420622Z if __name__ == "__main__":
2023-01-11T21:05:10.6420738Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6420863Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6421058Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6421247Z     arg1_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6421439Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6421560Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6421565Z 
2023-01-11T21:05:10.6421618Z ok (2.688s)
2023-01-11T21:05:10.6422059Z   test_scatter_add3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6422190Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6422450Z [2023-01-11 20:58:41,199] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 392
2023-01-11T21:05:10.6422711Z [2023-01-11 20:58:43,860] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 392
2023-01-11T21:05:10.6422716Z 
2023-01-11T21:05:10.6422807Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6422876Z import torch
2023-01-11T21:05:10.6422946Z import random
2023-01-11T21:05:10.6423059Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6423164Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6423181Z 
2023-01-11T21:05:10.6423244Z aten = torch.ops.aten
2023-01-11T21:05:10.6423403Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6423494Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6423499Z 
2023-01-11T21:05:10.6423503Z 
2023-01-11T21:05:10.6423637Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6423839Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6423957Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6424060Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6424163Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6424248Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6424307Z {
2023-01-11T21:05:10.6424403Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6424464Z     {
2023-01-11T21:05:10.6424539Z         #pragma omp for 
2023-01-11T21:05:10.6424620Z         for(long i0=0; i0<117; i0+=1)
2023-01-11T21:05:10.6424671Z         {
2023-01-11T21:05:10.6424804Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6424895Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6424956Z         }
2023-01-11T21:05:10.6425077Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6425163Z         for(long i0=1872; i0<1885; i0+=1)
2023-01-11T21:05:10.6425223Z         {
2023-01-11T21:05:10.6425293Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6425370Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6425430Z         }
2023-01-11T21:05:10.6425503Z         #pragma omp for 
2023-01-11T21:05:10.6425582Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6425641Z         {
2023-01-11T21:05:10.6425703Z             {
2023-01-11T21:05:10.6425753Z                 {
2023-01-11T21:05:10.6425843Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6425931Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:05:10.6426034Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:05:10.6426099Z                 }
2023-01-11T21:05:10.6426160Z             }
2023-01-11T21:05:10.6426219Z         }
2023-01-11T21:05:10.6426266Z     }
2023-01-11T21:05:10.6426325Z }
2023-01-11T21:05:10.6426404Z ''')
2023-01-11T21:05:10.6426409Z 
2023-01-11T21:05:10.6426413Z 
2023-01-11T21:05:10.6426501Z async_compile.wait(globals())
2023-01-11T21:05:10.6426571Z del async_compile
2023-01-11T21:05:10.6426575Z 
2023-01-11T21:05:10.6426643Z def call(args):
2023-01-11T21:05:10.6426723Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6426779Z     args.clear()
2023-01-11T21:05:10.6426990Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6427175Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6427245Z     del arg0_1
2023-01-11T21:05:10.6427309Z     del arg1_1
2023-01-11T21:05:10.6427374Z     del arg2_1
2023-01-11T21:05:10.6427445Z     return (buf0, )
2023-01-11T21:05:10.6427449Z 
2023-01-11T21:05:10.6427453Z 
2023-01-11T21:05:10.6427526Z if __name__ == "__main__":
2023-01-11T21:05:10.6427628Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6427748Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6427957Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6428155Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6428358Z     arg2_1 = rand_strided((1, 1, 10), (10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6428477Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6428482Z 
2023-01-11T21:05:10.6428547Z ok (2.706s)
2023-01-11T21:05:10.6428802Z   test_scatter_reduce1_cpu (__main__.CpuTests) ... [W TensorAdvancedIndexing.cpp:1739] Warning: scatter_reduce() is in beta and the API may change at any time. (function operator())
2023-01-11T21:05:10.6429218Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6429344Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6429600Z [2023-01-11 20:58:43,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 393
2023-01-11T21:05:10.6429863Z [2023-01-11 20:58:43,925] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 393
2023-01-11T21:05:10.6429868Z 
2023-01-11T21:05:10.6429960Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6430028Z import torch
2023-01-11T21:05:10.6430095Z import random
2023-01-11T21:05:10.6430208Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6430327Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6430333Z 
2023-01-11T21:05:10.6430397Z aten = torch.ops.aten
2023-01-11T21:05:10.6430555Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6430647Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6430652Z 
2023-01-11T21:05:10.6430655Z 
2023-01-11T21:05:10.6430787Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6430989Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6431107Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6431210Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6431311Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6431397Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6431456Z {
2023-01-11T21:05:10.6431551Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6431614Z     {
2023-01-11T21:05:10.6431689Z         #pragma omp for 
2023-01-11T21:05:10.6431769Z         for(long i0=0; i0<117; i0+=1)
2023-01-11T21:05:10.6431831Z         {
2023-01-11T21:05:10.6431953Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6432043Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6432107Z         }
2023-01-11T21:05:10.6432200Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6432283Z         for(long i0=1872; i0<1885; i0+=1)
2023-01-11T21:05:10.6432345Z         {
2023-01-11T21:05:10.6432426Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6432492Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6432551Z         }
2023-01-11T21:05:10.6432625Z         #pragma omp for 
2023-01-11T21:05:10.6432703Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6432763Z         {
2023-01-11T21:05:10.6432825Z             {
2023-01-11T21:05:10.6432875Z                 {
2023-01-11T21:05:10.6432967Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6433055Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:05:10.6433158Z                     atomic_add(&out_ptr0[tmp0], tmp1);
2023-01-11T21:05:10.6433223Z                 }
2023-01-11T21:05:10.6433284Z             }
2023-01-11T21:05:10.6433343Z         }
2023-01-11T21:05:10.6433390Z     }
2023-01-11T21:05:10.6433447Z }
2023-01-11T21:05:10.6433523Z ''')
2023-01-11T21:05:10.6433530Z 
2023-01-11T21:05:10.6433533Z 
2023-01-11T21:05:10.6433620Z async_compile.wait(globals())
2023-01-11T21:05:10.6433690Z del async_compile
2023-01-11T21:05:10.6433695Z 
2023-01-11T21:05:10.6433763Z def call(args):
2023-01-11T21:05:10.6433843Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6433900Z     args.clear()
2023-01-11T21:05:10.6434112Z     buf0 = empty_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6434298Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6434393Z     del arg0_1
2023-01-11T21:05:10.6434458Z     del arg1_1
2023-01-11T21:05:10.6434522Z     del arg2_1
2023-01-11T21:05:10.6434591Z     return (buf0, )
2023-01-11T21:05:10.6434595Z 
2023-01-11T21:05:10.6434602Z 
2023-01-11T21:05:10.6434678Z if __name__ == "__main__":
2023-01-11T21:05:10.6434777Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6434898Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6435105Z     arg0_1 = rand_strided((5, 29, 13), (377, 13, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6435304Z     arg1_1 = rand_strided((1, 1, 4), (4, 4, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6435506Z     arg2_1 = rand_strided((1, 1, 10), (10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6435625Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6435630Z 
2023-01-11T21:05:10.6435695Z ok (0.061s)
2023-01-11T21:05:10.6436173Z   test_scatter_reduce2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6436300Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6436546Z [2023-01-11 20:58:43,962] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 394
2023-01-11T21:05:10.6436807Z [2023-01-11 20:58:46,623] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 394
2023-01-11T21:05:10.6436813Z 
2023-01-11T21:05:10.6436905Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6436976Z import torch
2023-01-11T21:05:10.6437045Z import random
2023-01-11T21:05:10.6437160Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6437281Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6437286Z 
2023-01-11T21:05:10.6437361Z aten = torch.ops.aten
2023-01-11T21:05:10.6437483Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6437572Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6437578Z 
2023-01-11T21:05:10.6437582Z 
2023-01-11T21:05:10.6437712Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6437911Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6438028Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6438130Z                        const long* __restrict__ in_ptr1,
2023-01-11T21:05:10.6438231Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6438328Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6438375Z {
2023-01-11T21:05:10.6438470Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6438532Z     {
2023-01-11T21:05:10.6438607Z         #pragma omp for 
2023-01-11T21:05:10.6438686Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.6438747Z         {
2023-01-11T21:05:10.6438809Z             {
2023-01-11T21:05:10.6438862Z                 {
2023-01-11T21:05:10.6438952Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6439035Z                     out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6439099Z                 }
2023-01-11T21:05:10.6439162Z             }
2023-01-11T21:05:10.6439222Z         }
2023-01-11T21:05:10.6439283Z         #pragma omp for 
2023-01-11T21:05:10.6439361Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6439420Z         {
2023-01-11T21:05:10.6439498Z             #pragma GCC ivdep
2023-01-11T21:05:10.6439577Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6439639Z             {
2023-01-11T21:05:10.6439701Z                 {
2023-01-11T21:05:10.6439753Z                     {
2023-01-11T21:05:10.6439854Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:05:10.6439992Z                         auto tmp1 = static_cast<float>(0);
2023-01-11T21:05:10.6440088Z                         out_ptr0[i1 + (3*tmp0)] = tmp1;
2023-01-11T21:05:10.6440155Z                     }
2023-01-11T21:05:10.6440218Z                 }
2023-01-11T21:05:10.6440278Z             }
2023-01-11T21:05:10.6440326Z         }
2023-01-11T21:05:10.6440400Z         #pragma omp for 
2023-01-11T21:05:10.6440478Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6440539Z         {
2023-01-11T21:05:10.6440737Z             #pragma GCC ivdep
2023-01-11T21:05:10.6440820Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6440887Z             {
2023-01-11T21:05:10.6440937Z                 {
2023-01-11T21:05:10.6441001Z                     {
2023-01-11T21:05:10.6441104Z                         auto tmp0 = in_ptr1[i1 + (3*i0)];
2023-01-11T21:05:10.6441203Z                         auto tmp1 = in_ptr2[i1 + (3*i0)];
2023-01-11T21:05:10.6441315Z                         atomic_add(&out_ptr0[i1 + (3*tmp0)], tmp1);
2023-01-11T21:05:10.6441383Z                     }
2023-01-11T21:05:10.6441447Z                 }
2023-01-11T21:05:10.6441495Z             }
2023-01-11T21:05:10.6441556Z         }
2023-01-11T21:05:10.6441666Z     }
2023-01-11T21:05:10.6441726Z }
2023-01-11T21:05:10.6441806Z ''')
2023-01-11T21:05:10.6441811Z 
2023-01-11T21:05:10.6441815Z 
2023-01-11T21:05:10.6441904Z async_compile.wait(globals())
2023-01-11T21:05:10.6441961Z del async_compile
2023-01-11T21:05:10.6441979Z 
2023-01-11T21:05:10.6442035Z def call(args):
2023-01-11T21:05:10.6442113Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6442184Z     args.clear()
2023-01-11T21:05:10.6442384Z     buf0 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6442571Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6442640Z     del arg0_1
2023-01-11T21:05:10.6442708Z     del arg1_1
2023-01-11T21:05:10.6442759Z     del arg2_1
2023-01-11T21:05:10.6442830Z     return (buf0, )
2023-01-11T21:05:10.6442835Z 
2023-01-11T21:05:10.6442839Z 
2023-01-11T21:05:10.6442915Z if __name__ == "__main__":
2023-01-11T21:05:10.6443031Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6443155Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6443350Z     arg0_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6443539Z     arg1_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6443728Z     arg2_1 = rand_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6443840Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6443845Z 
2023-01-11T21:05:10.6443909Z ok (2.699s)
2023-01-11T21:05:10.6444365Z   test_scheduler_vertical_fusion1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6444495Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6444753Z [2023-01-11 20:58:46,982] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 395
2023-01-11T21:05:10.6445017Z [2023-01-11 20:58:49,663] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 395
2023-01-11T21:05:10.6445022Z 
2023-01-11T21:05:10.6445114Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6445182Z import torch
2023-01-11T21:05:10.6445249Z import random
2023-01-11T21:05:10.6445349Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6445468Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6445473Z 
2023-01-11T21:05:10.6445586Z aten = torch.ops.aten
2023-01-11T21:05:10.6445716Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6445807Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6445812Z 
2023-01-11T21:05:10.6445818Z 
2023-01-11T21:05:10.6445952Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6446157Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6446273Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6446362Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.6446465Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6446567Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6446669Z                        const float* __restrict__ in_ptr2)
2023-01-11T21:05:10.6446728Z {
2023-01-11T21:05:10.6446813Z     auto out_ptr1 = in_out_ptr1;
2023-01-11T21:05:10.6446908Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6446958Z     {
2023-01-11T21:05:10.6447034Z         #pragma omp for 
2023-01-11T21:05:10.6447115Z         for(long i0=0; i0<67626; i0+=1)
2023-01-11T21:05:10.6447179Z         {
2023-01-11T21:05:10.6447354Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6447488Z             auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6447712Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.061519070296458e-11));
2023-01-11T21:05:10.6447797Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6448006Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(-1.988366587925593e-08));
2023-01-11T21:05:10.6448088Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6448170Z             auto tmp5 = tmp0 * tmp4;
2023-01-11T21:05:10.6448388Z             auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(-3.087032500374211e-07));
2023-01-11T21:05:10.6448475Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.6448696Z             auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1.55093272922008e-10));
2023-01-11T21:05:10.6448781Z             auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.6448866Z             auto tmp11 = tmp7 + tmp10;
2023-01-11T21:05:10.6448952Z             auto tmp12 = tmp11.reciprocal();
2023-01-11T21:05:10.6449086Z             auto tmp13 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:05:10.6449172Z             auto tmp14 = tmp12 * tmp13;
2023-01-11T21:05:10.6449269Z             tmp11.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6449361Z             tmp14.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6449423Z         }
2023-01-11T21:05:10.6449518Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6449596Z         for(long i0=1082016; i0<1082016; i0+=1)
2023-01-11T21:05:10.6449658Z         {
2023-01-11T21:05:10.6449740Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6449825Z             auto tmp8 = in_ptr1[i0];
2023-01-11T21:05:10.6450001Z             auto tmp1 = static_cast<float>(-1.061519070296458e-11);
2023-01-11T21:05:10.6450082Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6450254Z             auto tmp3 = static_cast<float>(-1.988366587925593e-08);
2023-01-11T21:05:10.6450322Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6450402Z             auto tmp5 = tmp0 * tmp4;
2023-01-11T21:05:10.6450569Z             auto tmp6 = static_cast<float>(-3.087032500374211e-07);
2023-01-11T21:05:10.6450649Z             auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.6450818Z             auto tmp9 = static_cast<float>(1.55093272922008e-10);
2023-01-11T21:05:10.6450901Z             auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.6450985Z             auto tmp11 = tmp7 + tmp10;
2023-01-11T21:05:10.6451051Z             auto tmp12 = 1 / tmp11;
2023-01-11T21:05:10.6451151Z             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:05:10.6451235Z             auto tmp14 = tmp12 * tmp13;
2023-01-11T21:05:10.6451344Z             in_out_ptr0[i0] = tmp11;
2023-01-11T21:05:10.6451422Z             out_ptr1[i0] = tmp14;
2023-01-11T21:05:10.6451483Z         }
2023-01-11T21:05:10.6451558Z         #pragma omp for 
2023-01-11T21:05:10.6451628Z         for(long i0=0; i0<41616; i0+=1)
2023-01-11T21:05:10.6451689Z         {
2023-01-11T21:05:10.6451768Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.6451832Z             {
2023-01-11T21:05:10.6451980Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + (16*i1) + (26*i0));
2023-01-11T21:05:10.6452114Z                 auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i1);
2023-01-11T21:05:10.6452202Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6452294Z                 tmp2.store(in_out_ptr0 + (16*i1) + (26*i0));
2023-01-11T21:05:10.6452356Z             }
2023-01-11T21:05:10.6452446Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.6452534Z             for(long i1=16; i1<26; i1+=1)
2023-01-11T21:05:10.6452598Z             {
2023-01-11T21:05:10.6452697Z                 auto tmp0 = in_out_ptr0[i1 + (26*i0)];
2023-01-11T21:05:10.6452783Z                 auto tmp1 = in_ptr2[i1];
2023-01-11T21:05:10.6452882Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6452977Z                 in_out_ptr0[i1 + (26*i0)] = tmp2;
2023-01-11T21:05:10.6453039Z             }
2023-01-11T21:05:10.6453099Z         }
2023-01-11T21:05:10.6453173Z         #pragma omp for 
2023-01-11T21:05:10.6453252Z         for(long i0=0; i0<67626; i0+=1)
2023-01-11T21:05:10.6453312Z         {
2023-01-11T21:05:10.6453433Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6453567Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6453649Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6453745Z             tmp2.store(in_out_ptr1 + 16*i0);
2023-01-11T21:05:10.6453805Z         }
2023-01-11T21:05:10.6453896Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6453989Z         for(long i0=1082016; i0<1082016; i0+=1)
2023-01-11T21:05:10.6454048Z         {
2023-01-11T21:05:10.6454118Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.6454206Z             auto tmp1 = in_out_ptr0[i0];
2023-01-11T21:05:10.6454288Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6454367Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.6454430Z         }
2023-01-11T21:05:10.6454491Z     }
2023-01-11T21:05:10.6454538Z }
2023-01-11T21:05:10.6454615Z ''')
2023-01-11T21:05:10.6454620Z 
2023-01-11T21:05:10.6454625Z 
2023-01-11T21:05:10.6454711Z async_compile.wait(globals())
2023-01-11T21:05:10.6454784Z del async_compile
2023-01-11T21:05:10.6454789Z 
2023-01-11T21:05:10.6454858Z def call(args):
2023-01-11T21:05:10.6454938Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6455007Z     args.clear()
2023-01-11T21:05:10.6455220Z     buf0 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6455293Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6455503Z     buf2 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6455583Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6455664Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:05:10.6455872Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()))
2023-01-11T21:05:10.6455938Z     del arg0_1
2023-01-11T21:05:10.6456002Z     del arg1_1
2023-01-11T21:05:10.6456054Z     del arg2_1
2023-01-11T21:05:10.6456123Z     return (buf4, )
2023-01-11T21:05:10.6456128Z 
2023-01-11T21:05:10.6456132Z 
2023-01-11T21:05:10.6456206Z if __name__ == "__main__":
2023-01-11T21:05:10.6456319Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6456441Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6456654Z     arg0_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6456897Z     arg1_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6457087Z     arg2_1 = rand_strided((26, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6457196Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6457213Z 
2023-01-11T21:05:10.6457265Z ok (3.589s)
2023-01-11T21:05:10.6457707Z   test_select_scatter_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6457831Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6458087Z [2023-01-11 20:58:50,270] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 396
2023-01-11T21:05:10.6458351Z [2023-01-11 20:58:52,919] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 396
2023-01-11T21:05:10.6458356Z 
2023-01-11T21:05:10.6463442Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6463557Z import torch
2023-01-11T21:05:10.6463616Z import random
2023-01-11T21:05:10.6463738Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6463860Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6463867Z 
2023-01-11T21:05:10.6463943Z aten = torch.ops.aten
2023-01-11T21:05:10.6464079Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6464173Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6464178Z 
2023-01-11T21:05:10.6464182Z 
2023-01-11T21:05:10.6464339Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6464534Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6464658Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6464760Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6464864Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6464959Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6465054Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6465115Z {
2023-01-11T21:05:10.6465211Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6465260Z     {
2023-01-11T21:05:10.6465333Z         #pragma omp for 
2023-01-11T21:05:10.6465412Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6465472Z         {
2023-01-11T21:05:10.6465548Z             #pragma GCC ivdep
2023-01-11T21:05:10.6465633Z             for(long i1=0; i1<197; i1+=1)
2023-01-11T21:05:10.6465683Z             {
2023-01-11T21:05:10.6465762Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6465849Z                 for(long i2=0; i2<38; i2+=1)
2023-01-11T21:05:10.6465915Z                 {
2023-01-11T21:05:10.6465978Z                     {
2023-01-11T21:05:10.6466044Z                         {
2023-01-11T21:05:10.6466151Z                             auto tmp3 = in_ptr0[i2 + (38*i0)];
2023-01-11T21:05:10.6466249Z                             auto tmp4 = in_ptr1[i2 + (38*i1) + (7486*i0)];
2023-01-11T21:05:10.6466355Z                             auto tmp0 = static_cast<int>(i1);
2023-01-11T21:05:10.6466455Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:05:10.6466550Z                             auto tmp2 = tmp0 == tmp1;
2023-01-11T21:05:10.6466650Z                             auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:05:10.6466752Z                             out_ptr0[i2 + (38*i1) + (7486*i0)] = tmp5;
2023-01-11T21:05:10.6466818Z                         }
2023-01-11T21:05:10.6466882Z                     }
2023-01-11T21:05:10.6466933Z                 }
2023-01-11T21:05:10.6466991Z             }
2023-01-11T21:05:10.6467093Z         }
2023-01-11T21:05:10.6467170Z         #pragma omp for 
2023-01-11T21:05:10.6467249Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6467307Z         {
2023-01-11T21:05:10.6467373Z             #pragma GCC ivdep
2023-01-11T21:05:10.6467459Z             for(long i1=0; i1<7486; i1+=1)
2023-01-11T21:05:10.6467519Z             {
2023-01-11T21:05:10.6467580Z                 {
2023-01-11T21:05:10.6467642Z                     {
2023-01-11T21:05:10.6467734Z                         auto tmp3 = in_ptr2[i1];
2023-01-11T21:05:10.6467827Z                         auto tmp4 = in_ptr1[i1 + (7486*i0)];
2023-01-11T21:05:10.6467931Z                         auto tmp0 = static_cast<int>(i0);
2023-01-11T21:05:10.6468034Z                         auto tmp1 = static_cast<int>(1);
2023-01-11T21:05:10.6468114Z                         auto tmp2 = tmp0 == tmp1;
2023-01-11T21:05:10.6468214Z                         auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:05:10.6468310Z                         out_ptr1[i1 + (7486*i0)] = tmp5;
2023-01-11T21:05:10.6468381Z                     }
2023-01-11T21:05:10.6468445Z                 }
2023-01-11T21:05:10.6468507Z             }
2023-01-11T21:05:10.6468570Z         }
2023-01-11T21:05:10.6468617Z     }
2023-01-11T21:05:10.6468706Z }
2023-01-11T21:05:10.6468790Z ''')
2023-01-11T21:05:10.6468795Z 
2023-01-11T21:05:10.6468799Z 
2023-01-11T21:05:10.6468888Z async_compile.wait(globals())
2023-01-11T21:05:10.6468957Z del async_compile
2023-01-11T21:05:10.6468962Z 
2023-01-11T21:05:10.6469031Z def call(args):
2023-01-11T21:05:10.6469110Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.6469167Z     args.clear()
2023-01-11T21:05:10.6469382Z     buf0 = empty_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6469591Z     buf1 = empty_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6469806Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6469876Z     del arg0_1
2023-01-11T21:05:10.6469940Z     del arg1_1
2023-01-11T21:05:10.6470004Z     del arg2_1
2023-01-11T21:05:10.6470078Z     return (buf0, buf1, )
2023-01-11T21:05:10.6470086Z 
2023-01-11T21:05:10.6470090Z 
2023-01-11T21:05:10.6470152Z if __name__ == "__main__":
2023-01-11T21:05:10.6470266Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6470388Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6470598Z     arg0_1 = rand_strided((8, 197, 38), (7486, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6470792Z     arg1_1 = rand_strided((8, 38), (38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6470990Z     arg2_1 = rand_strided((197, 38), (38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6471113Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.6471118Z 
2023-01-11T21:05:10.6471184Z ok (2.827s)
2023-01-11T21:05:10.6471623Z   test_sgn_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6471736Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6471996Z [2023-01-11 20:58:53,082] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 397
2023-01-11T21:05:10.6472262Z [2023-01-11 20:58:55,763] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 397
2023-01-11T21:05:10.6472268Z 
2023-01-11T21:05:10.6472361Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6472429Z import torch
2023-01-11T21:05:10.6472497Z import random
2023-01-11T21:05:10.6472611Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6472768Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6472774Z 
2023-01-11T21:05:10.6472838Z aten = torch.ops.aten
2023-01-11T21:05:10.6472972Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6473061Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6473066Z 
2023-01-11T21:05:10.6473070Z 
2023-01-11T21:05:10.6473203Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6473405Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6473522Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6473620Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6473714Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6473761Z {
2023-01-11T21:05:10.6473857Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6473917Z     {
2023-01-11T21:05:10.6473994Z         #pragma omp for 
2023-01-11T21:05:10.6474077Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6474139Z         {
2023-01-11T21:05:10.6474278Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6474461Z             auto tmp1 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), decltype(tmp0)(0) < tmp0);
2023-01-11T21:05:10.6474632Z             auto tmp2 = decltype(tmp0)::blendv(decltype(tmp0)(0), decltype(tmp0)(1), tmp0 < decltype(tmp0)(0));
2023-01-11T21:05:10.6474756Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.6474887Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6474970Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.6475130Z             auto tmp6 = decltype(tmp5)::blendv(decltype(tmp5)(0), decltype(tmp5)(1), decltype(tmp5)(0) < tmp5);
2023-01-11T21:05:10.6475294Z             auto tmp7 = decltype(tmp5)::blendv(decltype(tmp5)(0), decltype(tmp5)(1), tmp5 < decltype(tmp5)(0));
2023-01-11T21:05:10.6475417Z             auto tmp8 = tmp6 - tmp7;
2023-01-11T21:05:10.6475535Z             auto tmp9 = tmp8 - tmp4;
2023-01-11T21:05:10.6475614Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6475705Z             tmp9.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6475770Z         }
2023-01-11T21:05:10.6475864Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6475943Z         for(long i0=32; i0<41; i0+=1)
2023-01-11T21:05:10.6476004Z         {
2023-01-11T21:05:10.6476085Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6476158Z             auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:05:10.6476241Z             auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:05:10.6476361Z             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.6476459Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.6476539Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.6476622Z             auto tmp6 = tmp5 > 0 ? 1 : 0;
2023-01-11T21:05:10.6476704Z             auto tmp7 = tmp5 < 0 ? 1 : 0;
2023-01-11T21:05:10.6476813Z             auto tmp8 = tmp6 - tmp7;
2023-01-11T21:05:10.6476929Z             auto tmp9 = tmp8 - tmp4;
2023-01-11T21:05:10.6477008Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6477084Z             out_ptr1[i0] = tmp9;
2023-01-11T21:05:10.6477145Z         }
2023-01-11T21:05:10.6477206Z     }
2023-01-11T21:05:10.6477252Z }
2023-01-11T21:05:10.6477327Z ''')
2023-01-11T21:05:10.6477332Z 
2023-01-11T21:05:10.6477336Z 
2023-01-11T21:05:10.6477423Z async_compile.wait(globals())
2023-01-11T21:05:10.6477494Z del async_compile
2023-01-11T21:05:10.6477498Z 
2023-01-11T21:05:10.6477567Z def call(args):
2023-01-11T21:05:10.6477634Z     arg0_1, = args
2023-01-11T21:05:10.6477704Z     args.clear()
2023-01-11T21:05:10.6477895Z     buf0 = empty_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6478072Z     buf1 = empty_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6478235Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6478330Z     del arg0_1
2023-01-11T21:05:10.6478405Z     return (buf0, buf1, )
2023-01-11T21:05:10.6478411Z 
2023-01-11T21:05:10.6478415Z 
2023-01-11T21:05:10.6478495Z if __name__ == "__main__":
2023-01-11T21:05:10.6478608Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6478732Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6478924Z     arg0_1 = rand_strided((41, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6479018Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6479022Z 
2023-01-11T21:05:10.6479086Z ok (2.724s)
2023-01-11T21:05:10.6479528Z   test_sgn_extremal_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6479654Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6479942Z [2023-01-11 20:58:55,792] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 398
2023-01-11T21:05:10.6480209Z [2023-01-11 20:58:58,461] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 398
2023-01-11T21:05:10.6480214Z 
2023-01-11T21:05:10.6480306Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6480375Z import torch
2023-01-11T21:05:10.6480443Z import random
2023-01-11T21:05:10.6480544Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6480810Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6480815Z 
2023-01-11T21:05:10.6480892Z aten = torch.ops.aten
2023-01-11T21:05:10.6481027Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6481119Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6481126Z 
2023-01-11T21:05:10.6481130Z 
2023-01-11T21:05:10.6481265Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6481470Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6481591Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6481677Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6481738Z {
2023-01-11T21:05:10.6481837Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6481897Z     {
2023-01-11T21:05:10.6481974Z         #pragma omp for 
2023-01-11T21:05:10.6482055Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6482118Z         {
2023-01-11T21:05:10.6482167Z             {
2023-01-11T21:05:10.6482230Z                 {
2023-01-11T21:05:10.6482323Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6482416Z                     auto tmp1 = tmp0 > 0 ? 1 : 0;
2023-01-11T21:05:10.6482509Z                     auto tmp2 = tmp0 < 0 ? 1 : 0;
2023-01-11T21:05:10.6482645Z                     auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.6482730Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6482782Z                 }
2023-01-11T21:05:10.6482844Z             }
2023-01-11T21:05:10.6482907Z         }
2023-01-11T21:05:10.6482966Z     }
2023-01-11T21:05:10.6483025Z }
2023-01-11T21:05:10.6483102Z ''')
2023-01-11T21:05:10.6483107Z 
2023-01-11T21:05:10.6483111Z 
2023-01-11T21:05:10.6483200Z async_compile.wait(globals())
2023-01-11T21:05:10.6483258Z del async_compile
2023-01-11T21:05:10.6483262Z 
2023-01-11T21:05:10.6483331Z def call(args):
2023-01-11T21:05:10.6483398Z     arg0_1, = args
2023-01-11T21:05:10.6483467Z     args.clear()
2023-01-11T21:05:10.6483658Z     buf0 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6483790Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6483856Z     del arg0_1
2023-01-11T21:05:10.6483912Z     return (buf0, )
2023-01-11T21:05:10.6483974Z 
2023-01-11T21:05:10.6483978Z 
2023-01-11T21:05:10.6484053Z if __name__ == "__main__":
2023-01-11T21:05:10.6484169Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6484294Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6484488Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6484595Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6484600Z 
2023-01-11T21:05:10.6484666Z ok (2.697s)
2023-01-11T21:05:10.6485121Z   test_shape_prop_torch_ones_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6485247Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6485495Z [2023-01-11 20:58:59,339] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 399
2023-01-11T21:05:10.6485807Z [2023-01-11 20:59:01,982] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 399
2023-01-11T21:05:10.6485813Z 
2023-01-11T21:05:10.6485910Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6485978Z import torch
2023-01-11T21:05:10.6486045Z import random
2023-01-11T21:05:10.6486157Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6486274Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6486279Z 
2023-01-11T21:05:10.6486356Z aten = torch.ops.aten
2023-01-11T21:05:10.6486475Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6486566Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6486570Z 
2023-01-11T21:05:10.6486574Z 
2023-01-11T21:05:10.6486709Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6486910Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6487029Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6487127Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6487188Z {
2023-01-11T21:05:10.6487283Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6487330Z     {
2023-01-11T21:05:10.6487404Z         #pragma omp for 
2023-01-11T21:05:10.6487490Z         for(long i0=0; i0<1572864; i0+=1)
2023-01-11T21:05:10.6487552Z         {
2023-01-11T21:05:10.6487689Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6487823Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6487907Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6487985Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6488047Z         }
2023-01-11T21:05:10.6488139Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6488231Z         for(long i0=25165824; i0<25165824; i0+=1)
2023-01-11T21:05:10.6488292Z         {
2023-01-11T21:05:10.6488373Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6488469Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6488541Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6488618Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6488677Z         }
2023-01-11T21:05:10.6488737Z     }
2023-01-11T21:05:10.6488794Z }
2023-01-11T21:05:10.6488870Z ''')
2023-01-11T21:05:10.6488875Z 
2023-01-11T21:05:10.6488879Z 
2023-01-11T21:05:10.6488965Z async_compile.wait(globals())
2023-01-11T21:05:10.6489022Z del async_compile
2023-01-11T21:05:10.6489027Z 
2023-01-11T21:05:10.6489096Z def call(args):
2023-01-11T21:05:10.6489162Z     arg0_1, = args
2023-01-11T21:05:10.6489230Z     args.clear()
2023-01-11T21:05:10.6489462Z     buf0 = empty_strided((8, 12, 512, 512), (3145728, 262144, 512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6489594Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6489693Z     del arg0_1
2023-01-11T21:05:10.6489749Z     return (buf0, )
2023-01-11T21:05:10.6489754Z 
2023-01-11T21:05:10.6489770Z 
2023-01-11T21:05:10.6489833Z if __name__ == "__main__":
2023-01-11T21:05:10.6489945Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6490065Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6490297Z     arg0_1 = rand_strided((8, 12, 512, 512), (3145728, 262144, 512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6490403Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6490408Z 
2023-01-11T21:05:10.6490471Z ok (10.326s)
2023-01-11T21:05:10.6490906Z   test_sigmoid_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6491032Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6491329Z [2023-01-11 20:59:08,830] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 400
2023-01-11T21:05:10.6491584Z [2023-01-11 20:59:11,466] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 400
2023-01-11T21:05:10.6491589Z 
2023-01-11T21:05:10.6491680Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6491748Z import torch
2023-01-11T21:05:10.6491816Z import random
2023-01-11T21:05:10.6491928Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6492047Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6492052Z 
2023-01-11T21:05:10.6492128Z aten = torch.ops.aten
2023-01-11T21:05:10.6492248Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6492339Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6492346Z 
2023-01-11T21:05:10.6492350Z 
2023-01-11T21:05:10.6492481Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6492686Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6492804Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6492906Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6493004Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6493099Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6493146Z {
2023-01-11T21:05:10.6493242Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6493302Z     {
2023-01-11T21:05:10.6493375Z         #pragma omp for 
2023-01-11T21:05:10.6493453Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6493515Z         {
2023-01-11T21:05:10.6493648Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6493768Z             auto tmp2 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6493902Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:05:10.6493987Z             auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.6494117Z             auto tmp4 = decltype(tmp3)(1)/(decltype(tmp3)(1) + tmp3.neg().exp());
2023-01-11T21:05:10.6494208Z             tmp1.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6494297Z             tmp4.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6494357Z         }
2023-01-11T21:05:10.6494450Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6494518Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6494579Z         {
2023-01-11T21:05:10.6494663Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6494742Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.6494875Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.6494957Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.6495076Z             auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.6495196Z             auto tmp5 = std::exp(-tmp4);
2023-01-11T21:05:10.6495276Z             auto tmp6 = 1 / (1 + tmp5);
2023-01-11T21:05:10.6495354Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6495431Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.6495493Z         }
2023-01-11T21:05:10.6495552Z     }
2023-01-11T21:05:10.6495597Z }
2023-01-11T21:05:10.6495672Z ''')
2023-01-11T21:05:10.6495677Z 
2023-01-11T21:05:10.6495681Z 
2023-01-11T21:05:10.6495769Z async_compile.wait(globals())
2023-01-11T21:05:10.6495839Z del async_compile
2023-01-11T21:05:10.6495844Z 
2023-01-11T21:05:10.6495912Z def call(args):
2023-01-11T21:05:10.6495986Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6496056Z     args.clear()
2023-01-11T21:05:10.6496250Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6496428Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6496615Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6496682Z     del arg0_1
2023-01-11T21:05:10.6496746Z     del arg1_1
2023-01-11T21:05:10.6496849Z     return (buf0, buf1, )
2023-01-11T21:05:10.6496855Z 
2023-01-11T21:05:10.6496859Z 
2023-01-11T21:05:10.6496935Z if __name__ == "__main__":
2023-01-11T21:05:10.6497049Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6497171Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6497351Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6497543Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6497655Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6497659Z 
2023-01-11T21:05:10.6497724Z ok (2.681s)
2023-01-11T21:05:10.6498163Z   test_signbit_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6498288Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6498645Z [2023-01-11 20:59:11,517] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 401
2023-01-11T21:05:10.6498919Z [2023-01-11 20:59:14,170] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 401
2023-01-11T21:05:10.6498924Z 
2023-01-11T21:05:10.6499018Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6499075Z import torch
2023-01-11T21:05:10.6499145Z import random
2023-01-11T21:05:10.6499260Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6499380Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6499387Z 
2023-01-11T21:05:10.6499465Z aten = torch.ops.aten
2023-01-11T21:05:10.6499598Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6499689Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6499695Z 
2023-01-11T21:05:10.6499700Z 
2023-01-11T21:05:10.6499832Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6500021Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6500140Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6500238Z                        bool* __restrict__ out_ptr0,
2023-01-11T21:05:10.6500333Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.6500396Z {
2023-01-11T21:05:10.6500494Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6500554Z     {
2023-01-11T21:05:10.6500617Z         #pragma omp for 
2023-01-11T21:05:10.6500699Z         for(long i0=0; i0<72; i0+=1)
2023-01-11T21:05:10.6500760Z         {
2023-01-11T21:05:10.6500856Z             {
2023-01-11T21:05:10.6500921Z                 {
2023-01-11T21:05:10.6501013Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6501120Z                     auto tmp1 = std::signbit(tmp0);
2023-01-11T21:05:10.6501231Z                     auto tmp2 = -tmp0;
2023-01-11T21:05:10.6501332Z                     auto tmp3 = std::signbit(tmp2);
2023-01-11T21:05:10.6501418Z                     auto tmp4 = tmp3 == 0;
2023-01-11T21:05:10.6501522Z                     auto tmp5 = static_cast<long>(tmp4);
2023-01-11T21:05:10.6501623Z                     auto tmp6 = static_cast<long>(1);
2023-01-11T21:05:10.6501715Z                     auto tmp7 = tmp5 & tmp6;
2023-01-11T21:05:10.6501799Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6501869Z                     out_ptr1[i0] = tmp7;
2023-01-11T21:05:10.6501931Z                 }
2023-01-11T21:05:10.6501993Z             }
2023-01-11T21:05:10.6502053Z         }
2023-01-11T21:05:10.6502113Z     }
2023-01-11T21:05:10.6502173Z }
2023-01-11T21:05:10.6502249Z ''')
2023-01-11T21:05:10.6502254Z 
2023-01-11T21:05:10.6502258Z 
2023-01-11T21:05:10.6502333Z async_compile.wait(globals())
2023-01-11T21:05:10.6502404Z del async_compile
2023-01-11T21:05:10.6502409Z 
2023-01-11T21:05:10.6502512Z def call(args):
2023-01-11T21:05:10.6502581Z     arg0_1, = args
2023-01-11T21:05:10.6502650Z     args.clear()
2023-01-11T21:05:10.6502856Z     buf0 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.6503060Z     buf1 = empty_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6503222Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6503277Z     del arg0_1
2023-01-11T21:05:10.6503352Z     return (buf0, buf1, )
2023-01-11T21:05:10.6503356Z 
2023-01-11T21:05:10.6503360Z 
2023-01-11T21:05:10.6503434Z if __name__ == "__main__":
2023-01-11T21:05:10.6503545Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6503668Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6503877Z     arg0_1 = rand_strided((1, 2, 6, 6), (72, 36, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6503985Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6503990Z 
2023-01-11T21:05:10.6504054Z ok (2.701s)
2023-01-11T21:05:10.6504472Z   test_silu_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6504598Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6504854Z [2023-01-11 20:59:14,208] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 402
2023-01-11T21:05:10.6505118Z [2023-01-11 20:59:16,861] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 402
2023-01-11T21:05:10.6505123Z 
2023-01-11T21:05:10.6505216Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6505287Z import torch
2023-01-11T21:05:10.6505356Z import random
2023-01-11T21:05:10.6505470Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6505576Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6505594Z 
2023-01-11T21:05:10.6505658Z aten = torch.ops.aten
2023-01-11T21:05:10.6505789Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6505879Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6505884Z 
2023-01-11T21:05:10.6505889Z 
2023-01-11T21:05:10.6506018Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6506223Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6506341Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6506470Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6506530Z {
2023-01-11T21:05:10.6506614Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6506673Z     {
2023-01-11T21:05:10.6506750Z         #pragma omp for 
2023-01-11T21:05:10.6506831Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6506891Z         {
2023-01-11T21:05:10.6507025Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6507157Z             auto tmp1 = decltype(tmp0)(1)/(decltype(tmp0)(1) + tmp0.neg().exp());
2023-01-11T21:05:10.6507227Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6507318Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6507378Z         }
2023-01-11T21:05:10.6507470Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6507551Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6507610Z         {
2023-01-11T21:05:10.6507679Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6507814Z             auto tmp1 = std::exp(-tmp0);
2023-01-11T21:05:10.6507896Z             auto tmp2 = 1 / (1 + tmp1);
2023-01-11T21:05:10.6507977Z             auto tmp3 = tmp0 * tmp2;
2023-01-11T21:05:10.6508081Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6508143Z         }
2023-01-11T21:05:10.6508200Z     }
2023-01-11T21:05:10.6508246Z }
2023-01-11T21:05:10.6508322Z ''')
2023-01-11T21:05:10.6508327Z 
2023-01-11T21:05:10.6508331Z 
2023-01-11T21:05:10.6508419Z async_compile.wait(globals())
2023-01-11T21:05:10.6508488Z del async_compile
2023-01-11T21:05:10.6508493Z 
2023-01-11T21:05:10.6508562Z def call(args):
2023-01-11T21:05:10.6508629Z     arg0_1, = args
2023-01-11T21:05:10.6508698Z     args.clear()
2023-01-11T21:05:10.6508890Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6509009Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6509077Z     del arg0_1
2023-01-11T21:05:10.6509146Z     return (buf0, )
2023-01-11T21:05:10.6509153Z 
2023-01-11T21:05:10.6509157Z 
2023-01-11T21:05:10.6509230Z if __name__ == "__main__":
2023-01-11T21:05:10.6509343Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6509466Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6509662Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6509755Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6509773Z 
2023-01-11T21:05:10.6509825Z ok (2.692s)
2023-01-11T21:05:10.6510269Z   test_simplify_loops_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6510395Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6510657Z [2023-01-11 20:59:16,892] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 403
2023-01-11T21:05:10.6510921Z [2023-01-11 20:59:19,532] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 403
2023-01-11T21:05:10.6510926Z 
2023-01-11T21:05:10.6511016Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6511084Z import torch
2023-01-11T21:05:10.6511152Z import random
2023-01-11T21:05:10.6511253Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6511372Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6511377Z 
2023-01-11T21:05:10.6511453Z aten = torch.ops.aten
2023-01-11T21:05:10.6511584Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6511674Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6511679Z 
2023-01-11T21:05:10.6511683Z 
2023-01-11T21:05:10.6511813Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6512086Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6512205Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6512312Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6512399Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6512460Z {
2023-01-11T21:05:10.6512556Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6512617Z     {
2023-01-11T21:05:10.6512703Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6512783Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.6512832Z         {
2023-01-11T21:05:10.6512914Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.6512977Z             {
2023-01-11T21:05:10.6513069Z                 for(long i2=0; i2<1; i2+=1)
2023-01-11T21:05:10.6513133Z                 {
2023-01-11T21:05:10.6513284Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i2) + (30*i1) + (120*i0));
2023-01-11T21:05:10.6513435Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i2) + (30*i0) + (180*i1));
2023-01-11T21:05:10.6513529Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6513655Z                     tmp2.store(out_ptr0 + (16*i2) + (30*i1) + (120*i0));
2023-01-11T21:05:10.6513721Z                 }
2023-01-11T21:05:10.6513816Z                 #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.6513907Z                 for(long i2=16; i2<30; i2+=1)
2023-01-11T21:05:10.6513971Z                 {
2023-01-11T21:05:10.6514078Z                     auto tmp0 = in_ptr0[i2 + (30*i1) + (120*i0)];
2023-01-11T21:05:10.6514182Z                     auto tmp1 = in_ptr1[i2 + (30*i0) + (180*i1)];
2023-01-11T21:05:10.6514260Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6514360Z                     out_ptr0[i2 + (30*i1) + (120*i0)] = tmp2;
2023-01-11T21:05:10.6514424Z                 }
2023-01-11T21:05:10.6514487Z             }
2023-01-11T21:05:10.6514552Z         }
2023-01-11T21:05:10.6514613Z     }
2023-01-11T21:05:10.6514673Z }
2023-01-11T21:05:10.6514738Z ''')
2023-01-11T21:05:10.6514742Z 
2023-01-11T21:05:10.6514746Z 
2023-01-11T21:05:10.6514837Z async_compile.wait(globals())
2023-01-11T21:05:10.6514910Z del async_compile
2023-01-11T21:05:10.6514915Z 
2023-01-11T21:05:10.6514984Z def call(args):
2023-01-11T21:05:10.6515058Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6515127Z     args.clear()
2023-01-11T21:05:10.6515350Z     buf0 = empty_strided((2, 3, 4, 5, 6), (360, 120, 30, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6515510Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6515564Z     del arg0_1
2023-01-11T21:05:10.6515631Z     del arg1_1
2023-01-11T21:05:10.6515701Z     return (buf0, )
2023-01-11T21:05:10.6515706Z 
2023-01-11T21:05:10.6515710Z 
2023-01-11T21:05:10.6515787Z if __name__ == "__main__":
2023-01-11T21:05:10.6515901Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6516026Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6516251Z     arg0_1 = rand_strided((2, 3, 4, 5, 6), (360, 120, 30, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6516456Z     arg1_1 = rand_strided((2, 3, 4, 5, 6), (90, 30, 180, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6516570Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6516575Z 
2023-01-11T21:05:10.6516641Z ok (2.673s)
2023-01-11T21:05:10.6517073Z   test_sin_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6517200Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6517488Z [2023-01-11 20:59:19,598] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 404
2023-01-11T21:05:10.6517755Z [2023-01-11 20:59:22,256] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 404
2023-01-11T21:05:10.6517760Z 
2023-01-11T21:05:10.6517854Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6517923Z import torch
2023-01-11T21:05:10.6517992Z import random
2023-01-11T21:05:10.6518091Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6518210Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6518215Z 
2023-01-11T21:05:10.6518291Z aten = torch.ops.aten
2023-01-11T21:05:10.6518425Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6518513Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6518519Z 
2023-01-11T21:05:10.6518523Z 
2023-01-11T21:05:10.6518654Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6518857Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6518978Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6519090Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6519191Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6519249Z {
2023-01-11T21:05:10.6519345Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6519404Z     {
2023-01-11T21:05:10.6519479Z         #pragma omp for 
2023-01-11T21:05:10.6519559Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6519608Z         {
2023-01-11T21:05:10.6519740Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6519822Z             auto tmp1 = tmp0.sin();
2023-01-11T21:05:10.6519954Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6520036Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6520167Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6520251Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.6520319Z             auto tmp6 = tmp5.sin();
2023-01-11T21:05:10.6520411Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6520500Z             tmp6.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6520562Z         }
2023-01-11T21:05:10.6520777Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6520859Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6520921Z         {
2023-01-11T21:05:10.6520990Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6521077Z             auto tmp1 = std::sin(tmp0);
2023-01-11T21:05:10.6521174Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.6521256Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6521354Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.6521438Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.6521525Z             auto tmp6 = std::sin(tmp5);
2023-01-11T21:05:10.6521593Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6521670Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.6521733Z         }
2023-01-11T21:05:10.6521794Z     }
2023-01-11T21:05:10.6521854Z }
2023-01-11T21:05:10.6521938Z ''')
2023-01-11T21:05:10.6521945Z 
2023-01-11T21:05:10.6521949Z 
2023-01-11T21:05:10.6522036Z async_compile.wait(globals())
2023-01-11T21:05:10.6522094Z del async_compile
2023-01-11T21:05:10.6522112Z 
2023-01-11T21:05:10.6522168Z def call(args):
2023-01-11T21:05:10.6522234Z     arg0_1, = args
2023-01-11T21:05:10.6522302Z     args.clear()
2023-01-11T21:05:10.6522501Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6522696Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6522855Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6522922Z     del arg0_1
2023-01-11T21:05:10.6523040Z     return (buf0, buf1, )
2023-01-11T21:05:10.6523045Z 
2023-01-11T21:05:10.6523050Z 
2023-01-11T21:05:10.6523124Z if __name__ == "__main__":
2023-01-11T21:05:10.6523239Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6523365Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6523567Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6523674Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6523679Z 
2023-01-11T21:05:10.6523745Z ok (2.725s)
2023-01-11T21:05:10.6524186Z   test_sizehint_issue1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6524313Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6524559Z [2023-01-11 20:59:22,522] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 405
2023-01-11T21:05:10.6524858Z [2023-01-11 20:59:25,264] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 405
2023-01-11T21:05:10.6524864Z 
2023-01-11T21:05:10.6524959Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6525028Z import torch
2023-01-11T21:05:10.6525099Z import random
2023-01-11T21:05:10.6525215Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6525336Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6525341Z 
2023-01-11T21:05:10.6525419Z aten = torch.ops.aten
2023-01-11T21:05:10.6525538Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6525628Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6525634Z 
2023-01-11T21:05:10.6525638Z 
2023-01-11T21:05:10.6525771Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6525976Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6526095Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6526195Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6526255Z {
2023-01-11T21:05:10.6526350Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6526397Z     {
2023-01-11T21:05:10.6526486Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6526566Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6526625Z         {
2023-01-11T21:05:10.6526711Z             for(long i1=0; i1<384; i1+=1)
2023-01-11T21:05:10.6526774Z             {
2023-01-11T21:05:10.6526842Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6526931Z                 for(long i2=0; i2<196; i2+=1)
2023-01-11T21:05:10.6526993Z                 {
2023-01-11T21:05:10.6527058Z                     {
2023-01-11T21:05:10.6527124Z                         {
2023-01-11T21:05:10.6527240Z                             auto tmp0 = static_cast<long>(4*(i2 / 14));
2023-01-11T21:05:10.6527351Z                             auto tmp1 = static_cast<long>((i1 / 4) % 4);
2023-01-11T21:05:10.6527438Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6527549Z                             auto tmp3 = static_cast<long>(4*(i2 % 14));
2023-01-11T21:05:10.6527658Z                             auto tmp4 = static_cast<long>(i1 % 4);
2023-01-11T21:05:10.6527750Z                             auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.6527874Z                             auto tmp6 = in_ptr0[tmp5 + (56*tmp2) + (3136*(i1 / 16)) + (75264*i0)];
2023-01-11T21:05:10.6527978Z                             out_ptr0[i2 + (196*i1) + (75264*i0)] = tmp6;
2023-01-11T21:05:10.6528043Z                         }
2023-01-11T21:05:10.6528108Z                     }
2023-01-11T21:05:10.6528158Z                 }
2023-01-11T21:05:10.6528220Z             }
2023-01-11T21:05:10.6528282Z         }
2023-01-11T21:05:10.6528375Z     }
2023-01-11T21:05:10.6528434Z }
2023-01-11T21:05:10.6528510Z ''')
2023-01-11T21:05:10.6528515Z 
2023-01-11T21:05:10.6528520Z 
2023-01-11T21:05:10.6528608Z async_compile.wait(globals())
2023-01-11T21:05:10.6528669Z del async_compile
2023-01-11T21:05:10.6528674Z 
2023-01-11T21:05:10.6528742Z def call(args):
2023-01-11T21:05:10.6528808Z     arg0_1, = args
2023-01-11T21:05:10.6528879Z     args.clear()
2023-01-11T21:05:10.6529093Z     buf0 = empty_strided((2, 384, 196), (75264, 196, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6529222Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6529288Z     del arg0_1
2023-01-11T21:05:10.6529345Z     return (buf0, )
2023-01-11T21:05:10.6529350Z 
2023-01-11T21:05:10.6529367Z 
2023-01-11T21:05:10.6529428Z if __name__ == "__main__":
2023-01-11T21:05:10.6529541Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6529663Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6529887Z     arg0_1 = rand_strided((2, 24, 56, 56), (75264, 3136, 56, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6529992Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6529997Z 
2023-01-11T21:05:10.6530105Z ok (3.083s)
2023-01-11T21:05:10.6530540Z   test_slice1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6530665Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6530907Z [2023-01-11 20:59:25,436] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 406
2023-01-11T21:05:10.6531169Z [2023-01-11 20:59:28,068] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 406
2023-01-11T21:05:10.6531176Z 
2023-01-11T21:05:10.6531267Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6531335Z import torch
2023-01-11T21:05:10.6531404Z import random
2023-01-11T21:05:10.6531519Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6531638Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6531643Z 
2023-01-11T21:05:10.6531720Z aten = torch.ops.aten
2023-01-11T21:05:10.6531840Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6531930Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6531935Z 
2023-01-11T21:05:10.6531939Z 
2023-01-11T21:05:10.6532068Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6532271Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6532391Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6532493Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6532590Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6532650Z {
2023-01-11T21:05:10.6532733Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6532793Z     {
2023-01-11T21:05:10.6532885Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6532966Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6533026Z         {
2023-01-11T21:05:10.6533109Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.6533171Z             {
2023-01-11T21:05:10.6533221Z                 {
2023-01-11T21:05:10.6533285Z                     {
2023-01-11T21:05:10.6533388Z                         auto tmp0 = in_ptr0[(2*i1) + (40*i0)];
2023-01-11T21:05:10.6533494Z                         auto tmp1 = in_ptr0[20 + (2*i1) + (40*i0)];
2023-01-11T21:05:10.6533587Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6533691Z                         auto tmp3 = static_cast<float>(1);
2023-01-11T21:05:10.6533782Z                         auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.6533894Z                         auto tmp5 = tmp1 + tmp3;
2023-01-11T21:05:10.6533983Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6534078Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.6534173Z                         out_ptr1[i1 + (10*i0)] = tmp6;
2023-01-11T21:05:10.6534238Z                     }
2023-01-11T21:05:10.6534301Z                 }
2023-01-11T21:05:10.6534362Z             }
2023-01-11T21:05:10.6534411Z         }
2023-01-11T21:05:10.6534470Z     }
2023-01-11T21:05:10.6534528Z }
2023-01-11T21:05:10.6534605Z ''')
2023-01-11T21:05:10.6534610Z 
2023-01-11T21:05:10.6534614Z 
2023-01-11T21:05:10.6534702Z async_compile.wait(globals())
2023-01-11T21:05:10.6534772Z del async_compile
2023-01-11T21:05:10.6534777Z 
2023-01-11T21:05:10.6534845Z def call(args):
2023-01-11T21:05:10.6534899Z     arg0_1, = args
2023-01-11T21:05:10.6534969Z     args.clear()
2023-01-11T21:05:10.6535165Z     buf0 = empty_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6535361Z     buf1 = empty_strided((2, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6535553Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6535622Z     del arg0_1
2023-01-11T21:05:10.6535698Z     return (buf0, buf1, )
2023-01-11T21:05:10.6535703Z 
2023-01-11T21:05:10.6535707Z 
2023-01-11T21:05:10.6535779Z if __name__ == "__main__":
2023-01-11T21:05:10.6535880Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6536002Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6536205Z     arg0_1 = rand_strided((2, 20, 2), (40, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6536310Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6536315Z 
2023-01-11T21:05:10.6536379Z ok (2.726s)
2023-01-11T21:05:10.6536817Z   test_slice2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6536945Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6537201Z [2023-01-11 20:59:28,158] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 407
2023-01-11T21:05:10.6537461Z [2023-01-11 20:59:30,813] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 407
2023-01-11T21:05:10.6537467Z 
2023-01-11T21:05:10.6537546Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6537614Z import torch
2023-01-11T21:05:10.6537682Z import random
2023-01-11T21:05:10.6537795Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6537914Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6537921Z 
2023-01-11T21:05:10.6537997Z aten = torch.ops.aten
2023-01-11T21:05:10.6538128Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6538205Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6538223Z 
2023-01-11T21:05:10.6538229Z 
2023-01-11T21:05:10.6538347Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6538651Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6538775Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6538873Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6538968Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6539029Z {
2023-01-11T21:05:10.6539128Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6539175Z     {
2023-01-11T21:05:10.6539251Z         #pragma omp for 
2023-01-11T21:05:10.6539332Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.6539394Z         {
2023-01-11T21:05:10.6539492Z             {
2023-01-11T21:05:10.6539555Z                 {
2023-01-11T21:05:10.6539651Z                     auto tmp0 = in_ptr0[1 + (4*i0)];
2023-01-11T21:05:10.6539735Z                     auto tmp1 = in_ptr0[42 + (4*i0)];
2023-01-11T21:05:10.6539830Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6539932Z                     auto tmp3 = static_cast<float>(1);
2023-01-11T21:05:10.6540022Z                     auto tmp4 = tmp0 + tmp3;
2023-01-11T21:05:10.6540121Z                     auto tmp5 = static_cast<float>(2);
2023-01-11T21:05:10.6540209Z                     auto tmp6 = tmp1 + tmp5;
2023-01-11T21:05:10.6540297Z                     auto tmp7 = tmp4 + tmp6;
2023-01-11T21:05:10.6540366Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6540447Z                     out_ptr1[i0] = tmp7;
2023-01-11T21:05:10.6540509Z                 }
2023-01-11T21:05:10.6540570Z             }
2023-01-11T21:05:10.6540630Z         }
2023-01-11T21:05:10.6540690Z     }
2023-01-11T21:05:10.6540749Z }
2023-01-11T21:05:10.6540815Z ''')
2023-01-11T21:05:10.6540821Z 
2023-01-11T21:05:10.6540825Z 
2023-01-11T21:05:10.6540912Z async_compile.wait(globals())
2023-01-11T21:05:10.6540982Z del async_compile
2023-01-11T21:05:10.6540987Z 
2023-01-11T21:05:10.6541087Z def call(args):
2023-01-11T21:05:10.6541156Z     arg0_1, = args
2023-01-11T21:05:10.6541225Z     args.clear()
2023-01-11T21:05:10.6541423Z     buf0 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6541605Z     buf1 = empty_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6541767Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6541834Z     del arg0_1
2023-01-11T21:05:10.6541909Z     return (buf0, buf1, )
2023-01-11T21:05:10.6541914Z 
2023-01-11T21:05:10.6541919Z 
2023-01-11T21:05:10.6541992Z if __name__ == "__main__":
2023-01-11T21:05:10.6542104Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6542228Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6542431Z     arg0_1 = rand_strided((2, 20, 2), (40, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6542526Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6542531Z 
2023-01-11T21:05:10.6542598Z ok (2.745s)
2023-01-11T21:05:10.6543038Z   test_slice_mutation1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6543162Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6543421Z [2023-01-11 20:59:30,918] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 408
2023-01-11T21:05:10.6543684Z [2023-01-11 20:59:33,650] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 408
2023-01-11T21:05:10.6543693Z 
2023-01-11T21:05:10.6543785Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6543854Z import torch
2023-01-11T21:05:10.6543924Z import random
2023-01-11T21:05:10.6544028Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6544146Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6544151Z 
2023-01-11T21:05:10.6544228Z aten = torch.ops.aten
2023-01-11T21:05:10.6544361Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6544452Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6544457Z 
2023-01-11T21:05:10.6544461Z 
2023-01-11T21:05:10.6544594Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6544797Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6544909Z extern "C" void kernel(float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6545023Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6545117Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6545210Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.6545272Z {
2023-01-11T21:05:10.6545370Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6545431Z     {
2023-01-11T21:05:10.6545507Z         #pragma omp for 
2023-01-11T21:05:10.6545574Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6545635Z         {
2023-01-11T21:05:10.6545775Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:05:10.6545908Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6545993Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6546085Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6546175Z             tmp2.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6546237Z         }
2023-01-11T21:05:10.6546318Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6546401Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6546463Z         {
2023-01-11T21:05:10.6546560Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:05:10.6546685Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6546769Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6546835Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6546911Z             out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.6546972Z         }
2023-01-11T21:05:10.6547046Z         #pragma omp for 
2023-01-11T21:05:10.6547127Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6547188Z         {
2023-01-11T21:05:10.6547253Z             {
2023-01-11T21:05:10.6547304Z                 {
2023-01-11T21:05:10.6547411Z                     auto tmp0 = static_cast<float>(3.0);
2023-01-11T21:05:10.6547502Z                     out_ptr0[3 + (8*i0)] = tmp0;
2023-01-11T21:05:10.6547566Z                 }
2023-01-11T21:05:10.6547628Z             }
2023-01-11T21:05:10.6547692Z         }
2023-01-11T21:05:10.6547767Z         #pragma omp for 
2023-01-11T21:05:10.6547833Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6547893Z         {
2023-01-11T21:05:10.6548032Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6548124Z             tmp0.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.6548186Z         }
2023-01-11T21:05:10.6548278Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6548358Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6548406Z         {
2023-01-11T21:05:10.6548490Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.6548568Z             out_ptr2[i0] = tmp0;
2023-01-11T21:05:10.6548629Z         }
2023-01-11T21:05:10.6548704Z         #pragma omp for 
2023-01-11T21:05:10.6548783Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6548830Z         {
2023-01-11T21:05:10.6548892Z             {
2023-01-11T21:05:10.6548956Z                 {
2023-01-11T21:05:10.6549062Z                     auto tmp0 = static_cast<float>(4.0);
2023-01-11T21:05:10.6549149Z                     out_ptr0[32 + i0] = tmp0;
2023-01-11T21:05:10.6549213Z                 }
2023-01-11T21:05:10.6549274Z             }
2023-01-11T21:05:10.6549324Z         }
2023-01-11T21:05:10.6549396Z         #pragma omp for 
2023-01-11T21:05:10.6549473Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6549534Z         {
2023-01-11T21:05:10.6549663Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6549795Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6549877Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6549954Z             tmp2.store(out_ptr3 + 16*i0);
2023-01-11T21:05:10.6550015Z         }
2023-01-11T21:05:10.6550107Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6550188Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6550249Z         {
2023-01-11T21:05:10.6550330Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.6550454Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6550525Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6550602Z             out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.6550664Z         }
2023-01-11T21:05:10.6550723Z     }
2023-01-11T21:05:10.6550781Z }
2023-01-11T21:05:10.6550859Z ''')
2023-01-11T21:05:10.6550864Z 
2023-01-11T21:05:10.6550869Z 
2023-01-11T21:05:10.6550956Z async_compile.wait(globals())
2023-01-11T21:05:10.6551014Z del async_compile
2023-01-11T21:05:10.6551033Z 
2023-01-11T21:05:10.6551089Z def call(args):
2023-01-11T21:05:10.6551156Z     arg0_1, = args
2023-01-11T21:05:10.6551226Z     args.clear()
2023-01-11T21:05:10.6551420Z     buf0 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6551611Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6551799Z     buf3 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6551987Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6552191Z     kernel_cpp_0(c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:05:10.6552279Z     return (buf0, buf1, buf3, buf5, )
2023-01-11T21:05:10.6552283Z 
2023-01-11T21:05:10.6552288Z 
2023-01-11T21:05:10.6552362Z if __name__ == "__main__":
2023-01-11T21:05:10.6552474Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6552598Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6552793Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6552898Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6552903Z 
2023-01-11T21:05:10.6552967Z ok (2.840s)
2023-01-11T21:05:10.6553287Z   test_slice_mutation2_cpu (__main__.CpuTests) ... [2023-01-11 20:59:33,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 409
2023-01-11T21:05:10.6553551Z [2023-01-11 20:59:36,448] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 409
2023-01-11T21:05:10.6553556Z 
2023-01-11T21:05:10.6553650Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6553718Z import torch
2023-01-11T21:05:10.6553787Z import random
2023-01-11T21:05:10.6553903Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6554021Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6554026Z 
2023-01-11T21:05:10.6554102Z aten = torch.ops.aten
2023-01-11T21:05:10.6554221Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6554309Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6554313Z 
2023-01-11T21:05:10.6554318Z 
2023-01-11T21:05:10.6554448Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6554651Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6554768Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6554868Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6554963Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6555057Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6555103Z {
2023-01-11T21:05:10.6555200Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6555260Z     {
2023-01-11T21:05:10.6555337Z         #pragma omp for 
2023-01-11T21:05:10.6555417Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.6555479Z         {
2023-01-11T21:05:10.6555616Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 20 + (16*i0));
2023-01-11T21:05:10.6555734Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6555817Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6555905Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6555967Z         }
2023-01-11T21:05:10.6556060Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6556174Z         for(long i0=16; i0<20; i0+=1)
2023-01-11T21:05:10.6556237Z         {
2023-01-11T21:05:10.6556310Z             auto tmp0 = in_ptr0[20 + i0];
2023-01-11T21:05:10.6556409Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6556490Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6556568Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6556628Z         }
2023-01-11T21:05:10.6556699Z         #pragma omp for 
2023-01-11T21:05:10.6556778Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.6556825Z         {
2023-01-11T21:05:10.6556957Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6557054Z             tmp0.store(out_ptr1 + 20 + (16*i0));
2023-01-11T21:05:10.6557113Z         }
2023-01-11T21:05:10.6557205Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6557284Z         for(long i0=16; i0<20; i0+=1)
2023-01-11T21:05:10.6557344Z         {
2023-01-11T21:05:10.6557415Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.6557494Z             out_ptr1[20 + i0] = tmp0;
2023-01-11T21:05:10.6557552Z         }
2023-01-11T21:05:10.6557625Z         #pragma omp for 
2023-01-11T21:05:10.6557732Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.6557791Z         {
2023-01-11T21:05:10.6557853Z             {
2023-01-11T21:05:10.6557903Z                 {
2023-01-11T21:05:10.6557999Z                     auto tmp0 = out_ptr1[1 + i0];
2023-01-11T21:05:10.6558102Z                     auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.6558191Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6558272Z                     out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.6558335Z                 }
2023-01-11T21:05:10.6558384Z             }
2023-01-11T21:05:10.6558444Z         }
2023-01-11T21:05:10.6558518Z         #pragma omp for 
2023-01-11T21:05:10.6558600Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.6558660Z         {
2023-01-11T21:05:10.6558722Z             {
2023-01-11T21:05:10.6558784Z                 {
2023-01-11T21:05:10.6558864Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:05:10.6558949Z                     out_ptr1[2 + i0] = tmp0;
2023-01-11T21:05:10.6559014Z                 }
2023-01-11T21:05:10.6559076Z             }
2023-01-11T21:05:10.6559136Z         }
2023-01-11T21:05:10.6559195Z     }
2023-01-11T21:05:10.6559253Z }
2023-01-11T21:05:10.6559317Z ''')
2023-01-11T21:05:10.6559323Z 
2023-01-11T21:05:10.6559328Z 
2023-01-11T21:05:10.6559415Z async_compile.wait(globals())
2023-01-11T21:05:10.6559483Z del async_compile
2023-01-11T21:05:10.6559488Z 
2023-01-11T21:05:10.6559557Z def call(args):
2023-01-11T21:05:10.6559627Z     arg0_1, = args
2023-01-11T21:05:10.6559696Z     args.clear()
2023-01-11T21:05:10.6559893Z     buf0 = empty_strided((1, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6560072Z     buf2 = empty_strided((1, 9), (9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6560258Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6560327Z     del arg0_1
2023-01-11T21:05:10.6560391Z     return ()
2023-01-11T21:05:10.6560396Z 
2023-01-11T21:05:10.6560402Z 
2023-01-11T21:05:10.6560476Z if __name__ == "__main__":
2023-01-11T21:05:10.6560705Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6560828Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6561028Z     arg0_1 = rand_strided((1, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6561122Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6561141Z 
2023-01-11T21:05:10.6561193Z ok (2.792s)
2023-01-11T21:05:10.6561639Z   test_slice_scatter2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6561831Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6562096Z [2023-01-11 20:59:36,526] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 410
2023-01-11T21:05:10.6562359Z [2023-01-11 20:59:39,154] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 410
2023-01-11T21:05:10.6562364Z 
2023-01-11T21:05:10.6562460Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6562529Z import torch
2023-01-11T21:05:10.6562600Z import random
2023-01-11T21:05:10.6562701Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6562822Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6562827Z 
2023-01-11T21:05:10.6562904Z aten = torch.ops.aten
2023-01-11T21:05:10.6563038Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6563133Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6563138Z 
2023-01-11T21:05:10.6563142Z 
2023-01-11T21:05:10.6563276Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6563516Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6563638Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6563725Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6563784Z {
2023-01-11T21:05:10.6563880Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6563941Z     {
2023-01-11T21:05:10.6564016Z         #pragma omp for 
2023-01-11T21:05:10.6564098Z         for(long i0=0; i0<37824; i0+=1)
2023-01-11T21:05:10.6564158Z         {
2023-01-11T21:05:10.6564278Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6564370Z             tmp0.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6564431Z         }
2023-01-11T21:05:10.6564526Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6564615Z         for(long i0=605184; i0<605184; i0+=1)
2023-01-11T21:05:10.6564675Z         {
2023-01-11T21:05:10.6564757Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6564825Z             out_ptr0[i0] = tmp0;
2023-01-11T21:05:10.6564886Z         }
2023-01-11T21:05:10.6564945Z     }
2023-01-11T21:05:10.6565003Z }
2023-01-11T21:05:10.6565079Z ''')
2023-01-11T21:05:10.6565084Z 
2023-01-11T21:05:10.6565089Z 
2023-01-11T21:05:10.6565176Z async_compile.wait(globals())
2023-01-11T21:05:10.6565247Z del async_compile
2023-01-11T21:05:10.6565252Z 
2023-01-11T21:05:10.6565308Z def call(args):
2023-01-11T21:05:10.6565380Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6565449Z     args.clear()
2023-01-11T21:05:10.6565663Z     buf0 = empty_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6565795Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6565864Z     del arg1_1
2023-01-11T21:05:10.6565936Z     return (buf0, )
2023-01-11T21:05:10.6565941Z 
2023-01-11T21:05:10.6565945Z 
2023-01-11T21:05:10.6566018Z if __name__ == "__main__":
2023-01-11T21:05:10.6566119Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6566239Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6566454Z     arg0_1 = rand_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6566668Z     arg1_1 = rand_strided((8, 197, 384), (75648, 384, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6566781Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6566786Z 
2023-01-11T21:05:10.6566851Z ok (3.079s)
2023-01-11T21:05:10.6567295Z   test_slice_scatter_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6567459Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6567719Z [2023-01-11 20:59:39,595] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 411
2023-01-11T21:05:10.6567969Z [2023-01-11 20:59:42,256] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 411
2023-01-11T21:05:10.6567987Z 
2023-01-11T21:05:10.6568067Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6568135Z import torch
2023-01-11T21:05:10.6568203Z import random
2023-01-11T21:05:10.6568318Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6568438Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6568443Z 
2023-01-11T21:05:10.6568520Z aten = torch.ops.aten
2023-01-11T21:05:10.6568650Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6568730Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6568734Z 
2023-01-11T21:05:10.6568750Z 
2023-01-11T21:05:10.6568869Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6569112Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6569235Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6569336Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6569433Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6569529Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6569591Z {
2023-01-11T21:05:10.6569675Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6569735Z     {
2023-01-11T21:05:10.6569810Z         #pragma omp for 
2023-01-11T21:05:10.6569891Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6569951Z         {
2023-01-11T21:05:10.6570030Z             #pragma GCC ivdep
2023-01-11T21:05:10.6570103Z             for(long i1=0; i1<100; i1+=1)
2023-01-11T21:05:10.6570168Z             {
2023-01-11T21:05:10.6570232Z                 {
2023-01-11T21:05:10.6570296Z                     {
2023-01-11T21:05:10.6570402Z                         auto tmp8 = in_ptr1[i1 + (100*i0)];
2023-01-11T21:05:10.6570506Z                         auto tmp0 = static_cast<int>(i1);
2023-01-11T21:05:10.6570609Z                         auto tmp1 = static_cast<int>(10);
2023-01-11T21:05:10.6570689Z                         auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.6570789Z                         auto tmp3 = static_cast<int>(90);
2023-01-11T21:05:10.6570882Z                         auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.6570975Z                         auto tmp5 = tmp2 & tmp4;
2023-01-11T21:05:10.6571059Z                         float tmp6 = 0.0;
2023-01-11T21:05:10.6571134Z                         if(tmp5)
2023-01-11T21:05:10.6571200Z                         {
2023-01-11T21:05:10.6571370Z                             auto tmp7 = in_ptr0[(-10) + i1 + (80*i0)];
2023-01-11T21:05:10.6571440Z                             tmp6 = tmp7;
2023-01-11T21:05:10.6571506Z                         }
2023-01-11T21:05:10.6571606Z                         auto tmp9 = tmp5 ? tmp6 : tmp8;
2023-01-11T21:05:10.6571716Z                         auto tmp10 = static_cast<int>(i1 % 2);
2023-01-11T21:05:10.6571817Z                         auto tmp11 = static_cast<int>(0);
2023-01-11T21:05:10.6571911Z                         auto tmp12 = tmp10 == tmp11;
2023-01-11T21:05:10.6572004Z                         auto tmp13 = tmp5 & tmp12;
2023-01-11T21:05:10.6572076Z                         float tmp14 = 0.0;
2023-01-11T21:05:10.6572150Z                         if(tmp13)
2023-01-11T21:05:10.6572216Z                         {
2023-01-11T21:05:10.6572390Z                             auto tmp15 = in_ptr0[(-5) + (80*i0) + (i1 / 2)];
2023-01-11T21:05:10.6572469Z                             tmp14 = tmp15;
2023-01-11T21:05:10.6572536Z                         }
2023-01-11T21:05:10.6572667Z                         auto tmp16 = tmp13 ? tmp14 : tmp8;
2023-01-11T21:05:10.6572749Z                         out_ptr0[i1 + (100*i0)] = tmp9;
2023-01-11T21:05:10.6572842Z                         out_ptr1[i1 + (100*i0)] = tmp16;
2023-01-11T21:05:10.6572909Z                     }
2023-01-11T21:05:10.6572972Z                 }
2023-01-11T21:05:10.6573037Z             }
2023-01-11T21:05:10.6573096Z         }
2023-01-11T21:05:10.6573154Z     }
2023-01-11T21:05:10.6573200Z }
2023-01-11T21:05:10.6573277Z ''')
2023-01-11T21:05:10.6573282Z 
2023-01-11T21:05:10.6573286Z 
2023-01-11T21:05:10.6573377Z async_compile.wait(globals())
2023-01-11T21:05:10.6573448Z del async_compile
2023-01-11T21:05:10.6573453Z 
2023-01-11T21:05:10.6573522Z def call(args):
2023-01-11T21:05:10.6573597Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6573667Z     args.clear()
2023-01-11T21:05:10.6573862Z     buf0 = empty_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6574070Z     buf1 = empty_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6574261Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6574356Z     del arg0_1
2023-01-11T21:05:10.6574425Z     del arg1_1
2023-01-11T21:05:10.6574502Z     return (buf0, buf1, )
2023-01-11T21:05:10.6574507Z 
2023-01-11T21:05:10.6574511Z 
2023-01-11T21:05:10.6574584Z if __name__ == "__main__":
2023-01-11T21:05:10.6574697Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6574806Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6575013Z     arg0_1 = rand_strided((4, 8, 100), (800, 100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6575218Z     arg1_1 = rand_strided((4, 8, 80), (640, 80, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6575333Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6575338Z 
2023-01-11T21:05:10.6575406Z ok (2.740s)
2023-01-11T21:05:10.6575847Z   test_softmax_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6575974Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6576236Z [2023-01-11 20:59:42,356] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 412
2023-01-11T21:05:10.6576498Z [2023-01-11 20:59:45,487] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 412
2023-01-11T21:05:10.6576504Z 
2023-01-11T21:05:10.6576597Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6576653Z import torch
2023-01-11T21:05:10.6576722Z import random
2023-01-11T21:05:10.6576836Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6576959Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6576964Z 
2023-01-11T21:05:10.6577041Z aten = torch.ops.aten
2023-01-11T21:05:10.6577175Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6577266Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6577271Z 
2023-01-11T21:05:10.6577275Z 
2023-01-11T21:05:10.6577393Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6577598Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6577712Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6577813Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.6577913Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:05:10.6578017Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6578119Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6578248Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6578331Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6578423Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6578615Z                        float* __restrict__ out_ptr6,
2023-01-11T21:05:10.6578714Z                        float* __restrict__ out_ptr7,
2023-01-11T21:05:10.6578805Z                        float* __restrict__ out_ptr8)
2023-01-11T21:05:10.6578866Z {
2023-01-11T21:05:10.6578951Z     auto out_ptr3 = in_out_ptr0;
2023-01-11T21:05:10.6579021Z     auto out_ptr4 = in_out_ptr1;
2023-01-11T21:05:10.6579105Z     auto out_ptr5 = in_out_ptr2;
2023-01-11T21:05:10.6579202Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6579263Z     {
2023-01-11T21:05:10.6579338Z         #pragma omp for 
2023-01-11T21:05:10.6579419Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6579485Z         {
2023-01-11T21:05:10.6579535Z             {
2023-01-11T21:05:10.6579602Z                 {
2023-01-11T21:05:10.6579850Z                     float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6580058Z                     float tmp4 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6580183Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6580250Z                     {
2023-01-11T21:05:10.6580317Z                         {
2023-01-11T21:05:10.6580408Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6580512Z                             auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.6580609Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6580711Z                             tmp3 = std::max(tmp3, tmp2);
2023-01-11T21:05:10.6580811Z                             tmp4 = std::max(tmp4, tmp1);
2023-01-11T21:05:10.6580878Z                         }
2023-01-11T21:05:10.6580945Z                     }
2023-01-11T21:05:10.6581016Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6581102Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.6581167Z                 }
2023-01-11T21:05:10.6581230Z             }
2023-01-11T21:05:10.6581291Z         }
2023-01-11T21:05:10.6581368Z         #pragma omp for 
2023-01-11T21:05:10.6581448Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6581496Z         {
2023-01-11T21:05:10.6581559Z             {
2023-01-11T21:05:10.6581622Z                 {
2023-01-11T21:05:10.6581833Z                     float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6581925Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6581991Z                     {
2023-01-11T21:05:10.6582057Z                         {
2023-01-11T21:05:10.6582147Z                             auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:05:10.6582246Z                             tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:05:10.6582311Z                         }
2023-01-11T21:05:10.6582375Z                     }
2023-01-11T21:05:10.6582459Z                     out_ptr2[i0] = tmp1;
2023-01-11T21:05:10.6582521Z                 }
2023-01-11T21:05:10.6582584Z             }
2023-01-11T21:05:10.6582631Z         }
2023-01-11T21:05:10.6582706Z         #pragma omp for 
2023-01-11T21:05:10.6582785Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6582846Z         {
2023-01-11T21:05:10.6582907Z             {
2023-01-11T21:05:10.6582970Z                 {
2023-01-11T21:05:10.6583038Z                     float tmp12 = 0;
2023-01-11T21:05:10.6583126Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6583190Z                     {
2023-01-11T21:05:10.6583255Z                         {
2023-01-11T21:05:10.6583357Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6583454Z                             auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.6583549Z                             auto tmp3 = out_ptr0[i0];
2023-01-11T21:05:10.6583642Z                             auto tmp6 = out_ptr2[i1];
2023-01-11T21:05:10.6583754Z                             auto tmp9 = out_ptr1[i0];
2023-01-11T21:05:10.6583848Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6583992Z                             auto tmp4 = tmp2 - tmp3;
2023-01-11T21:05:10.6584092Z                             auto tmp5 = std::exp(tmp4);
2023-01-11T21:05:10.6584234Z                             auto tmp7 = tmp0 - tmp6;
2023-01-11T21:05:10.6584332Z                             auto tmp8 = std::exp(tmp7);
2023-01-11T21:05:10.6584474Z                             auto tmp10 = tmp1 - tmp9;
2023-01-11T21:05:10.6584564Z                             auto tmp11 = std::exp(tmp10);
2023-01-11T21:05:10.6584658Z                             out_ptr3[i1 + (8*i0)] = tmp5;
2023-01-11T21:05:10.6584751Z                             out_ptr4[i1 + (8*i0)] = tmp8;
2023-01-11T21:05:10.6584847Z                             out_ptr5[i1 + (8*i0)] = tmp11;
2023-01-11T21:05:10.6584928Z                             tmp12 += tmp5;
2023-01-11T21:05:10.6584998Z                         }
2023-01-11T21:05:10.6585062Z                     }
2023-01-11T21:05:10.6585134Z                     out_ptr6[i0] = tmp12;
2023-01-11T21:05:10.6585197Z                 }
2023-01-11T21:05:10.6585291Z             }
2023-01-11T21:05:10.6585352Z         }
2023-01-11T21:05:10.6585426Z         #pragma omp for 
2023-01-11T21:05:10.6585505Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6585566Z         {
2023-01-11T21:05:10.6585633Z             #pragma GCC ivdep
2023-01-11T21:05:10.6585712Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6585774Z             {
2023-01-11T21:05:10.6585836Z                 {
2023-01-11T21:05:10.6585901Z                     {
2023-01-11T21:05:10.6586001Z                         auto tmp0 = out_ptr3[i1 + (8*i0)];
2023-01-11T21:05:10.6586094Z                         auto tmp1 = out_ptr6[i0];
2023-01-11T21:05:10.6586176Z                         auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6586274Z                         in_out_ptr0[i1 + (8*i0)] = tmp2;
2023-01-11T21:05:10.6586341Z                     }
2023-01-11T21:05:10.6586405Z                 }
2023-01-11T21:05:10.6586467Z             }
2023-01-11T21:05:10.6586528Z         }
2023-01-11T21:05:10.6586592Z         #pragma omp for 
2023-01-11T21:05:10.6586672Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6586731Z         {
2023-01-11T21:05:10.6586793Z             {
2023-01-11T21:05:10.6586855Z                 {
2023-01-11T21:05:10.6586932Z                     float tmp1 = 0;
2023-01-11T21:05:10.6587020Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6587072Z                     {
2023-01-11T21:05:10.6587138Z                         {
2023-01-11T21:05:10.6587241Z                             auto tmp0 = out_ptr4[i0 + (8*i1)];
2023-01-11T21:05:10.6587323Z                             tmp1 += tmp0;
2023-01-11T21:05:10.6587389Z                         }
2023-01-11T21:05:10.6587452Z                     }
2023-01-11T21:05:10.6587534Z                     out_ptr7[i0] = tmp1;
2023-01-11T21:05:10.6587587Z                 }
2023-01-11T21:05:10.6587649Z             }
2023-01-11T21:05:10.6587708Z         }
2023-01-11T21:05:10.6587781Z         #pragma omp for 
2023-01-11T21:05:10.6587862Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6587922Z         {
2023-01-11T21:05:10.6588000Z             #pragma GCC ivdep
2023-01-11T21:05:10.6588069Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6588129Z             {
2023-01-11T21:05:10.6588191Z                 {
2023-01-11T21:05:10.6588256Z                     {
2023-01-11T21:05:10.6588356Z                         auto tmp0 = out_ptr4[i1 + (8*i0)];
2023-01-11T21:05:10.6588449Z                         auto tmp1 = out_ptr7[i1];
2023-01-11T21:05:10.6588541Z                         auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6588626Z                         in_out_ptr1[i1 + (8*i0)] = tmp2;
2023-01-11T21:05:10.6588689Z                     }
2023-01-11T21:05:10.6588750Z                 }
2023-01-11T21:05:10.6588843Z             }
2023-01-11T21:05:10.6588904Z         }
2023-01-11T21:05:10.6588978Z         #pragma omp for 
2023-01-11T21:05:10.6589044Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6589104Z         {
2023-01-11T21:05:10.6589168Z             {
2023-01-11T21:05:10.6589231Z                 {
2023-01-11T21:05:10.6589308Z                     float tmp1 = 0;
2023-01-11T21:05:10.6589396Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6589460Z                     {
2023-01-11T21:05:10.6589513Z                         {
2023-01-11T21:05:10.6589615Z                             auto tmp0 = out_ptr5[i1 + (8*i0)];
2023-01-11T21:05:10.6589695Z                             tmp1 += tmp0;
2023-01-11T21:05:10.6589760Z                         }
2023-01-11T21:05:10.6589824Z                     }
2023-01-11T21:05:10.6589907Z                     out_ptr8[i0] = tmp1;
2023-01-11T21:05:10.6589969Z                 }
2023-01-11T21:05:10.6590018Z             }
2023-01-11T21:05:10.6590080Z         }
2023-01-11T21:05:10.6590153Z         #pragma omp for 
2023-01-11T21:05:10.6590231Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6590291Z         {
2023-01-11T21:05:10.6590370Z             #pragma GCC ivdep
2023-01-11T21:05:10.6590470Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6590531Z             {
2023-01-11T21:05:10.6590594Z                 {
2023-01-11T21:05:10.6590659Z                     {
2023-01-11T21:05:10.6590758Z                         auto tmp0 = out_ptr5[i1 + (8*i0)];
2023-01-11T21:05:10.6590851Z                         auto tmp1 = out_ptr8[i0];
2023-01-11T21:05:10.6590941Z                         auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6591024Z                         in_out_ptr2[i1 + (8*i0)] = tmp2;
2023-01-11T21:05:10.6591088Z                     }
2023-01-11T21:05:10.6591150Z                 }
2023-01-11T21:05:10.6591210Z             }
2023-01-11T21:05:10.6591270Z         }
2023-01-11T21:05:10.6591329Z     }
2023-01-11T21:05:10.6591387Z }
2023-01-11T21:05:10.6591454Z ''')
2023-01-11T21:05:10.6591461Z 
2023-01-11T21:05:10.6591465Z 
2023-01-11T21:05:10.6591554Z async_compile.wait(globals())
2023-01-11T21:05:10.6591624Z del async_compile
2023-01-11T21:05:10.6591629Z 
2023-01-11T21:05:10.6591700Z def call(args):
2023-01-11T21:05:10.6591774Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6591843Z     args.clear()
2023-01-11T21:05:10.6592039Z     buf0 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6592229Z     buf8 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6592405Z     buf4 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6592590Z     buf1 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6592773Z     buf5 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6592957Z     buf9 = empty_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6593143Z     buf2 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6593225Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6593410Z     buf6 = empty_strided((1, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6593480Z     buf7 = buf5; del buf5  # reuse
2023-01-11T21:05:10.6593673Z     buf10 = empty_strided((8, 1), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6593755Z     buf11 = buf9; del buf9  # reuse
2023-01-11T21:05:10.6594111Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf11.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf10.data_ptr()))
2023-01-11T21:05:10.6594178Z     del arg0_1
2023-01-11T21:05:10.6594242Z     del arg1_1
2023-01-11T21:05:10.6594325Z     return (buf3, buf7, buf11, )
2023-01-11T21:05:10.6594330Z 
2023-01-11T21:05:10.6594362Z 
2023-01-11T21:05:10.6594438Z if __name__ == "__main__":
2023-01-11T21:05:10.6594552Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6594662Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6594858Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6595049Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6595161Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6595167Z 
2023-01-11T21:05:10.6595230Z ok (3.225s)
2023-01-11T21:05:10.6595683Z   test_softmax_one_kernel_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6595809Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6596067Z [2023-01-11 20:59:45,539] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 413
2023-01-11T21:05:10.6596399Z [2023-01-11 20:59:48,242] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 413
2023-01-11T21:05:10.6596405Z 
2023-01-11T21:05:10.6596486Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6596554Z import torch
2023-01-11T21:05:10.6596623Z import random
2023-01-11T21:05:10.6596738Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6596859Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6596863Z 
2023-01-11T21:05:10.6596941Z aten = torch.ops.aten
2023-01-11T21:05:10.6597074Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6597165Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6597170Z 
2023-01-11T21:05:10.6597174Z 
2023-01-11T21:05:10.6597295Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6597502Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6597620Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6597725Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6597825Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6597921Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6597982Z {
2023-01-11T21:05:10.6598053Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.6598148Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6598207Z     {
2023-01-11T21:05:10.6598281Z         #pragma omp for 
2023-01-11T21:05:10.6598361Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6598422Z         {
2023-01-11T21:05:10.6598483Z             {
2023-01-11T21:05:10.6598859Z                 #pragma omp declare reduction(max:at::vec::Vectorized<float>:omp_out = at::vec::maximum(omp_out, omp_in)) initializer(omp_priv={{-std::numeric_limits<float>::infinity()}})
2023-01-11T21:05:10.6599063Z                 float tmp1 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6599184Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:05:10.6599271Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.6599335Z                 {
2023-01-11T21:05:10.6599477Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (32*i0));
2023-01-11T21:05:10.6599592Z                     tmp1_vec = at::vec::maximum(tmp1_vec, tmp0);
2023-01-11T21:05:10.6599655Z                 }
2023-01-11T21:05:10.6599863Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return at::vec::maximum(x, y);}, tmp1_vec);
2023-01-11T21:05:10.6599970Z                 #pragma omp simd simdlen(8)  reduction(max:tmp1)
2023-01-11T21:05:10.6600057Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:05:10.6600151Z                 {
2023-01-11T21:05:10.6600251Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.6600348Z                     tmp1 = std::max(tmp1, tmp0);
2023-01-11T21:05:10.6600414Z                 }
2023-01-11T21:05:10.6600493Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6600544Z             }
2023-01-11T21:05:10.6600724Z         }
2023-01-11T21:05:10.6600801Z         #pragma omp for 
2023-01-11T21:05:10.6600881Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6600943Z         {
2023-01-11T21:05:10.6601004Z             {
2023-01-11T21:05:10.6601192Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6601256Z                 float tmp4 = 0;
2023-01-11T21:05:10.6601376Z                 auto tmp4_vec = at::vec::Vectorized<float>(tmp4);
2023-01-11T21:05:10.6601465Z                 for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.6601531Z                 {
2023-01-11T21:05:10.6601673Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (32*i0));
2023-01-11T21:05:10.6601850Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:05:10.6601945Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6602037Z                     auto tmp3 = tmp2.exp();
2023-01-11T21:05:10.6602128Z                     tmp3.store(out_ptr1 + (16*i1) + (32*i0));
2023-01-11T21:05:10.6602210Z                     tmp4_vec += tmp3;
2023-01-11T21:05:10.6602274Z                 }
2023-01-11T21:05:10.6602471Z                 tmp4 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp4_vec);
2023-01-11T21:05:10.6602593Z                 #pragma omp simd simdlen(8)  reduction(+:tmp4)
2023-01-11T21:05:10.6602683Z                 for(long i1=32; i1<32; i1+=1)
2023-01-11T21:05:10.6602747Z                 {
2023-01-11T21:05:10.6602850Z                     auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.6602929Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:05:10.6603019Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6603120Z                     auto tmp3 = std::exp(tmp2);
2023-01-11T21:05:10.6603213Z                     out_ptr1[i1 + (32*i0)] = tmp3;
2023-01-11T21:05:10.6603289Z                     tmp4 += tmp3;
2023-01-11T21:05:10.6603352Z                 }
2023-01-11T21:05:10.6603432Z                 out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.6603481Z             }
2023-01-11T21:05:10.6603544Z         }
2023-01-11T21:05:10.6603622Z         #pragma omp for 
2023-01-11T21:05:10.6603702Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6603764Z         {
2023-01-11T21:05:10.6603845Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.6603910Z             {
2023-01-11T21:05:10.6604036Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + (16*i1) + (32*i0));
2023-01-11T21:05:10.6604161Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[i0]);
2023-01-11T21:05:10.6604248Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6604356Z                 tmp2.store(in_out_ptr0 + (16*i1) + (32*i0));
2023-01-11T21:05:10.6604419Z             }
2023-01-11T21:05:10.6604510Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.6604596Z             for(long i1=32; i1<32; i1+=1)
2023-01-11T21:05:10.6604644Z             {
2023-01-11T21:05:10.6604743Z                 auto tmp0 = out_ptr1[i1 + (32*i0)];
2023-01-11T21:05:10.6604828Z                 auto tmp1 = out_ptr2[i0];
2023-01-11T21:05:10.6604914Z                 auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6605006Z                 in_out_ptr0[i1 + (32*i0)] = tmp2;
2023-01-11T21:05:10.6605068Z             }
2023-01-11T21:05:10.6605130Z         }
2023-01-11T21:05:10.6605176Z     }
2023-01-11T21:05:10.6605236Z }
2023-01-11T21:05:10.6605317Z ''')
2023-01-11T21:05:10.6605323Z 
2023-01-11T21:05:10.6605364Z 
2023-01-11T21:05:10.6605458Z async_compile.wait(globals())
2023-01-11T21:05:10.6605527Z del async_compile
2023-01-11T21:05:10.6605532Z 
2023-01-11T21:05:10.6605601Z def call(args):
2023-01-11T21:05:10.6605670Z     arg0_1, = args
2023-01-11T21:05:10.6605729Z     args.clear()
2023-01-11T21:05:10.6605929Z     buf0 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6606127Z     buf1 = empty_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6606321Z     buf2 = empty_strided((16, 1), (1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6606404Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6606592Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6606660Z     del arg0_1
2023-01-11T21:05:10.6606733Z     return (buf3, )
2023-01-11T21:05:10.6606738Z 
2023-01-11T21:05:10.6606742Z 
2023-01-11T21:05:10.6606803Z if __name__ == "__main__":
2023-01-11T21:05:10.6606918Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6607041Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6607282Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6607391Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6607396Z 
2023-01-11T21:05:10.6607462Z ok (2.753s)
2023-01-11T21:05:10.6607896Z   test_sort_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6608022Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6608281Z [2023-01-11 20:59:48,277] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 414
2023-01-11T21:05:10.6608485Z [2023-01-11 20:59:48,288] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.sort
2023-01-11T21:05:10.6608747Z [2023-01-11 20:59:48,292] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 414
2023-01-11T21:05:10.6608753Z 
2023-01-11T21:05:10.6608844Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6608912Z import torch
2023-01-11T21:05:10.6608983Z import random
2023-01-11T21:05:10.6609096Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6609215Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6609220Z 
2023-01-11T21:05:10.6609295Z aten = torch.ops.aten
2023-01-11T21:05:10.6609414Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6609504Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6609509Z 
2023-01-11T21:05:10.6609514Z 
2023-01-11T21:05:10.6609599Z async_compile.wait(globals())
2023-01-11T21:05:10.6609670Z del async_compile
2023-01-11T21:05:10.6609677Z 
2023-01-11T21:05:10.6609747Z def call(args):
2023-01-11T21:05:10.6609814Z     arg0_1, = args
2023-01-11T21:05:10.6609884Z     args.clear()
2023-01-11T21:05:10.6609962Z     buf0 = aten.sort(arg0_1)
2023-01-11T21:05:10.6610017Z     del arg0_1
2023-01-11T21:05:10.6610083Z     buf1 = buf0[0]
2023-01-11T21:05:10.6610188Z     assert_size_stride(buf1, (1, 1, 8, 8), (64, 64, 8, 1))
2023-01-11T21:05:10.6610254Z     buf2 = buf0[1]
2023-01-11T21:05:10.6610357Z     assert_size_stride(buf2, (1, 1, 8, 8), (64, 64, 8, 1))
2023-01-11T21:05:10.6610420Z     del buf0
2023-01-11T21:05:10.6610482Z     return (buf1, buf2, )
2023-01-11T21:05:10.6610499Z 
2023-01-11T21:05:10.6610503Z 
2023-01-11T21:05:10.6610564Z if __name__ == "__main__":
2023-01-11T21:05:10.6610676Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6610795Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6611006Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6611138Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6611143Z 
2023-01-11T21:05:10.6611207Z ok (0.046s)
2023-01-11T21:05:10.6611641Z   test_split_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6611767Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6612023Z [2023-01-11 20:59:48,333] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 415
2023-01-11T21:05:10.6612271Z [2023-01-11 20:59:48,337] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 415
2023-01-11T21:05:10.6612697Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6612824Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6613079Z [2023-01-11 20:59:48,383] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 416
2023-01-11T21:05:10.6613340Z [2023-01-11 20:59:51,070] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 416
2023-01-11T21:05:10.6613346Z 
2023-01-11T21:05:10.6613436Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6613504Z import torch
2023-01-11T21:05:10.6613572Z import random
2023-01-11T21:05:10.6613686Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6613792Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6613799Z 
2023-01-11T21:05:10.6613876Z aten = torch.ops.aten
2023-01-11T21:05:10.6614008Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6614098Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6614103Z 
2023-01-11T21:05:10.6614110Z 
2023-01-11T21:05:10.6614196Z async_compile.wait(globals())
2023-01-11T21:05:10.6614267Z del async_compile
2023-01-11T21:05:10.6614272Z 
2023-01-11T21:05:10.6614339Z def call(args):
2023-01-11T21:05:10.6614405Z     arg0_1, = args
2023-01-11T21:05:10.6614462Z     args.clear()
2023-01-11T21:05:10.6614656Z     return (as_strided(arg0_1, (2, 2, 3), (20, 10, 1)), as_strided(arg0_1, (2, 2, 3), (20, 10, 1), 3), as_strided(arg0_1, (2, 2, 3), (20, 10, 1), 6), as_strided(arg0_1, (2, 2, 1), (20, 10, 1), 9), )
2023-01-11T21:05:10.6614661Z 
2023-01-11T21:05:10.6614666Z 
2023-01-11T21:05:10.6614740Z if __name__ == "__main__":
2023-01-11T21:05:10.6614850Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6614970Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6615178Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6615283Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6615290Z 
2023-01-11T21:05:10.6615295Z 
2023-01-11T21:05:10.6615386Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6615453Z import torch
2023-01-11T21:05:10.6615508Z import random
2023-01-11T21:05:10.6615620Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6615737Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6615742Z 
2023-01-11T21:05:10.6615819Z aten = torch.ops.aten
2023-01-11T21:05:10.6615951Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6616039Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6616044Z 
2023-01-11T21:05:10.6616048Z 
2023-01-11T21:05:10.6616177Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6616369Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6616516Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6616613Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6616673Z {
2023-01-11T21:05:10.6616768Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6616828Z     {
2023-01-11T21:05:10.6616902Z         #pragma omp for 
2023-01-11T21:05:10.6616969Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6617028Z         {
2023-01-11T21:05:10.6617161Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6617293Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6617377Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6617468Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6617529Z         }
2023-01-11T21:05:10.6617622Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6617690Z         for(long i0=32; i0<40; i0+=1)
2023-01-11T21:05:10.6617752Z         {
2023-01-11T21:05:10.6617832Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6617929Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6618038Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6618119Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6618179Z         }
2023-01-11T21:05:10.6618224Z     }
2023-01-11T21:05:10.6618282Z }
2023-01-11T21:05:10.6618359Z ''')
2023-01-11T21:05:10.6618364Z 
2023-01-11T21:05:10.6618369Z 
2023-01-11T21:05:10.6618455Z async_compile.wait(globals())
2023-01-11T21:05:10.6618600Z del async_compile
2023-01-11T21:05:10.6618607Z 
2023-01-11T21:05:10.6618678Z def call(args):
2023-01-11T21:05:10.6618747Z     arg0_1, = args
2023-01-11T21:05:10.6618803Z     args.clear()
2023-01-11T21:05:10.6619010Z     buf0 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6619145Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6619217Z     del arg0_1
2023-01-11T21:05:10.6619409Z     return (as_strided(buf0, (2, 2, 3), (20, 10, 1)), as_strided(buf0, (2, 2, 3), (20, 10, 1), 3), as_strided(buf0, (2, 2, 3), (20, 10, 1), 6), as_strided(buf0, (2, 2, 1), (20, 10, 0), 9), )
2023-01-11T21:05:10.6619415Z 
2023-01-11T21:05:10.6619419Z 
2023-01-11T21:05:10.6619495Z if __name__ == "__main__":
2023-01-11T21:05:10.6619609Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6619733Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6619926Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6620033Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6620039Z 
2023-01-11T21:05:10.6620105Z ok (2.784s)
2023-01-11T21:05:10.6620550Z   test_split_with_sizes_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6620680Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6620942Z [2023-01-11 20:59:51,140] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 417
2023-01-11T21:05:10.6621202Z [2023-01-11 20:59:53,965] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 417
2023-01-11T21:05:10.6621603Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6621725Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6622012Z [2023-01-11 20:59:54,033] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 418
2023-01-11T21:05:10.6622274Z [2023-01-11 20:59:56,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 418
2023-01-11T21:05:10.6622657Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6622780Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6623031Z [2023-01-11 20:59:56,896] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 419
2023-01-11T21:05:10.6623037Z 
2023-01-11T21:05:10.6623128Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6623197Z import torch
2023-01-11T21:05:10.6623271Z import random
2023-01-11T21:05:10.6623384Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6623502Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6623507Z 
2023-01-11T21:05:10.6623599Z aten = torch.ops.aten
2023-01-11T21:05:10.6623734Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6623825Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6623830Z 
2023-01-11T21:05:10.6623834Z 
2023-01-11T21:05:10.6623967Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6624171Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6624290Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6624388Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6624485Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6624565Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6624631Z {
2023-01-11T21:05:10.6624726Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6624786Z     {
2023-01-11T21:05:10.6624860Z         #pragma omp for 
2023-01-11T21:05:10.6624941Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6625003Z         {
2023-01-11T21:05:10.6625069Z             #pragma GCC ivdep
2023-01-11T21:05:10.6625150Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6625213Z             {
2023-01-11T21:05:10.6625275Z                 {
2023-01-11T21:05:10.6625340Z                     {
2023-01-11T21:05:10.6625442Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.6625548Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6625628Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6625735Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6625828Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6625923Z                         out_ptr0[i1 + (3*i0)] = tmp4;
2023-01-11T21:05:10.6625990Z                     }
2023-01-11T21:05:10.6626052Z                 }
2023-01-11T21:05:10.6626113Z             }
2023-01-11T21:05:10.6626161Z         }
2023-01-11T21:05:10.6626238Z         #pragma omp for 
2023-01-11T21:05:10.6626318Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6626378Z         {
2023-01-11T21:05:10.6626456Z             #pragma GCC ivdep
2023-01-11T21:05:10.6626537Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6626597Z             {
2023-01-11T21:05:10.6626647Z                 {
2023-01-11T21:05:10.6626710Z                     {
2023-01-11T21:05:10.6626814Z                         auto tmp0 = in_ptr0[3 + i1 + (10*i0)];
2023-01-11T21:05:10.6626925Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6627016Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6627119Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6627208Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6627320Z                         out_ptr1[i1 + (3*i0)] = tmp4;
2023-01-11T21:05:10.6627384Z                     }
2023-01-11T21:05:10.6627446Z                 }
2023-01-11T21:05:10.6627510Z             }
2023-01-11T21:05:10.6627570Z         }
2023-01-11T21:05:10.6627645Z         #pragma omp for 
2023-01-11T21:05:10.6627712Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6627772Z         {
2023-01-11T21:05:10.6627848Z             #pragma GCC ivdep
2023-01-11T21:05:10.6627927Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.6627989Z             {
2023-01-11T21:05:10.6628052Z                 {
2023-01-11T21:05:10.6628115Z                     {
2023-01-11T21:05:10.6628206Z                         auto tmp0 = in_ptr0[6 + i1 + (10*i0)];
2023-01-11T21:05:10.6628311Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6628402Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6628507Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6628599Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6628694Z                         out_ptr2[i1 + (4*i0)] = tmp4;
2023-01-11T21:05:10.6628787Z                     }
2023-01-11T21:05:10.6628838Z                 }
2023-01-11T21:05:10.6628899Z             }
2023-01-11T21:05:10.6628958Z         }
2023-01-11T21:05:10.6629017Z     }
2023-01-11T21:05:10.6629075Z }
2023-01-11T21:05:10.6629153Z ''')
2023-01-11T21:05:10.6629158Z 
2023-01-11T21:05:10.6629162Z 
2023-01-11T21:05:10.6629249Z async_compile.wait(globals())
2023-01-11T21:05:10.6629308Z del async_compile
2023-01-11T21:05:10.6629323Z 
2023-01-11T21:05:10.6629379Z def call(args):
2023-01-11T21:05:10.6629447Z     arg0_1, = args
2023-01-11T21:05:10.6629516Z     args.clear()
2023-01-11T21:05:10.6629717Z     buf0 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6629916Z     buf1 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6630115Z     buf2 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6630303Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6630359Z     del arg0_1
2023-01-11T21:05:10.6630440Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6630446Z 
2023-01-11T21:05:10.6630450Z 
2023-01-11T21:05:10.6630522Z if __name__ == "__main__":
2023-01-11T21:05:10.6630636Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6630757Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6630961Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6631068Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6631073Z 
2023-01-11T21:05:10.6631077Z 
2023-01-11T21:05:10.6631169Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6631224Z import torch
2023-01-11T21:05:10.6631295Z import random
2023-01-11T21:05:10.6631408Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6631526Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6631531Z 
2023-01-11T21:05:10.6631610Z aten = torch.ops.aten
2023-01-11T21:05:10.6631744Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6631835Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6631840Z 
2023-01-11T21:05:10.6631844Z 
2023-01-11T21:05:10.6631975Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6632166Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6632283Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6632381Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6632477Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6632569Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6632661Z {
2023-01-11T21:05:10.6632758Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6632805Z     {
2023-01-11T21:05:10.6632881Z         #pragma omp for 
2023-01-11T21:05:10.6632963Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6633024Z         {
2023-01-11T21:05:10.6633102Z             #pragma GCC ivdep
2023-01-11T21:05:10.6633181Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.6633244Z             {
2023-01-11T21:05:10.6633294Z                 {
2023-01-11T21:05:10.6633359Z                     {
2023-01-11T21:05:10.6633462Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.6633568Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6633660Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6633766Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6633856Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6633941Z                         out_ptr0[i1 + (4*i0)] = tmp4;
2023-01-11T21:05:10.6634006Z                     }
2023-01-11T21:05:10.6634067Z                 }
2023-01-11T21:05:10.6634129Z             }
2023-01-11T21:05:10.6634189Z         }
2023-01-11T21:05:10.6634298Z         #pragma omp for 
2023-01-11T21:05:10.6634379Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6634428Z         {
2023-01-11T21:05:10.6634508Z             #pragma GCC ivdep
2023-01-11T21:05:10.6634590Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6634652Z             {
2023-01-11T21:05:10.6634718Z                 {
2023-01-11T21:05:10.6634784Z                     {
2023-01-11T21:05:10.6634875Z                         auto tmp0 = in_ptr0[4 + i1 + (10*i0)];
2023-01-11T21:05:10.6634981Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6635073Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6635180Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6635275Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6635371Z                         out_ptr1[i1 + (3*i0)] = tmp4;
2023-01-11T21:05:10.6635438Z                     }
2023-01-11T21:05:10.6635503Z                 }
2023-01-11T21:05:10.6635554Z             }
2023-01-11T21:05:10.6635617Z         }
2023-01-11T21:05:10.6635691Z         #pragma omp for 
2023-01-11T21:05:10.6635772Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6635833Z         {
2023-01-11T21:05:10.6635911Z             #pragma GCC ivdep
2023-01-11T21:05:10.6635979Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6636041Z             {
2023-01-11T21:05:10.6636104Z                 {
2023-01-11T21:05:10.6636168Z                     {
2023-01-11T21:05:10.6636270Z                         auto tmp0 = in_ptr0[7 + i1 + (10*i0)];
2023-01-11T21:05:10.6636375Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6636466Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6636561Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6636653Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6636749Z                         out_ptr2[i1 + (3*i0)] = tmp4;
2023-01-11T21:05:10.6636816Z                     }
2023-01-11T21:05:10.6636880Z                 }
2023-01-11T21:05:10.6636942Z             }
2023-01-11T21:05:10.6637004Z         }
2023-01-11T21:05:10.6637050Z     }
2023-01-11T21:05:10.6637110Z }
2023-01-11T21:05:10.6637188Z ''')
2023-01-11T21:05:10.6637194Z 
2023-01-11T21:05:10.6637198Z 
2023-01-11T21:05:10.6637285Z async_compile.wait(globals())
2023-01-11T21:05:10.6637355Z del async_compile
2023-01-11T21:05:10.6637360Z 
2023-01-11T21:05:10.6637429Z def call(args):
2023-01-11T21:05:10.6637497Z     arg0_1, = args
2023-01-11T21:05:10.6637553Z     args.clear()
2023-01-11T21:05:10.6637757Z     buf0 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6637955Z     buf1 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6638179Z     buf2 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6638369Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6638438Z     del arg0_1
2023-01-11T21:05:10.6638518Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6638523Z 
2023-01-11T21:05:10.6638527Z 
2023-01-11T21:05:10.6638602Z if __name__ == "__main__":
2023-01-11T21:05:10.6638703Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6638826Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6639032Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6639140Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6639145Z 
2023-01-11T21:05:10.6639149Z 
2023-01-11T21:05:10.6639241Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6639314Z import torch
2023-01-11T21:05:10.6639385Z import random
2023-01-11T21:05:10.6639502Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6639609Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6639654Z 
2023-01-11T21:05:10.6639720Z aten = torch.ops.aten
2023-01-11T21:05:10.6639852Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6639942Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6639948Z 
2023-01-11T21:05:10.6639952Z 
2023-01-11T21:05:10.6640083Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6640285Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6640402Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6640501Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6640699Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6640794Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6640890Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.6640951Z {
2023-01-11T21:05:10.6641049Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6641115Z     {
2023-01-11T21:05:10.6641192Z         #pragma omp for 
2023-01-11T21:05:10.6641260Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6641324Z         {
2023-01-11T21:05:10.6641386Z             {
2023-01-11T21:05:10.6641449Z                 {
2023-01-11T21:05:10.6641542Z                     auto tmp0 = in_ptr0[10*i0];
2023-01-11T21:05:10.6641646Z                     auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6641738Z                     auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6641829Z                     auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6641920Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6642004Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6642068Z                 }
2023-01-11T21:05:10.6642132Z             }
2023-01-11T21:05:10.6642193Z         }
2023-01-11T21:05:10.6642267Z         #pragma omp for 
2023-01-11T21:05:10.6642335Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6642395Z         {
2023-01-11T21:05:10.6642478Z             #pragma GCC ivdep
2023-01-11T21:05:10.6642558Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.6642621Z             {
2023-01-11T21:05:10.6642683Z                 {
2023-01-11T21:05:10.6642735Z                     {
2023-01-11T21:05:10.6642837Z                         auto tmp0 = in_ptr0[1 + i1 + (10*i0)];
2023-01-11T21:05:10.6642945Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6643035Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6643141Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6643232Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6643325Z                         out_ptr1[i1 + (2*i0)] = tmp4;
2023-01-11T21:05:10.6643444Z                     }
2023-01-11T21:05:10.6643493Z                 }
2023-01-11T21:05:10.6643556Z             }
2023-01-11T21:05:10.6643617Z         }
2023-01-11T21:05:10.6643696Z         #pragma omp for 
2023-01-11T21:05:10.6643779Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6643840Z         {
2023-01-11T21:05:10.6643905Z             #pragma GCC ivdep
2023-01-11T21:05:10.6643990Z             for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6644053Z             {
2023-01-11T21:05:10.6644115Z                 {
2023-01-11T21:05:10.6644180Z                     {
2023-01-11T21:05:10.6644284Z                         auto tmp0 = in_ptr0[3 + i1 + (10*i0)];
2023-01-11T21:05:10.6644391Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6644470Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6644575Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6644666Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6644765Z                         out_ptr2[i1 + (3*i0)] = tmp4;
2023-01-11T21:05:10.6644831Z                     }
2023-01-11T21:05:10.6644894Z                 }
2023-01-11T21:05:10.6644956Z             }
2023-01-11T21:05:10.6645054Z         }
2023-01-11T21:05:10.6645132Z         #pragma omp for 
2023-01-11T21:05:10.6645210Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6645271Z         {
2023-01-11T21:05:10.6645347Z             #pragma GCC ivdep
2023-01-11T21:05:10.6645427Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.6645489Z             {
2023-01-11T21:05:10.6645539Z                 {
2023-01-11T21:05:10.6645602Z                     {
2023-01-11T21:05:10.6645703Z                         auto tmp0 = in_ptr0[6 + i1 + (10*i0)];
2023-01-11T21:05:10.6645806Z                         auto tmp1 = static_cast<float>(2.0);
2023-01-11T21:05:10.6645896Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6646002Z                         auto tmp3 = static_cast<float>(1.0);
2023-01-11T21:05:10.6646096Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6646177Z                         out_ptr3[i1 + (4*i0)] = tmp4;
2023-01-11T21:05:10.6646242Z                     }
2023-01-11T21:05:10.6646305Z                 }
2023-01-11T21:05:10.6646367Z             }
2023-01-11T21:05:10.6646429Z         }
2023-01-11T21:05:10.6646489Z     }
2023-01-11T21:05:10.6646535Z }
2023-01-11T21:05:10.6646616Z ''')
2023-01-11T21:05:10.6646622Z 
2023-01-11T21:05:10.6646626Z 
2023-01-11T21:05:10.6646713Z async_compile.wait(globals())
2023-01-11T21:05:10.6646783Z del async_compile
2023-01-11T21:05:10.6646789Z 
2023-01-11T21:05:10.6646857Z def call(args):
2023-01-11T21:05:10.6646924Z     arg0_1, = args
2023-01-11T21:05:10.6646993Z     args.clear()
2023-01-11T21:05:10.6647195Z     buf0 = empty_strided((2, 2, 1), (2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6647380Z     buf1 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6647578Z     buf2 = empty_strided((2, 2, 3), (6, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6647773Z     buf3 = empty_strided((2, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6647984Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.6648053Z     del arg0_1
2023-01-11T21:05:10.6648138Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:05:10.6648143Z 
2023-01-11T21:05:10.6648147Z 
2023-01-11T21:05:10.6648221Z if __name__ == "__main__":
2023-01-11T21:05:10.6648336Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6648444Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6648646Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6648752Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6649021Z [2023-01-11 20:59:59,728] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 419
2023-01-11T21:05:10.6649056Z 
2023-01-11T21:05:10.6649123Z ok (8.658s)
2023-01-11T21:05:10.6649560Z   test_squeeze1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6649685Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6649942Z [2023-01-11 20:59:59,825] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 420
2023-01-11T21:05:10.6650203Z [2023-01-11 21:00:02,495] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 420
2023-01-11T21:05:10.6650208Z 
2023-01-11T21:05:10.6650303Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6650361Z import torch
2023-01-11T21:05:10.6650429Z import random
2023-01-11T21:05:10.6650541Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6650691Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6650696Z 
2023-01-11T21:05:10.6650775Z aten = torch.ops.aten
2023-01-11T21:05:10.6650908Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6650998Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6651003Z 
2023-01-11T21:05:10.6651007Z 
2023-01-11T21:05:10.6651139Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6651332Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6651449Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6651548Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6651643Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6651705Z {
2023-01-11T21:05:10.6651801Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6651862Z     {
2023-01-11T21:05:10.6651923Z         #pragma omp for 
2023-01-11T21:05:10.6652003Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6652065Z         {
2023-01-11T21:05:10.6652126Z             {
2023-01-11T21:05:10.6652190Z                 {
2023-01-11T21:05:10.6652281Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6652385Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6652464Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6652566Z                     auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.6652654Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6652740Z                     auto tmp5 = tmp0 + tmp3;
2023-01-11T21:05:10.6652822Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6652907Z                     out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.6652973Z                 }
2023-01-11T21:05:10.6653023Z             }
2023-01-11T21:05:10.6653084Z         }
2023-01-11T21:05:10.6653143Z     }
2023-01-11T21:05:10.6653202Z }
2023-01-11T21:05:10.6653277Z ''')
2023-01-11T21:05:10.6653282Z 
2023-01-11T21:05:10.6653286Z 
2023-01-11T21:05:10.6653377Z async_compile.wait(globals())
2023-01-11T21:05:10.6653448Z del async_compile
2023-01-11T21:05:10.6653453Z 
2023-01-11T21:05:10.6653509Z def call(args):
2023-01-11T21:05:10.6653575Z     arg0_1, = args
2023-01-11T21:05:10.6653643Z     args.clear()
2023-01-11T21:05:10.6653844Z     buf0 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6654043Z     buf1 = empty_strided((2, 2, 2), (4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6654204Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6654270Z     del arg0_1
2023-01-11T21:05:10.6654334Z     return (buf0, buf1, )
2023-01-11T21:05:10.6654339Z 
2023-01-11T21:05:10.6654356Z 
2023-01-11T21:05:10.6654449Z if __name__ == "__main__":
2023-01-11T21:05:10.6654561Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6654680Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6654908Z     arg0_1 = rand_strided((1, 2, 1, 2, 2, 1, 1), (8, 4, 4, 2, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6655013Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6655018Z 
2023-01-11T21:05:10.6655082Z ok (2.764s)
2023-01-11T21:05:10.6655519Z   test_squeeze2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6655643Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6655890Z [2023-01-11 21:00:02,548] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 421
2023-01-11T21:05:10.6656185Z [2023-01-11 21:00:05,196] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 421
2023-01-11T21:05:10.6656191Z 
2023-01-11T21:05:10.6656283Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6656350Z import torch
2023-01-11T21:05:10.6656420Z import random
2023-01-11T21:05:10.6656533Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6656652Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6656657Z 
2023-01-11T21:05:10.6656733Z aten = torch.ops.aten
2023-01-11T21:05:10.6656854Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6656944Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6656949Z 
2023-01-11T21:05:10.6656953Z 
2023-01-11T21:05:10.6657085Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6657287Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6657408Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6657507Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6657604Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6657663Z {
2023-01-11T21:05:10.6657747Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6657808Z     {
2023-01-11T21:05:10.6657883Z         #pragma omp for 
2023-01-11T21:05:10.6657963Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.6658025Z         {
2023-01-11T21:05:10.6658162Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6658294Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6658365Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6658588Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6658676Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6658760Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:05:10.6658853Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6658944Z             tmp5.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6659007Z         }
2023-01-11T21:05:10.6659088Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6659169Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.6659231Z         {
2023-01-11T21:05:10.6659315Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6659414Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6659494Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6659594Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.6659663Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6659744Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:05:10.6659824Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6659902Z             out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.6659964Z         }
2023-01-11T21:05:10.6660063Z     }
2023-01-11T21:05:10.6660121Z }
2023-01-11T21:05:10.6660187Z ''')
2023-01-11T21:05:10.6660192Z 
2023-01-11T21:05:10.6660209Z 
2023-01-11T21:05:10.6660285Z async_compile.wait(globals())
2023-01-11T21:05:10.6660357Z del async_compile
2023-01-11T21:05:10.6660361Z 
2023-01-11T21:05:10.6660429Z def call(args):
2023-01-11T21:05:10.6660496Z     arg0_1, = args
2023-01-11T21:05:10.6660567Z     args.clear()
2023-01-11T21:05:10.6660784Z     buf0 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6661003Z     buf1 = empty_strided((2, 1, 2, 2, 2, 1), (8, 8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6661147Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6661213Z     del arg0_1
2023-01-11T21:05:10.6661288Z     return (buf0, buf1, )
2023-01-11T21:05:10.6661293Z 
2023-01-11T21:05:10.6661297Z 
2023-01-11T21:05:10.6661374Z if __name__ == "__main__":
2023-01-11T21:05:10.6661486Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6661606Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6661863Z     arg0_1 = rand_strided((1, 2, 1, 2, 2, 2, 1), (16, 8, 8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6661970Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6661975Z 
2023-01-11T21:05:10.6662027Z ok (2.701s)
2023-01-11T21:05:10.6662458Z   test_stack_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6662583Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6662842Z [2023-01-11 21:00:05,243] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 422
2023-01-11T21:05:10.6663105Z [2023-01-11 21:00:08,074] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 422
2023-01-11T21:05:10.6663111Z 
2023-01-11T21:05:10.6663204Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6663274Z import torch
2023-01-11T21:05:10.6663342Z import random
2023-01-11T21:05:10.6663454Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6663560Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6663565Z 
2023-01-11T21:05:10.6663640Z aten = torch.ops.aten
2023-01-11T21:05:10.6663770Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6663860Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6663866Z 
2023-01-11T21:05:10.6663870Z 
2023-01-11T21:05:10.6664001Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6664203Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6664323Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6664427Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6664514Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6664609Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6664668Z {
2023-01-11T21:05:10.6664763Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6664823Z     {
2023-01-11T21:05:10.6664898Z         #pragma omp for 
2023-01-11T21:05:10.6664980Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.6665028Z         {
2023-01-11T21:05:10.6665107Z             #pragma GCC ivdep
2023-01-11T21:05:10.6665191Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.6665254Z             {
2023-01-11T21:05:10.6665318Z                 {
2023-01-11T21:05:10.6665386Z                     {
2023-01-11T21:05:10.6665481Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.6665566Z                         out_ptr0[(2*i1) + (32*i0)] = tmp0;
2023-01-11T21:05:10.6665672Z                     }
2023-01-11T21:05:10.6665735Z                 }
2023-01-11T21:05:10.6665798Z             }
2023-01-11T21:05:10.6665859Z         }
2023-01-11T21:05:10.6665937Z         #pragma omp for 
2023-01-11T21:05:10.6666005Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.6666065Z         {
2023-01-11T21:05:10.6666143Z             #pragma GCC ivdep
2023-01-11T21:05:10.6666226Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.6666289Z             {
2023-01-11T21:05:10.6666352Z                 {
2023-01-11T21:05:10.6666416Z                     {
2023-01-11T21:05:10.6666496Z                         auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6666596Z                         out_ptr1[(2*i1) + (32*i0)] = tmp0;
2023-01-11T21:05:10.6666661Z                     }
2023-01-11T21:05:10.6666724Z                 }
2023-01-11T21:05:10.6666786Z             }
2023-01-11T21:05:10.6666846Z         }
2023-01-11T21:05:10.6666908Z     }
2023-01-11T21:05:10.6666953Z }
2023-01-11T21:05:10.6667032Z ''')
2023-01-11T21:05:10.6667037Z 
2023-01-11T21:05:10.6667041Z 
2023-01-11T21:05:10.6667130Z async_compile.wait(globals())
2023-01-11T21:05:10.6667228Z del async_compile
2023-01-11T21:05:10.6667233Z 
2023-01-11T21:05:10.6667303Z def call(args):
2023-01-11T21:05:10.6667375Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6667443Z     args.clear()
2023-01-11T21:05:10.6667636Z     buf2 = empty_strided((12, 16, 2), (32, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6667746Z     buf0 = as_strided(buf2, (12, 16, 1), (32, 2, 1))  # alias
2023-01-11T21:05:10.6667849Z     buf1 = as_strided(buf2, (12, 16, 1), (32, 2, 1), 1)  # alias
2023-01-11T21:05:10.6668041Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6668109Z     del arg0_1
2023-01-11T21:05:10.6668175Z     del arg1_1
2023-01-11T21:05:10.6668244Z     return (buf2, )
2023-01-11T21:05:10.6668251Z 
2023-01-11T21:05:10.6668255Z 
2023-01-11T21:05:10.6668330Z if __name__ == "__main__":
2023-01-11T21:05:10.6668429Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6668553Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6668750Z     arg0_1 = rand_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6668946Z     arg1_1 = rand_strided((12, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6669060Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6669065Z 
2023-01-11T21:05:10.6669130Z ok (2.878s)
2023-01-11T21:05:10.6669558Z   test_std_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6669687Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6669945Z [2023-01-11 21:00:08,166] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 423
2023-01-11T21:05:10.6669951Z 
2023-01-11T21:05:10.6670030Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6670097Z import torch
2023-01-11T21:05:10.6670165Z import random
2023-01-11T21:05:10.6670278Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6670396Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6670401Z 
2023-01-11T21:05:10.6670477Z aten = torch.ops.aten
2023-01-11T21:05:10.6670609Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6670685Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6670702Z 
2023-01-11T21:05:10.6670706Z 
2023-01-11T21:05:10.6670826Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6671028Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6671174Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6671274Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.6671375Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:05:10.6671472Z                        float* __restrict__ in_out_ptr3,
2023-01-11T21:05:10.6671569Z                        float* __restrict__ in_out_ptr4,
2023-01-11T21:05:10.6671653Z                        float* __restrict__ in_out_ptr5,
2023-01-11T21:05:10.6671750Z                        float* __restrict__ in_out_ptr6,
2023-01-11T21:05:10.6671852Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6671948Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6672042Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6672134Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.6672225Z                        float* __restrict__ out_ptr5,
2023-01-11T21:05:10.6672317Z                        float* __restrict__ out_ptr7,
2023-01-11T21:05:10.6672402Z                        float* __restrict__ out_ptr10,
2023-01-11T21:05:10.6672524Z                        float* __restrict__ out_ptr12,
2023-01-11T21:05:10.6672621Z                        float* __restrict__ out_ptr14)
2023-01-11T21:05:10.6672679Z {
2023-01-11T21:05:10.6672762Z     auto out_ptr6 = in_out_ptr0;
2023-01-11T21:05:10.6672843Z     auto out_ptr8 = in_out_ptr1;
2023-01-11T21:05:10.6672925Z     auto out_ptr11 = in_out_ptr2;
2023-01-11T21:05:10.6672993Z     auto out_ptr13 = in_out_ptr3;
2023-01-11T21:05:10.6673074Z     auto out_ptr1 = in_out_ptr4;
2023-01-11T21:05:10.6673153Z     auto out_ptr3 = in_out_ptr5;
2023-01-11T21:05:10.6673232Z     auto out_ptr9 = in_out_ptr6;
2023-01-11T21:05:10.6673291Z     {
2023-01-11T21:05:10.6673480Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6673555Z         float tmp1 = 0;
2023-01-11T21:05:10.6673658Z         auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:05:10.6673760Z         #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6673820Z         {
2023-01-11T21:05:10.6673928Z             #pragma omp for reduction(+:tmp1_vec) 
2023-01-11T21:05:10.6674011Z             for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6674074Z             {
2023-01-11T21:05:10.6674207Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6674272Z                 tmp1_vec += tmp0;
2023-01-11T21:05:10.6674334Z             }
2023-01-11T21:05:10.6674526Z             tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:05:10.6674647Z             #pragma omp for simd simdlen(8) reduction(+:tmp1) 
2023-01-11T21:05:10.6674734Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6674795Z             {
2023-01-11T21:05:10.6674884Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6674957Z                 tmp1 += tmp0;
2023-01-11T21:05:10.6675006Z             }
2023-01-11T21:05:10.6675064Z         }
2023-01-11T21:05:10.6675141Z         out_ptr0[0] = tmp1;
2023-01-11T21:05:10.6675200Z     }
2023-01-11T21:05:10.6675258Z     {
2023-01-11T21:05:10.6675441Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6675514Z         float tmp6 = 0;
2023-01-11T21:05:10.6675616Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:05:10.6675688Z         float tmp7 = 0;
2023-01-11T21:05:10.6675800Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:05:10.6675902Z         #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6675964Z         {
2023-01-11T21:05:10.6676093Z             #pragma omp for reduction(+:tmp6_vec) reduction(+:tmp7_vec) 
2023-01-11T21:05:10.6676178Z             for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6676254Z             {
2023-01-11T21:05:10.6676386Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6676511Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr0[0]);
2023-01-11T21:05:10.6676645Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:05:10.6676732Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6676866Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6676951Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:05:10.6677029Z                 tmp6_vec += tmp5;
2023-01-11T21:05:10.6677093Z                 tmp7_vec += tmp0;
2023-01-11T21:05:10.6677154Z             }
2023-01-11T21:05:10.6677398Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:05:10.6677587Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:05:10.6677731Z             #pragma omp for simd simdlen(8) reduction(+:tmp6) reduction(+:tmp7) 
2023-01-11T21:05:10.6677852Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6677915Z             {
2023-01-11T21:05:10.6678000Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6678073Z                 auto tmp1 = out_ptr0[0];
2023-01-11T21:05:10.6678173Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:05:10.6678258Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6678385Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6678467Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6678540Z                 tmp6 += tmp5;
2023-01-11T21:05:10.6678612Z                 tmp7 += tmp0;
2023-01-11T21:05:10.6678663Z             }
2023-01-11T21:05:10.6678725Z         }
2023-01-11T21:05:10.6678799Z         out_ptr1[0] = tmp6;
2023-01-11T21:05:10.6678876Z         out_ptr2[0] = tmp7;
2023-01-11T21:05:10.6678937Z     }
2023-01-11T21:05:10.6678994Z     {
2023-01-11T21:05:10.6679179Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6679243Z         float tmp6 = 0;
2023-01-11T21:05:10.6679358Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:05:10.6679430Z         float tmp7 = 0;
2023-01-11T21:05:10.6679543Z         auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:05:10.6679646Z         #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6679707Z         {
2023-01-11T21:05:10.6679838Z             #pragma omp for reduction(+:tmp6_vec) reduction(+:tmp7_vec) 
2023-01-11T21:05:10.6679909Z             for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6679971Z             {
2023-01-11T21:05:10.6680101Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6680221Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr2[0]);
2023-01-11T21:05:10.6680357Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:05:10.6680442Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6680575Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6680792Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:05:10.6680859Z                 tmp6_vec += tmp5;
2023-01-11T21:05:10.6680935Z                 tmp7_vec += tmp0;
2023-01-11T21:05:10.6681000Z             }
2023-01-11T21:05:10.6681193Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:05:10.6681379Z             tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:05:10.6681519Z             #pragma omp for simd simdlen(8) reduction(+:tmp6) reduction(+:tmp7) 
2023-01-11T21:05:10.6681612Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6681758Z             {
2023-01-11T21:05:10.6681830Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6681913Z                 auto tmp1 = out_ptr2[0];
2023-01-11T21:05:10.6682017Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:05:10.6682102Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6682232Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6682315Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6682392Z                 tmp6 += tmp5;
2023-01-11T21:05:10.6682450Z                 tmp7 += tmp0;
2023-01-11T21:05:10.6682512Z             }
2023-01-11T21:05:10.6682575Z         }
2023-01-11T21:05:10.6682651Z         out_ptr3[0] = tmp6;
2023-01-11T21:05:10.6682724Z         out_ptr4[0] = tmp7;
2023-01-11T21:05:10.6682786Z     }
2023-01-11T21:05:10.6682868Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6682929Z     {
2023-01-11T21:05:10.6683005Z         #pragma omp for 
2023-01-11T21:05:10.6683089Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6683151Z         {
2023-01-11T21:05:10.6683215Z             {
2023-01-11T21:05:10.6683278Z                 {
2023-01-11T21:05:10.6683344Z                     float tmp1 = 0;
2023-01-11T21:05:10.6683486Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6683553Z                     {
2023-01-11T21:05:10.6683620Z                         {
2023-01-11T21:05:10.6683727Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6683807Z                             tmp1 += tmp0;
2023-01-11T21:05:10.6683872Z                         }
2023-01-11T21:05:10.6683924Z                     }
2023-01-11T21:05:10.6684007Z                     out_ptr5[i0] = tmp1;
2023-01-11T21:05:10.6684069Z                 }
2023-01-11T21:05:10.6684131Z             }
2023-01-11T21:05:10.6684190Z         }
2023-01-11T21:05:10.6684264Z         #pragma omp for 
2023-01-11T21:05:10.6684399Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6684449Z         {
2023-01-11T21:05:10.6684510Z             {
2023-01-11T21:05:10.6684573Z                 {
2023-01-11T21:05:10.6684651Z                     float tmp6 = 0;
2023-01-11T21:05:10.6684731Z                     float tmp7 = 0;
2023-01-11T21:05:10.6684823Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6684874Z                     {
2023-01-11T21:05:10.6684942Z                         {
2023-01-11T21:05:10.6685048Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6685145Z                             auto tmp1 = out_ptr5[i0];
2023-01-11T21:05:10.6685254Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:05:10.6685349Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6685491Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6685583Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6685650Z                             tmp6 += tmp5;
2023-01-11T21:05:10.6685732Z                             tmp7 += tmp0;
2023-01-11T21:05:10.6685798Z                         }
2023-01-11T21:05:10.6685862Z                     }
2023-01-11T21:05:10.6685945Z                     out_ptr6[i0] = tmp6;
2023-01-11T21:05:10.6686028Z                     out_ptr7[i0] = tmp7;
2023-01-11T21:05:10.6686078Z                 }
2023-01-11T21:05:10.6686138Z             }
2023-01-11T21:05:10.6686197Z         }
2023-01-11T21:05:10.6686272Z         #pragma omp for 
2023-01-11T21:05:10.6686352Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6686413Z         {
2023-01-11T21:05:10.6686546Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr6 + 16*i0);
2023-01-11T21:05:10.6686666Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:05:10.6686748Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6686842Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6686902Z         }
2023-01-11T21:05:10.6686994Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6687102Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.6687162Z         {
2023-01-11T21:05:10.6687232Z             auto tmp0 = out_ptr6[i0];
2023-01-11T21:05:10.6687331Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:05:10.6687412Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6687492Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6687552Z         }
2023-01-11T21:05:10.6687625Z         #pragma omp for 
2023-01-11T21:05:10.6687705Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6687753Z         {
2023-01-11T21:05:10.6687814Z             {
2023-01-11T21:05:10.6687878Z                 {
2023-01-11T21:05:10.6687954Z                     float tmp6 = 0;
2023-01-11T21:05:10.6688042Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6688107Z                     {
2023-01-11T21:05:10.6688172Z                         {
2023-01-11T21:05:10.6688262Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6688361Z                             auto tmp1 = out_ptr7[i0];
2023-01-11T21:05:10.6688466Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:05:10.6688592Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6688736Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6688826Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6688905Z                             tmp6 += tmp5;
2023-01-11T21:05:10.6688959Z                         }
2023-01-11T21:05:10.6689027Z                     }
2023-01-11T21:05:10.6689110Z                     out_ptr8[i0] = tmp6;
2023-01-11T21:05:10.6689172Z                 }
2023-01-11T21:05:10.6689233Z             }
2023-01-11T21:05:10.6689294Z         }
2023-01-11T21:05:10.6689366Z         #pragma omp for 
2023-01-11T21:05:10.6689431Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6689492Z         {
2023-01-11T21:05:10.6689628Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr8 + 16*i0);
2023-01-11T21:05:10.6689760Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:05:10.6689843Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6689940Z             tmp2.store(in_out_ptr1 + 16*i0);
2023-01-11T21:05:10.6690000Z         }
2023-01-11T21:05:10.6690079Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6690158Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.6690218Z         {
2023-01-11T21:05:10.6690300Z             auto tmp0 = out_ptr8[i0];
2023-01-11T21:05:10.6690394Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:05:10.6690476Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6690556Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.6690604Z         }
2023-01-11T21:05:10.6690663Z     }
2023-01-11T21:05:10.6690721Z     {
2023-01-11T21:05:10.6690904Z         #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6690979Z         float tmp6 = 0;
2023-01-11T21:05:10.6691094Z         auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:05:10.6691195Z         #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6691244Z         {
2023-01-11T21:05:10.6691348Z             #pragma omp for reduction(+:tmp6_vec) 
2023-01-11T21:05:10.6691431Z             for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6691494Z             {
2023-01-11T21:05:10.6691628Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6691751Z                 auto tmp1 = at::vec::Vectorized<float>(out_ptr4[0]);
2023-01-11T21:05:10.6691885Z                 auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(256));
2023-01-11T21:05:10.6691972Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6692086Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6692172Z                 auto tmp5 = tmp4.pow(2);
2023-01-11T21:05:10.6692252Z                 tmp6_vec += tmp5;
2023-01-11T21:05:10.6692362Z             }
2023-01-11T21:05:10.6692555Z             tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:05:10.6692679Z             #pragma omp for simd simdlen(8) reduction(+:tmp6) 
2023-01-11T21:05:10.6692768Z             for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6692818Z             {
2023-01-11T21:05:10.6692902Z                 auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6692986Z                 auto tmp1 = out_ptr4[0];
2023-01-11T21:05:10.6693087Z                 auto tmp2 = static_cast<float>(256);
2023-01-11T21:05:10.6693172Z                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6693299Z                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6693386Z                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6693447Z                 tmp6 += tmp5;
2023-01-11T21:05:10.6693511Z             }
2023-01-11T21:05:10.6693572Z         }
2023-01-11T21:05:10.6693651Z         out_ptr9[0] = tmp6;
2023-01-11T21:05:10.6693713Z     }
2023-01-11T21:05:10.6693810Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6693870Z     {
2023-01-11T21:05:10.6693931Z         #pragma omp for 
2023-01-11T21:05:10.6694040Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6694103Z         {
2023-01-11T21:05:10.6694164Z             {
2023-01-11T21:05:10.6694229Z                 {
2023-01-11T21:05:10.6694308Z                     float tmp1 = 0;
2023-01-11T21:05:10.6694399Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6694452Z                     {
2023-01-11T21:05:10.6694519Z                         {
2023-01-11T21:05:10.6694622Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:05:10.6694703Z                             tmp1 += tmp0;
2023-01-11T21:05:10.6694772Z                         }
2023-01-11T21:05:10.6694835Z                     }
2023-01-11T21:05:10.6694918Z                     out_ptr10[i0] = tmp1;
2023-01-11T21:05:10.6694971Z                 }
2023-01-11T21:05:10.6695034Z             }
2023-01-11T21:05:10.6695095Z         }
2023-01-11T21:05:10.6695170Z         #pragma omp for 
2023-01-11T21:05:10.6695253Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6695316Z         {
2023-01-11T21:05:10.6695365Z             {
2023-01-11T21:05:10.6695427Z                 {
2023-01-11T21:05:10.6695505Z                     float tmp6 = 0;
2023-01-11T21:05:10.6695583Z                     float tmp7 = 0;
2023-01-11T21:05:10.6695674Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6695739Z                     {
2023-01-11T21:05:10.6695805Z                         {
2023-01-11T21:05:10.6695894Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:05:10.6695993Z                             auto tmp1 = out_ptr10[i0];
2023-01-11T21:05:10.6696097Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:05:10.6696191Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6696334Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6696427Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6696508Z                             tmp6 += tmp5;
2023-01-11T21:05:10.6696576Z                             tmp7 += tmp0;
2023-01-11T21:05:10.6696642Z                         }
2023-01-11T21:05:10.6696706Z                     }
2023-01-11T21:05:10.6696787Z                     out_ptr11[i0] = tmp6;
2023-01-11T21:05:10.6696866Z                     out_ptr12[i0] = tmp7;
2023-01-11T21:05:10.6696928Z                 }
2023-01-11T21:05:10.6696990Z             }
2023-01-11T21:05:10.6697037Z         }
2023-01-11T21:05:10.6697111Z         #pragma omp for 
2023-01-11T21:05:10.6697189Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6697249Z         {
2023-01-11T21:05:10.6697382Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr11 + 16*i0);
2023-01-11T21:05:10.6697513Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(7));
2023-01-11T21:05:10.6697628Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6697697Z             auto tmp3 = tmp2.sqrt();
2023-01-11T21:05:10.6697793Z             tmp3.store(in_out_ptr2 + 16*i0);
2023-01-11T21:05:10.6697853Z         }
2023-01-11T21:05:10.6697946Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6698026Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.6698086Z         {
2023-01-11T21:05:10.6698169Z             auto tmp0 = out_ptr11[i0];
2023-01-11T21:05:10.6698254Z             auto tmp1 = static_cast<float>(7);
2023-01-11T21:05:10.6698336Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6698425Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:05:10.6698599Z             in_out_ptr2[i0] = tmp3;
2023-01-11T21:05:10.6698666Z         }
2023-01-11T21:05:10.6698741Z         #pragma omp for 
2023-01-11T21:05:10.6698820Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6698868Z         {
2023-01-11T21:05:10.6698933Z             {
2023-01-11T21:05:10.6698998Z                 {
2023-01-11T21:05:10.6699079Z                     float tmp6 = 0;
2023-01-11T21:05:10.6699169Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6699275Z                     {
2023-01-11T21:05:10.6699344Z                         {
2023-01-11T21:05:10.6699434Z                             auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:05:10.6699534Z                             auto tmp1 = out_ptr12[i0];
2023-01-11T21:05:10.6699637Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:05:10.6699732Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6699876Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6699967Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6700046Z                             tmp6 += tmp5;
2023-01-11T21:05:10.6700101Z                         }
2023-01-11T21:05:10.6700166Z                     }
2023-01-11T21:05:10.6700249Z                     out_ptr13[i0] = tmp6;
2023-01-11T21:05:10.6700312Z                 }
2023-01-11T21:05:10.6700373Z             }
2023-01-11T21:05:10.6700433Z         }
2023-01-11T21:05:10.6700506Z         #pragma omp for 
2023-01-11T21:05:10.6700574Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6700633Z         {
2023-01-11T21:05:10.6700767Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr13 + 16*i0);
2023-01-11T21:05:10.6700900Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(8));
2023-01-11T21:05:10.6700982Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6701063Z             auto tmp3 = tmp2.sqrt();
2023-01-11T21:05:10.6701158Z             tmp3.store(in_out_ptr3 + 16*i0);
2023-01-11T21:05:10.6701205Z         }
2023-01-11T21:05:10.6701297Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6701376Z         for(long i0=32; i0<32; i0+=1)
2023-01-11T21:05:10.6701435Z         {
2023-01-11T21:05:10.6701520Z             auto tmp0 = out_ptr13[i0];
2023-01-11T21:05:10.6701619Z             auto tmp1 = static_cast<float>(8);
2023-01-11T21:05:10.6701700Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6701777Z             auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:05:10.6701858Z             in_out_ptr3[i0] = tmp3;
2023-01-11T21:05:10.6701918Z         }
2023-01-11T21:05:10.6701992Z         #pragma omp for 
2023-01-11T21:05:10.6702069Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6702128Z         {
2023-01-11T21:05:10.6702205Z             #pragma GCC ivdep
2023-01-11T21:05:10.6702272Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6702334Z             {
2023-01-11T21:05:10.6702395Z                 {
2023-01-11T21:05:10.6702460Z                     {
2023-01-11T21:05:10.6702562Z                         auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.6702697Z                         auto tmp1 = in_ptr0[8 + i1 + (32*i0)];
2023-01-11T21:05:10.6702831Z                         auto tmp3 = in_ptr0[16 + i1 + (32*i0)];
2023-01-11T21:05:10.6703016Z                         auto tmp5 = in_ptr0[24 + i1 + (32*i0)];
2023-01-11T21:05:10.6703111Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6703208Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6703301Z                         auto tmp6 = tmp4 + tmp5;
2023-01-11T21:05:10.6703407Z                         auto tmp7 = static_cast<float>(4);
2023-01-11T21:05:10.6703498Z                         auto tmp8 = tmp6 / tmp7;
2023-01-11T21:05:10.6703640Z                         auto tmp9 = tmp0 - tmp8;
2023-01-11T21:05:10.6703721Z                         auto tmp10 = tmp9 * tmp9;
2023-01-11T21:05:10.6703860Z                         auto tmp11 = tmp1 - tmp8;
2023-01-11T21:05:10.6703959Z                         auto tmp12 = tmp11 * tmp11;
2023-01-11T21:05:10.6704051Z                         auto tmp13 = tmp10 + tmp12;
2023-01-11T21:05:10.6704192Z                         auto tmp14 = tmp3 - tmp8;
2023-01-11T21:05:10.6704286Z                         auto tmp15 = tmp14 * tmp14;
2023-01-11T21:05:10.6704380Z                         auto tmp16 = tmp13 + tmp15;
2023-01-11T21:05:10.6704505Z                         auto tmp17 = tmp5 - tmp8;
2023-01-11T21:05:10.6704634Z                         auto tmp18 = tmp17 * tmp17;
2023-01-11T21:05:10.6704729Z                         auto tmp19 = tmp16 + tmp18;
2023-01-11T21:05:10.6704833Z                         auto tmp20 = static_cast<float>(3);
2023-01-11T21:05:10.6704925Z                         auto tmp21 = tmp19 / tmp20;
2023-01-11T21:05:10.6705027Z                         auto tmp22 = std::sqrt(tmp21);
2023-01-11T21:05:10.6705122Z                         out_ptr14[i1 + (8*i0)] = tmp22;
2023-01-11T21:05:10.6705175Z                     }
2023-01-11T21:05:10.6705237Z                 }
2023-01-11T21:05:10.6705298Z             }
2023-01-11T21:05:10.6705359Z         }
2023-01-11T21:05:10.6705436Z         #pragma omp single
2023-01-11T21:05:10.6705497Z         {
2023-01-11T21:05:10.6705558Z             {
2023-01-11T21:05:10.6705609Z                 {
2023-01-11T21:05:10.6705701Z                     auto tmp0 = out_ptr1[0];
2023-01-11T21:05:10.6705803Z                     auto tmp1 = static_cast<float>(255);
2023-01-11T21:05:10.6705895Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6705980Z                     in_out_ptr4[0] = tmp2;
2023-01-11T21:05:10.6706044Z                 }
2023-01-11T21:05:10.6706107Z             }
2023-01-11T21:05:10.6706154Z         }
2023-01-11T21:05:10.6706233Z         #pragma omp single
2023-01-11T21:05:10.6706295Z         {
2023-01-11T21:05:10.6706359Z             {
2023-01-11T21:05:10.6706423Z                 {
2023-01-11T21:05:10.6706511Z                     auto tmp0 = out_ptr3[0];
2023-01-11T21:05:10.6706615Z                     auto tmp1 = static_cast<float>(256);
2023-01-11T21:05:10.6706691Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6706778Z                     in_out_ptr5[0] = tmp2;
2023-01-11T21:05:10.6706840Z                 }
2023-01-11T21:05:10.6706903Z             }
2023-01-11T21:05:10.6706963Z         }
2023-01-11T21:05:10.6707039Z         #pragma omp single
2023-01-11T21:05:10.6707087Z         {
2023-01-11T21:05:10.6707148Z             {
2023-01-11T21:05:10.6707213Z                 {
2023-01-11T21:05:10.6707301Z                     auto tmp0 = out_ptr9[0];
2023-01-11T21:05:10.6707406Z                     auto tmp1 = static_cast<float>(256);
2023-01-11T21:05:10.6707493Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6707589Z                     auto tmp3 = std::sqrt(tmp2);
2023-01-11T21:05:10.6707661Z                     in_out_ptr6[0] = tmp3;
2023-01-11T21:05:10.6707724Z                 }
2023-01-11T21:05:10.6707784Z             }
2023-01-11T21:05:10.6707844Z         }
2023-01-11T21:05:10.6707903Z     }
2023-01-11T21:05:10.6707961Z }
2023-01-11T21:05:10.6708038Z ''')
2023-01-11T21:05:10.6708044Z 
2023-01-11T21:05:10.6708048Z 
2023-01-11T21:05:10.6708126Z async_compile.wait(globals())
2023-01-11T21:05:10.6708228Z del async_compile
2023-01-11T21:05:10.6708233Z 
2023-01-11T21:05:10.6708301Z def call(args):
2023-01-11T21:05:10.6708367Z     arg0_1, = args
2023-01-11T21:05:10.6708437Z     args.clear()
2023-01-11T21:05:10.6708650Z     buf0 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6708833Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6709024Z     buf2 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6709206Z     buf3 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6709412Z     buf10 = empty_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6709621Z     buf4 = empty_strided((2, 4, 4, 1), (16, 4, 1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6709819Z     buf5 = empty_strided((2, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6710024Z     buf7 = empty_strided((2, 4, 4, 1), (16, 4, 1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6710111Z     buf6 = buf5; del buf5  # reuse
2023-01-11T21:05:10.6710310Z     buf8 = empty_strided((2, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6710406Z     buf9 = buf8; del buf8  # reuse
2023-01-11T21:05:10.6710588Z     buf11 = empty_strided((), (), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6710794Z     buf12 = empty_strided((1, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6710985Z     buf13 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6711187Z     buf15 = empty_strided((1, 1, 4, 8), (32, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6711271Z     buf14 = buf13; del buf13  # reuse
2023-01-11T21:05:10.6711459Z     buf16 = empty_strided((4, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6711540Z     buf17 = buf16; del buf16  # reuse
2023-01-11T21:05:10.6711731Z     buf18 = empty_strided((2, 4, 1, 8), (32, 8, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6711817Z     buf19 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6711897Z     buf20 = buf3; del buf3  # reuse
2023-01-11T21:05:10.6711982Z     buf21 = buf11; del buf11  # reuse
2023-01-11T21:05:10.6712449Z     kernel_cpp_0(c_void_p(buf6.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf14.data_ptr()), c_void_p(buf17.data_ptr()), c_void_p(buf19.data_ptr()), c_void_p(buf20.data_ptr()), c_void_p(buf21.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf12.data_ptr()), c_void_p(buf15.data_ptr()), c_void_p(buf18.data_ptr()))
2023-01-11T21:05:10.6712517Z     del arg0_1
2023-01-11T21:05:10.6712636Z     return (buf19, buf20, buf6, buf9, buf21, buf14, buf17, buf18, )
2023-01-11T21:05:10.6712641Z 
2023-01-11T21:05:10.6712645Z 
2023-01-11T21:05:10.6712720Z if __name__ == "__main__":
2023-01-11T21:05:10.6712836Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6712946Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6713160Z     arg0_1 = rand_strided((2, 4, 4, 8), (128, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6713268Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6713618Z [2023-01-11 21:00:11,449] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 423
2023-01-11T21:05:10.6713627Z 
2023-01-11T21:05:10.6713713Z ok (3.380s)
2023-01-11T21:05:10.6714120Z   test_strided_inputs_cpu (__main__.CpuTests) ... [2023-01-11 21:00:11,485] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 424
2023-01-11T21:05:10.6714385Z [2023-01-11 21:00:14,113] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 424
2023-01-11T21:05:10.6714391Z 
2023-01-11T21:05:10.6714484Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6714593Z import torch
2023-01-11T21:05:10.6714649Z import random
2023-01-11T21:05:10.6714763Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6714882Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6714889Z 
2023-01-11T21:05:10.6714966Z aten = torch.ops.aten
2023-01-11T21:05:10.6715100Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6715190Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6715196Z 
2023-01-11T21:05:10.6715200Z 
2023-01-11T21:05:10.6715332Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6715534Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6715641Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6715742Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6715840Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6715901Z {
2023-01-11T21:05:10.6715999Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6716058Z     {
2023-01-11T21:05:10.6716133Z         #pragma omp for 
2023-01-11T21:05:10.6716202Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.6716266Z         {
2023-01-11T21:05:10.6716357Z             {
2023-01-11T21:05:10.6716421Z                 {
2023-01-11T21:05:10.6716514Z                     auto tmp0 = in_ptr0[2*i0];
2023-01-11T21:05:10.6716604Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.6716694Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6716765Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6716827Z                 }
2023-01-11T21:05:10.6716888Z             }
2023-01-11T21:05:10.6716949Z         }
2023-01-11T21:05:10.6717008Z     }
2023-01-11T21:05:10.6717066Z }
2023-01-11T21:05:10.6717130Z ''')
2023-01-11T21:05:10.6717135Z 
2023-01-11T21:05:10.6717151Z 
2023-01-11T21:05:10.6717227Z async_compile.wait(globals())
2023-01-11T21:05:10.6717297Z del async_compile
2023-01-11T21:05:10.6717304Z 
2023-01-11T21:05:10.6717372Z def call(args):
2023-01-11T21:05:10.6717446Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6717515Z     args.clear()
2023-01-11T21:05:10.6717714Z     buf0 = empty_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6717876Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6717930Z     del arg0_1
2023-01-11T21:05:10.6717994Z     del arg1_1
2023-01-11T21:05:10.6718064Z     return (buf0, )
2023-01-11T21:05:10.6718069Z 
2023-01-11T21:05:10.6718073Z 
2023-01-11T21:05:10.6718147Z if __name__ == "__main__":
2023-01-11T21:05:10.6718259Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6718381Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6718578Z     arg0_1 = rand_strided((8, 16), (32, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6718775Z     arg1_1 = rand_strided((8, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6718880Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6718885Z 
2023-01-11T21:05:10.6718949Z ok (2.655s)
2023-01-11T21:05:10.6719382Z   test_sum1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6719509Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6719766Z [2023-01-11 21:00:14,155] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 425
2023-01-11T21:05:10.6720026Z [2023-01-11 21:00:16,815] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 425
2023-01-11T21:05:10.6720031Z 
2023-01-11T21:05:10.6720122Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6720219Z import torch
2023-01-11T21:05:10.6720286Z import random
2023-01-11T21:05:10.6720387Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6720507Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6720512Z 
2023-01-11T21:05:10.6720760Z aten = torch.ops.aten
2023-01-11T21:05:10.6720898Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6720988Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6720992Z 
2023-01-11T21:05:10.6720996Z 
2023-01-11T21:05:10.6721132Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6721340Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6721459Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6721548Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6721648Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6721713Z {
2023-01-11T21:05:10.6721811Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6721873Z     {
2023-01-11T21:05:10.6721949Z         #pragma omp for 
2023-01-11T21:05:10.6722029Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6722147Z         {
2023-01-11T21:05:10.6722213Z             {
2023-01-11T21:05:10.6722278Z                 {
2023-01-11T21:05:10.6722358Z                     float tmp3 = 0;
2023-01-11T21:05:10.6722450Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6722516Z                     {
2023-01-11T21:05:10.6722570Z                         {
2023-01-11T21:05:10.6722676Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6722779Z                             auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.6722874Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6722957Z                             tmp3 += tmp2;
2023-01-11T21:05:10.6723025Z                         }
2023-01-11T21:05:10.6723094Z                     }
2023-01-11T21:05:10.6723164Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6723228Z                 }
2023-01-11T21:05:10.6723290Z             }
2023-01-11T21:05:10.6723352Z         }
2023-01-11T21:05:10.6723415Z     }
2023-01-11T21:05:10.6723476Z }
2023-01-11T21:05:10.6723553Z ''')
2023-01-11T21:05:10.6723559Z 
2023-01-11T21:05:10.6723563Z 
2023-01-11T21:05:10.6723638Z async_compile.wait(globals())
2023-01-11T21:05:10.6723709Z del async_compile
2023-01-11T21:05:10.6723714Z 
2023-01-11T21:05:10.6723783Z def call(args):
2023-01-11T21:05:10.6723855Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6723925Z     args.clear()
2023-01-11T21:05:10.6724118Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6724279Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6724350Z     del arg0_1
2023-01-11T21:05:10.6724402Z     del arg1_1
2023-01-11T21:05:10.6724474Z     return (buf0, )
2023-01-11T21:05:10.6724479Z 
2023-01-11T21:05:10.6724483Z 
2023-01-11T21:05:10.6724557Z if __name__ == "__main__":
2023-01-11T21:05:10.6724671Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6724796Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6724991Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6725184Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6725286Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6725303Z 
2023-01-11T21:05:10.6725355Z ok (2.705s)
2023-01-11T21:05:10.6725788Z   test_sum2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6725958Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6726221Z [2023-01-11 21:00:16,892] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 426
2023-01-11T21:05:10.6726483Z [2023-01-11 21:00:19,579] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 426
2023-01-11T21:05:10.6726487Z 
2023-01-11T21:05:10.6726580Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6726650Z import torch
2023-01-11T21:05:10.6726719Z import random
2023-01-11T21:05:10.6726833Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6726939Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6726944Z 
2023-01-11T21:05:10.6727020Z aten = torch.ops.aten
2023-01-11T21:05:10.6727151Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6727240Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6727248Z 
2023-01-11T21:05:10.6727252Z 
2023-01-11T21:05:10.6727383Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6727586Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6727730Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6727835Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6727919Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6728012Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6728072Z {
2023-01-11T21:05:10.6728168Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6728232Z     {
2023-01-11T21:05:10.6728307Z         #pragma omp for 
2023-01-11T21:05:10.6728386Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6728434Z         {
2023-01-11T21:05:10.6728512Z             #pragma GCC ivdep
2023-01-11T21:05:10.6728597Z             for(long i1=0; i1<21; i1+=1)
2023-01-11T21:05:10.6728659Z             {
2023-01-11T21:05:10.6728723Z                 {
2023-01-11T21:05:10.6728788Z                     {
2023-01-11T21:05:10.6728855Z                         float tmp3 = 0;
2023-01-11T21:05:10.6728949Z                         for(long i2=0; i2<27; i2+=1)
2023-01-11T21:05:10.6729016Z                         {
2023-01-11T21:05:10.6729084Z                             {
2023-01-11T21:05:10.6729197Z                                 auto tmp0 = in_ptr0[i1 + (21*i2) + (567*i0)];
2023-01-11T21:05:10.6729306Z                                 auto tmp1 = in_ptr1[i1 + (21*i2) + (567*i0)];
2023-01-11T21:05:10.6729405Z                                 auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6729490Z                                 tmp3 += tmp2;
2023-01-11T21:05:10.6729545Z                             }
2023-01-11T21:05:10.6729610Z                         }
2023-01-11T21:05:10.6729704Z                         out_ptr0[i1 + (21*i0)] = tmp3;
2023-01-11T21:05:10.6729769Z                     }
2023-01-11T21:05:10.6729832Z                 }
2023-01-11T21:05:10.6729896Z             }
2023-01-11T21:05:10.6729944Z         }
2023-01-11T21:05:10.6730020Z         #pragma omp for 
2023-01-11T21:05:10.6730100Z         for(long i0=0; i0<216; i0+=1)
2023-01-11T21:05:10.6730163Z         {
2023-01-11T21:05:10.6730225Z             {
2023-01-11T21:05:10.6730416Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6730492Z                 float tmp3 = 0;
2023-01-11T21:05:10.6730610Z                 auto tmp3_vec = at::vec::Vectorized<float>(tmp3);
2023-01-11T21:05:10.6730686Z                 for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.6730749Z                 {
2023-01-11T21:05:10.6730890Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (21*i0));
2023-01-11T21:05:10.6731030Z                     auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i1) + (21*i0));
2023-01-11T21:05:10.6731121Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6731229Z                     tmp3_vec += tmp2;
2023-01-11T21:05:10.6731293Z                 }
2023-01-11T21:05:10.6731491Z                 tmp3 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp3_vec);
2023-01-11T21:05:10.6731597Z                 #pragma omp simd simdlen(8)  reduction(+:tmp3)
2023-01-11T21:05:10.6731685Z                 for(long i1=16; i1<21; i1+=1)
2023-01-11T21:05:10.6731748Z                 {
2023-01-11T21:05:10.6731846Z                     auto tmp0 = in_ptr0[i1 + (21*i0)];
2023-01-11T21:05:10.6731943Z                     auto tmp1 = in_ptr1[i1 + (21*i0)];
2023-01-11T21:05:10.6732032Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6732107Z                     tmp3 += tmp2;
2023-01-11T21:05:10.6732158Z                 }
2023-01-11T21:05:10.6732238Z                 out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.6732299Z             }
2023-01-11T21:05:10.6732358Z         }
2023-01-11T21:05:10.6732421Z     }
2023-01-11T21:05:10.6732479Z }
2023-01-11T21:05:10.6732545Z ''')
2023-01-11T21:05:10.6732555Z 
2023-01-11T21:05:10.6732559Z 
2023-01-11T21:05:10.6737468Z async_compile.wait(globals())
2023-01-11T21:05:10.6737628Z del async_compile
2023-01-11T21:05:10.6737635Z 
2023-01-11T21:05:10.6737707Z def call(args):
2023-01-11T21:05:10.6737784Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6737854Z     args.clear()
2023-01-11T21:05:10.6738064Z     buf0 = empty_strided((8, 21), (21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6738271Z     buf1 = empty_strided((8, 9, 3), (27, 3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6738553Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6738627Z     del arg0_1
2023-01-11T21:05:10.6738691Z     del arg1_1
2023-01-11T21:05:10.6738766Z     return (buf0, buf1, )
2023-01-11T21:05:10.6738773Z 
2023-01-11T21:05:10.6738782Z 
2023-01-11T21:05:10.6738857Z if __name__ == "__main__":
2023-01-11T21:05:10.6738971Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6739083Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6739303Z     arg0_1 = rand_strided((8, 9, 3, 21), (567, 63, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6739515Z     arg1_1 = rand_strided((8, 9, 3, 21), (567, 63, 21, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6739630Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6739635Z 
2023-01-11T21:05:10.6739700Z ok (2.769s)
2023-01-11T21:05:10.6740135Z   test_sum3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6740263Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6740525Z [2023-01-11 21:00:19,631] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 427
2023-01-11T21:05:10.6740793Z [2023-01-11 21:00:22,281] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 427
2023-01-11T21:05:10.6740799Z 
2023-01-11T21:05:10.6740893Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6740949Z import torch
2023-01-11T21:05:10.6741017Z import random
2023-01-11T21:05:10.6741136Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6741255Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6741262Z 
2023-01-11T21:05:10.6741337Z aten = torch.ops.aten
2023-01-11T21:05:10.6741470Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6741559Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6741564Z 
2023-01-11T21:05:10.6741568Z 
2023-01-11T21:05:10.6741737Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6741938Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6742058Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6742160Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6742257Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6742351Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6742442Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6742500Z {
2023-01-11T21:05:10.6742584Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6742644Z     {
2023-01-11T21:05:10.6742720Z         #pragma omp for 
2023-01-11T21:05:10.6742802Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.6742864Z         {
2023-01-11T21:05:10.6742926Z             {
2023-01-11T21:05:10.6742975Z                 {
2023-01-11T21:05:10.6743053Z                     float tmp3 = 0;
2023-01-11T21:05:10.6743146Z                     for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.6743211Z                     {
2023-01-11T21:05:10.6743277Z                         {
2023-01-11T21:05:10.6743401Z                             auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.6743502Z                             auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.6743596Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6743692Z                             out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.6743761Z                             tmp3 += tmp2;
2023-01-11T21:05:10.6743828Z                         }
2023-01-11T21:05:10.6743892Z                     }
2023-01-11T21:05:10.6743975Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.6744039Z                 }
2023-01-11T21:05:10.6744099Z             }
2023-01-11T21:05:10.6744159Z         }
2023-01-11T21:05:10.6744221Z         #pragma omp for 
2023-01-11T21:05:10.6744300Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.6744362Z         {
2023-01-11T21:05:10.6744424Z             {
2023-01-11T21:05:10.6744486Z                 {
2023-01-11T21:05:10.6744575Z                     auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6744679Z                     auto tmp1 = static_cast<float>(10);
2023-01-11T21:05:10.6744756Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6744837Z                     out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.6744899Z                 }
2023-01-11T21:05:10.6744962Z             }
2023-01-11T21:05:10.6745022Z         }
2023-01-11T21:05:10.6745082Z     }
2023-01-11T21:05:10.6745127Z }
2023-01-11T21:05:10.6745205Z ''')
2023-01-11T21:05:10.6745210Z 
2023-01-11T21:05:10.6745215Z 
2023-01-11T21:05:10.6745303Z async_compile.wait(globals())
2023-01-11T21:05:10.6745372Z del async_compile
2023-01-11T21:05:10.6745377Z 
2023-01-11T21:05:10.6745445Z def call(args):
2023-01-11T21:05:10.6745517Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6745587Z     args.clear()
2023-01-11T21:05:10.6745789Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6745969Z     buf1 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6746159Z     buf2 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6746372Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6746439Z     del arg0_1
2023-01-11T21:05:10.6746504Z     del arg1_1
2023-01-11T21:05:10.6746584Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6746589Z 
2023-01-11T21:05:10.6746593Z 
2023-01-11T21:05:10.6746667Z if __name__ == "__main__":
2023-01-11T21:05:10.6746779Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6746887Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6747084Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6747311Z     arg1_1 = rand_strided((1, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6747426Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6747431Z 
2023-01-11T21:05:10.6747498Z ok (2.698s)
2023-01-11T21:05:10.6747931Z   test_sum4_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6748056Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6748313Z [2023-01-11 21:00:22,331] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 428
2023-01-11T21:05:10.6748574Z [2023-01-11 21:00:25,145] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 428
2023-01-11T21:05:10.6748582Z 
2023-01-11T21:05:10.6748674Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6748730Z import torch
2023-01-11T21:05:10.6748800Z import random
2023-01-11T21:05:10.6748940Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6749062Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6749067Z 
2023-01-11T21:05:10.6749145Z aten = torch.ops.aten
2023-01-11T21:05:10.6749277Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6749367Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6749372Z 
2023-01-11T21:05:10.6749377Z 
2023-01-11T21:05:10.6749496Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6749698Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6749817Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6749916Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6750014Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6750107Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6750200Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.6750290Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.6750337Z {
2023-01-11T21:05:10.6750434Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6750494Z     {
2023-01-11T21:05:10.6750569Z         #pragma omp for 
2023-01-11T21:05:10.6750650Z         for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.6750711Z         {
2023-01-11T21:05:10.6750849Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6750970Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6751053Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6751144Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6751205Z         }
2023-01-11T21:05:10.6751300Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6751384Z         for(long i0=1024; i0<1024; i0+=1)
2023-01-11T21:05:10.6751445Z         {
2023-01-11T21:05:10.6751514Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6751615Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6751698Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6751776Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6751837Z         }
2023-01-11T21:05:10.6751910Z         #pragma omp for 
2023-01-11T21:05:10.6751990Z         for(long i0=0; i0<128; i0+=1)
2023-01-11T21:05:10.6752038Z         {
2023-01-11T21:05:10.6752100Z             {
2023-01-11T21:05:10.6752162Z                 {
2023-01-11T21:05:10.6752241Z                     float tmp1 = 0;
2023-01-11T21:05:10.6752330Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6752396Z                     {
2023-01-11T21:05:10.6752450Z                         {
2023-01-11T21:05:10.6752555Z                             auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6752670Z                             tmp1 += tmp0;
2023-01-11T21:05:10.6752737Z                         }
2023-01-11T21:05:10.6752803Z                     }
2023-01-11T21:05:10.6752890Z                     out_ptr1[i0] = tmp1;
2023-01-11T21:05:10.6752952Z                 }
2023-01-11T21:05:10.6753003Z             }
2023-01-11T21:05:10.6753062Z         }
2023-01-11T21:05:10.6753135Z         #pragma omp for 
2023-01-11T21:05:10.6753215Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6753275Z         {
2023-01-11T21:05:10.6753408Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6753537Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(3));
2023-01-11T21:05:10.6753608Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6753696Z             tmp2.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.6753757Z         }
2023-01-11T21:05:10.6753852Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6753936Z         for(long i0=128; i0<128; i0+=1)
2023-01-11T21:05:10.6753997Z         {
2023-01-11T21:05:10.6754080Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.6754194Z             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.6754279Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6754358Z             out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.6754419Z         }
2023-01-11T21:05:10.6754493Z         #pragma omp for 
2023-01-11T21:05:10.6754573Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6754636Z         {
2023-01-11T21:05:10.6754686Z             {
2023-01-11T21:05:10.6754750Z                 {
2023-01-11T21:05:10.6754828Z                     float tmp1 = 0;
2023-01-11T21:05:10.6754919Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6754985Z                     {
2023-01-11T21:05:10.6755052Z                         {
2023-01-11T21:05:10.6755157Z                             auto tmp0 = out_ptr2[i1 + (8*i0)];
2023-01-11T21:05:10.6755228Z                             tmp1 += tmp0;
2023-01-11T21:05:10.6755293Z                         }
2023-01-11T21:05:10.6755358Z                     }
2023-01-11T21:05:10.6755441Z                     out_ptr3[i0] = tmp1;
2023-01-11T21:05:10.6755507Z                 }
2023-01-11T21:05:10.6755569Z             }
2023-01-11T21:05:10.6755617Z         }
2023-01-11T21:05:10.6755691Z         #pragma omp for 
2023-01-11T21:05:10.6755771Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.6755834Z         {
2023-01-11T21:05:10.6755966Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr3 + 16*i0);
2023-01-11T21:05:10.6756097Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(5));
2023-01-11T21:05:10.6756179Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6756269Z             tmp2.store(out_ptr4 + 16*i0);
2023-01-11T21:05:10.6756317Z         }
2023-01-11T21:05:10.6756409Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6756490Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.6756554Z         {
2023-01-11T21:05:10.6756636Z             auto tmp0 = out_ptr3[i0];
2023-01-11T21:05:10.6756733Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:05:10.6756804Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6756883Z             out_ptr4[i0] = tmp2;
2023-01-11T21:05:10.6756942Z         }
2023-01-11T21:05:10.6757003Z     }
2023-01-11T21:05:10.6757062Z }
2023-01-11T21:05:10.6757143Z ''')
2023-01-11T21:05:10.6757149Z 
2023-01-11T21:05:10.6757153Z 
2023-01-11T21:05:10.6757241Z async_compile.wait(globals())
2023-01-11T21:05:10.6757299Z del async_compile
2023-01-11T21:05:10.6757316Z 
2023-01-11T21:05:10.6757372Z def call(args):
2023-01-11T21:05:10.6757439Z     arg0_1, = args
2023-01-11T21:05:10.6757509Z     args.clear()
2023-01-11T21:05:10.6757727Z     buf0 = empty_strided((1, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6757936Z     buf1 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6758169Z     buf2 = empty_strided((1, 16, 8), (128, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6758363Z     buf3 = empty_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6758545Z     buf4 = empty_strided((1, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6758780Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.6758849Z     del arg0_1
2023-01-11T21:05:10.6758945Z     return (buf4, buf3, buf2, buf1, buf0, )
2023-01-11T21:05:10.6758950Z 
2023-01-11T21:05:10.6758955Z 
2023-01-11T21:05:10.6759031Z if __name__ == "__main__":
2023-01-11T21:05:10.6759146Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6759269Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6759483Z     arg0_1 = rand_strided((1, 16, 8, 8), (1024, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6759593Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6759598Z 
2023-01-11T21:05:10.6759651Z ok (2.866s)
2023-01-11T21:05:10.6760108Z   test_sum5_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6760235Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6760496Z [2023-01-11 21:00:25,199] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 429
2023-01-11T21:05:10.6760895Z [2023-01-11 21:00:27,910] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 429
2023-01-11T21:05:10.6760901Z 
2023-01-11T21:05:10.6760996Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6761068Z import torch
2023-01-11T21:05:10.6761140Z import random
2023-01-11T21:05:10.6761256Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6761365Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6761371Z 
2023-01-11T21:05:10.6761449Z aten = torch.ops.aten
2023-01-11T21:05:10.6761582Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6761674Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6761680Z 
2023-01-11T21:05:10.6761684Z 
2023-01-11T21:05:10.6761822Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6762025Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6762142Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6762248Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6762334Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6762398Z {
2023-01-11T21:05:10.6762484Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.6762581Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6762641Z     {
2023-01-11T21:05:10.6762716Z         #pragma omp for 
2023-01-11T21:05:10.6762797Z         for(long i0=0; i0<136; i0+=1)
2023-01-11T21:05:10.6762845Z         {
2023-01-11T21:05:10.6762907Z             {
2023-01-11T21:05:10.6762969Z                 {
2023-01-11T21:05:10.6763046Z                     float tmp3 = 0;
2023-01-11T21:05:10.6763137Z                     for(long i1=0; i1<9; i1+=1)
2023-01-11T21:05:10.6763202Z                     {
2023-01-11T21:05:10.6763256Z                         {
2023-01-11T21:05:10.6763358Z                             auto tmp0 = in_ptr0[i1 + (9*i0)];
2023-01-11T21:05:10.6763466Z                             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6763561Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6763641Z                             tmp3 += tmp2;
2023-01-11T21:05:10.6763766Z                         }
2023-01-11T21:05:10.6763832Z                     }
2023-01-11T21:05:10.6763903Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6763967Z                 }
2023-01-11T21:05:10.6764032Z             }
2023-01-11T21:05:10.6764095Z         }
2023-01-11T21:05:10.6764170Z         #pragma omp for 
2023-01-11T21:05:10.6764250Z         for(long i0=0; i0<17; i0+=1)
2023-01-11T21:05:10.6764313Z         {
2023-01-11T21:05:10.6764361Z             {
2023-01-11T21:05:10.6764425Z                 {
2023-01-11T21:05:10.6764505Z                     float tmp3 = 0;
2023-01-11T21:05:10.6764595Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6764661Z                     {
2023-01-11T21:05:10.6764727Z                         {
2023-01-11T21:05:10.6764831Z                             auto tmp0 = out_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6764923Z                             auto tmp1 = static_cast<float>(3);
2023-01-11T21:05:10.6765025Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6765107Z                             tmp3 += tmp2;
2023-01-11T21:05:10.6765177Z                         }
2023-01-11T21:05:10.6765242Z                     }
2023-01-11T21:05:10.6765380Z                     out_ptr1[i0] = tmp3;
2023-01-11T21:05:10.6765449Z                 }
2023-01-11T21:05:10.6765499Z             }
2023-01-11T21:05:10.6765557Z         }
2023-01-11T21:05:10.6765632Z         #pragma omp for 
2023-01-11T21:05:10.6765711Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.6765773Z         {
2023-01-11T21:05:10.6765913Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6766047Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(5));
2023-01-11T21:05:10.6766118Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6766214Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6766276Z         }
2023-01-11T21:05:10.6766369Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6766454Z         for(long i0=16; i0<17; i0+=1)
2023-01-11T21:05:10.6766516Z         {
2023-01-11T21:05:10.6766600Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.6766686Z             auto tmp1 = static_cast<float>(5);
2023-01-11T21:05:10.6766769Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6766851Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6766912Z         }
2023-01-11T21:05:10.6766973Z     }
2023-01-11T21:05:10.6767031Z }
2023-01-11T21:05:10.6767098Z ''')
2023-01-11T21:05:10.6767116Z 
2023-01-11T21:05:10.6767120Z 
2023-01-11T21:05:10.6767196Z async_compile.wait(globals())
2023-01-11T21:05:10.6767266Z del async_compile
2023-01-11T21:05:10.6767271Z 
2023-01-11T21:05:10.6767338Z def call(args):
2023-01-11T21:05:10.6767408Z     arg0_1, = args
2023-01-11T21:05:10.6767477Z     args.clear()
2023-01-11T21:05:10.6767683Z     buf0 = empty_strided((1, 17, 8), (136, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6767880Z     buf1 = empty_strided((1, 17), (17, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6767950Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6768112Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6768179Z     del arg0_1
2023-01-11T21:05:10.6768249Z     return (buf2, )
2023-01-11T21:05:10.6768254Z 
2023-01-11T21:05:10.6768258Z 
2023-01-11T21:05:10.6768332Z if __name__ == "__main__":
2023-01-11T21:05:10.6768445Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6768567Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6768779Z     arg0_1 = rand_strided((1, 17, 8, 9), (1224, 72, 9, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6768873Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6768878Z 
2023-01-11T21:05:10.6768941Z ok (2.761s)
2023-01-11T21:05:10.6769380Z   test_sum_dtype_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6769536Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6769796Z [2023-01-11 21:00:27,958] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 430
2023-01-11T21:05:10.6770058Z [2023-01-11 21:00:30,627] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 430
2023-01-11T21:05:10.6770064Z 
2023-01-11T21:05:10.6770156Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6770225Z import torch
2023-01-11T21:05:10.6770292Z import random
2023-01-11T21:05:10.6770392Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6770511Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6770518Z 
2023-01-11T21:05:10.6770595Z aten = torch.ops.aten
2023-01-11T21:05:10.6770728Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6770819Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6770888Z 
2023-01-11T21:05:10.6770892Z 
2023-01-11T21:05:10.6771026Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6771231Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6771352Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6771439Z                        double* __restrict__ out_ptr0,
2023-01-11T21:05:10.6771537Z                        double* __restrict__ out_ptr1,
2023-01-11T21:05:10.6771630Z                        double* __restrict__ out_ptr2)
2023-01-11T21:05:10.6771691Z {
2023-01-11T21:05:10.6771789Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6771856Z     {
2023-01-11T21:05:10.6771932Z         #pragma omp for 
2023-01-11T21:05:10.6772002Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6772066Z         {
2023-01-11T21:05:10.6772129Z             {
2023-01-11T21:05:10.6772195Z                 {
2023-01-11T21:05:10.6772275Z                     double tmp2 = 0;
2023-01-11T21:05:10.6772370Z                     for(long i1=0; i1<32; i1+=1)
2023-01-11T21:05:10.6772437Z                     {
2023-01-11T21:05:10.6772491Z                         {
2023-01-11T21:05:10.6772596Z                             auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.6772711Z                             auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.6772793Z                             tmp2 += tmp1;
2023-01-11T21:05:10.6772861Z                         }
2023-01-11T21:05:10.6772926Z                     }
2023-01-11T21:05:10.6773012Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6773063Z                 }
2023-01-11T21:05:10.6773124Z             }
2023-01-11T21:05:10.6773184Z         }
2023-01-11T21:05:10.6773244Z     }
2023-01-11T21:05:10.6773305Z     {
2023-01-11T21:05:10.6773365Z         {
2023-01-11T21:05:10.6773430Z             double tmp2 = 0;
2023-01-11T21:05:10.6773532Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6773596Z             {
2023-01-11T21:05:10.6773700Z                 #pragma omp for reduction(+:tmp2) 
2023-01-11T21:05:10.6773789Z                 for(long i0=0; i0<1024; i0+=1)
2023-01-11T21:05:10.6773853Z                 {
2023-01-11T21:05:10.6773916Z                     {
2023-01-11T21:05:10.6773996Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6774105Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.6774182Z                         tmp2 += tmp1;
2023-01-11T21:05:10.6774246Z                     }
2023-01-11T21:05:10.6774307Z                 }
2023-01-11T21:05:10.6774369Z             }
2023-01-11T21:05:10.6774443Z             out_ptr1[0] = tmp2;
2023-01-11T21:05:10.6774492Z         }
2023-01-11T21:05:10.6774552Z     }
2023-01-11T21:05:10.6774678Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6774737Z     {
2023-01-11T21:05:10.6774811Z         #pragma omp for 
2023-01-11T21:05:10.6774894Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6774943Z         {
2023-01-11T21:05:10.6775023Z             #pragma GCC ivdep
2023-01-11T21:05:10.6775105Z             for(long i1=0; i1<32; i1+=1)
2023-01-11T21:05:10.6775167Z             {
2023-01-11T21:05:10.6775228Z                 {
2023-01-11T21:05:10.6775292Z                     {
2023-01-11T21:05:10.6775392Z                         auto tmp0 = in_ptr0[i1 + (32*i0)];
2023-01-11T21:05:10.6775473Z                         auto tmp2 = out_ptr0[i1];
2023-01-11T21:05:10.6775566Z                         auto tmp4 = out_ptr1[0];
2023-01-11T21:05:10.6775674Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.6775765Z                         auto tmp3 = tmp1 * tmp2;
2023-01-11T21:05:10.6775855Z                         auto tmp5 = tmp3 + tmp4;
2023-01-11T21:05:10.6775954Z                         out_ptr2[i1 + (32*i0)] = tmp5;
2023-01-11T21:05:10.6776019Z                     }
2023-01-11T21:05:10.6776069Z                 }
2023-01-11T21:05:10.6776130Z             }
2023-01-11T21:05:10.6776216Z         }
2023-01-11T21:05:10.6776277Z     }
2023-01-11T21:05:10.6776334Z }
2023-01-11T21:05:10.6776412Z ''')
2023-01-11T21:05:10.6776417Z 
2023-01-11T21:05:10.6776421Z 
2023-01-11T21:05:10.6776509Z async_compile.wait(globals())
2023-01-11T21:05:10.6776567Z del async_compile
2023-01-11T21:05:10.6776584Z 
2023-01-11T21:05:10.6776640Z def call(args):
2023-01-11T21:05:10.6776708Z     arg0_1, = args
2023-01-11T21:05:10.6776779Z     args.clear()
2023-01-11T21:05:10.6776971Z     buf0 = empty_strided((32, ), (1, ), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.6777153Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.6777348Z     buf2 = empty_strided((32, 32), (32, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.6777537Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6777592Z     del arg0_1
2023-01-11T21:05:10.6777661Z     return (buf2, )
2023-01-11T21:05:10.6777668Z 
2023-01-11T21:05:10.6777673Z 
2023-01-11T21:05:10.6777746Z if __name__ == "__main__":
2023-01-11T21:05:10.6777859Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6777981Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6778178Z     arg0_1 = rand_strided((32, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6778283Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6778288Z 
2023-01-11T21:05:10.6778352Z ok (2.718s)
2023-01-11T21:05:10.6778862Z   test_sum_int_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6778996Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6779264Z [2023-01-11 21:00:30,673] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 431
2023-01-11T21:05:10.6779532Z [2023-01-11 21:00:33,299] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 431
2023-01-11T21:05:10.6779931Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6780058Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6780312Z [2023-01-11 21:00:33,341] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 432
2023-01-11T21:05:10.6780609Z [2023-01-11 21:00:36,007] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 432
2023-01-11T21:05:10.6781010Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6781133Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6781385Z [2023-01-11 21:00:36,048] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 433
2023-01-11T21:05:10.6781644Z [2023-01-11 21:00:38,669] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 433
2023-01-11T21:05:10.6781650Z 
2023-01-11T21:05:10.6781733Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6781802Z import torch
2023-01-11T21:05:10.6781870Z import random
2023-01-11T21:05:10.6781984Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6782130Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6782136Z 
2023-01-11T21:05:10.6782217Z aten = torch.ops.aten
2023-01-11T21:05:10.6782349Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6782427Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6782445Z 
2023-01-11T21:05:10.6782449Z 
2023-01-11T21:05:10.6782570Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6782774Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6782889Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6782991Z                        const bool* __restrict__ in_ptr0,
2023-01-11T21:05:10.6783087Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.6783149Z {
2023-01-11T21:05:10.6783232Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.6783279Z     {
2023-01-11T21:05:10.6783341Z         {
2023-01-11T21:05:10.6783415Z             long tmp2 = 0;
2023-01-11T21:05:10.6783490Z             long tmp3 = 0;
2023-01-11T21:05:10.6783593Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6783655Z             {
2023-01-11T21:05:10.6783783Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:05:10.6783857Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.6783922Z                 {
2023-01-11T21:05:10.6783987Z                     {
2023-01-11T21:05:10.6784079Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6784190Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:05:10.6784267Z                         tmp2 += tmp1;
2023-01-11T21:05:10.6784344Z                         tmp3 += tmp1;
2023-01-11T21:05:10.6784394Z                     }
2023-01-11T21:05:10.6784460Z                 }
2023-01-11T21:05:10.6784522Z             }
2023-01-11T21:05:10.6784601Z             out_ptr0[0] = tmp2;
2023-01-11T21:05:10.6784677Z             out_ptr1[0] = tmp3;
2023-01-11T21:05:10.6784738Z         }
2023-01-11T21:05:10.6784786Z     }
2023-01-11T21:05:10.6784846Z     {
2023-01-11T21:05:10.6784906Z         {
2023-01-11T21:05:10.6784989Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:05:10.6785070Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:05:10.6785167Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:05:10.6785249Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6785316Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6785395Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:05:10.6785455Z         }
2023-01-11T21:05:10.6785514Z     }
2023-01-11T21:05:10.6785573Z }
2023-01-11T21:05:10.6785650Z ''')
2023-01-11T21:05:10.6785655Z 
2023-01-11T21:05:10.6785660Z 
2023-01-11T21:05:10.6785749Z async_compile.wait(globals())
2023-01-11T21:05:10.6785844Z del async_compile
2023-01-11T21:05:10.6785849Z 
2023-01-11T21:05:10.6785918Z def call(args):
2023-01-11T21:05:10.6785984Z     arg0_1, = args
2023-01-11T21:05:10.6786055Z     args.clear()
2023-01-11T21:05:10.6786237Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6786415Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6786498Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6786645Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6786712Z     del arg0_1
2023-01-11T21:05:10.6786782Z     return (buf2, )
2023-01-11T21:05:10.6786787Z 
2023-01-11T21:05:10.6786791Z 
2023-01-11T21:05:10.6786866Z if __name__ == "__main__":
2023-01-11T21:05:10.6786978Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6787101Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6787288Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.6787397Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6787402Z 
2023-01-11T21:05:10.6787406Z 
2023-01-11T21:05:10.6787498Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6787580Z import torch
2023-01-11T21:05:10.6787653Z import random
2023-01-11T21:05:10.6787766Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6787885Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6787889Z 
2023-01-11T21:05:10.6787967Z aten = torch.ops.aten
2023-01-11T21:05:10.6788099Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6788190Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6788195Z 
2023-01-11T21:05:10.6788198Z 
2023-01-11T21:05:10.6788318Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6788524Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6788637Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6788753Z                        const unsigned char* __restrict__ in_ptr0,
2023-01-11T21:05:10.6788849Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.6788910Z {
2023-01-11T21:05:10.6788996Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.6789058Z     {
2023-01-11T21:05:10.6789107Z         {
2023-01-11T21:05:10.6789179Z             long tmp2 = 0;
2023-01-11T21:05:10.6789251Z             long tmp3 = 0;
2023-01-11T21:05:10.6789355Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6789417Z             {
2023-01-11T21:05:10.6789543Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:05:10.6789618Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.6789682Z                 {
2023-01-11T21:05:10.6789745Z                     {
2023-01-11T21:05:10.6789839Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6789945Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:05:10.6790026Z                         tmp2 += tmp1;
2023-01-11T21:05:10.6790101Z                         tmp3 += tmp1;
2023-01-11T21:05:10.6790152Z                     }
2023-01-11T21:05:10.6790217Z                 }
2023-01-11T21:05:10.6790278Z             }
2023-01-11T21:05:10.6790355Z             out_ptr0[0] = tmp2;
2023-01-11T21:05:10.6790430Z             out_ptr1[0] = tmp3;
2023-01-11T21:05:10.6790490Z         }
2023-01-11T21:05:10.6790548Z     }
2023-01-11T21:05:10.6790594Z     {
2023-01-11T21:05:10.6790653Z         {
2023-01-11T21:05:10.6790736Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:05:10.6790816Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:05:10.6790911Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:05:10.6790992Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6791070Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6791136Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:05:10.6791196Z         }
2023-01-11T21:05:10.6791283Z     }
2023-01-11T21:05:10.6791342Z }
2023-01-11T21:05:10.6791419Z ''')
2023-01-11T21:05:10.6791424Z 
2023-01-11T21:05:10.6791428Z 
2023-01-11T21:05:10.6791515Z async_compile.wait(globals())
2023-01-11T21:05:10.6791588Z del async_compile
2023-01-11T21:05:10.6791593Z 
2023-01-11T21:05:10.6791649Z def call(args):
2023-01-11T21:05:10.6791716Z     arg0_1, = args
2023-01-11T21:05:10.6791785Z     args.clear()
2023-01-11T21:05:10.6791964Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6792139Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6792220Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6792380Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6792434Z     del arg0_1
2023-01-11T21:05:10.6792504Z     return (buf2, )
2023-01-11T21:05:10.6792508Z 
2023-01-11T21:05:10.6792513Z 
2023-01-11T21:05:10.6792585Z if __name__ == "__main__":
2023-01-11T21:05:10.6792698Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6792818Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6793035Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.uint8)
2023-01-11T21:05:10.6793142Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6793147Z 
2023-01-11T21:05:10.6793151Z 
2023-01-11T21:05:10.6793241Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6793296Z import torch
2023-01-11T21:05:10.6793365Z import random
2023-01-11T21:05:10.6793478Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6793596Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6793601Z 
2023-01-11T21:05:10.6793676Z aten = torch.ops.aten
2023-01-11T21:05:10.6793806Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6793896Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6793901Z 
2023-01-11T21:05:10.6793905Z 
2023-01-11T21:05:10.6794038Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6794226Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6794341Z extern "C" void kernel(long* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6794441Z                        const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.6794537Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.6794596Z {
2023-01-11T21:05:10.6794679Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.6794738Z     {
2023-01-11T21:05:10.6794786Z         {
2023-01-11T21:05:10.6794859Z             long tmp2 = 0;
2023-01-11T21:05:10.6794930Z             long tmp3 = 0;
2023-01-11T21:05:10.6795035Z             #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6795097Z             {
2023-01-11T21:05:10.6795221Z                 #pragma omp for reduction(+:tmp2) reduction(+:tmp3) 
2023-01-11T21:05:10.6795308Z                 for(long i0=0; i0<64; i0+=1)
2023-01-11T21:05:10.6795360Z                 {
2023-01-11T21:05:10.6795426Z                     {
2023-01-11T21:05:10.6795519Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6795625Z                         auto tmp1 = static_cast<long>(tmp0);
2023-01-11T21:05:10.6795704Z                         tmp2 += tmp1;
2023-01-11T21:05:10.6795780Z                         tmp3 += tmp1;
2023-01-11T21:05:10.6795843Z                     }
2023-01-11T21:05:10.6795892Z                 }
2023-01-11T21:05:10.6795953Z             }
2023-01-11T21:05:10.6796030Z             out_ptr0[0] = tmp2;
2023-01-11T21:05:10.6796103Z             out_ptr1[0] = tmp3;
2023-01-11T21:05:10.6796163Z         }
2023-01-11T21:05:10.6796223Z     }
2023-01-11T21:05:10.6796268Z     {
2023-01-11T21:05:10.6796327Z         {
2023-01-11T21:05:10.6796409Z             auto tmp0 = out_ptr0[0];
2023-01-11T21:05:10.6796489Z             auto tmp3 = out_ptr1[0];
2023-01-11T21:05:10.6796584Z             auto tmp1 = static_cast<long>(2);
2023-01-11T21:05:10.6796666Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6796774Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6796841Z             in_out_ptr0[0] = tmp4;
2023-01-11T21:05:10.6796900Z         }
2023-01-11T21:05:10.6796959Z     }
2023-01-11T21:05:10.6797020Z }
2023-01-11T21:05:10.6797097Z ''')
2023-01-11T21:05:10.6797102Z 
2023-01-11T21:05:10.6797106Z 
2023-01-11T21:05:10.6797193Z async_compile.wait(globals())
2023-01-11T21:05:10.6797265Z del async_compile
2023-01-11T21:05:10.6797271Z 
2023-01-11T21:05:10.6797339Z def call(args):
2023-01-11T21:05:10.6797394Z     arg0_1, = args
2023-01-11T21:05:10.6797464Z     args.clear()
2023-01-11T21:05:10.6797642Z     buf0 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6797818Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6797900Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6798064Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6798133Z     del arg0_1
2023-01-11T21:05:10.6798191Z     return (buf2, )
2023-01-11T21:05:10.6798196Z 
2023-01-11T21:05:10.6798200Z 
2023-01-11T21:05:10.6798273Z if __name__ == "__main__":
2023-01-11T21:05:10.6798411Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6798533Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6798724Z     arg0_1 = rand_strided((64, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.6798830Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6798835Z 
2023-01-11T21:05:10.6798904Z ok (8.038s)
2023-01-11T21:05:10.6799344Z   test_sum_keepdims_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6799472Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6799718Z [2023-01-11 21:00:38,704] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 434
2023-01-11T21:05:10.6799984Z [2023-01-11 21:00:38,718] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 434
2023-01-11T21:05:10.6799990Z 
2023-01-11T21:05:10.6800080Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6800147Z import torch
2023-01-11T21:05:10.6800215Z import random
2023-01-11T21:05:10.6800329Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6800448Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6800453Z 
2023-01-11T21:05:10.6800529Z aten = torch.ops.aten
2023-01-11T21:05:10.6800767Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6800858Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6800863Z 
2023-01-11T21:05:10.6800867Z 
2023-01-11T21:05:10.6801003Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6801208Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6801330Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6801433Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6801533Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6801594Z {
2023-01-11T21:05:10.6801677Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6801737Z     {
2023-01-11T21:05:10.6801813Z         #pragma omp for 
2023-01-11T21:05:10.6801895Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6801958Z         {
2023-01-11T21:05:10.6802023Z             {
2023-01-11T21:05:10.6802074Z                 {
2023-01-11T21:05:10.6802153Z                     float tmp3 = 0;
2023-01-11T21:05:10.6802246Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6802313Z                     {
2023-01-11T21:05:10.6802441Z                         {
2023-01-11T21:05:10.6802546Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.6802649Z                             auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.6802733Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6802816Z                             tmp3 += tmp2;
2023-01-11T21:05:10.6802884Z                         }
2023-01-11T21:05:10.6802949Z                     }
2023-01-11T21:05:10.6803031Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6803094Z                 }
2023-01-11T21:05:10.6803157Z             }
2023-01-11T21:05:10.6803206Z         }
2023-01-11T21:05:10.6803266Z     }
2023-01-11T21:05:10.6803326Z }
2023-01-11T21:05:10.6803405Z ''')
2023-01-11T21:05:10.6803410Z 
2023-01-11T21:05:10.6803414Z 
2023-01-11T21:05:10.6803502Z async_compile.wait(globals())
2023-01-11T21:05:10.6803572Z del async_compile
2023-01-11T21:05:10.6803578Z 
2023-01-11T21:05:10.6803647Z def call(args):
2023-01-11T21:05:10.6803710Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6803781Z     args.clear()
2023-01-11T21:05:10.6803978Z     buf0 = empty_strided((8, 1), (1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6804189Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6804258Z     del arg0_1
2023-01-11T21:05:10.6804322Z     del arg1_1
2023-01-11T21:05:10.6804392Z     return (buf0, )
2023-01-11T21:05:10.6804397Z 
2023-01-11T21:05:10.6804401Z 
2023-01-11T21:05:10.6804475Z if __name__ == "__main__":
2023-01-11T21:05:10.6804575Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6804694Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6804891Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6805081Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6805194Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6805202Z 
2023-01-11T21:05:10.6805268Z ok (0.047s)
2023-01-11T21:05:10.6805702Z   test_tanh_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6805830Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6806086Z [2023-01-11 21:00:38,771] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 435
2023-01-11T21:05:10.6806336Z [2023-01-11 21:00:41,448] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 435
2023-01-11T21:05:10.6806355Z 
2023-01-11T21:05:10.6806434Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6806502Z import torch
2023-01-11T21:05:10.6806574Z import random
2023-01-11T21:05:10.6806687Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6806806Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6806811Z 
2023-01-11T21:05:10.6806888Z aten = torch.ops.aten
2023-01-11T21:05:10.6807023Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6807100Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6807105Z 
2023-01-11T21:05:10.6807109Z 
2023-01-11T21:05:10.6807241Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6807442Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6807558Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6807656Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6807752Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6807811Z {
2023-01-11T21:05:10.6807894Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6807982Z     {
2023-01-11T21:05:10.6808056Z         #pragma omp for 
2023-01-11T21:05:10.6808138Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.6808200Z         {
2023-01-11T21:05:10.6808342Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6808428Z             auto tmp1 = tmp0.tanh();
2023-01-11T21:05:10.6808559Z             auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6808629Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6808758Z             auto tmp4 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6808840Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.6808919Z             auto tmp6 = tmp5.tanh();
2023-01-11T21:05:10.6809009Z             tmp3.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6809098Z             tmp6.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6809158Z         }
2023-01-11T21:05:10.6809240Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6809322Z         for(long i0=256; i0<256; i0+=1)
2023-01-11T21:05:10.6809383Z         {
2023-01-11T21:05:10.6809464Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6809554Z             auto tmp1 = std::tanh(tmp0);
2023-01-11T21:05:10.6809680Z             auto tmp2 = static_cast<float>(2);
2023-01-11T21:05:10.6809763Z             auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6809847Z             auto tmp4 = static_cast<float>(1);
2023-01-11T21:05:10.6809930Z             auto tmp5 = tmp0 + tmp4;
2023-01-11T21:05:10.6810019Z             auto tmp6 = std::tanh(tmp5);
2023-01-11T21:05:10.6810097Z             out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6810172Z             out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.6810234Z         }
2023-01-11T21:05:10.6810294Z     }
2023-01-11T21:05:10.6810339Z }
2023-01-11T21:05:10.6810417Z ''')
2023-01-11T21:05:10.6810422Z 
2023-01-11T21:05:10.6810427Z 
2023-01-11T21:05:10.6810512Z async_compile.wait(globals())
2023-01-11T21:05:10.6810583Z del async_compile
2023-01-11T21:05:10.6810590Z 
2023-01-11T21:05:10.6810658Z def call(args):
2023-01-11T21:05:10.6810726Z     arg0_1, = args
2023-01-11T21:05:10.6810795Z     args.clear()
2023-01-11T21:05:10.6810986Z     buf0 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6811184Z     buf1 = empty_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6811344Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6811412Z     del arg0_1
2023-01-11T21:05:10.6811487Z     return (buf0, buf1, )
2023-01-11T21:05:10.6811492Z 
2023-01-11T21:05:10.6811496Z 
2023-01-11T21:05:10.6811571Z if __name__ == "__main__":
2023-01-11T21:05:10.6811683Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6811804Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6811988Z     arg0_1 = rand_strided((16, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6812096Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6812102Z 
2023-01-11T21:05:10.6812166Z ok (2.734s)
2023-01-11T21:05:10.6812603Z   test_tensor1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6812727Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6812987Z [2023-01-11 21:00:41,489] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 436
2023-01-11T21:05:10.6813248Z [2023-01-11 21:00:44,119] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 436
2023-01-11T21:05:10.6813254Z 
2023-01-11T21:05:10.6813346Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6813446Z import torch
2023-01-11T21:05:10.6813501Z import random
2023-01-11T21:05:10.6813614Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6813737Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6813742Z 
2023-01-11T21:05:10.6813822Z aten = torch.ops.aten
2023-01-11T21:05:10.6813953Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6814045Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6814049Z 
2023-01-11T21:05:10.6814053Z 
2023-01-11T21:05:10.6814186Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6814389Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6814495Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6814595Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6814695Z                        long* __restrict__ out_ptr1)
2023-01-11T21:05:10.6814753Z {
2023-01-11T21:05:10.6814850Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6814914Z     {
2023-01-11T21:05:10.6814990Z         #pragma omp for 
2023-01-11T21:05:10.6815058Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.6815121Z         {
2023-01-11T21:05:10.6815211Z             {
2023-01-11T21:05:10.6815277Z                 {
2023-01-11T21:05:10.6815369Z                     auto tmp2 = in_ptr0[i0];
2023-01-11T21:05:10.6815472Z                     auto tmp0 = static_cast<long>(1);
2023-01-11T21:05:10.6815577Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.6815654Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6815738Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6815802Z                 }
2023-01-11T21:05:10.6815864Z             }
2023-01-11T21:05:10.6815925Z         }
2023-01-11T21:05:10.6816004Z         #pragma omp single
2023-01-11T21:05:10.6816066Z         {
2023-01-11T21:05:10.6816114Z             {
2023-01-11T21:05:10.6816177Z                 {
2023-01-11T21:05:10.6816281Z                     auto tmp0 = static_cast<long>(5);
2023-01-11T21:05:10.6816361Z                     out_ptr1[0] = tmp0;
2023-01-11T21:05:10.6816425Z                 }
2023-01-11T21:05:10.6816487Z             }
2023-01-11T21:05:10.6816537Z         }
2023-01-11T21:05:10.6816598Z     }
2023-01-11T21:05:10.6816656Z }
2023-01-11T21:05:10.6816734Z ''')
2023-01-11T21:05:10.6816739Z 
2023-01-11T21:05:10.6816743Z 
2023-01-11T21:05:10.6816831Z async_compile.wait(globals())
2023-01-11T21:05:10.6816904Z del async_compile
2023-01-11T21:05:10.6816909Z 
2023-01-11T21:05:10.6816978Z def call(args):
2023-01-11T21:05:10.6817033Z     arg0_1, = args
2023-01-11T21:05:10.6817103Z     args.clear()
2023-01-11T21:05:10.6817295Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6817473Z     buf1 = empty_strided((), (), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6817635Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6817706Z     del arg0_1
2023-01-11T21:05:10.6817781Z     return (buf0, buf1, )
2023-01-11T21:05:10.6817786Z 
2023-01-11T21:05:10.6817791Z 
2023-01-11T21:05:10.6817866Z if __name__ == "__main__":
2023-01-11T21:05:10.6817967Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6818088Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6818278Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6818384Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6818388Z 
2023-01-11T21:05:10.6818454Z ok (2.670s)
2023-01-11T21:05:10.6819002Z   test_tensor2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6819165Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6819430Z [2023-01-11 21:00:44,158] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 437
2023-01-11T21:05:10.6819694Z [2023-01-11 21:00:46,762] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 437
2023-01-11T21:05:10.6819699Z 
2023-01-11T21:05:10.6819793Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6819849Z import torch
2023-01-11T21:05:10.6819920Z import random
2023-01-11T21:05:10.6820035Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6820156Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6820161Z 
2023-01-11T21:05:10.6820240Z aten = torch.ops.aten
2023-01-11T21:05:10.6820372Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6820464Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6820612Z constant0 = None  # 4ebd4ff1c68a89413a036eaaf84436373c4ec2939ac1d7f84e9908772a109281
2023-01-11T21:05:10.6820619Z 
2023-01-11T21:05:10.6820637Z 
2023-01-11T21:05:10.6820756Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6820989Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6821110Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.6821211Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6821310Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6821369Z {
2023-01-11T21:05:10.6821464Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6821512Z     {
2023-01-11T21:05:10.6821588Z         #pragma omp for 
2023-01-11T21:05:10.6821668Z         for(long i0=0; i0<19; i0+=1)
2023-01-11T21:05:10.6821731Z         {
2023-01-11T21:05:10.6821792Z             {
2023-01-11T21:05:10.6821854Z                 {
2023-01-11T21:05:10.6821932Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6822025Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:05:10.6822129Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.6822219Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.6822303Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6822368Z                 }
2023-01-11T21:05:10.6822430Z             }
2023-01-11T21:05:10.6822478Z         }
2023-01-11T21:05:10.6822536Z     }
2023-01-11T21:05:10.6822595Z }
2023-01-11T21:05:10.6822673Z ''')
2023-01-11T21:05:10.6822678Z 
2023-01-11T21:05:10.6822682Z 
2023-01-11T21:05:10.6822767Z async_compile.wait(globals())
2023-01-11T21:05:10.6822838Z del async_compile
2023-01-11T21:05:10.6822843Z 
2023-01-11T21:05:10.6822911Z def call(args):
2023-01-11T21:05:10.6822977Z     arg0_1, = args
2023-01-11T21:05:10.6823033Z     args.clear()
2023-01-11T21:05:10.6823225Z     buf0 = empty_strided((19, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6823392Z     kernel_cpp_0(c_void_p(constant0.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6823461Z     del arg0_1
2023-01-11T21:05:10.6823530Z     return (buf0, )
2023-01-11T21:05:10.6823535Z 
2023-01-11T21:05:10.6823539Z 
2023-01-11T21:05:10.6823614Z if __name__ == "__main__":
2023-01-11T21:05:10.6823726Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6823833Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6824031Z     constant0 = rand_strided((19, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6824221Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6824326Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6824331Z 
2023-01-11T21:05:10.6824395Z ok (2.643s)
2023-01-11T21:05:10.6824830Z   test_tensor3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6824992Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6825250Z [2023-01-11 21:00:46,821] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 438
2023-01-11T21:05:10.6825510Z [2023-01-11 21:00:49,493] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 438
2023-01-11T21:05:10.6825515Z 
2023-01-11T21:05:10.6825608Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6825664Z import torch
2023-01-11T21:05:10.6825732Z import random
2023-01-11T21:05:10.6825849Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6825968Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6825973Z 
2023-01-11T21:05:10.6826052Z aten = torch.ops.aten
2023-01-11T21:05:10.6826186Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6826275Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6826280Z 
2023-01-11T21:05:10.6826284Z 
2023-01-11T21:05:10.6826401Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6826634Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6826754Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6826849Z                        long* __restrict__ out_ptr0,
2023-01-11T21:05:10.6826944Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.6827040Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6827099Z {
2023-01-11T21:05:10.6827173Z     #pragma GCC ivdep
2023-01-11T21:05:10.6827240Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6827299Z     {
2023-01-11T21:05:10.6827362Z         {
2023-01-11T21:05:10.6827423Z             {
2023-01-11T21:05:10.6827521Z                 auto tmp0 = static_cast<long>(i0);
2023-01-11T21:05:10.6827619Z                 auto tmp1 = static_cast<long>(1);
2023-01-11T21:05:10.6827693Z                 auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.6827789Z                 auto tmp3 = static_cast<long>(2);
2023-01-11T21:05:10.6827884Z                 auto tmp4 = tmp2 ? tmp1 : tmp3;
2023-01-11T21:05:10.6827970Z                 auto tmp5 = tmp4 + tmp1;
2023-01-11T21:05:10.6828050Z                 out_ptr0[i0] = tmp5;
2023-01-11T21:05:10.6828111Z             }
2023-01-11T21:05:10.6828172Z         }
2023-01-11T21:05:10.6828219Z     }
2023-01-11T21:05:10.6828293Z     #pragma GCC ivdep
2023-01-11T21:05:10.6828369Z     for(long i0=0; i0<3; i0+=1)
2023-01-11T21:05:10.6828428Z     {
2023-01-11T21:05:10.6828487Z         {
2023-01-11T21:05:10.6828548Z             {
2023-01-11T21:05:10.6828646Z                 auto tmp0 = static_cast<long>(i0);
2023-01-11T21:05:10.6828730Z                 auto tmp1 = static_cast<long>(1);
2023-01-11T21:05:10.6828813Z                 auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.6828908Z                 auto tmp3 = static_cast<long>(2);
2023-01-11T21:05:10.6828990Z                 auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.6829082Z                 auto tmp5 = static_cast<long>(3);
2023-01-11T21:05:10.6829178Z                 auto tmp6 = tmp4 ? tmp3 : tmp5;
2023-01-11T21:05:10.6829271Z                 auto tmp7 = tmp2 ? tmp1 : tmp6;
2023-01-11T21:05:10.6829344Z                 auto tmp8 = tmp7 + tmp3;
2023-01-11T21:05:10.6829424Z                 out_ptr1[i0] = tmp8;
2023-01-11T21:05:10.6829485Z             }
2023-01-11T21:05:10.6829544Z         }
2023-01-11T21:05:10.6829604Z     }
2023-01-11T21:05:10.6829699Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6829759Z     {
2023-01-11T21:05:10.6829821Z         #pragma omp for 
2023-01-11T21:05:10.6829900Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6829961Z         {
2023-01-11T21:05:10.6830021Z             {
2023-01-11T21:05:10.6830084Z                 {
2023-01-11T21:05:10.6830205Z                     auto tmp12 = in_ptr0[i0];
2023-01-11T21:05:10.6830295Z                     auto tmp0 = static_cast<long>(i0);
2023-01-11T21:05:10.6830395Z                     auto tmp1 = static_cast<long>(2);
2023-01-11T21:05:10.6830487Z                     auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.6830586Z                     auto tmp3 = static_cast<long>(1);
2023-01-11T21:05:10.6830674Z                     auto tmp4 = tmp0 < tmp3;
2023-01-11T21:05:10.6830766Z                     auto tmp5 = tmp4 ? tmp3 : tmp1;
2023-01-11T21:05:10.6830862Z                     auto tmp6 = static_cast<long>(3);
2023-01-11T21:05:10.6830950Z                     auto tmp7 = tmp0 < tmp6;
2023-01-11T21:05:10.6831034Z                     auto tmp8 = static_cast<long>(4);
2023-01-11T21:05:10.6831128Z                     auto tmp9 = tmp7 ? tmp6 : tmp8;
2023-01-11T21:05:10.6831223Z                     auto tmp10 = tmp2 ? tmp5 : tmp9;
2023-01-11T21:05:10.6831330Z                     auto tmp11 = static_cast<float>(tmp10);
2023-01-11T21:05:10.6831423Z                     auto tmp13 = tmp11 + tmp12;
2023-01-11T21:05:10.6831506Z                     out_ptr2[i0] = tmp13;
2023-01-11T21:05:10.6831570Z                 }
2023-01-11T21:05:10.6831618Z             }
2023-01-11T21:05:10.6831706Z         }
2023-01-11T21:05:10.6831767Z     }
2023-01-11T21:05:10.6831825Z }
2023-01-11T21:05:10.6831902Z ''')
2023-01-11T21:05:10.6831908Z 
2023-01-11T21:05:10.6831912Z 
2023-01-11T21:05:10.6831999Z async_compile.wait(globals())
2023-01-11T21:05:10.6832069Z del async_compile
2023-01-11T21:05:10.6832074Z 
2023-01-11T21:05:10.6832130Z def call(args):
2023-01-11T21:05:10.6832197Z     arg0_1, = args
2023-01-11T21:05:10.6832265Z     args.clear()
2023-01-11T21:05:10.6832453Z     buf0 = empty_strided((2, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6832639Z     buf1 = empty_strided((3, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6832826Z     buf2 = empty_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6833016Z     buf3 = empty_strided((0, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6833190Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6833258Z     del arg0_1
2023-01-11T21:05:10.6833345Z     return (buf3, buf0, buf1, buf2, )
2023-01-11T21:05:10.6833350Z 
2023-01-11T21:05:10.6833354Z 
2023-01-11T21:05:10.6833428Z if __name__ == "__main__":
2023-01-11T21:05:10.6833540Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6833661Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6833852Z     arg0_1 = rand_strided((4, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6833957Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6833962Z 
2023-01-11T21:05:10.6834026Z ok (2.733s)
2023-01-11T21:05:10.6834471Z   test_tmp_not_defined_issue1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6834599Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6834966Z Failed to collect metadata on function, produced code may be suboptimal.  Known situations this can occur are inference mode only compilation involving resize_ or prims (!schema.hasAnyAliasInfo() INTERNAL ASSERT FAILED); if your situation looks different please file a bug to PyTorch.
2023-01-11T21:05:10.6835059Z Traceback (most recent call last):
2023-01-11T21:05:10.6835329Z   File "/opt/conda/lib/python3.7/site-packages/torch/_functorch/aot_autograd.py", line 1275, in aot_wrapper_dedupe
2023-01-11T21:05:10.6835397Z     )(*flat_args)
2023-01-11T21:05:10.6835643Z   File "/opt/conda/lib/python3.7/site-packages/torch/_functorch/aot_autograd.py", line 289, in inner
2023-01-11T21:05:10.6835743Z     outs = f(*f_args)
2023-01-11T21:05:10.6836005Z   File "/opt/conda/lib/python3.7/site-packages/torch/_functorch/aot_autograd.py", line 2327, in functional_call
2023-01-11T21:05:10.6836116Z     out = Interpreter(mod).run(*args[params_len:], **kwargs)
2023-01-11T21:05:10.6836341Z   File "/opt/conda/lib/python3.7/site-packages/torch/fx/interpreter.py", line 136, in run
2023-01-11T21:05:10.6836436Z     self.env[node] = self.run_node(node)
2023-01-11T21:05:10.6836666Z   File "/opt/conda/lib/python3.7/site-packages/torch/fx/interpreter.py", line 177, in run_node
2023-01-11T21:05:10.6836780Z     return getattr(self, n.op)(n.target, args, kwargs)
2023-01-11T21:05:10.6837019Z   File "/opt/conda/lib/python3.7/site-packages/torch/fx/interpreter.py", line 249, in call_function
2023-01-11T21:05:10.6837104Z     return target(*args, **kwargs)
2023-01-11T21:05:10.6837313Z   File "/opt/conda/lib/python3.7/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:05:10.6837400Z     return self._op(*args, **kwargs or {})
2023-01-11T21:05:10.6837657Z   File "/opt/conda/lib/python3.7/site-packages/torch/_inductor/overrides.py", line 36, in __torch_function__
2023-01-11T21:05:10.6837767Z     return func(*args, **kwargs)
2023-01-11T21:05:10.6837982Z   File "/opt/conda/lib/python3.7/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:05:10.6838078Z     return self._op(*args, **kwargs or {})
2023-01-11T21:05:10.6838318Z   File "/opt/conda/lib/python3.7/site-packages/torch/_prims/__init__.py", line 285, in _autograd_impl
2023-01-11T21:05:10.6838437Z     return backwards_not_supported(_prim)(*args, **kwargs)
2023-01-11T21:05:10.6838695Z   File "/opt/conda/lib/python3.7/site-packages/torch/_prims_common/wrappers.py", line 309, in _autograd_impl
2023-01-11T21:05:10.6838780Z     return redispatch_prim(args, kwargs)
2023-01-11T21:05:10.6839038Z   File "/opt/conda/lib/python3.7/site-packages/torch/_prims_common/wrappers.py", line 279, in redispatch_prim
2023-01-11T21:05:10.6839126Z     return prim(*args, **kwargs)
2023-01-11T21:05:10.6839336Z   File "/opt/conda/lib/python3.7/site-packages/torch/_ops.py", line 284, in __call__
2023-01-11T21:05:10.6839435Z     return self._op(*args, **kwargs or {})
2023-01-11T21:05:10.6839930Z RuntimeError: !schema.hasAnyAliasInfo() INTERNAL ASSERT FAILED at "/var/lib/jenkins/workspace/aten/src/ATen/FunctionalizeFallbackKernel.cpp":32, please report a bug to PyTorch. mutating and aliasing ops should all have codegen'd kernels
2023-01-11T21:05:10.6839936Z 
2023-01-11T21:05:10.6840181Z While executing %broadcast_in_dim_default : [#users=1] = call_function[target=torch.ops.prims.broadcast_in_dim.default](args = (%var_default_1, [1, 512, 1], [0, 1]), kwargs = {})
2023-01-11T21:05:10.6840259Z Original traceback:
2023-01-11T21:05:10.6840329Z Module stack: {}
2023-01-11T21:05:10.6840442Z   File "inductor/test_torchinductor.py", line 4724, in forward
2023-01-11T21:05:10.6840525Z     var_default_1, [1, 512, 1], [0, 1]
2023-01-11T21:05:10.6840774Z  |   File "inductor/test_torchinductor.py", line 318, in run
2023-01-11T21:05:10.6840857Z     return model(*ex, **kwargs)
2023-01-11T21:05:10.6840861Z 
2023-01-11T21:05:10.6841124Z [2023-01-11 21:00:49,944] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 439
2023-01-11T21:05:10.6841130Z 
2023-01-11T21:05:10.6841222Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6841294Z import torch
2023-01-11T21:05:10.6841350Z import random
2023-01-11T21:05:10.6841467Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6841588Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6841593Z 
2023-01-11T21:05:10.6841671Z aten = torch.ops.aten
2023-01-11T21:05:10.6841806Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6841897Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6841902Z 
2023-01-11T21:05:10.6841906Z 
2023-01-11T21:05:10.6842039Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6842295Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6842398Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6842503Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.6842608Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6842710Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6842813Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.6842915Z                        const float* __restrict__ in_ptr3,
2023-01-11T21:05:10.6843018Z                        const float* __restrict__ in_ptr4,
2023-01-11T21:05:10.6843118Z                        const float* __restrict__ in_ptr5,
2023-01-11T21:05:10.6843204Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6843301Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6843395Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.6843492Z                        float* __restrict__ out_ptr5)
2023-01-11T21:05:10.6843553Z {
2023-01-11T21:05:10.6843638Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.6843785Z     auto out_ptr4 = in_out_ptr1;
2023-01-11T21:05:10.6843875Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6843935Z     {
2023-01-11T21:05:10.6844010Z         #pragma omp for 
2023-01-11T21:05:10.6844091Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.6844155Z         {
2023-01-11T21:05:10.6844218Z             {
2023-01-11T21:05:10.6844413Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6844478Z                 float tmp1 = 0;
2023-01-11T21:05:10.6844599Z                 auto tmp1_vec = at::vec::Vectorized<float>(tmp1);
2023-01-11T21:05:10.6844690Z                 for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.6844756Z                 {
2023-01-11T21:05:10.6844904Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (1024*i0));
2023-01-11T21:05:10.6844998Z                     tmp1_vec += tmp0;
2023-01-11T21:05:10.6845090Z                 }
2023-01-11T21:05:10.6845327Z                 tmp1 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp1_vec);
2023-01-11T21:05:10.6845435Z                 #pragma omp simd simdlen(8)  reduction(+:tmp1)
2023-01-11T21:05:10.6845525Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:05:10.6845587Z                 {
2023-01-11T21:05:10.6845687Z                     auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:05:10.6845763Z                     tmp1 += tmp0;
2023-01-11T21:05:10.6845825Z                 }
2023-01-11T21:05:10.6845905Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6845954Z             }
2023-01-11T21:05:10.6846015Z         }
2023-01-11T21:05:10.6846090Z         #pragma omp for 
2023-01-11T21:05:10.6846174Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.6846236Z         {
2023-01-11T21:05:10.6846299Z             {
2023-01-11T21:05:10.6846488Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6846552Z                 float tmp6 = 0;
2023-01-11T21:05:10.6846674Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:05:10.6846762Z                 for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.6846826Z                 {
2023-01-11T21:05:10.6846967Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (1024*i0));
2023-01-11T21:05:10.6847092Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr0[i0]);
2023-01-11T21:05:10.6847229Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:05:10.6847320Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6847449Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6847577Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:05:10.6847658Z                     tmp6_vec += tmp5;
2023-01-11T21:05:10.6847721Z                 }
2023-01-11T21:05:10.6847919Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:05:10.6848041Z                 #pragma omp simd simdlen(8)  reduction(+:tmp6)
2023-01-11T21:05:10.6848132Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:05:10.6848196Z                 {
2023-01-11T21:05:10.6848282Z                     auto tmp0 = in_ptr0[i1 + (1024*i0)];
2023-01-11T21:05:10.6848374Z                     auto tmp1 = out_ptr0[i0];
2023-01-11T21:05:10.6848477Z                     auto tmp2 = static_cast<float>(1024);
2023-01-11T21:05:10.6848567Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6848702Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6848793Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6848870Z                     tmp6 += tmp5;
2023-01-11T21:05:10.6848920Z                 }
2023-01-11T21:05:10.6848999Z                 out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.6849094Z             }
2023-01-11T21:05:10.6849157Z         }
2023-01-11T21:05:10.6849233Z         #pragma omp for 
2023-01-11T21:05:10.6849311Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6849376Z         {
2023-01-11T21:05:10.6849498Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6849633Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:05:10.6849717Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6849812Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6849873Z         }
2023-01-11T21:05:10.6849966Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6850047Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:05:10.6850097Z         {
2023-01-11T21:05:10.6850181Z             auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.6850282Z             auto tmp1 = static_cast<float>(1024);
2023-01-11T21:05:10.6850365Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6850448Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6850509Z         }
2023-01-11T21:05:10.6850583Z         #pragma omp for 
2023-01-11T21:05:10.6850651Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.6850711Z         {
2023-01-11T21:05:10.6850777Z             {
2023-01-11T21:05:10.6850961Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6851039Z                 float tmp9 = 0;
2023-01-11T21:05:10.6851159Z                 auto tmp9_vec = at::vec::Vectorized<float>(tmp9);
2023-01-11T21:05:10.6851247Z                 for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.6851297Z                 {
2023-01-11T21:05:10.6851441Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i1) + (1024*i0));
2023-01-11T21:05:10.6851567Z                     auto tmp1 = at::vec::Vectorized<float>(in_ptr2[i0]);
2023-01-11T21:05:10.6851693Z                     auto tmp3 = at::vec::Vectorized<float>(in_ptr3[i0]);
2023-01-11T21:05:10.6851827Z                     auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr4 + 16*i1);
2023-01-11T21:05:10.6851956Z                     auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr5 + 16*i1);
2023-01-11T21:05:10.6852091Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.6852180Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.6852254Z                     auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.6852339Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6852443Z                     tmp8.store(out_ptr2 + (16*i1) + (1024*i0));
2023-01-11T21:05:10.6852525Z                     tmp9_vec += tmp8;
2023-01-11T21:05:10.6852589Z                 }
2023-01-11T21:05:10.6852824Z                 tmp9 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp9_vec);
2023-01-11T21:05:10.6852946Z                 #pragma omp simd simdlen(8)  reduction(+:tmp9)
2023-01-11T21:05:10.6853037Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:05:10.6853088Z                 {
2023-01-11T21:05:10.6853184Z                     auto tmp0 = in_ptr1[i1 + (1024*i0)];
2023-01-11T21:05:10.6853273Z                     auto tmp1 = in_ptr2[i0];
2023-01-11T21:05:10.6853360Z                     auto tmp3 = in_ptr3[i0];
2023-01-11T21:05:10.6853446Z                     auto tmp5 = in_ptr4[i1];
2023-01-11T21:05:10.6853532Z                     auto tmp7 = in_ptr5[i1];
2023-01-11T21:05:10.6853663Z                     auto tmp2 = tmp0 - tmp1;
2023-01-11T21:05:10.6853737Z                     auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.6853823Z                     auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.6853909Z                     auto tmp8 = tmp6 + tmp7;
2023-01-11T21:05:10.6854006Z                     out_ptr2[i1 + (1024*i0)] = tmp8;
2023-01-11T21:05:10.6854082Z                     tmp9 += tmp8;
2023-01-11T21:05:10.6854148Z                 }
2023-01-11T21:05:10.6854254Z                 out_ptr3[i0] = tmp9;
2023-01-11T21:05:10.6854305Z             }
2023-01-11T21:05:10.6854365Z         }
2023-01-11T21:05:10.6854440Z         #pragma omp for 
2023-01-11T21:05:10.6854521Z         for(long i0=0; i0<512; i0+=1)
2023-01-11T21:05:10.6854583Z         {
2023-01-11T21:05:10.6854645Z             {
2023-01-11T21:05:10.6854835Z                 #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out += omp_in) initializer(omp_priv={{0}})
2023-01-11T21:05:10.6854899Z                 float tmp6 = 0;
2023-01-11T21:05:10.6855017Z                 auto tmp6_vec = at::vec::Vectorized<float>(tmp6);
2023-01-11T21:05:10.6855092Z                 float tmp7 = 0;
2023-01-11T21:05:10.6855208Z                 auto tmp7_vec = at::vec::Vectorized<float>(tmp7);
2023-01-11T21:05:10.6855298Z                 for(long i1=0; i1<64; i1+=1)
2023-01-11T21:05:10.6855361Z                 {
2023-01-11T21:05:10.6855506Z                     auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + (16*i1) + (1024*i0));
2023-01-11T21:05:10.6855629Z                     auto tmp1 = at::vec::Vectorized<float>(out_ptr3[i0]);
2023-01-11T21:05:10.6855753Z                     auto tmp2 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:05:10.6855842Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6855975Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6856063Z                     auto tmp5 = tmp4.pow(2);
2023-01-11T21:05:10.6856141Z                     tmp6_vec += tmp5;
2023-01-11T21:05:10.6856217Z                     tmp7_vec += tmp0;
2023-01-11T21:05:10.6856280Z                 }
2023-01-11T21:05:10.6856471Z                 tmp6 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp6_vec);
2023-01-11T21:05:10.6856667Z                 tmp7 = at::vec::vec_reduce_all<float>([](at::vec::Vectorized<float>& x, at::vec::Vectorized<float>&y) {return x + y;}, tmp7_vec);
2023-01-11T21:05:10.6856795Z                 #pragma omp simd simdlen(8)  reduction(+:tmp6) reduction(+:tmp7)
2023-01-11T21:05:10.6856886Z                 for(long i1=1024; i1<1024; i1+=1)
2023-01-11T21:05:10.6856949Z                 {
2023-01-11T21:05:10.6857049Z                     auto tmp0 = out_ptr2[i1 + (1024*i0)];
2023-01-11T21:05:10.6857140Z                     auto tmp1 = out_ptr3[i0];
2023-01-11T21:05:10.6857242Z                     auto tmp2 = static_cast<float>(1024);
2023-01-11T21:05:10.6857330Z                     auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.6857451Z                     auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.6857538Z                     auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.6857612Z                     tmp6 += tmp5;
2023-01-11T21:05:10.6857685Z                     tmp7 += tmp0;
2023-01-11T21:05:10.6857811Z                 }
2023-01-11T21:05:10.6857892Z                 out_ptr4[i0] = tmp6;
2023-01-11T21:05:10.6857971Z                 out_ptr5[i0] = tmp7;
2023-01-11T21:05:10.6858021Z             }
2023-01-11T21:05:10.6858084Z         }
2023-01-11T21:05:10.6858162Z         #pragma omp for 
2023-01-11T21:05:10.6858243Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6858306Z         {
2023-01-11T21:05:10.6858441Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr4 + 16*i0);
2023-01-11T21:05:10.6858669Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1024));
2023-01-11T21:05:10.6858739Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6858943Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1e-05));
2023-01-11T21:05:10.6859024Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6859121Z             tmp4.store(in_out_ptr1 + 16*i0);
2023-01-11T21:05:10.6859186Z         }
2023-01-11T21:05:10.6859283Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6859367Z         for(long i0=512; i0<512; i0+=1)
2023-01-11T21:05:10.6859415Z         {
2023-01-11T21:05:10.6859498Z             auto tmp0 = out_ptr4[i0];
2023-01-11T21:05:10.6859635Z             auto tmp1 = static_cast<float>(1024);
2023-01-11T21:05:10.6859719Z             auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.6859868Z             auto tmp3 = static_cast<float>(1e-05);
2023-01-11T21:05:10.6859948Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6860027Z             in_out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.6860074Z         }
2023-01-11T21:05:10.6860134Z     }
2023-01-11T21:05:10.6860192Z }
2023-01-11T21:05:10.6860268Z ''')
2023-01-11T21:05:10.6860274Z 
2023-01-11T21:05:10.6860278Z 
2023-01-11T21:05:10.6860365Z async_compile.wait(globals())
2023-01-11T21:05:10.6860438Z del async_compile
2023-01-11T21:05:10.6860443Z 
2023-01-11T21:05:10.6860511Z def call(args):
2023-01-11T21:05:10.6860603Z     arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1 = args
2023-01-11T21:05:10.6860675Z     args.clear()
2023-01-11T21:05:10.6860885Z     buf0 = empty_strided((1, 512, 1), (512, 1, 512), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6861087Z     buf1 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6861170Z     buf2 = buf1; del buf1  # reuse
2023-01-11T21:05:10.6861388Z     buf3 = empty_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6861595Z     buf4 = empty_strided((1, 512, 1), (512, 1, 512), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6861789Z     buf5 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6861969Z     buf6 = empty_strided((1, 512), (512, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6862082Z     buf7 = as_strided(buf5, (1, 512, 1), (512, 1, 1)); del buf5  # reuse
2023-01-11T21:05:10.6862454Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(arg3_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(arg4_1.data_ptr()), c_void_p(arg5_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf6.data_ptr()))
2023-01-11T21:05:10.6862524Z     del arg0_1
2023-01-11T21:05:10.6862589Z     del arg1_1
2023-01-11T21:05:10.6862652Z     del arg2_1
2023-01-11T21:05:10.6862714Z     del arg3_1
2023-01-11T21:05:10.6862778Z     del arg4_1
2023-01-11T21:05:10.6862829Z     del arg5_1
2023-01-11T21:05:10.6862909Z     return (buf2, buf6, buf7, )
2023-01-11T21:05:10.6862914Z 
2023-01-11T21:05:10.6862918Z 
2023-01-11T21:05:10.6862992Z if __name__ == "__main__":
2023-01-11T21:05:10.6863105Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6863228Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6863425Z     arg0_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6863618Z     arg1_1 = rand_strided((1024, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6863864Z     arg2_1 = rand_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6864068Z     arg3_1 = rand_strided((1, 512, 1024), (524288, 1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6864273Z     arg4_1 = rand_strided((1, 512, 1), (512, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6864476Z     arg5_1 = rand_strided((1, 512, 1), (512, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6864615Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1]))
2023-01-11T21:05:10.6864882Z [2023-01-11 21:00:52,725] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 439
2023-01-11T21:05:10.6864888Z 
2023-01-11T21:05:10.6864954Z ok (3.491s)
2023-01-11T21:05:10.6865117Z   test_tmp_not_defined_issue2_cpu (__main__.CpuTests) ... skip: TODO: debug this with asan (0.002s)
2023-01-11T21:05:10.6865263Z   test_to_device_constant_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:05:10.6865394Z   test_to_device_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:05:10.6865848Z   test_to_dtype_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6865976Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6866235Z [2023-01-11 21:00:53,166] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 440
2023-01-11T21:05:10.6866498Z [2023-01-11 21:00:55,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 440
2023-01-11T21:05:10.6866503Z 
2023-01-11T21:05:10.6866598Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6866666Z import torch
2023-01-11T21:05:10.6866735Z import random
2023-01-11T21:05:10.6866850Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6866970Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6866975Z 
2023-01-11T21:05:10.6867040Z aten = torch.ops.aten
2023-01-11T21:05:10.6867172Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6867260Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6867265Z 
2023-01-11T21:05:10.6867269Z 
2023-01-11T21:05:10.6867402Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6867603Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6867723Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.6867820Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6867915Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:05:10.6867965Z {
2023-01-11T21:05:10.6868059Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6868119Z     {
2023-01-11T21:05:10.6868195Z         #pragma omp for 
2023-01-11T21:05:10.6868278Z         for(long i0=0; i0<40; i0+=1)
2023-01-11T21:05:10.6868338Z         {
2023-01-11T21:05:10.6868388Z             {
2023-01-11T21:05:10.6868452Z                 {
2023-01-11T21:05:10.6868541Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6868643Z                     auto tmp1 = static_cast<double>(1);
2023-01-11T21:05:10.6868733Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6868839Z                     auto tmp3 = static_cast<float>(tmp2);
2023-01-11T21:05:10.6868941Z                     auto tmp4 = static_cast<bool>(tmp0);
2023-01-11T21:05:10.6869013Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.6869095Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.6869155Z                 }
2023-01-11T21:05:10.6869243Z             }
2023-01-11T21:05:10.6869304Z         }
2023-01-11T21:05:10.6869363Z     }
2023-01-11T21:05:10.6869421Z }
2023-01-11T21:05:10.6869486Z ''')
2023-01-11T21:05:10.6869491Z 
2023-01-11T21:05:10.6869496Z 
2023-01-11T21:05:10.6869586Z async_compile.wait(globals())
2023-01-11T21:05:10.6869658Z del async_compile
2023-01-11T21:05:10.6869663Z 
2023-01-11T21:05:10.6869732Z def call(args):
2023-01-11T21:05:10.6869804Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6869873Z     args.clear()
2023-01-11T21:05:10.6870076Z     buf0 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6870271Z     buf1 = empty_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.6870419Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6870511Z     return (arg0_1, buf0, arg1_1, buf1, )
2023-01-11T21:05:10.6870516Z 
2023-01-11T21:05:10.6870520Z 
2023-01-11T21:05:10.6870594Z if __name__ == "__main__":
2023-01-11T21:05:10.6870710Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6870830Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6871061Z     arg0_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6871260Z     arg1_1 = rand_strided((2, 2, 10), (20, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.6871373Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6871378Z 
2023-01-11T21:05:10.6871431Z ok (2.835s)
2023-01-11T21:05:10.6871865Z   test_topk_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6871991Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6872252Z [2023-01-11 21:00:55,857] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 441
2023-01-11T21:05:10.6872469Z [2023-01-11 21:00:55,867] torch._inductor.ir: [WARNING] Using FallbackKernel: aten.topk
2023-01-11T21:05:10.6872728Z [2023-01-11 21:00:55,871] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 441
2023-01-11T21:05:10.6872733Z 
2023-01-11T21:05:10.6872823Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6872892Z import torch
2023-01-11T21:05:10.6872960Z import random
2023-01-11T21:05:10.6873062Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6873181Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6873186Z 
2023-01-11T21:05:10.6873262Z aten = torch.ops.aten
2023-01-11T21:05:10.6873394Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6873483Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6873490Z 
2023-01-11T21:05:10.6873494Z 
2023-01-11T21:05:10.6873580Z async_compile.wait(globals())
2023-01-11T21:05:10.6873649Z del async_compile
2023-01-11T21:05:10.6873653Z 
2023-01-11T21:05:10.6873721Z def call(args):
2023-01-11T21:05:10.6873778Z     arg0_1, = args
2023-01-11T21:05:10.6873845Z     args.clear()
2023-01-11T21:05:10.6873928Z     buf0 = aten.topk(arg0_1, 2)
2023-01-11T21:05:10.6873992Z     del arg0_1
2023-01-11T21:05:10.6874058Z     buf1 = buf0[0]
2023-01-11T21:05:10.6874163Z     assert_size_stride(buf1, (1, 1, 8, 2), (16, 16, 2, 1))
2023-01-11T21:05:10.6874230Z     buf2 = buf0[1]
2023-01-11T21:05:10.6874320Z     assert_size_stride(buf2, (1, 1, 8, 2), (16, 16, 2, 1))
2023-01-11T21:05:10.6874383Z     del buf0
2023-01-11T21:05:10.6874457Z     return (buf1, buf2, )
2023-01-11T21:05:10.6874462Z 
2023-01-11T21:05:10.6874466Z 
2023-01-11T21:05:10.6874539Z if __name__ == "__main__":
2023-01-11T21:05:10.6874652Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6874771Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6875012Z     arg0_1 = rand_strided((1, 1, 8, 8), (64, 64, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6875119Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6875126Z 
2023-01-11T21:05:10.6875179Z ok (0.043s)
2023-01-11T21:05:10.6875620Z   test_transpose_add_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6875745Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6876000Z [2023-01-11 21:00:55,898] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 442
2023-01-11T21:05:10.6876262Z [2023-01-11 21:00:58,530] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 442
2023-01-11T21:05:10.6876270Z 
2023-01-11T21:05:10.6876359Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6876427Z import torch
2023-01-11T21:05:10.6876522Z import random
2023-01-11T21:05:10.6876636Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6876744Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6876749Z 
2023-01-11T21:05:10.6876824Z aten = torch.ops.aten
2023-01-11T21:05:10.6876955Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6877043Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6877048Z 
2023-01-11T21:05:10.6877053Z 
2023-01-11T21:05:10.6877184Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6877386Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6877504Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6877605Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6877693Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6877753Z {
2023-01-11T21:05:10.6877849Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6877911Z     {
2023-01-11T21:05:10.6877988Z         #pragma omp for 
2023-01-11T21:05:10.6878068Z         for(long i0=0; i0<32; i0+=1)
2023-01-11T21:05:10.6878130Z         {
2023-01-11T21:05:10.6878196Z             #pragma GCC ivdep
2023-01-11T21:05:10.6878280Z             for(long i1=0; i1<16; i1+=1)
2023-01-11T21:05:10.6878343Z             {
2023-01-11T21:05:10.6878409Z                 {
2023-01-11T21:05:10.6878475Z                     {
2023-01-11T21:05:10.6878577Z                         auto tmp0 = in_ptr0[i0 + (32*i1)];
2023-01-11T21:05:10.6878665Z                         auto tmp1 = in_ptr1[i1 + (16*i0)];
2023-01-11T21:05:10.6878758Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6878854Z                         out_ptr0[i0 + (32*i1)] = tmp2;
2023-01-11T21:05:10.6878924Z                     }
2023-01-11T21:05:10.6878987Z                 }
2023-01-11T21:05:10.6879051Z             }
2023-01-11T21:05:10.6879111Z         }
2023-01-11T21:05:10.6879158Z     }
2023-01-11T21:05:10.6879219Z }
2023-01-11T21:05:10.6879297Z ''')
2023-01-11T21:05:10.6879302Z 
2023-01-11T21:05:10.6879306Z 
2023-01-11T21:05:10.6879396Z async_compile.wait(globals())
2023-01-11T21:05:10.6879466Z del async_compile
2023-01-11T21:05:10.6879471Z 
2023-01-11T21:05:10.6879541Z def call(args):
2023-01-11T21:05:10.6879618Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6879674Z     args.clear()
2023-01-11T21:05:10.6879874Z     buf0 = empty_strided((32, 16), (1, 32), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6880034Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6880101Z     del arg0_1
2023-01-11T21:05:10.6880165Z     del arg1_1
2023-01-11T21:05:10.6880236Z     return (buf0, )
2023-01-11T21:05:10.6880271Z 
2023-01-11T21:05:10.6880275Z 
2023-01-11T21:05:10.6880350Z if __name__ == "__main__":
2023-01-11T21:05:10.6880462Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6880573Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6880895Z     arg0_1 = rand_strided((16, 32), (32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6881094Z     arg1_1 = rand_strided((32, 16), (16, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6881209Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6881214Z 
2023-01-11T21:05:10.6881281Z ok (2.664s)
2023-01-11T21:05:10.6881716Z   test_transpose_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6881846Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6882177Z [2023-01-11 21:00:58,584] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 443
2023-01-11T21:05:10.6882443Z [2023-01-11 21:01:01,308] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 443
2023-01-11T21:05:10.6882448Z 
2023-01-11T21:05:10.6882542Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6882599Z import torch
2023-01-11T21:05:10.6882668Z import random
2023-01-11T21:05:10.6882782Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6882902Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6882907Z 
2023-01-11T21:05:10.6882987Z aten = torch.ops.aten
2023-01-11T21:05:10.6883120Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6883211Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6883215Z 
2023-01-11T21:05:10.6883222Z 
2023-01-11T21:05:10.6883355Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6883548Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6883671Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6883775Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6883875Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6883971Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6884031Z {
2023-01-11T21:05:10.6884129Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6884176Z     {
2023-01-11T21:05:10.6884252Z         #pragma omp for 
2023-01-11T21:05:10.6884333Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6884394Z         {
2023-01-11T21:05:10.6884473Z             #pragma GCC ivdep
2023-01-11T21:05:10.6884555Z             for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.6884605Z             {
2023-01-11T21:05:10.6884672Z                 {
2023-01-11T21:05:10.6884735Z                     {
2023-01-11T21:05:10.6884837Z                         auto tmp0 = in_ptr0[i0 + (8*i1)];
2023-01-11T21:05:10.6884937Z                         auto tmp1 = in_ptr1[i1 + (8*i0)];
2023-01-11T21:05:10.6885033Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6885125Z                         out_ptr0[i0 + (8*i1)] = tmp2;
2023-01-11T21:05:10.6885178Z                     }
2023-01-11T21:05:10.6885241Z                 }
2023-01-11T21:05:10.6885302Z             }
2023-01-11T21:05:10.6885363Z         }
2023-01-11T21:05:10.6885438Z         #pragma omp for 
2023-01-11T21:05:10.6885516Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6885575Z         {
2023-01-11T21:05:10.6885700Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6885834Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6885916Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6886088Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(10));
2023-01-11T21:05:10.6886171Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6886260Z             tmp4.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6886323Z         }
2023-01-11T21:05:10.6886417Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6886484Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6886544Z         {
2023-01-11T21:05:10.6886626Z             auto tmp0 = in_ptr1[i0];
2023-01-11T21:05:10.6886721Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.6886803Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.6886900Z             auto tmp3 = static_cast<float>(10);
2023-01-11T21:05:10.6886981Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6887045Z             out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.6887105Z         }
2023-01-11T21:05:10.6887165Z     }
2023-01-11T21:05:10.6887223Z }
2023-01-11T21:05:10.6887302Z ''')
2023-01-11T21:05:10.6887309Z 
2023-01-11T21:05:10.6887313Z 
2023-01-11T21:05:10.6887400Z async_compile.wait(globals())
2023-01-11T21:05:10.6887470Z del async_compile
2023-01-11T21:05:10.6887475Z 
2023-01-11T21:05:10.6887531Z def call(args):
2023-01-11T21:05:10.6887631Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6887702Z     args.clear()
2023-01-11T21:05:10.6887896Z     buf0 = empty_strided((8, 8), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6888087Z     buf1 = empty_strided((8, 8), (1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6888272Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6888340Z     del arg0_1
2023-01-11T21:05:10.6888392Z     del arg1_1
2023-01-11T21:05:10.6888467Z     return (buf0, buf1, )
2023-01-11T21:05:10.6888472Z 
2023-01-11T21:05:10.6888476Z 
2023-01-11T21:05:10.6888551Z if __name__ == "__main__":
2023-01-11T21:05:10.6888663Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6888788Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6888980Z     arg0_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6889175Z     arg1_1 = rand_strided((8, 8), (8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6889288Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6889293Z 
2023-01-11T21:05:10.6889345Z ok (2.778s)
2023-01-11T21:05:10.6889694Z   test_transposed_propagates_cpu (__main__.CpuTests) ... [2023-01-11 21:01:01,342] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 444
2023-01-11T21:05:10.6889956Z [2023-01-11 21:01:03,985] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 444
2023-01-11T21:05:10.6889961Z 
2023-01-11T21:05:10.6890052Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6890121Z import torch
2023-01-11T21:05:10.6890190Z import random
2023-01-11T21:05:10.6890301Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6890422Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6890427Z 
2023-01-11T21:05:10.6890491Z aten = torch.ops.aten
2023-01-11T21:05:10.6890622Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6890710Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6890715Z 
2023-01-11T21:05:10.6890720Z 
2023-01-11T21:05:10.6890851Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6891053Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6891171Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6891272Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.6891370Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.6891417Z {
2023-01-11T21:05:10.6891510Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6891570Z     {
2023-01-11T21:05:10.6891673Z         #pragma omp for 
2023-01-11T21:05:10.6891753Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.6891813Z         {
2023-01-11T21:05:10.6891943Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6892064Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.6892148Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6892237Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6892298Z         }
2023-01-11T21:05:10.6892392Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6892473Z         for(long i0=64; i0<64; i0+=1)
2023-01-11T21:05:10.6892532Z         {
2023-01-11T21:05:10.6892602Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6892680Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.6892759Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6892839Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6892899Z         }
2023-01-11T21:05:10.6892961Z     }
2023-01-11T21:05:10.6893019Z }
2023-01-11T21:05:10.6893083Z ''')
2023-01-11T21:05:10.6893089Z 
2023-01-11T21:05:10.6893092Z 
2023-01-11T21:05:10.6893177Z async_compile.wait(globals())
2023-01-11T21:05:10.6893248Z del async_compile
2023-01-11T21:05:10.6893282Z 
2023-01-11T21:05:10.6893352Z def call(args):
2023-01-11T21:05:10.6893425Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6893494Z     args.clear()
2023-01-11T21:05:10.6893706Z     buf0 = empty_strided((1, 4, 4, 4), (64, 4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6893856Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.6893922Z     del arg0_1
2023-01-11T21:05:10.6893986Z     del arg1_1
2023-01-11T21:05:10.6894055Z     return (buf0, )
2023-01-11T21:05:10.6894060Z 
2023-01-11T21:05:10.6894064Z 
2023-01-11T21:05:10.6894137Z if __name__ == "__main__":
2023-01-11T21:05:10.6894248Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6894374Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6894584Z     arg0_1 = rand_strided((1, 4, 4, 4), (64, 4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6894776Z     arg1_1 = rand_strided((4, 4, 4), (4, 1, 16), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6894889Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6894894Z 
2023-01-11T21:05:10.6894957Z ok (2.672s)
2023-01-11T21:05:10.6895091Z   test_triton_conv_cpu (__main__.CpuTests) ... skip: requires cuda (0.002s)
2023-01-11T21:05:10.6895416Z   test_triton_mm2_cpu (__main__.CpuTests) ... [2023-01-11 21:01:04,132] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 445
2023-01-11T21:05:10.6895676Z [2023-01-11 21:01:06,747] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 445
2023-01-11T21:05:10.6895681Z 
2023-01-11T21:05:10.6895772Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6895839Z import torch
2023-01-11T21:05:10.6895909Z import random
2023-01-11T21:05:10.6896010Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6896127Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6896132Z 
2023-01-11T21:05:10.6896210Z aten = torch.ops.aten
2023-01-11T21:05:10.6896342Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6896431Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6896436Z 
2023-01-11T21:05:10.6896440Z 
2023-01-11T21:05:10.6896571Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6896772Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6896887Z extern "C" void kernel(float* __restrict__ in_out_ptr0)
2023-01-11T21:05:10.6896934Z {
2023-01-11T21:05:10.6897030Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6897089Z     {
2023-01-11T21:05:10.6897163Z         #pragma omp for 
2023-01-11T21:05:10.6897244Z         for(long i0=0; i0<65536; i0+=1)
2023-01-11T21:05:10.6897334Z         {
2023-01-11T21:05:10.6897458Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6897584Z             auto tmp1 = at::vec::clamp_min(tmp0, decltype(tmp0)(0));
2023-01-11T21:05:10.6897679Z             tmp1.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.6897740Z         }
2023-01-11T21:05:10.6897835Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6897925Z         for(long i0=1048576; i0<1048576; i0+=1)
2023-01-11T21:05:10.6897986Z         {
2023-01-11T21:05:10.6898072Z             auto tmp0 = in_out_ptr0[i0];
2023-01-11T21:05:10.6898145Z             auto tmp1 = tmp0 * (tmp0>0);
2023-01-11T21:05:10.6898226Z             in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6898286Z         }
2023-01-11T21:05:10.6898345Z     }
2023-01-11T21:05:10.6898403Z }
2023-01-11T21:05:10.6898571Z ''')
2023-01-11T21:05:10.6898579Z 
2023-01-11T21:05:10.6898585Z 
2023-01-11T21:05:10.6898675Z async_compile.wait(globals())
2023-01-11T21:05:10.6898736Z del async_compile
2023-01-11T21:05:10.6898741Z 
2023-01-11T21:05:10.6898815Z def call(args):
2023-01-11T21:05:10.6898888Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.6898960Z     args.clear()
2023-01-11T21:05:10.6899206Z     buf0 = empty_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6899302Z     aten.mm.out(arg0_1, arg1_1, out=buf0)
2023-01-11T21:05:10.6899370Z     del arg0_1
2023-01-11T21:05:10.6899422Z     del arg1_1
2023-01-11T21:05:10.6899509Z     buf1 = buf0; del buf0  # reuse
2023-01-11T21:05:10.6899609Z     kernel_cpp_0(c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6899679Z     return (buf1, )
2023-01-11T21:05:10.6899684Z 
2023-01-11T21:05:10.6899688Z 
2023-01-11T21:05:10.6899760Z if __name__ == "__main__":
2023-01-11T21:05:10.6899872Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6899992Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6900186Z     arg0_1 = rand_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6900388Z     arg1_1 = rand_strided((1024, 1024), (1024, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6900499Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.6900507Z 
2023-01-11T21:05:10.6900574Z ok (2.953s)
2023-01-11T21:05:10.6901007Z   test_triu_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6901133Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6901387Z [2023-01-11 21:01:07,021] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 446
2023-01-11T21:05:10.6901649Z [2023-01-11 21:01:09,697] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 446
2023-01-11T21:05:10.6901656Z 
2023-01-11T21:05:10.6901749Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6901817Z import torch
2023-01-11T21:05:10.6901872Z import random
2023-01-11T21:05:10.6901987Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6902106Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6902111Z 
2023-01-11T21:05:10.6902187Z aten = torch.ops.aten
2023-01-11T21:05:10.6902319Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6902408Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6902413Z 
2023-01-11T21:05:10.6902417Z 
2023-01-11T21:05:10.6902548Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6902749Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6902855Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6902952Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6903077Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6903170Z                        float* __restrict__ out_ptr2)
2023-01-11T21:05:10.6903230Z {
2023-01-11T21:05:10.6903326Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6903387Z     {
2023-01-11T21:05:10.6903464Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.6903546Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.6903606Z         {
2023-01-11T21:05:10.6903691Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.6903753Z             {
2023-01-11T21:05:10.6903832Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6903921Z                 for(long i2=0; i2<10; i2+=1)
2023-01-11T21:05:10.6903972Z                 {
2023-01-11T21:05:10.6904037Z                     {
2023-01-11T21:05:10.6904102Z                         {
2023-01-11T21:05:10.6904211Z                             auto tmp3 = in_ptr0[i2 + (10*i1) + (100*i0)];
2023-01-11T21:05:10.6904396Z                             auto tmp0 = static_cast<int>((-1) + i2 + ((-1)*i1));
2023-01-11T21:05:10.6904501Z                             auto tmp1 = static_cast<int>(0);
2023-01-11T21:05:10.6904629Z                             auto tmp2 = tmp0 >= tmp1;
2023-01-11T21:05:10.6904724Z                             auto tmp4 = static_cast<float>(0);
2023-01-11T21:05:10.6904826Z                             auto tmp5 = tmp2 ? tmp3 : tmp4;
2023-01-11T21:05:10.6905000Z                             auto tmp6 = static_cast<int>(i2 + ((-1)*i1));
2023-01-11T21:05:10.6905095Z                             auto tmp7 = tmp6 >= tmp1;
2023-01-11T21:05:10.6905195Z                             auto tmp8 = tmp7 ? tmp3 : tmp4;
2023-01-11T21:05:10.6905377Z                             auto tmp9 = static_cast<int>((-2) + i2 + ((-1)*i1));
2023-01-11T21:05:10.6905473Z                             auto tmp10 = tmp9 >= tmp1;
2023-01-11T21:05:10.6905565Z                             auto tmp11 = tmp10 ? tmp3 : tmp4;
2023-01-11T21:05:10.6905669Z                             out_ptr0[i2 + (10*i1) + (100*i0)] = tmp5;
2023-01-11T21:05:10.6905768Z                             out_ptr1[i2 + (10*i1) + (100*i0)] = tmp8;
2023-01-11T21:05:10.6905872Z                             out_ptr2[i2 + (10*i1) + (100*i0)] = tmp11;
2023-01-11T21:05:10.6905938Z                         }
2023-01-11T21:05:10.6906003Z                     }
2023-01-11T21:05:10.6906065Z                 }
2023-01-11T21:05:10.6906127Z             }
2023-01-11T21:05:10.6906174Z         }
2023-01-11T21:05:10.6906235Z     }
2023-01-11T21:05:10.6906292Z }
2023-01-11T21:05:10.6906368Z ''')
2023-01-11T21:05:10.6906373Z 
2023-01-11T21:05:10.6906377Z 
2023-01-11T21:05:10.6906467Z async_compile.wait(globals())
2023-01-11T21:05:10.6906540Z del async_compile
2023-01-11T21:05:10.6906545Z 
2023-01-11T21:05:10.6906613Z def call(args):
2023-01-11T21:05:10.6906669Z     arg0_1, = args
2023-01-11T21:05:10.6906739Z     args.clear()
2023-01-11T21:05:10.6906947Z     buf0 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6907154Z     buf1 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6907358Z     buf2 = empty_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6907546Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()))
2023-01-11T21:05:10.6907612Z     del arg0_1
2023-01-11T21:05:10.6907681Z     return (buf0, buf1, buf2, )
2023-01-11T21:05:10.6907697Z 
2023-01-11T21:05:10.6907701Z 
2023-01-11T21:05:10.6907764Z if __name__ == "__main__":
2023-01-11T21:05:10.6907876Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6907998Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6908205Z     arg0_1 = rand_strided((2, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6908312Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6908347Z 
2023-01-11T21:05:10.6908413Z ok (2.763s)
2023-01-11T21:05:10.6908850Z   test_unbind_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6908975Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6909232Z [2023-01-11 21:01:09,811] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 447
2023-01-11T21:05:10.6909482Z [2023-01-11 21:01:09,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 447
2023-01-11T21:05:10.6909487Z 
2023-01-11T21:05:10.6909580Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6909647Z import torch
2023-01-11T21:05:10.6909714Z import random
2023-01-11T21:05:10.6909829Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6909950Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6909955Z 
2023-01-11T21:05:10.6910033Z aten = torch.ops.aten
2023-01-11T21:05:10.6910179Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6910272Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6910276Z 
2023-01-11T21:05:10.6910280Z 
2023-01-11T21:05:10.6910366Z async_compile.wait(globals())
2023-01-11T21:05:10.6910436Z del async_compile
2023-01-11T21:05:10.6910441Z 
2023-01-11T21:05:10.6910511Z def call(args):
2023-01-11T21:05:10.6910579Z     arg0_1, = args
2023-01-11T21:05:10.6910650Z     args.clear()
2023-01-11T21:05:10.6910926Z     return (as_strided(arg0_1, (4, 4), (4, 1)), as_strided(arg0_1, (4, 4), (4, 1), 16), as_strided(arg0_1, (4, 4), (4, 1), 32), as_strided(arg0_1, (4, 4), (4, 1), 48), as_strided(arg0_1, (4, 4), (16, 4)), as_strided(arg0_1, (4, 4), (16, 4), 1), as_strided(arg0_1, (4, 4), (16, 4), 2), as_strided(arg0_1, (4, 4), (16, 4), 3), )
2023-01-11T21:05:10.6910934Z 
2023-01-11T21:05:10.6910938Z 
2023-01-11T21:05:10.6911012Z if __name__ == "__main__":
2023-01-11T21:05:10.6911123Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6911233Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6911438Z     arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6911546Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6911550Z 
2023-01-11T21:05:10.6911615Z ok (0.121s)
2023-01-11T21:05:10.6912067Z   test_unroll_small_reduction_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6912196Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6912456Z [2023-01-11 21:01:09,918] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 448
2023-01-11T21:05:10.6912719Z [2023-01-11 21:01:12,624] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 448
2023-01-11T21:05:10.6913118Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6913239Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6913481Z [2023-01-11 21:01:12,707] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 449
2023-01-11T21:05:10.6913499Z 
2023-01-11T21:05:10.6913579Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6913677Z import torch
2023-01-11T21:05:10.6913745Z import random
2023-01-11T21:05:10.6913858Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6913979Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6913984Z 
2023-01-11T21:05:10.6914062Z aten = torch.ops.aten
2023-01-11T21:05:10.6914195Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6914273Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6914278Z 
2023-01-11T21:05:10.6914282Z 
2023-01-11T21:05:10.6914421Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6914623Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6914745Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6914844Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6914939Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.6915037Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6915130Z                        long* __restrict__ out_ptr3,
2023-01-11T21:05:10.6915210Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.6915329Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:05:10.6915421Z                        bool* __restrict__ out_ptr6,
2023-01-11T21:05:10.6915512Z                        long* __restrict__ out_ptr7,
2023-01-11T21:05:10.6915602Z                        long* __restrict__ out_ptr8,
2023-01-11T21:05:10.6915695Z                        float* __restrict__ out_ptr9,
2023-01-11T21:05:10.6915794Z                        float* __restrict__ out_ptr10)
2023-01-11T21:05:10.6915842Z {
2023-01-11T21:05:10.6915938Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6915999Z     {
2023-01-11T21:05:10.6916074Z         #pragma omp for 
2023-01-11T21:05:10.6916155Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6916217Z         {
2023-01-11T21:05:10.6916268Z             {
2023-01-11T21:05:10.6916332Z                 {
2023-01-11T21:05:10.6916427Z                     auto tmp0 = in_ptr0[3*i0];
2023-01-11T21:05:10.6916524Z                     auto tmp1 = in_ptr0[1 + (3*i0)];
2023-01-11T21:05:10.6916619Z                     auto tmp3 = in_ptr0[2 + (3*i0)];
2023-01-11T21:05:10.6916750Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::min(tmp0, tmp1);
2023-01-11T21:05:10.6916874Z                     auto tmp4 = (tmp3 != tmp3) ? tmp3 : std::min(tmp2, tmp3);
2023-01-11T21:05:10.6916974Z                     auto tmp5 = static_cast<long>(0);
2023-01-11T21:05:10.6917058Z                     auto tmp6 = static_cast<long>(1);
2023-01-11T21:05:10.6917153Z                     auto tmp7 = tmp1 < tmp0;
2023-01-11T21:05:10.6917247Z                     auto tmp8 = tmp7 ? tmp6 : tmp5;
2023-01-11T21:05:10.6917346Z                     auto tmp9 = static_cast<long>(2);
2023-01-11T21:05:10.6917441Z                     auto tmp10 = tmp3 < tmp2;
2023-01-11T21:05:10.6917538Z                     auto tmp11 = tmp10 ? tmp9 : tmp8;
2023-01-11T21:05:10.6917661Z                     auto tmp12 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:05:10.6917787Z                     auto tmp13 = (tmp3 != tmp3) ? tmp3 : std::max(tmp12, tmp3);
2023-01-11T21:05:10.6917865Z                     auto tmp14 = tmp1 > tmp0;
2023-01-11T21:05:10.6917960Z                     auto tmp15 = tmp14 ? tmp6 : tmp5;
2023-01-11T21:05:10.6918049Z                     auto tmp16 = tmp3 > tmp12;
2023-01-11T21:05:10.6918148Z                     auto tmp17 = tmp16 ? tmp9 : tmp15;
2023-01-11T21:05:10.6918238Z                     auto tmp18 = tmp0 + tmp1;
2023-01-11T21:05:10.6918327Z                     auto tmp19 = tmp18 + tmp3;
2023-01-11T21:05:10.6918429Z                     auto tmp20 = static_cast<float>(1);
2023-01-11T21:05:10.6918505Z                     auto tmp21 = tmp0 > tmp20;
2023-01-11T21:05:10.6918610Z                     auto tmp22 = static_cast<long>(tmp21);
2023-01-11T21:05:10.6918743Z                     auto tmp23 = static_cast<bool>(tmp22);
2023-01-11T21:05:10.6918832Z                     auto tmp24 = tmp1 > tmp20;
2023-01-11T21:05:10.6918936Z                     auto tmp25 = static_cast<long>(tmp24);
2023-01-11T21:05:10.6919041Z                     auto tmp26 = static_cast<bool>(tmp25);
2023-01-11T21:05:10.6919132Z                     auto tmp27 = tmp23 || tmp26;
2023-01-11T21:05:10.6919209Z                     auto tmp28 = tmp3 > tmp20;
2023-01-11T21:05:10.6919311Z                     auto tmp29 = static_cast<long>(tmp28);
2023-01-11T21:05:10.6919413Z                     auto tmp30 = static_cast<bool>(tmp29);
2023-01-11T21:05:10.6919503Z                     auto tmp31 = tmp27 || tmp30;
2023-01-11T21:05:10.6919606Z                     auto tmp32 = static_cast<float>(0);
2023-01-11T21:05:10.6919695Z                     auto tmp33 = tmp0 > tmp32;
2023-01-11T21:05:10.6919784Z                     auto tmp34 = tmp33 == 0;
2023-01-11T21:05:10.6919888Z                     auto tmp35 = static_cast<long>(tmp34);
2023-01-11T21:05:10.6919981Z                     auto tmp36 = static_cast<bool>(tmp35);
2023-01-11T21:05:10.6920069Z                     auto tmp37 = tmp1 > tmp32;
2023-01-11T21:05:10.6920199Z                     auto tmp38 = tmp37 == 0;
2023-01-11T21:05:10.6920302Z                     auto tmp39 = static_cast<long>(tmp38);
2023-01-11T21:05:10.6920401Z                     auto tmp40 = static_cast<bool>(tmp39);
2023-01-11T21:05:10.6920492Z                     auto tmp41 = tmp36 || tmp40;
2023-01-11T21:05:10.6920580Z                     auto tmp42 = tmp3 > tmp32;
2023-01-11T21:05:10.6920770Z                     auto tmp43 = tmp42 == 0;
2023-01-11T21:05:10.6920873Z                     auto tmp44 = static_cast<long>(tmp43);
2023-01-11T21:05:10.6920975Z                     auto tmp45 = static_cast<bool>(tmp44);
2023-01-11T21:05:10.6921067Z                     auto tmp46 = tmp41 || tmp45;
2023-01-11T21:05:10.6921156Z                     auto tmp47 = tmp46 == 0;
2023-01-11T21:05:10.6921245Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6921330Z                     out_ptr1[i0] = tmp11;
2023-01-11T21:05:10.6921401Z                     out_ptr2[i0] = tmp13;
2023-01-11T21:05:10.6921486Z                     out_ptr3[i0] = tmp17;
2023-01-11T21:05:10.6921568Z                     out_ptr4[i0] = tmp19;
2023-01-11T21:05:10.6921648Z                     out_ptr5[i0] = tmp31;
2023-01-11T21:05:10.6921729Z                     out_ptr6[i0] = tmp47;
2023-01-11T21:05:10.6921812Z                     out_ptr7[i0] = tmp11;
2023-01-11T21:05:10.6921894Z                     out_ptr8[i0] = tmp17;
2023-01-11T21:05:10.6921963Z                     out_ptr9[i0] = tmp4;
2023-01-11T21:05:10.6922049Z                     out_ptr10[i0] = tmp13;
2023-01-11T21:05:10.6922114Z                 }
2023-01-11T21:05:10.6922176Z             }
2023-01-11T21:05:10.6922237Z         }
2023-01-11T21:05:10.6922296Z     }
2023-01-11T21:05:10.6922355Z }
2023-01-11T21:05:10.6922425Z ''')
2023-01-11T21:05:10.6922431Z 
2023-01-11T21:05:10.6922437Z 
2023-01-11T21:05:10.6922527Z async_compile.wait(globals())
2023-01-11T21:05:10.6922597Z del async_compile
2023-01-11T21:05:10.6922602Z 
2023-01-11T21:05:10.6922670Z def call(args):
2023-01-11T21:05:10.6922738Z     arg0_1, = args
2023-01-11T21:05:10.6922811Z     args.clear()
2023-01-11T21:05:10.6923005Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6923178Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6923367Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6923550Z     buf3 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6923734Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6923913Z     buf5 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.6924091Z     buf6 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.6924325Z     buf7 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6924506Z     buf8 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6924680Z     buf9 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6924870Z     buf10 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6925246Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf6.data_ptr()), c_void_p(buf7.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()))
2023-01-11T21:05:10.6925317Z     del arg0_1
2023-01-11T21:05:10.6925446Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, )
2023-01-11T21:05:10.6925451Z 
2023-01-11T21:05:10.6925455Z 
2023-01-11T21:05:10.6925532Z if __name__ == "__main__":
2023-01-11T21:05:10.6925648Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6925770Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6926000Z     arg0_1 = rand_strided((8, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6926096Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6926114Z 
2023-01-11T21:05:10.6926118Z 
2023-01-11T21:05:10.6926198Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6926267Z import torch
2023-01-11T21:05:10.6926336Z import random
2023-01-11T21:05:10.6926449Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6926568Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6926573Z 
2023-01-11T21:05:10.6926648Z aten = torch.ops.aten
2023-01-11T21:05:10.6926780Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6926858Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6926876Z 
2023-01-11T21:05:10.6926880Z 
2023-01-11T21:05:10.6927001Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6927202Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6927318Z extern "C" void kernel(bool* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6927421Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6927518Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6927611Z                        long* __restrict__ out_ptr1,
2023-01-11T21:05:10.6927703Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6927784Z                        long* __restrict__ out_ptr3,
2023-01-11T21:05:10.6927876Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.6927968Z                        bool* __restrict__ out_ptr5,
2023-01-11T21:05:10.6928059Z                        long* __restrict__ out_ptr7,
2023-01-11T21:05:10.6928148Z                        long* __restrict__ out_ptr8,
2023-01-11T21:05:10.6928241Z                        float* __restrict__ out_ptr9,
2023-01-11T21:05:10.6928340Z                        float* __restrict__ out_ptr10)
2023-01-11T21:05:10.6928386Z {
2023-01-11T21:05:10.6928470Z     auto out_ptr6 = in_out_ptr0;
2023-01-11T21:05:10.6928570Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6928631Z     {
2023-01-11T21:05:10.6928706Z         #pragma omp for 
2023-01-11T21:05:10.6928786Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6928847Z         {
2023-01-11T21:05:10.6928896Z             {
2023-01-11T21:05:10.6928958Z                 {
2023-01-11T21:05:10.6929088Z                     float tmp1 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6929210Z                     struct IndexValue_11 {size_t index; float value;};
2023-01-11T21:05:10.6929347Z                     IndexValue_11 tmp2{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6929485Z                     #pragma omp declare reduction(argmin : struct IndexValue_11 :\
2023-01-11T21:05:10.6929667Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6929813Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6929945Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6930185Z                     float tmp3 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6930305Z                     struct IndexValue_12 {size_t index; float value;};
2023-01-11T21:05:10.6930534Z                     IndexValue_12 tmp4{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6930673Z                     #pragma omp declare reduction(argmax : struct IndexValue_12 :\
2023-01-11T21:05:10.6930821Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6930966Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6931206Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6931286Z                     float tmp5 = 0;
2023-01-11T21:05:10.6931379Z                     bool tmp10 = 0;
2023-01-11T21:05:10.6931457Z                     bool tmp16 = 0;
2023-01-11T21:05:10.6931576Z                     struct IndexValue_13 {size_t index; float value;};
2023-01-11T21:05:10.6931714Z                     IndexValue_13 tmp17{0, std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6931852Z                     #pragma omp declare reduction(argmin : struct IndexValue_13 :\
2023-01-11T21:05:10.6932002Z                         omp_out.value = omp_in.value > omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6932146Z                         omp_out.index = omp_in.value > omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6932289Z                     	initializer(omp_priv = {0, std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6932398Z                     struct IndexValue_14 {size_t index; float value;};
2023-01-11T21:05:10.6932632Z                     IndexValue_14 tmp18{0, -std::numeric_limits<float>::infinity()};
2023-01-11T21:05:10.6932771Z                     #pragma omp declare reduction(argmax : struct IndexValue_14 :\
2023-01-11T21:05:10.6932916Z                         omp_out.value = omp_in.value < omp_out.value ? omp_out.value : omp_in.value,\
2023-01-11T21:05:10.6933058Z                         omp_out.index = omp_in.value < omp_out.value ? omp_out.index : omp_in.index)\
2023-01-11T21:05:10.6933295Z                     	initializer(omp_priv = {0, -std::numeric_limits<float>::infinity()})
2023-01-11T21:05:10.6933418Z                     float tmp19 = std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6933625Z                     float tmp20 = -std::numeric_limits<float>::infinity();
2023-01-11T21:05:10.6933713Z                     for(long i1=0; i1<3; i1+=1)
2023-01-11T21:05:10.6933769Z                     {
2023-01-11T21:05:10.6933836Z                         {
2023-01-11T21:05:10.6933938Z                             auto tmp0 = in_ptr0[i1 + (3*i0)];
2023-01-11T21:05:10.6934046Z                             auto tmp6 = static_cast<float>(1);
2023-01-11T21:05:10.6934141Z                             auto tmp7 = tmp0 > tmp6;
2023-01-11T21:05:10.6934249Z                             auto tmp8 = static_cast<long>(tmp7);
2023-01-11T21:05:10.6934355Z                             auto tmp9 = static_cast<bool>(tmp8);
2023-01-11T21:05:10.6934450Z                             auto tmp11 = static_cast<float>(0);
2023-01-11T21:05:10.6934547Z                             auto tmp12 = tmp0 > tmp11;
2023-01-11T21:05:10.6934639Z                             auto tmp13 = tmp12 == 0;
2023-01-11T21:05:10.6934748Z                             auto tmp14 = static_cast<long>(tmp13);
2023-01-11T21:05:10.6934853Z                             auto tmp15 = static_cast<bool>(tmp14);
2023-01-11T21:05:10.6934987Z                             tmp1 = std::min(tmp1, tmp0);
2023-01-11T21:05:10.6935082Z                             if (tmp2.value > tmp0) {
2023-01-11T21:05:10.6935194Z                                 tmp2.index = i1; tmp2.value = tmp0;
2023-01-11T21:05:10.6935252Z                             }
2023-01-11T21:05:10.6935354Z                             tmp3 = std::max(tmp3, tmp0);
2023-01-11T21:05:10.6935448Z                             if (tmp4.value < tmp0) {
2023-01-11T21:05:10.6935557Z                                 tmp4.index = i1; tmp4.value = tmp0;
2023-01-11T21:05:10.6935625Z                             }
2023-01-11T21:05:10.6935706Z                             tmp5 += tmp0;
2023-01-11T21:05:10.6935801Z                             tmp10 = tmp10 || tmp9;
2023-01-11T21:05:10.6935879Z                             tmp16 = tmp16 || tmp15;
2023-01-11T21:05:10.6935973Z                             if (tmp17.value > tmp0) {
2023-01-11T21:05:10.6936084Z                                 tmp17.index = i1; tmp17.value = tmp0;
2023-01-11T21:05:10.6936155Z                             }
2023-01-11T21:05:10.6936250Z                             if (tmp18.value < tmp0) {
2023-01-11T21:05:10.6936387Z                                 tmp18.index = i1; tmp18.value = tmp0;
2023-01-11T21:05:10.6936456Z                             }
2023-01-11T21:05:10.6936561Z                             tmp19 = std::min(tmp19, tmp0);
2023-01-11T21:05:10.6936651Z                             tmp20 = std::max(tmp20, tmp0);
2023-01-11T21:05:10.6936717Z                         }
2023-01-11T21:05:10.6936781Z                     }
2023-01-11T21:05:10.6936865Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6936956Z                     out_ptr1[i0] = tmp2.index;
2023-01-11T21:05:10.6937037Z                     out_ptr2[i0] = tmp3;
2023-01-11T21:05:10.6937125Z                     out_ptr3[i0] = tmp4.index;
2023-01-11T21:05:10.6937194Z                     out_ptr4[i0] = tmp5;
2023-01-11T21:05:10.6937276Z                     out_ptr5[i0] = tmp10;
2023-01-11T21:05:10.6937359Z                     out_ptr6[i0] = tmp16;
2023-01-11T21:05:10.6937452Z                     out_ptr7[i0] = tmp17.index;
2023-01-11T21:05:10.6937543Z                     out_ptr8[i0] = tmp18.index;
2023-01-11T21:05:10.6937626Z                     out_ptr9[i0] = tmp19;
2023-01-11T21:05:10.6937710Z                     out_ptr10[i0] = tmp20;
2023-01-11T21:05:10.6937761Z                 }
2023-01-11T21:05:10.6937823Z             }
2023-01-11T21:05:10.6937887Z         }
2023-01-11T21:05:10.6937962Z         #pragma omp for 
2023-01-11T21:05:10.6938041Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6938103Z         {
2023-01-11T21:05:10.6938152Z             {
2023-01-11T21:05:10.6938214Z                 {
2023-01-11T21:05:10.6938304Z                     auto tmp0 = out_ptr6[i0];
2023-01-11T21:05:10.6938391Z                     auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.6938562Z                     in_out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.6938628Z                 }
2023-01-11T21:05:10.6938692Z             }
2023-01-11T21:05:10.6938739Z         }
2023-01-11T21:05:10.6938800Z     }
2023-01-11T21:05:10.6938860Z }
2023-01-11T21:05:10.6938942Z ''')
2023-01-11T21:05:10.6938948Z 
2023-01-11T21:05:10.6938953Z 
2023-01-11T21:05:10.6939045Z async_compile.wait(globals())
2023-01-11T21:05:10.6939116Z del async_compile
2023-01-11T21:05:10.6939121Z 
2023-01-11T21:05:10.6939188Z def call(args):
2023-01-11T21:05:10.6939242Z     arg0_1, = args
2023-01-11T21:05:10.6939313Z     args.clear()
2023-01-11T21:05:10.6939505Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6939694Z     buf1 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6939884Z     buf2 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6940067Z     buf3 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6940250Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6940468Z     buf5 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.6940635Z     buf6 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.6940814Z     buf8 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6940998Z     buf9 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.6941188Z     buf10 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6941375Z     buf11 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6941459Z     buf7 = buf6; del buf6  # reuse
2023-01-11T21:05:10.6941832Z     kernel_cpp_0(c_void_p(buf7.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(buf10.data_ptr()), c_void_p(buf11.data_ptr()))
2023-01-11T21:05:10.6941902Z     del arg0_1
2023-01-11T21:05:10.6942018Z     return (buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf8, buf9, buf10, buf11, )
2023-01-11T21:05:10.6942036Z 
2023-01-11T21:05:10.6942040Z 
2023-01-11T21:05:10.6942158Z if __name__ == "__main__":
2023-01-11T21:05:10.6942273Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6942395Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6942593Z     arg0_1 = rand_strided((8, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6942701Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6942969Z [2023-01-11 21:01:15,390] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 449
2023-01-11T21:05:10.6942974Z 
2023-01-11T21:05:10.6943041Z ok (5.572s)
2023-01-11T21:05:10.6943184Z   test_unspec_inputs_cpu (__main__.CpuTests) ... skip: requires cuda (0.001s)
2023-01-11T21:05:10.6943613Z   test_unsqueeze_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6943740Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6943998Z [2023-01-11 21:01:15,466] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 450
2023-01-11T21:05:10.6944259Z [2023-01-11 21:01:18,238] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 450
2023-01-11T21:05:10.6944264Z 
2023-01-11T21:05:10.6944356Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6944426Z import torch
2023-01-11T21:05:10.6944496Z import random
2023-01-11T21:05:10.6944612Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6944736Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6944741Z 
2023-01-11T21:05:10.6944805Z aten = torch.ops.aten
2023-01-11T21:05:10.6944938Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6945032Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6945037Z 
2023-01-11T21:05:10.6945042Z 
2023-01-11T21:05:10.6945174Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6945377Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6945496Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6945597Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6945697Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6945776Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.6945868Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.6945927Z {
2023-01-11T21:05:10.6946023Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6946112Z     {
2023-01-11T21:05:10.6946188Z         #pragma omp for 
2023-01-11T21:05:10.6946270Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.6946319Z         {
2023-01-11T21:05:10.6946455Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6946586Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6946670Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6946799Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6946881Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6946961Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:05:10.6947038Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6947128Z             tmp5.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6947217Z             tmp4.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.6947305Z             tmp5.store(out_ptr3 + 16*i0);
2023-01-11T21:05:10.6947371Z         }
2023-01-11T21:05:10.6947466Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6947548Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.6947596Z         {
2023-01-11T21:05:10.6947709Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6947808Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6947890Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6947987Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.6948069Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6948150Z             auto tmp5 = tmp0 + tmp3;
2023-01-11T21:05:10.6948216Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.6948291Z             out_ptr1[i0] = tmp5;
2023-01-11T21:05:10.6948367Z             out_ptr2[i0] = tmp4;
2023-01-11T21:05:10.6948441Z             out_ptr3[i0] = tmp5;
2023-01-11T21:05:10.6948502Z         }
2023-01-11T21:05:10.6948563Z     }
2023-01-11T21:05:10.6948622Z }
2023-01-11T21:05:10.6948686Z ''')
2023-01-11T21:05:10.6948693Z 
2023-01-11T21:05:10.6948697Z 
2023-01-11T21:05:10.6948786Z async_compile.wait(globals())
2023-01-11T21:05:10.6948857Z del async_compile
2023-01-11T21:05:10.6948862Z 
2023-01-11T21:05:10.6948930Z def call(args):
2023-01-11T21:05:10.6949002Z     arg0_1, = args
2023-01-11T21:05:10.6949074Z     args.clear()
2023-01-11T21:05:10.6949289Z     buf0 = empty_strided((2, 2, 2, 2, 1), (8, 4, 2, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6949498Z     buf1 = empty_strided((2, 2, 1, 2, 2), (8, 4, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6949697Z     buf2 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6949904Z     buf3 = empty_strided((2, 2, 2, 1, 2), (8, 4, 2, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6950117Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.6950189Z     del arg0_1
2023-01-11T21:05:10.6950275Z     return (buf0, buf1, buf2, buf3, )
2023-01-11T21:05:10.6950280Z 
2023-01-11T21:05:10.6950284Z 
2023-01-11T21:05:10.6950359Z if __name__ == "__main__":
2023-01-11T21:05:10.6950474Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6950596Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6950789Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6950895Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6950900Z 
2023-01-11T21:05:10.6950966Z ok (2.846s)
2023-01-11T21:05:10.6951413Z   test_unsqueeze_inplace_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6951568Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6951828Z [2023-01-11 21:01:18,317] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 451
2023-01-11T21:05:10.6952093Z [2023-01-11 21:01:20,962] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 451
2023-01-11T21:05:10.6952099Z 
2023-01-11T21:05:10.6952190Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6952257Z import torch
2023-01-11T21:05:10.6952313Z import random
2023-01-11T21:05:10.6952426Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6952545Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6952550Z 
2023-01-11T21:05:10.6952626Z aten = torch.ops.aten
2023-01-11T21:05:10.6952758Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6952846Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6952852Z 
2023-01-11T21:05:10.6952859Z 
2023-01-11T21:05:10.6952990Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6953193Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6953323Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6953422Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6953516Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6953575Z {
2023-01-11T21:05:10.6953670Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6953729Z     {
2023-01-11T21:05:10.6953804Z         #pragma omp for 
2023-01-11T21:05:10.6953871Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.6953933Z         {
2023-01-11T21:05:10.6954064Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.6954194Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.6954276Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6954408Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.6954489Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6954580Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.6954658Z             tmp4.store(out_ptr1 + 16*i0);
2023-01-11T21:05:10.6954719Z         }
2023-01-11T21:05:10.6954811Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.6954891Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.6954954Z         {
2023-01-11T21:05:10.6955036Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.6955131Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.6955199Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6955296Z             auto tmp3 = static_cast<float>(2);
2023-01-11T21:05:10.6955378Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.6955457Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.6955532Z             out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.6955593Z         }
2023-01-11T21:05:10.6955645Z     }
2023-01-11T21:05:10.6955703Z }
2023-01-11T21:05:10.6955780Z ''')
2023-01-11T21:05:10.6955785Z 
2023-01-11T21:05:10.6955789Z 
2023-01-11T21:05:10.6955878Z async_compile.wait(globals())
2023-01-11T21:05:10.6955949Z del async_compile
2023-01-11T21:05:10.6955954Z 
2023-01-11T21:05:10.6956023Z def call(args):
2023-01-11T21:05:10.6956090Z     arg0_1, = args
2023-01-11T21:05:10.6956160Z     args.clear()
2023-01-11T21:05:10.6956363Z     buf0 = empty_strided((2, 2, 1, 2, 2), (8, 4, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6956578Z     buf1 = empty_strided((1, 2, 2, 2, 2), (16, 8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6956741Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6956807Z     del arg0_1
2023-01-11T21:05:10.6956882Z     return (buf0, buf1, )
2023-01-11T21:05:10.6956887Z 
2023-01-11T21:05:10.6956892Z 
2023-01-11T21:05:10.6956965Z if __name__ == "__main__":
2023-01-11T21:05:10.6957110Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6957229Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6957428Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6957534Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6957539Z 
2023-01-11T21:05:10.6957604Z ok (2.721s)
2023-01-11T21:05:10.6958052Z   test_upsample_bicubic2d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6958177Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6958431Z [2023-01-11 21:01:23,817] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 452
2023-01-11T21:05:10.6958439Z 
2023-01-11T21:05:10.6958530Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6958598Z import torch
2023-01-11T21:05:10.6958666Z import random
2023-01-11T21:05:10.6958810Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6958930Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6958935Z 
2023-01-11T21:05:10.6959011Z aten = torch.ops.aten
2023-01-11T21:05:10.6959144Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6959233Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6959238Z 
2023-01-11T21:05:10.6959242Z 
2023-01-11T21:05:10.6959373Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6959577Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6959696Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6959782Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.6959878Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.6959936Z {
2023-01-11T21:05:10.6960032Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6960093Z     {
2023-01-11T21:05:10.6960171Z         #pragma omp for 
2023-01-11T21:05:10.6960251Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.6960300Z         {
2023-01-11T21:05:10.6960377Z             #pragma GCC ivdep
2023-01-11T21:05:10.6960462Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:05:10.6960525Z             {
2023-01-11T21:05:10.6960715Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6960807Z                 for(long i2=0; i2<128; i2+=1)
2023-01-11T21:05:10.6960873Z                 {
2023-01-11T21:05:10.6960927Z                     {
2023-01-11T21:05:10.6960994Z                         {
2023-01-11T21:05:10.6961108Z                             auto tmp0 = static_cast<float>(i2);
2023-01-11T21:05:10.6961212Z                             auto tmp1 = 0.2440944881889764 * tmp0;
2023-01-11T21:05:10.6961320Z                             auto tmp2 = std::floor(tmp1);
2023-01-11T21:05:10.6961466Z                             auto tmp3 = tmp1 - tmp2;
2023-01-11T21:05:10.6961576Z                             auto tmp4 = static_cast<float>(i1);
2023-01-11T21:05:10.6961664Z                             auto tmp5 = 0.49606299212598426 * tmp4;
2023-01-11T21:05:10.6961765Z                             auto tmp6 = std::floor(tmp5);
2023-01-11T21:05:10.6961908Z                             auto tmp7 = tmp5 - tmp6;
2023-01-11T21:05:10.6962015Z                             auto tmp8 = static_cast<int>(tmp6);
2023-01-11T21:05:10.6962124Z                             auto tmp9 = static_cast<int>(tmp2);
2023-01-11T21:05:10.6962265Z                             auto tmp10 = tmp8 + -1;
2023-01-11T21:05:10.6962356Z                             auto tmp11 = tmp8 + 0;
2023-01-11T21:05:10.6962432Z                             auto tmp12 = tmp8 + 1;
2023-01-11T21:05:10.6962519Z                             auto tmp13 = tmp8 + 2;
2023-01-11T21:05:10.6962711Z                             auto tmp14 = tmp9 + -1;
2023-01-11T21:05:10.6962798Z                             auto tmp15 = tmp9 + 0;
2023-01-11T21:05:10.6962890Z                             auto tmp16 = tmp9 + 1;
2023-01-11T21:05:10.6962977Z                             auto tmp17 = tmp9 + 2;
2023-01-11T21:05:10.6963107Z                             auto tmp18 = (tmp10 != tmp10) ? tmp10 : std::min(63, tmp10);
2023-01-11T21:05:10.6963235Z                             auto tmp19 = (tmp18 != tmp18) ? tmp18 : std::max(0, tmp18);
2023-01-11T21:05:10.6963348Z                             auto tmp20 = (tmp14 != tmp14) ? tmp14 : std::min(31, tmp14);
2023-01-11T21:05:10.6963474Z                             auto tmp21 = (tmp20 != tmp20) ? tmp20 : std::max(0, tmp20);
2023-01-11T21:05:10.6963592Z                             auto tmp22 = in_ptr0[tmp21 + (32*tmp19) + (2048*i0)];
2023-01-11T21:05:10.6963716Z                             auto tmp23 = (tmp15 != tmp15) ? tmp15 : std::min(31, tmp15);
2023-01-11T21:05:10.6963843Z                             auto tmp24 = (tmp23 != tmp23) ? tmp23 : std::max(0, tmp23);
2023-01-11T21:05:10.6963993Z                             auto tmp25 = in_ptr0[tmp24 + (32*tmp19) + (2048*i0)];
2023-01-11T21:05:10.6964120Z                             auto tmp26 = (tmp16 != tmp16) ? tmp16 : std::min(31, tmp16);
2023-01-11T21:05:10.6964242Z                             auto tmp27 = (tmp26 != tmp26) ? tmp26 : std::max(0, tmp26);
2023-01-11T21:05:10.6964359Z                             auto tmp28 = in_ptr0[tmp27 + (32*tmp19) + (2048*i0)];
2023-01-11T21:05:10.6964482Z                             auto tmp29 = (tmp17 != tmp17) ? tmp17 : std::min(31, tmp17);
2023-01-11T21:05:10.6964592Z                             auto tmp30 = (tmp29 != tmp29) ? tmp29 : std::max(0, tmp29);
2023-01-11T21:05:10.6964708Z                             auto tmp31 = in_ptr0[tmp30 + (32*tmp19) + (2048*i0)];
2023-01-11T21:05:10.6964802Z                             auto tmp32 = tmp3 + 1.0;
2023-01-11T21:05:10.6964951Z                             auto tmp33 = -0.75 * tmp32;
2023-01-11T21:05:10.6965101Z                             auto tmp34 = tmp33 - -3.75;
2023-01-11T21:05:10.6965197Z                             auto tmp35 = tmp34 * tmp32;
2023-01-11T21:05:10.6965345Z                             auto tmp36 = tmp35 + -6.0;
2023-01-11T21:05:10.6965427Z                             auto tmp37 = tmp36 * tmp32;
2023-01-11T21:05:10.6965571Z                             auto tmp38 = tmp37 - -3.0;
2023-01-11T21:05:10.6965664Z                             auto tmp39 = 1.25 * tmp3;
2023-01-11T21:05:10.6965807Z                             auto tmp40 = tmp39 - 2.25;
2023-01-11T21:05:10.6965899Z                             auto tmp41 = tmp40 * tmp3;
2023-01-11T21:05:10.6965992Z                             auto tmp42 = tmp41 * tmp3;
2023-01-11T21:05:10.6966086Z                             auto tmp43 = tmp42 + 1.0;
2023-01-11T21:05:10.6966226Z                             auto tmp44 = 1.0 - tmp3;
2023-01-11T21:05:10.6966307Z                             auto tmp45 = 1.25 * tmp44;
2023-01-11T21:05:10.6966450Z                             auto tmp46 = tmp45 - 2.25;
2023-01-11T21:05:10.6966547Z                             auto tmp47 = tmp46 * tmp44;
2023-01-11T21:05:10.6966639Z                             auto tmp48 = tmp47 * tmp44;
2023-01-11T21:05:10.6966732Z                             auto tmp49 = tmp48 + 1.0;
2023-01-11T21:05:10.6966820Z                             auto tmp50 = tmp44 + 1.0;
2023-01-11T21:05:10.6966960Z                             auto tmp51 = -0.75 * tmp50;
2023-01-11T21:05:10.6967091Z                             auto tmp52 = tmp51 - -3.75;
2023-01-11T21:05:10.6967184Z                             auto tmp53 = tmp52 * tmp50;
2023-01-11T21:05:10.6967327Z                             auto tmp54 = tmp53 + -6.0;
2023-01-11T21:05:10.6967419Z                             auto tmp55 = tmp54 * tmp50;
2023-01-11T21:05:10.6967562Z                             auto tmp56 = tmp55 - -3.0;
2023-01-11T21:05:10.6967684Z                             auto tmp57 = tmp22 * tmp38;
2023-01-11T21:05:10.6967776Z                             auto tmp58 = tmp25 * tmp43;
2023-01-11T21:05:10.6967866Z                             auto tmp59 = tmp28 * tmp49;
2023-01-11T21:05:10.6967949Z                             auto tmp60 = tmp31 * tmp56;
2023-01-11T21:05:10.6968040Z                             auto tmp61 = tmp59 + tmp60;
2023-01-11T21:05:10.6968132Z                             auto tmp62 = tmp58 + tmp61;
2023-01-11T21:05:10.6968223Z                             auto tmp63 = tmp57 + tmp62;
2023-01-11T21:05:10.6968349Z                             auto tmp64 = (tmp11 != tmp11) ? tmp11 : std::min(63, tmp11);
2023-01-11T21:05:10.6968473Z                             auto tmp65 = (tmp64 != tmp64) ? tmp64 : std::max(0, tmp64);
2023-01-11T21:05:10.6968590Z                             auto tmp66 = in_ptr0[tmp21 + (32*tmp65) + (2048*i0)];
2023-01-11T21:05:10.6968703Z                             auto tmp67 = in_ptr0[tmp24 + (32*tmp65) + (2048*i0)];
2023-01-11T21:05:10.6968807Z                             auto tmp68 = in_ptr0[tmp27 + (32*tmp65) + (2048*i0)];
2023-01-11T21:05:10.6968918Z                             auto tmp69 = in_ptr0[tmp30 + (32*tmp65) + (2048*i0)];
2023-01-11T21:05:10.6969040Z                             auto tmp70 = tmp66 * tmp38;
2023-01-11T21:05:10.6969134Z                             auto tmp71 = tmp67 * tmp43;
2023-01-11T21:05:10.6969227Z                             auto tmp72 = tmp68 * tmp49;
2023-01-11T21:05:10.6969319Z                             auto tmp73 = tmp69 * tmp56;
2023-01-11T21:05:10.6969410Z                             auto tmp74 = tmp72 + tmp73;
2023-01-11T21:05:10.6969489Z                             auto tmp75 = tmp71 + tmp74;
2023-01-11T21:05:10.6969580Z                             auto tmp76 = tmp70 + tmp75;
2023-01-11T21:05:10.6969704Z                             auto tmp77 = (tmp12 != tmp12) ? tmp12 : std::min(63, tmp12);
2023-01-11T21:05:10.6969830Z                             auto tmp78 = (tmp77 != tmp77) ? tmp77 : std::max(0, tmp77);
2023-01-11T21:05:10.6969945Z                             auto tmp79 = in_ptr0[tmp21 + (32*tmp78) + (2048*i0)];
2023-01-11T21:05:10.6970059Z                             auto tmp80 = in_ptr0[tmp24 + (32*tmp78) + (2048*i0)];
2023-01-11T21:05:10.6970173Z                             auto tmp81 = in_ptr0[tmp27 + (32*tmp78) + (2048*i0)];
2023-01-11T21:05:10.6970285Z                             auto tmp82 = in_ptr0[tmp30 + (32*tmp78) + (2048*i0)];
2023-01-11T21:05:10.6970377Z                             auto tmp83 = tmp79 * tmp38;
2023-01-11T21:05:10.6970458Z                             auto tmp84 = tmp80 * tmp43;
2023-01-11T21:05:10.6970550Z                             auto tmp85 = tmp81 * tmp49;
2023-01-11T21:05:10.6970642Z                             auto tmp86 = tmp82 * tmp56;
2023-01-11T21:05:10.6970732Z                             auto tmp87 = tmp85 + tmp86;
2023-01-11T21:05:10.6970823Z                             auto tmp88 = tmp84 + tmp87;
2023-01-11T21:05:10.6970915Z                             auto tmp89 = tmp83 + tmp88;
2023-01-11T21:05:10.6971043Z                             auto tmp90 = (tmp13 != tmp13) ? tmp13 : std::min(63, tmp13);
2023-01-11T21:05:10.6971157Z                             auto tmp91 = (tmp90 != tmp90) ? tmp90 : std::max(0, tmp90);
2023-01-11T21:05:10.6971272Z                             auto tmp92 = in_ptr0[tmp21 + (32*tmp91) + (2048*i0)];
2023-01-11T21:05:10.6971384Z                             auto tmp93 = in_ptr0[tmp24 + (32*tmp91) + (2048*i0)];
2023-01-11T21:05:10.6971497Z                             auto tmp94 = in_ptr0[tmp27 + (32*tmp91) + (2048*i0)];
2023-01-11T21:05:10.6971608Z                             auto tmp95 = in_ptr0[tmp30 + (32*tmp91) + (2048*i0)];
2023-01-11T21:05:10.6971701Z                             auto tmp96 = tmp92 * tmp38;
2023-01-11T21:05:10.6971793Z                             auto tmp97 = tmp93 * tmp43;
2023-01-11T21:05:10.6971885Z                             auto tmp98 = tmp94 * tmp49;
2023-01-11T21:05:10.6971993Z                             auto tmp99 = tmp95 * tmp56;
2023-01-11T21:05:10.6972092Z                             auto tmp100 = tmp98 + tmp99;
2023-01-11T21:05:10.6972188Z                             auto tmp101 = tmp97 + tmp100;
2023-01-11T21:05:10.6972284Z                             auto tmp102 = tmp96 + tmp101;
2023-01-11T21:05:10.6972378Z                             auto tmp103 = tmp7 + 1.0;
2023-01-11T21:05:10.6972529Z                             auto tmp104 = -0.75 * tmp103;
2023-01-11T21:05:10.6972679Z                             auto tmp105 = tmp104 - -3.75;
2023-01-11T21:05:10.6972779Z                             auto tmp106 = tmp105 * tmp103;
2023-01-11T21:05:10.6972915Z                             auto tmp107 = tmp106 + -6.0;
2023-01-11T21:05:10.6973011Z                             auto tmp108 = tmp107 * tmp103;
2023-01-11T21:05:10.6973158Z                             auto tmp109 = tmp108 - -3.0;
2023-01-11T21:05:10.6973251Z                             auto tmp110 = 1.25 * tmp7;
2023-01-11T21:05:10.6973402Z                             auto tmp111 = tmp110 - 2.25;
2023-01-11T21:05:10.6973495Z                             auto tmp112 = tmp111 * tmp7;
2023-01-11T21:05:10.6973589Z                             auto tmp113 = tmp112 * tmp7;
2023-01-11T21:05:10.6973699Z                             auto tmp114 = tmp113 + 1.0;
2023-01-11T21:05:10.6973844Z                             auto tmp115 = 1.0 - tmp7;
2023-01-11T21:05:10.6973935Z                             auto tmp116 = 1.25 * tmp115;
2023-01-11T21:05:10.6974080Z                             auto tmp117 = tmp116 - 2.25;
2023-01-11T21:05:10.6974178Z                             auto tmp118 = tmp117 * tmp115;
2023-01-11T21:05:10.6974274Z                             auto tmp119 = tmp118 * tmp115;
2023-01-11T21:05:10.6974367Z                             auto tmp120 = tmp119 + 1.0;
2023-01-11T21:05:10.6974460Z                             auto tmp121 = tmp115 + 1.0;
2023-01-11T21:05:10.6974596Z                             auto tmp122 = -0.75 * tmp121;
2023-01-11T21:05:10.6974746Z                             auto tmp123 = tmp122 - -3.75;
2023-01-11T21:05:10.6974841Z                             auto tmp124 = tmp123 * tmp121;
2023-01-11T21:05:10.6974990Z                             auto tmp125 = tmp124 + -6.0;
2023-01-11T21:05:10.6975086Z                             auto tmp126 = tmp125 * tmp121;
2023-01-11T21:05:10.6975234Z                             auto tmp127 = tmp126 - -3.0;
2023-01-11T21:05:10.6975329Z                             auto tmp128 = tmp63 * tmp109;
2023-01-11T21:05:10.6975412Z                             auto tmp129 = tmp76 * tmp114;
2023-01-11T21:05:10.6975506Z                             auto tmp130 = tmp89 * tmp120;
2023-01-11T21:05:10.6975602Z                             auto tmp131 = tmp102 * tmp127;
2023-01-11T21:05:10.6975697Z                             auto tmp132 = tmp130 + tmp131;
2023-01-11T21:05:10.6975794Z                             auto tmp133 = tmp129 + tmp132;
2023-01-11T21:05:10.6975888Z                             auto tmp134 = tmp128 + tmp133;
2023-01-11T21:05:10.6975997Z                             out_ptr0[i2 + (128*i1) + (16384*i0)] = tmp134;
2023-01-11T21:05:10.6976064Z                         }
2023-01-11T21:05:10.6976116Z                     }
2023-01-11T21:05:10.6976181Z                 }
2023-01-11T21:05:10.6976242Z             }
2023-01-11T21:05:10.6976304Z         }
2023-01-11T21:05:10.6976381Z         #pragma omp for 
2023-01-11T21:05:10.6976461Z         for(long i0=0; i0<12; i0+=1)
2023-01-11T21:05:10.6976508Z         {
2023-01-11T21:05:10.6976589Z             #pragma GCC ivdep
2023-01-11T21:05:10.6976674Z             for(long i1=0; i1<128; i1+=1)
2023-01-11T21:05:10.6976738Z             {
2023-01-11T21:05:10.6976819Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6976909Z                 for(long i2=0; i2<256; i2+=1)
2023-01-11T21:05:10.6976975Z                 {
2023-01-11T21:05:10.6977026Z                     {
2023-01-11T21:05:10.6977092Z                         {
2023-01-11T21:05:10.6977202Z                             auto tmp0 = static_cast<float>(i2);
2023-01-11T21:05:10.6977325Z                             auto tmp1 = tmp0 + 0.5;
2023-01-11T21:05:10.6977416Z                             auto tmp2 = 0.125 * tmp1;
2023-01-11T21:05:10.6977560Z                             auto tmp3 = tmp2 - 0.5;
2023-01-11T21:05:10.6977664Z                             auto tmp4 = std::floor(tmp3);
2023-01-11T21:05:10.6977795Z                             auto tmp5 = tmp3 - tmp4;
2023-01-11T21:05:10.6977902Z                             auto tmp6 = static_cast<float>(i1);
2023-01-11T21:05:10.6977993Z                             auto tmp7 = tmp6 + 0.5;
2023-01-11T21:05:10.6978082Z                             auto tmp8 = 0.5 * tmp7;
2023-01-11T21:05:10.6978218Z                             auto tmp9 = tmp8 - 0.5;
2023-01-11T21:05:10.6978322Z                             auto tmp10 = std::floor(tmp9);
2023-01-11T21:05:10.6978559Z                             auto tmp11 = tmp9 - tmp10;
2023-01-11T21:05:10.6978672Z                             auto tmp12 = static_cast<int>(tmp10);
2023-01-11T21:05:10.6978770Z                             auto tmp13 = static_cast<int>(tmp4);
2023-01-11T21:05:10.6978916Z                             auto tmp14 = tmp12 + -1;
2023-01-11T21:05:10.6979043Z                             auto tmp15 = tmp12 + 0;
2023-01-11T21:05:10.6979136Z                             auto tmp16 = tmp12 + 1;
2023-01-11T21:05:10.6979224Z                             auto tmp17 = tmp12 + 2;
2023-01-11T21:05:10.6979365Z                             auto tmp18 = tmp13 + -1;
2023-01-11T21:05:10.6979454Z                             auto tmp19 = tmp13 + 0;
2023-01-11T21:05:10.6979528Z                             auto tmp20 = tmp13 + 1;
2023-01-11T21:05:10.6979619Z                             auto tmp21 = tmp13 + 2;
2023-01-11T21:05:10.6979749Z                             auto tmp22 = (tmp14 != tmp14) ? tmp14 : std::min(63, tmp14);
2023-01-11T21:05:10.6979874Z                             auto tmp23 = (tmp22 != tmp22) ? tmp22 : std::max(0, tmp22);
2023-01-11T21:05:10.6980003Z                             auto tmp24 = (tmp18 != tmp18) ? tmp18 : std::min(31, tmp18);
2023-01-11T21:05:10.6980126Z                             auto tmp25 = (tmp24 != tmp24) ? tmp24 : std::max(0, tmp24);
2023-01-11T21:05:10.6980246Z                             auto tmp26 = in_ptr0[tmp25 + (32*tmp23) + (2048*i0)];
2023-01-11T21:05:10.6980370Z                             auto tmp27 = (tmp19 != tmp19) ? tmp19 : std::min(31, tmp19);
2023-01-11T21:05:10.6980494Z                             auto tmp28 = (tmp27 != tmp27) ? tmp27 : std::max(0, tmp27);
2023-01-11T21:05:10.6980595Z                             auto tmp29 = in_ptr0[tmp28 + (32*tmp23) + (2048*i0)];
2023-01-11T21:05:10.6980718Z                             auto tmp30 = (tmp20 != tmp20) ? tmp20 : std::min(31, tmp20);
2023-01-11T21:05:10.6980840Z                             auto tmp31 = (tmp30 != tmp30) ? tmp30 : std::max(0, tmp30);
2023-01-11T21:05:10.6980957Z                             auto tmp32 = in_ptr0[tmp31 + (32*tmp23) + (2048*i0)];
2023-01-11T21:05:10.6981081Z                             auto tmp33 = (tmp21 != tmp21) ? tmp21 : std::min(31, tmp21);
2023-01-11T21:05:10.6981206Z                             auto tmp34 = (tmp33 != tmp33) ? tmp33 : std::max(0, tmp33);
2023-01-11T21:05:10.6981323Z                             auto tmp35 = in_ptr0[tmp34 + (32*tmp23) + (2048*i0)];
2023-01-11T21:05:10.6981420Z                             auto tmp36 = tmp5 + 1.0;
2023-01-11T21:05:10.6981566Z                             auto tmp37 = -0.75 * tmp36;
2023-01-11T21:05:10.6981699Z                             auto tmp38 = tmp37 - -3.75;
2023-01-11T21:05:10.6981795Z                             auto tmp39 = tmp38 * tmp36;
2023-01-11T21:05:10.6981940Z                             auto tmp40 = tmp39 + -6.0;
2023-01-11T21:05:10.6982033Z                             auto tmp41 = tmp40 * tmp36;
2023-01-11T21:05:10.6982178Z                             auto tmp42 = tmp41 - -3.0;
2023-01-11T21:05:10.6982269Z                             auto tmp43 = 1.25 * tmp5;
2023-01-11T21:05:10.6982444Z                             auto tmp44 = tmp43 - 2.25;
2023-01-11T21:05:10.6982525Z                             auto tmp45 = tmp44 * tmp5;
2023-01-11T21:05:10.6982620Z                             auto tmp46 = tmp45 * tmp5;
2023-01-11T21:05:10.6982713Z                             auto tmp47 = tmp46 + 1.0;
2023-01-11T21:05:10.6982855Z                             auto tmp48 = 1.0 - tmp5;
2023-01-11T21:05:10.6982946Z                             auto tmp49 = 1.25 * tmp48;
2023-01-11T21:05:10.6983089Z                             auto tmp50 = tmp49 - 2.25;
2023-01-11T21:05:10.6983182Z                             auto tmp51 = tmp50 * tmp48;
2023-01-11T21:05:10.6983263Z                             auto tmp52 = tmp51 * tmp48;
2023-01-11T21:05:10.6983356Z                             auto tmp53 = tmp52 + 1.0;
2023-01-11T21:05:10.6983446Z                             auto tmp54 = tmp48 + 1.0;
2023-01-11T21:05:10.6983587Z                             auto tmp55 = -0.75 * tmp54;
2023-01-11T21:05:10.6983735Z                             auto tmp56 = tmp55 - -3.75;
2023-01-11T21:05:10.6983826Z                             auto tmp57 = tmp56 * tmp54;
2023-01-11T21:05:10.6983970Z                             auto tmp58 = tmp57 + -6.0;
2023-01-11T21:05:10.6984086Z                             auto tmp59 = tmp58 * tmp54;
2023-01-11T21:05:10.6984219Z                             auto tmp60 = tmp59 - -3.0;
2023-01-11T21:05:10.6984311Z                             auto tmp61 = tmp26 * tmp42;
2023-01-11T21:05:10.6984403Z                             auto tmp62 = tmp29 * tmp47;
2023-01-11T21:05:10.6984492Z                             auto tmp63 = tmp32 * tmp53;
2023-01-11T21:05:10.6984584Z                             auto tmp64 = tmp35 * tmp60;
2023-01-11T21:05:10.6984674Z                             auto tmp65 = tmp63 + tmp64;
2023-01-11T21:05:10.6984765Z                             auto tmp66 = tmp62 + tmp65;
2023-01-11T21:05:10.6984843Z                             auto tmp67 = tmp61 + tmp66;
2023-01-11T21:05:10.6984972Z                             auto tmp68 = (tmp15 != tmp15) ? tmp15 : std::min(63, tmp15);
2023-01-11T21:05:10.6985094Z                             auto tmp69 = (tmp68 != tmp68) ? tmp68 : std::max(0, tmp68);
2023-01-11T21:05:10.6985212Z                             auto tmp70 = in_ptr0[tmp25 + (32*tmp69) + (2048*i0)];
2023-01-11T21:05:10.6985329Z                             auto tmp71 = in_ptr0[tmp28 + (32*tmp69) + (2048*i0)];
2023-01-11T21:05:10.6985443Z                             auto tmp72 = in_ptr0[tmp31 + (32*tmp69) + (2048*i0)];
2023-01-11T21:05:10.6985555Z                             auto tmp73 = in_ptr0[tmp34 + (32*tmp69) + (2048*i0)];
2023-01-11T21:05:10.6985650Z                             auto tmp74 = tmp70 * tmp42;
2023-01-11T21:05:10.6985730Z                             auto tmp75 = tmp71 * tmp47;
2023-01-11T21:05:10.6985822Z                             auto tmp76 = tmp72 * tmp53;
2023-01-11T21:05:10.6985916Z                             auto tmp77 = tmp73 * tmp60;
2023-01-11T21:05:10.6986010Z                             auto tmp78 = tmp76 + tmp77;
2023-01-11T21:05:10.6986099Z                             auto tmp79 = tmp75 + tmp78;
2023-01-11T21:05:10.6986189Z                             auto tmp80 = tmp74 + tmp79;
2023-01-11T21:05:10.6986360Z                             auto tmp81 = (tmp16 != tmp16) ? tmp16 : std::min(63, tmp16);
2023-01-11T21:05:10.6986540Z                             auto tmp82 = (tmp81 != tmp81) ? tmp81 : std::max(0, tmp81);
2023-01-11T21:05:10.6986657Z                             auto tmp83 = in_ptr0[tmp25 + (32*tmp82) + (2048*i0)];
2023-01-11T21:05:10.6986775Z                             auto tmp84 = in_ptr0[tmp28 + (32*tmp82) + (2048*i0)];
2023-01-11T21:05:10.6986887Z                             auto tmp85 = in_ptr0[tmp31 + (32*tmp82) + (2048*i0)];
2023-01-11T21:05:10.6987000Z                             auto tmp86 = in_ptr0[tmp34 + (32*tmp82) + (2048*i0)];
2023-01-11T21:05:10.6987091Z                             auto tmp87 = tmp83 * tmp42;
2023-01-11T21:05:10.6987185Z                             auto tmp88 = tmp84 * tmp47;
2023-01-11T21:05:10.6987314Z                             auto tmp89 = tmp85 * tmp53;
2023-01-11T21:05:10.6987407Z                             auto tmp90 = tmp86 * tmp60;
2023-01-11T21:05:10.6987487Z                             auto tmp91 = tmp89 + tmp90;
2023-01-11T21:05:10.6987579Z                             auto tmp92 = tmp88 + tmp91;
2023-01-11T21:05:10.6987670Z                             auto tmp93 = tmp87 + tmp92;
2023-01-11T21:05:10.6987799Z                             auto tmp94 = (tmp17 != tmp17) ? tmp17 : std::min(63, tmp17);
2023-01-11T21:05:10.6987923Z                             auto tmp95 = (tmp94 != tmp94) ? tmp94 : std::max(0, tmp94);
2023-01-11T21:05:10.6988038Z                             auto tmp96 = in_ptr0[tmp25 + (32*tmp95) + (2048*i0)];
2023-01-11T21:05:10.6988150Z                             auto tmp97 = in_ptr0[tmp28 + (32*tmp95) + (2048*i0)];
2023-01-11T21:05:10.6988263Z                             auto tmp98 = in_ptr0[tmp31 + (32*tmp95) + (2048*i0)];
2023-01-11T21:05:10.6988365Z                             auto tmp99 = in_ptr0[tmp34 + (32*tmp95) + (2048*i0)];
2023-01-11T21:05:10.6988463Z                             auto tmp100 = tmp96 * tmp42;
2023-01-11T21:05:10.6988588Z                             auto tmp101 = tmp97 * tmp47;
2023-01-11T21:05:10.6988686Z                             auto tmp102 = tmp98 * tmp53;
2023-01-11T21:05:10.6988780Z                             auto tmp103 = tmp99 * tmp60;
2023-01-11T21:05:10.6988878Z                             auto tmp104 = tmp102 + tmp103;
2023-01-11T21:05:10.6988975Z                             auto tmp105 = tmp101 + tmp104;
2023-01-11T21:05:10.6989071Z                             auto tmp106 = tmp100 + tmp105;
2023-01-11T21:05:10.6989153Z                             auto tmp107 = tmp11 + 1.0;
2023-01-11T21:05:10.6989309Z                             auto tmp108 = -0.75 * tmp107;
2023-01-11T21:05:10.6989460Z                             auto tmp109 = tmp108 - -3.75;
2023-01-11T21:05:10.6989561Z                             auto tmp110 = tmp109 * tmp107;
2023-01-11T21:05:10.6989707Z                             auto tmp111 = tmp110 + -6.0;
2023-01-11T21:05:10.6989802Z                             auto tmp112 = tmp111 * tmp107;
2023-01-11T21:05:10.6989951Z                             auto tmp113 = tmp112 - -3.0;
2023-01-11T21:05:10.6990031Z                             auto tmp114 = 1.25 * tmp11;
2023-01-11T21:05:10.6990177Z                             auto tmp115 = tmp114 - 2.25;
2023-01-11T21:05:10.6990274Z                             auto tmp116 = tmp115 * tmp11;
2023-01-11T21:05:10.6990371Z                             auto tmp117 = tmp116 * tmp11;
2023-01-11T21:05:10.6990465Z                             auto tmp118 = tmp117 + 1.0;
2023-01-11T21:05:10.6990610Z                             auto tmp119 = 1.0 - tmp11;
2023-01-11T21:05:10.6990704Z                             auto tmp120 = 1.25 * tmp119;
2023-01-11T21:05:10.6990852Z                             auto tmp121 = tmp120 - 2.25;
2023-01-11T21:05:10.6990939Z                             auto tmp122 = tmp121 * tmp119;
2023-01-11T21:05:10.6991034Z                             auto tmp123 = tmp122 * tmp119;
2023-01-11T21:05:10.6991125Z                             auto tmp124 = tmp123 + 1.0;
2023-01-11T21:05:10.6991221Z                             auto tmp125 = tmp119 + 1.0;
2023-01-11T21:05:10.6991367Z                             auto tmp126 = -0.75 * tmp125;
2023-01-11T21:05:10.6991513Z                             auto tmp127 = tmp126 - -3.75;
2023-01-11T21:05:10.6991610Z                             auto tmp128 = tmp127 * tmp125;
2023-01-11T21:05:10.6991744Z                             auto tmp129 = tmp128 + -6.0;
2023-01-11T21:05:10.6991840Z                             auto tmp130 = tmp129 * tmp125;
2023-01-11T21:05:10.6991986Z                             auto tmp131 = tmp130 - -3.0;
2023-01-11T21:05:10.6992082Z                             auto tmp132 = tmp67 * tmp113;
2023-01-11T21:05:10.6992176Z                             auto tmp133 = tmp80 * tmp118;
2023-01-11T21:05:10.6992303Z                             auto tmp134 = tmp93 * tmp124;
2023-01-11T21:05:10.6992401Z                             auto tmp135 = tmp106 * tmp131;
2023-01-11T21:05:10.6992497Z                             auto tmp136 = tmp134 + tmp135;
2023-01-11T21:05:10.6992579Z                             auto tmp137 = tmp133 + tmp136;
2023-01-11T21:05:10.6992674Z                             auto tmp138 = tmp132 + tmp137;
2023-01-11T21:05:10.6992781Z                             out_ptr1[i2 + (256*i1) + (32768*i0)] = tmp138;
2023-01-11T21:05:10.6992851Z                         }
2023-01-11T21:05:10.6992917Z                     }
2023-01-11T21:05:10.6992980Z                 }
2023-01-11T21:05:10.6993042Z             }
2023-01-11T21:05:10.6993090Z         }
2023-01-11T21:05:10.6993149Z     }
2023-01-11T21:05:10.6993209Z }
2023-01-11T21:05:10.6993285Z ''')
2023-01-11T21:05:10.6993292Z 
2023-01-11T21:05:10.6993296Z 
2023-01-11T21:05:10.6993386Z async_compile.wait(globals())
2023-01-11T21:05:10.6993459Z del async_compile
2023-01-11T21:05:10.6993465Z 
2023-01-11T21:05:10.6993532Z def call(args):
2023-01-11T21:05:10.6993587Z     arg0_1, = args
2023-01-11T21:05:10.6993657Z     args.clear()
2023-01-11T21:05:10.6993933Z     buf0 = empty_strided((4, 3, 128, 128), (49152, 16384, 128, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6994158Z     buf1 = empty_strided((4, 3, 128, 256), (98304, 32768, 256, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6994319Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.6994385Z     del arg0_1
2023-01-11T21:05:10.6994461Z     return (buf0, buf1, )
2023-01-11T21:05:10.6994465Z 
2023-01-11T21:05:10.6994470Z 
2023-01-11T21:05:10.6994544Z if __name__ == "__main__":
2023-01-11T21:05:10.6994645Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.6994768Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.6994987Z     arg0_1 = rand_strided((4, 3, 64, 32), (6144, 2048, 32, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.6995095Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.6995366Z [2023-01-11 21:01:26,827] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 452
2023-01-11T21:05:10.6995371Z 
2023-01-11T21:05:10.6995438Z ok (6.030s)
2023-01-11T21:05:10.6995894Z   test_upsample_bilinear2d_a_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.6996022Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.6996281Z [2023-01-11 21:01:28,083] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 453
2023-01-11T21:05:10.6996289Z 
2023-01-11T21:05:10.6996383Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.6996440Z import torch
2023-01-11T21:05:10.6996511Z import random
2023-01-11T21:05:10.6996624Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.6996744Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.6996749Z 
2023-01-11T21:05:10.6996827Z aten = torch.ops.aten
2023-01-11T21:05:10.6996959Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.6997050Z async_compile = AsyncCompile()
2023-01-11T21:05:10.6997055Z 
2023-01-11T21:05:10.6997059Z 
2023-01-11T21:05:10.6997179Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.6997383Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.6997497Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.6997597Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.6997699Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.6997825Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.6997919Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.6997979Z {
2023-01-11T21:05:10.6998053Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.6998134Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:05:10.6998230Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.6998291Z     {
2023-01-11T21:05:10.6998367Z         #pragma omp for 
2023-01-11T21:05:10.6998447Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.6998495Z         {
2023-01-11T21:05:10.6998573Z             #pragma GCC ivdep
2023-01-11T21:05:10.6998656Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:05:10.6998717Z             {
2023-01-11T21:05:10.6998796Z                 #pragma GCC ivdep
2023-01-11T21:05:10.6998883Z                 for(long i2=0; i2<45; i2+=1)
2023-01-11T21:05:10.6998946Z                 {
2023-01-11T21:05:10.6998998Z                     {
2023-01-11T21:05:10.6999066Z                         {
2023-01-11T21:05:10.6999173Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.6999281Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:05:10.6999402Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.6999519Z                             auto tmp3 = static_cast<float>(0.8222222222222222);
2023-01-11T21:05:10.6999614Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.6999744Z                             auto tmp5 = tmp4 - tmp1;
2023-01-11T21:05:10.6999855Z                             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:05:10.6999985Z                             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:05:10.7000088Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:05:10.7000194Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:05:10.7000301Z                             auto tmp10 = static_cast<float>(i2);
2023-01-11T21:05:10.7000398Z                             auto tmp11 = tmp10 + tmp1;
2023-01-11T21:05:10.7000517Z                             auto tmp12 = static_cast<float>(0.8444444444444444);
2023-01-11T21:05:10.7000734Z                             auto tmp13 = tmp11 * tmp12;
2023-01-11T21:05:10.7000882Z                             auto tmp14 = tmp13 - tmp1;
2023-01-11T21:05:10.7001012Z                             auto tmp15 = (tmp6 != tmp6) ? tmp6 : std::max(tmp14, tmp6);
2023-01-11T21:05:10.7001118Z                             auto tmp16 = std::floor(tmp15);
2023-01-11T21:05:10.7001228Z                             auto tmp17 = static_cast<long>(tmp16);
2023-01-11T21:05:10.7001346Z                             auto tmp18 = in_ptr0[tmp17 + (38*tmp9) + (1406*i0)];
2023-01-11T21:05:10.7001455Z                             auto tmp19 = static_cast<float>(1.0);
2023-01-11T21:05:10.7001568Z                             auto tmp20 = static_cast<float>(tmp9);
2023-01-11T21:05:10.7001704Z                             auto tmp21 = tmp7 - tmp20;
2023-01-11T21:05:10.7001852Z                             auto tmp22 = tmp19 - tmp21;
2023-01-11T21:05:10.7001951Z                             auto tmp23 = tmp18 * tmp22;
2023-01-11T21:05:10.7002058Z                             auto tmp24 = std::ceil(tmp7);
2023-01-11T21:05:10.7002167Z                             auto tmp25 = static_cast<float>(36.0);
2023-01-11T21:05:10.7002298Z                             auto tmp26 = (tmp25 != tmp25) ? tmp25 : std::min(tmp24, tmp25);
2023-01-11T21:05:10.7002407Z                             auto tmp27 = static_cast<long>(tmp26);
2023-01-11T21:05:10.7002527Z                             auto tmp28 = in_ptr0[tmp17 + (38*tmp27) + (1406*i0)];
2023-01-11T21:05:10.7002609Z                             auto tmp29 = tmp28 * tmp21;
2023-01-11T21:05:10.7002704Z                             auto tmp30 = tmp23 + tmp29;
2023-01-11T21:05:10.7002808Z                             out_ptr0[i2 + (45*i1) + (2025*i0)] = tmp30;
2023-01-11T21:05:10.7002932Z                         }
2023-01-11T21:05:10.7003000Z                     }
2023-01-11T21:05:10.7003064Z                 }
2023-01-11T21:05:10.7003127Z             }
2023-01-11T21:05:10.7003178Z         }
2023-01-11T21:05:10.7003257Z         #pragma omp for 
2023-01-11T21:05:10.7003338Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7003400Z         {
2023-01-11T21:05:10.7003479Z             #pragma GCC ivdep
2023-01-11T21:05:10.7003564Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:05:10.7003628Z             {
2023-01-11T21:05:10.7003697Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7003786Z                 for(long i2=0; i2<45; i2+=1)
2023-01-11T21:05:10.7003851Z                 {
2023-01-11T21:05:10.7003918Z                     {
2023-01-11T21:05:10.7003989Z                         {
2023-01-11T21:05:10.7004097Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7004207Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:05:10.7004292Z                             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7004405Z                             auto tmp3 = static_cast<float>(0.8222222222222222);
2023-01-11T21:05:10.7004533Z                             auto tmp4 = tmp2 * tmp3;
2023-01-11T21:05:10.7004677Z                             auto tmp5 = tmp4 - tmp1;
2023-01-11T21:05:10.7004786Z                             auto tmp6 = static_cast<float>(0.0);
2023-01-11T21:05:10.7004916Z                             auto tmp7 = (tmp6 != tmp6) ? tmp6 : std::max(tmp5, tmp6);
2023-01-11T21:05:10.7005018Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:05:10.7005125Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:05:10.7005219Z                             auto tmp10 = static_cast<float>(i2);
2023-01-11T21:05:10.7005315Z                             auto tmp11 = tmp10 + tmp1;
2023-01-11T21:05:10.7005434Z                             auto tmp12 = static_cast<float>(0.8444444444444444);
2023-01-11T21:05:10.7005531Z                             auto tmp13 = tmp11 * tmp12;
2023-01-11T21:05:10.7005678Z                             auto tmp14 = tmp13 - tmp1;
2023-01-11T21:05:10.7005810Z                             auto tmp15 = (tmp6 != tmp6) ? tmp6 : std::max(tmp14, tmp6);
2023-01-11T21:05:10.7005914Z                             auto tmp16 = std::ceil(tmp15);
2023-01-11T21:05:10.7006009Z                             auto tmp17 = static_cast<float>(37.0);
2023-01-11T21:05:10.7006139Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:05:10.7006249Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:05:10.7006365Z                             auto tmp20 = in_ptr0[tmp19 + (38*tmp9) + (1406*i0)];
2023-01-11T21:05:10.7006473Z                             auto tmp21 = static_cast<float>(1.0);
2023-01-11T21:05:10.7006582Z                             auto tmp22 = static_cast<float>(tmp9);
2023-01-11T21:05:10.7006732Z                             auto tmp23 = tmp7 - tmp22;
2023-01-11T21:05:10.7006878Z                             auto tmp24 = tmp21 - tmp23;
2023-01-11T21:05:10.7006962Z                             auto tmp25 = tmp20 * tmp24;
2023-01-11T21:05:10.7007064Z                             auto tmp26 = std::ceil(tmp7);
2023-01-11T21:05:10.7007171Z                             auto tmp27 = static_cast<float>(36.0);
2023-01-11T21:05:10.7007300Z                             auto tmp28 = (tmp27 != tmp27) ? tmp27 : std::min(tmp26, tmp27);
2023-01-11T21:05:10.7007407Z                             auto tmp29 = static_cast<long>(tmp28);
2023-01-11T21:05:10.7007523Z                             auto tmp30 = in_ptr0[tmp19 + (38*tmp29) + (1406*i0)];
2023-01-11T21:05:10.7007620Z                             auto tmp31 = tmp30 * tmp23;
2023-01-11T21:05:10.7007713Z                             auto tmp32 = tmp25 + tmp31;
2023-01-11T21:05:10.7007805Z                             out_ptr1[i2 + (45*i1) + (2025*i0)] = tmp32;
2023-01-11T21:05:10.7007909Z                         }
2023-01-11T21:05:10.7007973Z                     }
2023-01-11T21:05:10.7008036Z                 }
2023-01-11T21:05:10.7008101Z             }
2023-01-11T21:05:10.7008165Z         }
2023-01-11T21:05:10.7008239Z         #pragma omp for 
2023-01-11T21:05:10.7008308Z         for(long i0=0; i0<360; i0+=1)
2023-01-11T21:05:10.7008369Z         {
2023-01-11T21:05:10.7008448Z             #pragma GCC ivdep
2023-01-11T21:05:10.7008533Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:05:10.7008597Z             {
2023-01-11T21:05:10.7008660Z                 {
2023-01-11T21:05:10.7008726Z                     {
2023-01-11T21:05:10.7008816Z                         auto tmp0 = out_ptr0[i1 + (45*i0)];
2023-01-11T21:05:10.7008919Z                         auto tmp16 = out_ptr1[i1 + (45*i0)];
2023-01-11T21:05:10.7009026Z                         auto tmp1 = static_cast<float>(1.0);
2023-01-11T21:05:10.7009133Z                         auto tmp2 = static_cast<float>(i1);
2023-01-11T21:05:10.7009241Z                         auto tmp3 = static_cast<float>(0.5);
2023-01-11T21:05:10.7009333Z                         auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7009474Z                         auto tmp5 = static_cast<float>(0.8444444444444444);
2023-01-11T21:05:10.7009555Z                         auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.7009694Z                         auto tmp7 = tmp6 - tmp3;
2023-01-11T21:05:10.7009797Z                         auto tmp8 = static_cast<float>(0.0);
2023-01-11T21:05:10.7009925Z                         auto tmp9 = (tmp8 != tmp8) ? tmp8 : std::max(tmp7, tmp8);
2023-01-11T21:05:10.7010030Z                         auto tmp10 = std::floor(tmp9);
2023-01-11T21:05:10.7010140Z                         auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:05:10.7010250Z                         auto tmp12 = static_cast<float>(tmp11);
2023-01-11T21:05:10.7010394Z                         auto tmp13 = tmp9 - tmp12;
2023-01-11T21:05:10.7010521Z                         auto tmp14 = tmp1 - tmp13;
2023-01-11T21:05:10.7010610Z                         auto tmp15 = tmp0 * tmp14;
2023-01-11T21:05:10.7010703Z                         auto tmp17 = tmp16 * tmp13;
2023-01-11T21:05:10.7010796Z                         auto tmp18 = tmp15 + tmp17;
2023-01-11T21:05:10.7010893Z                         in_out_ptr0[i1 + (45*i0)] = tmp18;
2023-01-11T21:05:10.7010959Z                     }
2023-01-11T21:05:10.7011024Z                 }
2023-01-11T21:05:10.7011074Z             }
2023-01-11T21:05:10.7011134Z         }
2023-01-11T21:05:10.7011209Z         #pragma omp for 
2023-01-11T21:05:10.7011290Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7011351Z         {
2023-01-11T21:05:10.7011430Z             #pragma GCC ivdep
2023-01-11T21:05:10.7011514Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:05:10.7011564Z             {
2023-01-11T21:05:10.7011644Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7011732Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:05:10.7011798Z                 {
2023-01-11T21:05:10.7011863Z                     {
2023-01-11T21:05:10.7011929Z                         {
2023-01-11T21:05:10.7012024Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7012140Z                             auto tmp1 = static_cast<float>(0.4931506849315068);
2023-01-11T21:05:10.7012234Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7012336Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:05:10.7012443Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:05:10.7012549Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:05:10.7012666Z                             auto tmp6 = static_cast<float>(0.49333333333333335);
2023-01-11T21:05:10.7012761Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.7012849Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:05:10.7012988Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:05:10.7013101Z                             auto tmp10 = in_ptr0[tmp9 + (38*tmp4) + (1406*i0)];
2023-01-11T21:05:10.7013210Z                             auto tmp11 = static_cast<float>(1.0);
2023-01-11T21:05:10.7013320Z                             auto tmp12 = static_cast<float>(tmp4);
2023-01-11T21:05:10.7013466Z                             auto tmp13 = tmp2 - tmp12;
2023-01-11T21:05:10.7013617Z                             auto tmp14 = tmp11 - tmp13;
2023-01-11T21:05:10.7013711Z                             auto tmp15 = tmp10 * tmp14;
2023-01-11T21:05:10.7013801Z                             auto tmp16 = std::ceil(tmp2);
2023-01-11T21:05:10.7013908Z                             auto tmp17 = static_cast<float>(36.0);
2023-01-11T21:05:10.7014037Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:05:10.7014148Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:05:10.7014265Z                             auto tmp20 = in_ptr0[tmp9 + (38*tmp19) + (1406*i0)];
2023-01-11T21:05:10.7014358Z                             auto tmp21 = tmp20 * tmp13;
2023-01-11T21:05:10.7014478Z                             auto tmp22 = tmp15 + tmp21;
2023-01-11T21:05:10.7014589Z                             auto tmp23 = static_cast<float>(tmp9);
2023-01-11T21:05:10.7014723Z                             auto tmp24 = tmp7 - tmp23;
2023-01-11T21:05:10.7014867Z                             auto tmp25 = tmp11 - tmp24;
2023-01-11T21:05:10.7014961Z                             auto tmp26 = tmp22 * tmp25;
2023-01-11T21:05:10.7015065Z                             out_ptr2[i2 + (76*i1) + (5624*i0)] = tmp26;
2023-01-11T21:05:10.7015134Z                         }
2023-01-11T21:05:10.7015198Z                     }
2023-01-11T21:05:10.7015262Z                 }
2023-01-11T21:05:10.7015310Z             }
2023-01-11T21:05:10.7015370Z         }
2023-01-11T21:05:10.7015445Z         #pragma omp for 
2023-01-11T21:05:10.7015527Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7015589Z         {
2023-01-11T21:05:10.7015668Z             #pragma GCC ivdep
2023-01-11T21:05:10.7015740Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:05:10.7015804Z             {
2023-01-11T21:05:10.7015883Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7015972Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:05:10.7016035Z                 {
2023-01-11T21:05:10.7016098Z                     {
2023-01-11T21:05:10.7016163Z                         {
2023-01-11T21:05:10.7016257Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7016370Z                             auto tmp1 = static_cast<float>(0.4931506849315068);
2023-01-11T21:05:10.7016464Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7016567Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:05:10.7016674Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:05:10.7016782Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:05:10.7016897Z                             auto tmp6 = static_cast<float>(0.49333333333333335);
2023-01-11T21:05:10.7016994Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.7017081Z                             auto tmp8 = std::ceil(tmp7);
2023-01-11T21:05:10.7017189Z                             auto tmp9 = static_cast<float>(37.0);
2023-01-11T21:05:10.7017317Z                             auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp8, tmp9);
2023-01-11T21:05:10.7017426Z                             auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:05:10.7017541Z                             auto tmp12 = in_ptr0[tmp11 + (38*tmp4) + (1406*i0)];
2023-01-11T21:05:10.7017650Z                             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:05:10.7017758Z                             auto tmp14 = static_cast<float>(tmp4);
2023-01-11T21:05:10.7017903Z                             auto tmp15 = tmp2 - tmp14;
2023-01-11T21:05:10.7018069Z                             auto tmp16 = tmp13 - tmp15;
2023-01-11T21:05:10.7018166Z                             auto tmp17 = tmp12 * tmp16;
2023-01-11T21:05:10.7018270Z                             auto tmp18 = std::ceil(tmp2);
2023-01-11T21:05:10.7018377Z                             auto tmp19 = static_cast<float>(36.0);
2023-01-11T21:05:10.7018596Z                             auto tmp20 = (tmp19 != tmp19) ? tmp19 : std::min(tmp18, tmp19);
2023-01-11T21:05:10.7018706Z                             auto tmp21 = static_cast<long>(tmp20);
2023-01-11T21:05:10.7018823Z                             auto tmp22 = in_ptr0[tmp11 + (38*tmp21) + (1406*i0)];
2023-01-11T21:05:10.7018914Z                             auto tmp23 = tmp22 * tmp15;
2023-01-11T21:05:10.7023832Z                             auto tmp24 = tmp17 + tmp23;
2023-01-11T21:05:10.7023969Z                             auto tmp25 = std::floor(tmp7);
2023-01-11T21:05:10.7024084Z                             auto tmp26 = static_cast<long>(tmp25);
2023-01-11T21:05:10.7024203Z                             auto tmp27 = static_cast<float>(tmp26);
2023-01-11T21:05:10.7024373Z                             auto tmp28 = tmp7 - tmp27;
2023-01-11T21:05:10.7024525Z                             auto tmp29 = tmp24 * tmp28;
2023-01-11T21:05:10.7024632Z                             out_ptr3[i2 + (76*i1) + (5624*i0)] = tmp29;
2023-01-11T21:05:10.7024701Z                         }
2023-01-11T21:05:10.7024766Z                     }
2023-01-11T21:05:10.7024829Z                 }
2023-01-11T21:05:10.7024892Z             }
2023-01-11T21:05:10.7024953Z         }
2023-01-11T21:05:10.7025019Z         #pragma omp for 
2023-01-11T21:05:10.7025100Z         for(long i0=0; i0<2812; i0+=1)
2023-01-11T21:05:10.7025162Z         {
2023-01-11T21:05:10.7025300Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr2 + 16*i0);
2023-01-11T21:05:10.7025432Z             auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr3 + 16*i0);
2023-01-11T21:05:10.7025517Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7025613Z             tmp2.store(in_out_ptr1 + 16*i0);
2023-01-11T21:05:10.7025661Z         }
2023-01-11T21:05:10.7025755Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7025843Z         for(long i0=44992; i0<44992; i0+=1)
2023-01-11T21:05:10.7025904Z         {
2023-01-11T21:05:10.7025987Z             auto tmp0 = out_ptr2[i0];
2023-01-11T21:05:10.7026068Z             auto tmp1 = out_ptr3[i0];
2023-01-11T21:05:10.7026150Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7026219Z             in_out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.7026279Z         }
2023-01-11T21:05:10.7026339Z     }
2023-01-11T21:05:10.7026397Z }
2023-01-11T21:05:10.7026477Z ''')
2023-01-11T21:05:10.7026484Z 
2023-01-11T21:05:10.7026489Z 
2023-01-11T21:05:10.7026576Z async_compile.wait(globals())
2023-01-11T21:05:10.7026646Z del async_compile
2023-01-11T21:05:10.7026651Z 
2023-01-11T21:05:10.7026720Z def call(args):
2023-01-11T21:05:10.7026779Z     arg0_1, = args
2023-01-11T21:05:10.7026848Z     args.clear()
2023-01-11T21:05:10.7027072Z     buf0 = empty_strided((2, 4, 45, 45), (8100, 2025, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7027293Z     buf1 = empty_strided((2, 4, 45, 45), (8100, 2025, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7027374Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:05:10.7027592Z     buf3 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7027808Z     buf4 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7027877Z     buf5 = buf3; del buf3  # reuse
2023-01-11T21:05:10.7028089Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(buf5.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.7028156Z     del arg0_1
2023-01-11T21:05:10.7028231Z     return (buf2, buf5, )
2023-01-11T21:05:10.7028236Z 
2023-01-11T21:05:10.7028310Z 
2023-01-11T21:05:10.7028384Z if __name__ == "__main__":
2023-01-11T21:05:10.7028497Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7028620Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7028840Z     arg0_1 = rand_strided((2, 4, 37, 38), (5624, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7028935Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7029213Z [2023-01-11 21:01:31,118] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 453
2023-01-11T21:05:10.7029218Z 
2023-01-11T21:05:10.7029285Z ok (4.188s)
2023-01-11T21:05:10.7029724Z   test_upsample_bilinear2d_b_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7029853Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7030146Z [2023-01-11 21:01:31,731] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 454
2023-01-11T21:05:10.7030412Z [2023-01-11 21:01:34,500] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 454
2023-01-11T21:05:10.7030417Z 
2023-01-11T21:05:10.7030514Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7030584Z import torch
2023-01-11T21:05:10.7030653Z import random
2023-01-11T21:05:10.7030767Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7030885Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7030891Z 
2023-01-11T21:05:10.7030955Z aten = torch.ops.aten
2023-01-11T21:05:10.7031087Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7031177Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7031185Z 
2023-01-11T21:05:10.7031189Z 
2023-01-11T21:05:10.7031321Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7031524Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7031640Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.7031745Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7031843Z                        float* __restrict__ out_ptr1)
2023-01-11T21:05:10.7031890Z {
2023-01-11T21:05:10.7031973Z     auto out_ptr0 = in_out_ptr0;
2023-01-11T21:05:10.7032069Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7032130Z     {
2023-01-11T21:05:10.7032216Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.7032295Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7032355Z         {
2023-01-11T21:05:10.7032426Z             for(long i1=0; i1<80; i1+=1)
2023-01-11T21:05:10.7032489Z             {
2023-01-11T21:05:10.7032569Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7032661Z                 for(long i2=0; i2<118; i2+=1)
2023-01-11T21:05:10.7032723Z                 {
2023-01-11T21:05:10.7032789Z                     {
2023-01-11T21:05:10.7032843Z                         {
2023-01-11T21:05:10.7032953Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7033069Z                             auto tmp1 = static_cast<float>(0.4936708860759494);
2023-01-11T21:05:10.7033166Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7033268Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:05:10.7033376Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:05:10.7033482Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:05:10.7033597Z                             auto tmp6 = static_cast<float>(0.49572649572649574);
2023-01-11T21:05:10.7033680Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.7033782Z                             auto tmp8 = std::floor(tmp7);
2023-01-11T21:05:10.7033923Z                             auto tmp9 = static_cast<long>(tmp8);
2023-01-11T21:05:10.7034040Z                             auto tmp10 = in_ptr0[tmp9 + (59*tmp4) + (2360*i0)];
2023-01-11T21:05:10.7034148Z                             auto tmp11 = static_cast<float>(1.0);
2023-01-11T21:05:10.7034259Z                             auto tmp12 = static_cast<float>(tmp4);
2023-01-11T21:05:10.7034408Z                             auto tmp13 = tmp2 - tmp12;
2023-01-11T21:05:10.7034556Z                             auto tmp14 = tmp11 - tmp13;
2023-01-11T21:05:10.7034639Z                             auto tmp15 = tmp10 * tmp14;
2023-01-11T21:05:10.7034740Z                             auto tmp16 = std::ceil(tmp2);
2023-01-11T21:05:10.7034848Z                             auto tmp17 = static_cast<float>(39.0);
2023-01-11T21:05:10.7034981Z                             auto tmp18 = (tmp17 != tmp17) ? tmp17 : std::min(tmp16, tmp17);
2023-01-11T21:05:10.7035091Z                             auto tmp19 = static_cast<long>(tmp18);
2023-01-11T21:05:10.7035207Z                             auto tmp20 = in_ptr0[tmp9 + (59*tmp19) + (2360*i0)];
2023-01-11T21:05:10.7035350Z                             auto tmp21 = tmp20 * tmp13;
2023-01-11T21:05:10.7035445Z                             auto tmp22 = tmp15 + tmp21;
2023-01-11T21:05:10.7035541Z                             auto tmp23 = static_cast<float>(tmp9);
2023-01-11T21:05:10.7035690Z                             auto tmp24 = tmp7 - tmp23;
2023-01-11T21:05:10.7035836Z                             auto tmp25 = tmp11 - tmp24;
2023-01-11T21:05:10.7035931Z                             auto tmp26 = tmp22 * tmp25;
2023-01-11T21:05:10.7036037Z                             out_ptr0[i2 + (118*i1) + (9440*i0)] = tmp26;
2023-01-11T21:05:10.7036104Z                         }
2023-01-11T21:05:10.7036169Z                     }
2023-01-11T21:05:10.7036219Z                 }
2023-01-11T21:05:10.7036280Z             }
2023-01-11T21:05:10.7036343Z         }
2023-01-11T21:05:10.7036434Z         #pragma omp for  collapse(2)
2023-01-11T21:05:10.7036516Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7036580Z         {
2023-01-11T21:05:10.7036668Z             for(long i1=0; i1<80; i1+=1)
2023-01-11T21:05:10.7036718Z             {
2023-01-11T21:05:10.7036799Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7036888Z                 for(long i2=0; i2<118; i2+=1)
2023-01-11T21:05:10.7036953Z                 {
2023-01-11T21:05:10.7037018Z                     {
2023-01-11T21:05:10.7037084Z                         {
2023-01-11T21:05:10.7037179Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7037294Z                             auto tmp1 = static_cast<float>(0.4936708860759494);
2023-01-11T21:05:10.7037387Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7037491Z                             auto tmp3 = std::floor(tmp2);
2023-01-11T21:05:10.7037598Z                             auto tmp4 = static_cast<long>(tmp3);
2023-01-11T21:05:10.7037706Z                             auto tmp5 = static_cast<float>(i2);
2023-01-11T21:05:10.7037823Z                             auto tmp6 = static_cast<float>(0.49572649572649574);
2023-01-11T21:05:10.7037919Z                             auto tmp7 = tmp5 * tmp6;
2023-01-11T21:05:10.7038007Z                             auto tmp8 = std::ceil(tmp7);
2023-01-11T21:05:10.7038116Z                             auto tmp9 = static_cast<float>(58.0);
2023-01-11T21:05:10.7038245Z                             auto tmp10 = (tmp9 != tmp9) ? tmp9 : std::min(tmp8, tmp9);
2023-01-11T21:05:10.7038353Z                             auto tmp11 = static_cast<long>(tmp10);
2023-01-11T21:05:10.7038467Z                             auto tmp12 = in_ptr0[tmp11 + (59*tmp4) + (2360*i0)];
2023-01-11T21:05:10.7038575Z                             auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:05:10.7038684Z                             auto tmp14 = static_cast<float>(tmp4);
2023-01-11T21:05:10.7038863Z                             auto tmp15 = tmp2 - tmp14;
2023-01-11T21:05:10.7038997Z                             auto tmp16 = tmp13 - tmp15;
2023-01-11T21:05:10.7039090Z                             auto tmp17 = tmp12 * tmp16;
2023-01-11T21:05:10.7039196Z                             auto tmp18 = std::ceil(tmp2);
2023-01-11T21:05:10.7039303Z                             auto tmp19 = static_cast<float>(39.0);
2023-01-11T21:05:10.7039433Z                             auto tmp20 = (tmp19 != tmp19) ? tmp19 : std::min(tmp18, tmp19);
2023-01-11T21:05:10.7039545Z                             auto tmp21 = static_cast<long>(tmp20);
2023-01-11T21:05:10.7039663Z                             auto tmp22 = in_ptr0[tmp11 + (59*tmp21) + (2360*i0)];
2023-01-11T21:05:10.7039757Z                             auto tmp23 = tmp22 * tmp15;
2023-01-11T21:05:10.7039839Z                             auto tmp24 = tmp17 + tmp23;
2023-01-11T21:05:10.7039943Z                             auto tmp25 = std::floor(tmp7);
2023-01-11T21:05:10.7040051Z                             auto tmp26 = static_cast<long>(tmp25);
2023-01-11T21:05:10.7040161Z                             auto tmp27 = static_cast<float>(tmp26);
2023-01-11T21:05:10.7040337Z                             auto tmp28 = tmp7 - tmp27;
2023-01-11T21:05:10.7040434Z                             auto tmp29 = tmp24 * tmp28;
2023-01-11T21:05:10.7040540Z                             out_ptr1[i2 + (118*i1) + (9440*i0)] = tmp29;
2023-01-11T21:05:10.7040747Z                         }
2023-01-11T21:05:10.7040801Z                     }
2023-01-11T21:05:10.7040865Z                 }
2023-01-11T21:05:10.7040928Z             }
2023-01-11T21:05:10.7040989Z         }
2023-01-11T21:05:10.7041067Z         #pragma omp for 
2023-01-11T21:05:10.7041149Z         for(long i0=0; i0<1180; i0+=1)
2023-01-11T21:05:10.7041198Z         {
2023-01-11T21:05:10.7041334Z             auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7041470Z             auto tmp1 = at::vec::Vectorized<float>::loadu(out_ptr1 + 16*i0);
2023-01-11T21:05:10.7041558Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7041655Z             tmp2.store(in_out_ptr0 + 16*i0);
2023-01-11T21:05:10.7041717Z         }
2023-01-11T21:05:10.7041814Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7041888Z         for(long i0=18880; i0<18880; i0+=1)
2023-01-11T21:05:10.7041951Z         {
2023-01-11T21:05:10.7042036Z             auto tmp0 = out_ptr0[i0];
2023-01-11T21:05:10.7042118Z             auto tmp1 = out_ptr1[i0];
2023-01-11T21:05:10.7042201Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7042282Z             in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7042343Z         }
2023-01-11T21:05:10.7042390Z     }
2023-01-11T21:05:10.7042451Z }
2023-01-11T21:05:10.7042530Z ''')
2023-01-11T21:05:10.7042536Z 
2023-01-11T21:05:10.7042540Z 
2023-01-11T21:05:10.7042629Z async_compile.wait(globals())
2023-01-11T21:05:10.7042701Z del async_compile
2023-01-11T21:05:10.7042707Z 
2023-01-11T21:05:10.7042778Z def call(args):
2023-01-11T21:05:10.7042846Z     arg0_1, = args
2023-01-11T21:05:10.7042903Z     args.clear()
2023-01-11T21:05:10.7043129Z     buf0 = empty_strided((1, 2, 80, 118), (18880, 9440, 118, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7043351Z     buf1 = empty_strided((1, 2, 80, 118), (18880, 9440, 118, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7043435Z     buf2 = buf0; del buf0  # reuse
2023-01-11T21:05:10.7043594Z     kernel_cpp_0(c_void_p(buf2.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.7043662Z     del arg0_1
2023-01-11T21:05:10.7043732Z     return (buf2, )
2023-01-11T21:05:10.7043738Z 
2023-01-11T21:05:10.7043742Z 
2023-01-11T21:05:10.7043816Z if __name__ == "__main__":
2023-01-11T21:05:10.7043917Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7044039Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7044260Z     arg0_1 = rand_strided((1, 2, 40, 59), (4720, 2360, 59, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7044425Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7044430Z 
2023-01-11T21:05:10.7044497Z ok (3.330s)
2023-01-11T21:05:10.7044949Z   test_upsample_nearest1d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7045075Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7045340Z [2023-01-11 21:01:35,051] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 455
2023-01-11T21:05:10.7045608Z [2023-01-11 21:01:37,736] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 455
2023-01-11T21:05:10.7045613Z 
2023-01-11T21:05:10.7045712Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7045768Z import torch
2023-01-11T21:05:10.7045839Z import random
2023-01-11T21:05:10.7045954Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7046113Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7046119Z 
2023-01-11T21:05:10.7046198Z aten = torch.ops.aten
2023-01-11T21:05:10.7046332Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7046422Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7046428Z 
2023-01-11T21:05:10.7046432Z 
2023-01-11T21:05:10.7046563Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7046758Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7046877Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7046978Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.7047073Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.7047169Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.7047261Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.7047355Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.7047402Z {
2023-01-11T21:05:10.7047499Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7047558Z     {
2023-01-11T21:05:10.7047633Z         #pragma omp for 
2023-01-11T21:05:10.7047714Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7047774Z         {
2023-01-11T21:05:10.7047852Z             #pragma GCC ivdep
2023-01-11T21:05:10.7047924Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:05:10.7047986Z             {
2023-01-11T21:05:10.7048048Z                 {
2023-01-11T21:05:10.7048112Z                     {
2023-01-11T21:05:10.7048219Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7048327Z                         auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:05:10.7048421Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7048514Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7048617Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:05:10.7048713Z                         out_ptr0[i1 + (74*i0)] = tmp4;
2023-01-11T21:05:10.7048807Z                         out_ptr1[i1 + (74*i0)] = tmp4;
2023-01-11T21:05:10.7048871Z                     }
2023-01-11T21:05:10.7048934Z                 }
2023-01-11T21:05:10.7048996Z             }
2023-01-11T21:05:10.7049044Z         }
2023-01-11T21:05:10.7049118Z         #pragma omp for 
2023-01-11T21:05:10.7049198Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7049258Z         {
2023-01-11T21:05:10.7049335Z             #pragma GCC ivdep
2023-01-11T21:05:10.7049420Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:05:10.7049469Z             {
2023-01-11T21:05:10.7049530Z                 {
2023-01-11T21:05:10.7049594Z                     {
2023-01-11T21:05:10.7049750Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7049864Z                         auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:05:10.7049959Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7050066Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7050169Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:05:10.7050250Z                         out_ptr2[i1 + (70*i0)] = tmp4;
2023-01-11T21:05:10.7050315Z                     }
2023-01-11T21:05:10.7050378Z                 }
2023-01-11T21:05:10.7050439Z             }
2023-01-11T21:05:10.7050499Z         }
2023-01-11T21:05:10.7050574Z         #pragma omp for 
2023-01-11T21:05:10.7050640Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7050702Z         {
2023-01-11T21:05:10.7050782Z             #pragma GCC ivdep
2023-01-11T21:05:10.7050867Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:05:10.7050931Z             {
2023-01-11T21:05:10.7050997Z                 {
2023-01-11T21:05:10.7051062Z                     {
2023-01-11T21:05:10.7051153Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7051293Z                         auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:05:10.7051389Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7051495Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7051598Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:05:10.7051694Z                         out_ptr3[i1 + (45*i0)] = tmp4;
2023-01-11T21:05:10.7051759Z                     }
2023-01-11T21:05:10.7051809Z                 }
2023-01-11T21:05:10.7051870Z             }
2023-01-11T21:05:10.7051929Z         }
2023-01-11T21:05:10.7052004Z         #pragma omp for 
2023-01-11T21:05:10.7052082Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7052141Z         {
2023-01-11T21:05:10.7052218Z             #pragma GCC ivdep
2023-01-11T21:05:10.7052292Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:05:10.7052356Z             {
2023-01-11T21:05:10.7052419Z                 {
2023-01-11T21:05:10.7052483Z                     {
2023-01-11T21:05:10.7052591Z                         auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7052703Z                         auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:05:10.7052795Z                         auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7052887Z                         auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7052988Z                         auto tmp4 = in_ptr0[tmp3 + (37*i0)];
2023-01-11T21:05:10.7053082Z                         out_ptr4[i1 + (36*i0)] = tmp4;
2023-01-11T21:05:10.7053147Z                     }
2023-01-11T21:05:10.7053209Z                 }
2023-01-11T21:05:10.7053271Z             }
2023-01-11T21:05:10.7053331Z         }
2023-01-11T21:05:10.7053379Z     }
2023-01-11T21:05:10.7053437Z }
2023-01-11T21:05:10.7053517Z ''')
2023-01-11T21:05:10.7053522Z 
2023-01-11T21:05:10.7053526Z 
2023-01-11T21:05:10.7053615Z async_compile.wait(globals())
2023-01-11T21:05:10.7053686Z del async_compile
2023-01-11T21:05:10.7053691Z 
2023-01-11T21:05:10.7053762Z def call(args):
2023-01-11T21:05:10.7053829Z     arg0_1, = args
2023-01-11T21:05:10.7053887Z     args.clear()
2023-01-11T21:05:10.7054094Z     buf0 = empty_strided((2, 4, 74), (296, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7054293Z     buf4 = empty_strided((2, 4, 74), (296, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7054488Z     buf1 = empty_strided((2, 4, 70), (280, 70, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7054679Z     buf2 = empty_strided((2, 4, 45), (180, 45, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7054869Z     buf3 = empty_strided((2, 4, 36), (144, 36, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7055105Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.7055203Z     del arg0_1
2023-01-11T21:05:10.7055286Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.7055304Z 
2023-01-11T21:05:10.7055309Z 
2023-01-11T21:05:10.7055372Z if __name__ == "__main__":
2023-01-11T21:05:10.7055484Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7055604Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7055813Z     arg0_1 = rand_strided((2, 4, 37), (148, 37, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7055917Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7055923Z 
2023-01-11T21:05:10.7055987Z ok (3.229s)
2023-01-11T21:05:10.7056444Z   test_upsample_nearest2d_backward_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7056599Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7056862Z [2023-01-11 21:01:37,793] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 456
2023-01-11T21:05:10.7056868Z 
2023-01-11T21:05:10.7056947Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7057014Z import torch
2023-01-11T21:05:10.7057083Z import random
2023-01-11T21:05:10.7057197Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7057316Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7057322Z 
2023-01-11T21:05:10.7057397Z aten = torch.ops.aten
2023-01-11T21:05:10.7057530Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7057608Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7057625Z 
2023-01-11T21:05:10.7057632Z 
2023-01-11T21:05:10.7057750Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7057955Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7058075Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7058173Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.7058269Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.7058361Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.7058452Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.7058619Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.7058679Z {
2023-01-11T21:05:10.7058781Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7058845Z     {
2023-01-11T21:05:10.7058922Z         #pragma omp for 
2023-01-11T21:05:10.7059004Z         for(long i0=0; i0<27; i0+=1)
2023-01-11T21:05:10.7059066Z         {
2023-01-11T21:05:10.7059130Z             #pragma GCC ivdep
2023-01-11T21:05:10.7059215Z             for(long i1=0; i1<6; i1+=1)
2023-01-11T21:05:10.7059279Z             {
2023-01-11T21:05:10.7059343Z                 {
2023-01-11T21:05:10.7059411Z                     {
2023-01-11T21:05:10.7059518Z                         auto tmp0 = in_ptr0[(2*i1) + (24*i0)];
2023-01-11T21:05:10.7059626Z                         auto tmp1 = in_ptr0[1 + (2*i1) + (24*i0)];
2023-01-11T21:05:10.7059719Z                         auto tmp3 = in_ptr0[12 + (2*i1) + (24*i0)];
2023-01-11T21:05:10.7059820Z                         auto tmp5 = in_ptr0[13 + (2*i1) + (24*i0)];
2023-01-11T21:05:10.7059913Z                         auto tmp2 = tmp1 + tmp0;
2023-01-11T21:05:10.7060004Z                         auto tmp4 = tmp3 + tmp2;
2023-01-11T21:05:10.7060094Z                         auto tmp6 = tmp5 + tmp4;
2023-01-11T21:05:10.7060202Z                         auto tmp7 = static_cast<float>(1.0);
2023-01-11T21:05:10.7060292Z                         auto tmp8 = tmp6 * tmp7;
2023-01-11T21:05:10.7060415Z                         out_ptr0[i1 + (6*i0)] = tmp8;
2023-01-11T21:05:10.7060480Z                     }
2023-01-11T21:05:10.7060542Z                 }
2023-01-11T21:05:10.7060603Z             }
2023-01-11T21:05:10.7060666Z         }
2023-01-11T21:05:10.7060742Z         #pragma omp for 
2023-01-11T21:05:10.7060822Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.7060869Z         {
2023-01-11T21:05:10.7060947Z             #pragma GCC ivdep
2023-01-11T21:05:10.7061028Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.7061090Z             {
2023-01-11T21:05:10.7061168Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7061255Z                 for(long i2=0; i2<5; i2+=1)
2023-01-11T21:05:10.7061316Z                 {
2023-01-11T21:05:10.7061367Z                     {
2023-01-11T21:05:10.7061433Z                         {
2023-01-11T21:05:10.7061550Z                             auto tmp0 = static_cast<long>(((3 + (6*i1)) / 4));
2023-01-11T21:05:10.7061664Z                             auto tmp1 = static_cast<long>(((9 + (6*i1)) / 4));
2023-01-11T21:05:10.7061762Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.7061907Z                             auto tmp3 = static_cast<long>(((4 + (12*i2)) / 5));
2023-01-11T21:05:10.7062025Z                             auto tmp4 = static_cast<long>(((16 + (12*i2)) / 5));
2023-01-11T21:05:10.7062107Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:05:10.7062197Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:05:10.7062284Z                             float tmp7 = 0.0;
2023-01-11T21:05:10.7062359Z                             if(tmp6)
2023-01-11T21:05:10.7062427Z                             {
2023-01-11T21:05:10.7062551Z                                 auto tmp8 = in_ptr0[(12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:05:10.7062637Z                                 tmp7 = tmp8;
2023-01-11T21:05:10.7062693Z                             }
2023-01-11T21:05:10.7062812Z                             auto tmp9 = static_cast<long>(1 + (((4 + (12*i2)) / 5)));
2023-01-11T21:05:10.7062906Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:05:10.7063003Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:05:10.7063092Z                             float tmp12 = 0.0;
2023-01-11T21:05:10.7063166Z                             if(tmp11)
2023-01-11T21:05:10.7063234Z                             {
2023-01-11T21:05:10.7063358Z                                 auto tmp13 = in_ptr0[1 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:05:10.7063432Z                                 tmp12 = tmp13;
2023-01-11T21:05:10.7063500Z                             }
2023-01-11T21:05:10.7063594Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:05:10.7063712Z                             auto tmp15 = static_cast<long>(2 + (((4 + (12*i2)) / 5)));
2023-01-11T21:05:10.7063807Z                             auto tmp16 = tmp15 < tmp4;
2023-01-11T21:05:10.7063903Z                             auto tmp17 = tmp2 & tmp16;
2023-01-11T21:05:10.7063990Z                             float tmp18 = 0.0;
2023-01-11T21:05:10.7064052Z                             if(tmp17)
2023-01-11T21:05:10.7064121Z                             {
2023-01-11T21:05:10.7064247Z                                 auto tmp19 = in_ptr0[2 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:05:10.7064333Z                                 tmp18 = tmp19;
2023-01-11T21:05:10.7064399Z                             }
2023-01-11T21:05:10.7064494Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:05:10.7064611Z                             auto tmp21 = static_cast<long>(1 + (((3 + (6*i1)) / 4)));
2023-01-11T21:05:10.7064705Z                             auto tmp22 = tmp21 < tmp1;
2023-01-11T21:05:10.7064788Z                             auto tmp23 = tmp22 & tmp5;
2023-01-11T21:05:10.7064874Z                             float tmp24 = 0.0;
2023-01-11T21:05:10.7064979Z                             if(tmp23)
2023-01-11T21:05:10.7065047Z                             {
2023-01-11T21:05:10.7065177Z                                 auto tmp25 = in_ptr0[12 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:05:10.7065262Z                                 tmp24 = tmp25;
2023-01-11T21:05:10.7065328Z                             }
2023-01-11T21:05:10.7065411Z                             auto tmp26 = tmp24 + tmp20;
2023-01-11T21:05:10.7065504Z                             auto tmp27 = tmp22 & tmp10;
2023-01-11T21:05:10.7065591Z                             float tmp28 = 0.0;
2023-01-11T21:05:10.7065666Z                             if(tmp27)
2023-01-11T21:05:10.7065733Z                             {
2023-01-11T21:05:10.7065861Z                                 auto tmp29 = in_ptr0[13 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:05:10.7065946Z                                 tmp28 = tmp29;
2023-01-11T21:05:10.7066003Z                             }
2023-01-11T21:05:10.7066097Z                             auto tmp30 = tmp28 + tmp26;
2023-01-11T21:05:10.7066192Z                             auto tmp31 = tmp22 & tmp16;
2023-01-11T21:05:10.7066307Z                             float tmp32 = 0.0;
2023-01-11T21:05:10.7066384Z                             if(tmp31)
2023-01-11T21:05:10.7066451Z                             {
2023-01-11T21:05:10.7066577Z                                 auto tmp33 = in_ptr0[14 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((4 + (12*i2)) / 5))];
2023-01-11T21:05:10.7066662Z                                 tmp32 = tmp33;
2023-01-11T21:05:10.7066716Z                             }
2023-01-11T21:05:10.7066810Z                             auto tmp34 = tmp32 + tmp30;
2023-01-11T21:05:10.7066913Z                             out_ptr1[i2 + (5*i1) + (20*i0)] = tmp34;
2023-01-11T21:05:10.7066980Z                         }
2023-01-11T21:05:10.7067045Z                     }
2023-01-11T21:05:10.7067108Z                 }
2023-01-11T21:05:10.7067172Z             }
2023-01-11T21:05:10.7067220Z         }
2023-01-11T21:05:10.7067295Z         #pragma omp for 
2023-01-11T21:05:10.7067377Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.7067439Z         {
2023-01-11T21:05:10.7067517Z             #pragma GCC ivdep
2023-01-11T21:05:10.7067599Z             for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.7067647Z             {
2023-01-11T21:05:10.7067727Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7067814Z                 for(long i2=0; i2<8; i2+=1)
2023-01-11T21:05:10.7067880Z                 {
2023-01-11T21:05:10.7067944Z                     {
2023-01-11T21:05:10.7068010Z                         {
2023-01-11T21:05:10.7068117Z                             auto tmp0 = static_cast<long>(3*i1);
2023-01-11T21:05:10.7068218Z                             auto tmp1 = static_cast<long>(3 + (3*i1));
2023-01-11T21:05:10.7068312Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.7068426Z                             auto tmp3 = static_cast<long>(((7 + (12*i2)) / 8));
2023-01-11T21:05:10.7068543Z                             auto tmp4 = static_cast<long>(((19 + (12*i2)) / 8));
2023-01-11T21:05:10.7068640Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:05:10.7068737Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:05:10.7068824Z                             float tmp7 = 0.0;
2023-01-11T21:05:10.7068900Z                             if(tmp6)
2023-01-11T21:05:10.7068957Z                             {
2023-01-11T21:05:10.7069076Z                                 auto tmp8 = in_ptr0[(36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7069163Z                                 tmp7 = tmp8;
2023-01-11T21:05:10.7069231Z                             }
2023-01-11T21:05:10.7069348Z                             auto tmp9 = static_cast<long>(1 + (((7 + (12*i2)) / 8)));
2023-01-11T21:05:10.7069445Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:05:10.7069572Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:05:10.7069647Z                             float tmp12 = 0.0;
2023-01-11T21:05:10.7069723Z                             if(tmp11)
2023-01-11T21:05:10.7069793Z                             {
2023-01-11T21:05:10.7069917Z                                 auto tmp13 = in_ptr0[1 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7070002Z                                 tmp12 = tmp13;
2023-01-11T21:05:10.7070070Z                             }
2023-01-11T21:05:10.7070165Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:05:10.7070265Z                             auto tmp15 = static_cast<long>(1 + (3*i1));
2023-01-11T21:05:10.7070362Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:05:10.7070456Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:05:10.7070547Z                             float tmp18 = 0.0;
2023-01-11T21:05:10.7070623Z                             if(tmp17)
2023-01-11T21:05:10.7070696Z                             {
2023-01-11T21:05:10.7070817Z                                 auto tmp19 = in_ptr0[12 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7070944Z                                 tmp18 = tmp19;
2023-01-11T21:05:10.7071000Z                             }
2023-01-11T21:05:10.7071098Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:05:10.7071194Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:05:10.7071281Z                             float tmp22 = 0.0;
2023-01-11T21:05:10.7071357Z                             if(tmp21)
2023-01-11T21:05:10.7071425Z                             {
2023-01-11T21:05:10.7071547Z                                 auto tmp23 = in_ptr0[13 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7071620Z                                 tmp22 = tmp23;
2023-01-11T21:05:10.7071687Z                             }
2023-01-11T21:05:10.7071783Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:05:10.7071897Z                             auto tmp25 = static_cast<long>(2 + (3*i1));
2023-01-11T21:05:10.7071991Z                             auto tmp26 = tmp25 < tmp1;
2023-01-11T21:05:10.7072090Z                             auto tmp27 = tmp26 & tmp5;
2023-01-11T21:05:10.7072178Z                             float tmp28 = 0.0;
2023-01-11T21:05:10.7072240Z                             if(tmp27)
2023-01-11T21:05:10.7072313Z                             {
2023-01-11T21:05:10.7072433Z                                 auto tmp29 = in_ptr0[24 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7072519Z                                 tmp28 = tmp29;
2023-01-11T21:05:10.7072587Z                             }
2023-01-11T21:05:10.7072683Z                             auto tmp30 = tmp28 + tmp24;
2023-01-11T21:05:10.7072777Z                             auto tmp31 = tmp26 & tmp10;
2023-01-11T21:05:10.7072865Z                             float tmp32 = 0.0;
2023-01-11T21:05:10.7072929Z                             if(tmp31)
2023-01-11T21:05:10.7072996Z                             {
2023-01-11T21:05:10.7073116Z                                 auto tmp33 = in_ptr0[25 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7073205Z                                 tmp32 = tmp33;
2023-01-11T21:05:10.7073273Z                             }
2023-01-11T21:05:10.7073368Z                             auto tmp34 = tmp32 + tmp30;
2023-01-11T21:05:10.7073454Z                             float tmp35 = 0.0;
2023-01-11T21:05:10.7073516Z                             if(tmp6)
2023-01-11T21:05:10.7073584Z                             {
2023-01-11T21:05:10.7073701Z                                 auto tmp36 = in_ptr0[(36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7073784Z                                 tmp35 = tmp36;
2023-01-11T21:05:10.7073851Z                             }
2023-01-11T21:05:10.7073938Z                             float tmp37 = 0.0;
2023-01-11T21:05:10.7074044Z                             if(tmp11)
2023-01-11T21:05:10.7074098Z                             {
2023-01-11T21:05:10.7074216Z                                 auto tmp38 = in_ptr0[1 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7074300Z                                 tmp37 = tmp38;
2023-01-11T21:05:10.7074367Z                             }
2023-01-11T21:05:10.7074462Z                             auto tmp39 = tmp37 + tmp35;
2023-01-11T21:05:10.7074547Z                             float tmp40 = 0.0;
2023-01-11T21:05:10.7074622Z                             if(tmp17)
2023-01-11T21:05:10.7074676Z                             {
2023-01-11T21:05:10.7074795Z                                 auto tmp41 = in_ptr0[12 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7074877Z                                 tmp40 = tmp41;
2023-01-11T21:05:10.7074943Z                             }
2023-01-11T21:05:10.7075038Z                             auto tmp42 = tmp40 + tmp39;
2023-01-11T21:05:10.7075126Z                             float tmp43 = 0.0;
2023-01-11T21:05:10.7075200Z                             if(tmp21)
2023-01-11T21:05:10.7075266Z                             {
2023-01-11T21:05:10.7075406Z                                 auto tmp44 = in_ptr0[13 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7075491Z                                 tmp43 = tmp44;
2023-01-11T21:05:10.7075559Z                             }
2023-01-11T21:05:10.7075654Z                             auto tmp45 = tmp43 + tmp42;
2023-01-11T21:05:10.7075739Z                             float tmp46 = 0.0;
2023-01-11T21:05:10.7075812Z                             if(tmp27)
2023-01-11T21:05:10.7075880Z                             {
2023-01-11T21:05:10.7075986Z                                 auto tmp47 = in_ptr0[24 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7076068Z                                 tmp46 = tmp47;
2023-01-11T21:05:10.7076134Z                             }
2023-01-11T21:05:10.7076231Z                             auto tmp48 = tmp46 + tmp45;
2023-01-11T21:05:10.7076318Z                             float tmp49 = 0.0;
2023-01-11T21:05:10.7076392Z                             if(tmp31)
2023-01-11T21:05:10.7076461Z                             {
2023-01-11T21:05:10.7076567Z                                 auto tmp50 = in_ptr0[25 + (36*i1) + (72*i0) + (((7 + (12*i2)) / 8))];
2023-01-11T21:05:10.7076650Z                                 tmp49 = tmp50;
2023-01-11T21:05:10.7076717Z                             }
2023-01-11T21:05:10.7076810Z                             auto tmp51 = tmp49 + tmp48;
2023-01-11T21:05:10.7076913Z                             out_ptr2[i2 + (8*i1) + (16*i0)] = tmp34;
2023-01-11T21:05:10.7077015Z                             out_ptr3[i2 + (8*i1) + (16*i0)] = tmp51;
2023-01-11T21:05:10.7077080Z                         }
2023-01-11T21:05:10.7077144Z                     }
2023-01-11T21:05:10.7077196Z                 }
2023-01-11T21:05:10.7077256Z             }
2023-01-11T21:05:10.7077318Z         }
2023-01-11T21:05:10.7077393Z         #pragma omp for 
2023-01-11T21:05:10.7077472Z         for(long i0=0; i0<9; i0+=1)
2023-01-11T21:05:10.7077533Z         {
2023-01-11T21:05:10.7077597Z             #pragma GCC ivdep
2023-01-11T21:05:10.7077682Z             for(long i1=0; i1<4; i1+=1)
2023-01-11T21:05:10.7077743Z             {
2023-01-11T21:05:10.7077822Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7077909Z                 for(long i2=0; i2<7; i2+=1)
2023-01-11T21:05:10.7077972Z                 {
2023-01-11T21:05:10.7078036Z                     {
2023-01-11T21:05:10.7078089Z                         {
2023-01-11T21:05:10.7078203Z                             auto tmp0 = static_cast<long>(((3 + (6*i1)) / 4));
2023-01-11T21:05:10.7078319Z                             auto tmp1 = static_cast<long>(((9 + (6*i1)) / 4));
2023-01-11T21:05:10.7078414Z                             auto tmp2 = tmp0 < tmp1;
2023-01-11T21:05:10.7078529Z                             auto tmp3 = static_cast<long>(((6 + (12*i2)) / 7));
2023-01-11T21:05:10.7078676Z                             auto tmp4 = static_cast<long>(((18 + (12*i2)) / 7));
2023-01-11T21:05:10.7078770Z                             auto tmp5 = tmp3 < tmp4;
2023-01-11T21:05:10.7078864Z                             auto tmp6 = tmp2 & tmp5;
2023-01-11T21:05:10.7078939Z                             float tmp7 = 0.0;
2023-01-11T21:05:10.7079014Z                             if(tmp6)
2023-01-11T21:05:10.7079081Z                             {
2023-01-11T21:05:10.7079206Z                                 auto tmp8 = in_ptr0[(12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:05:10.7079291Z                                 tmp7 = tmp8;
2023-01-11T21:05:10.7079358Z                             }
2023-01-11T21:05:10.7079476Z                             auto tmp9 = static_cast<long>(1 + (((6 + (12*i2)) / 7)));
2023-01-11T21:05:10.7079558Z                             auto tmp10 = tmp9 < tmp4;
2023-01-11T21:05:10.7079653Z                             auto tmp11 = tmp2 & tmp10;
2023-01-11T21:05:10.7079743Z                             float tmp12 = 0.0;
2023-01-11T21:05:10.7079816Z                             if(tmp11)
2023-01-11T21:05:10.7079882Z                             {
2023-01-11T21:05:10.7080034Z                                 auto tmp13 = in_ptr0[1 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:05:10.7080121Z                                 tmp12 = tmp13;
2023-01-11T21:05:10.7080175Z                             }
2023-01-11T21:05:10.7080271Z                             auto tmp14 = tmp12 + tmp7;
2023-01-11T21:05:10.7080390Z                             auto tmp15 = static_cast<long>(1 + (((3 + (6*i1)) / 4)));
2023-01-11T21:05:10.7080484Z                             auto tmp16 = tmp15 < tmp1;
2023-01-11T21:05:10.7080579Z                             auto tmp17 = tmp16 & tmp5;
2023-01-11T21:05:10.7080787Z                             float tmp18 = 0.0;
2023-01-11T21:05:10.7080863Z                             if(tmp17)
2023-01-11T21:05:10.7080935Z                             {
2023-01-11T21:05:10.7081051Z                                 auto tmp19 = in_ptr0[12 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:05:10.7081139Z                                 tmp18 = tmp19;
2023-01-11T21:05:10.7081208Z                             }
2023-01-11T21:05:10.7081307Z                             auto tmp20 = tmp18 + tmp14;
2023-01-11T21:05:10.7081403Z                             auto tmp21 = tmp16 & tmp10;
2023-01-11T21:05:10.7081493Z                             float tmp22 = 0.0;
2023-01-11T21:05:10.7081568Z                             if(tmp21)
2023-01-11T21:05:10.7081622Z                             {
2023-01-11T21:05:10.7081750Z                                 auto tmp23 = in_ptr0[13 + (12*(((3 + (6*i1)) / 4))) + (72*i0) + (((6 + (12*i2)) / 7))];
2023-01-11T21:05:10.7081837Z                                 tmp22 = tmp23;
2023-01-11T21:05:10.7081910Z                             }
2023-01-11T21:05:10.7082008Z                             auto tmp24 = tmp22 + tmp20;
2023-01-11T21:05:10.7082115Z                             out_ptr4[i2 + (7*i1) + (28*i0)] = tmp24;
2023-01-11T21:05:10.7082183Z                         }
2023-01-11T21:05:10.7082237Z                     }
2023-01-11T21:05:10.7082301Z                 }
2023-01-11T21:05:10.7082364Z             }
2023-01-11T21:05:10.7082425Z         }
2023-01-11T21:05:10.7082487Z     }
2023-01-11T21:05:10.7082545Z }
2023-01-11T21:05:10.7082643Z ''')
2023-01-11T21:05:10.7082651Z 
2023-01-11T21:05:10.7082655Z 
2023-01-11T21:05:10.7082731Z async_compile.wait(globals())
2023-01-11T21:05:10.7082805Z del async_compile
2023-01-11T21:05:10.7082811Z 
2023-01-11T21:05:10.7082880Z def call(args):
2023-01-11T21:05:10.7082950Z     arg0_1, = args
2023-01-11T21:05:10.7083022Z     args.clear()
2023-01-11T21:05:10.7083238Z     buf0 = empty_strided((3, 3, 3, 6), (54, 18, 6, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7083449Z     buf1 = empty_strided((3, 3, 4, 5), (60, 20, 5, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7083709Z     buf2 = empty_strided((3, 3, 2, 8), (48, 16, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7083904Z     buf3 = empty_strided((3, 3, 2, 8), (48, 16, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7084108Z     buf4 = empty_strided((3, 3, 4, 7), (84, 28, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7084343Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()))
2023-01-11T21:05:10.7084414Z     del arg0_1
2023-01-11T21:05:10.7084512Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.7084517Z 
2023-01-11T21:05:10.7084522Z 
2023-01-11T21:05:10.7084603Z if __name__ == "__main__":
2023-01-11T21:05:10.7084719Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7084842Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7085042Z     arg0_1 = rand_strided((3, 3, 6, 12), (216, 72, 12, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7085150Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7085455Z [2023-01-11 21:01:41,101] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 456
2023-01-11T21:05:10.7085461Z 
2023-01-11T21:05:10.7085532Z ok (3.365s)
2023-01-11T21:05:10.7085980Z   test_upsample_nearest2d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7086106Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7086364Z [2023-01-11 21:01:41,927] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 457
2023-01-11T21:05:10.7086629Z [2023-01-11 21:01:44,705] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 457
2023-01-11T21:05:10.7086635Z 
2023-01-11T21:05:10.7086730Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7086800Z import torch
2023-01-11T21:05:10.7086856Z import random
2023-01-11T21:05:10.7086969Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7087088Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7087093Z 
2023-01-11T21:05:10.7087169Z aten = torch.ops.aten
2023-01-11T21:05:10.7087302Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7087392Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7087397Z 
2023-01-11T21:05:10.7087401Z 
2023-01-11T21:05:10.7087532Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7087736Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7087841Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7087941Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.7088038Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.7088135Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.7088228Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.7088320Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.7088380Z {
2023-01-11T21:05:10.7088464Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7088525Z     {
2023-01-11T21:05:10.7088600Z         #pragma omp for 
2023-01-11T21:05:10.7088680Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7088741Z         {
2023-01-11T21:05:10.7088818Z             #pragma GCC ivdep
2023-01-11T21:05:10.7088903Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:05:10.7088953Z             {
2023-01-11T21:05:10.7089032Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7089121Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:05:10.7089214Z                 {
2023-01-11T21:05:10.7089281Z                     {
2023-01-11T21:05:10.7089348Z                         {
2023-01-11T21:05:10.7089457Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7089554Z                             auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:05:10.7089649Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7089756Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7089861Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7089954Z                             auto tmp5 = tmp4 * tmp1;
2023-01-11T21:05:10.7090057Z                             auto tmp6 = static_cast<int>(tmp5);
2023-01-11T21:05:10.7090172Z                             auto tmp7 = in_ptr0[tmp6 + (38*tmp3) + (1406*i0)];
2023-01-11T21:05:10.7090264Z                             out_ptr0[i2 + (76*i1) + (5624*i0)] = tmp7;
2023-01-11T21:05:10.7090368Z                             out_ptr1[i2 + (76*i1) + (5624*i0)] = tmp7;
2023-01-11T21:05:10.7090434Z                         }
2023-01-11T21:05:10.7090499Z                     }
2023-01-11T21:05:10.7090587Z                 }
2023-01-11T21:05:10.7090650Z             }
2023-01-11T21:05:10.7090710Z         }
2023-01-11T21:05:10.7090774Z         #pragma omp for 
2023-01-11T21:05:10.7090853Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7090914Z         {
2023-01-11T21:05:10.7090993Z             #pragma GCC ivdep
2023-01-11T21:05:10.7091076Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:05:10.7091138Z             {
2023-01-11T21:05:10.7091216Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7091292Z                 for(long i2=0; i2<75; i2+=1)
2023-01-11T21:05:10.7091356Z                 {
2023-01-11T21:05:10.7091419Z                     {
2023-01-11T21:05:10.7091485Z                         {
2023-01-11T21:05:10.7091593Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7091711Z                             auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:05:10.7091805Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7091900Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7092007Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7092121Z                             auto tmp5 = static_cast<float>(0.5066666666666667);
2023-01-11T21:05:10.7092216Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.7092321Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:05:10.7092437Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:05:10.7092539Z                             out_ptr2[i2 + (75*i1) + (5250*i0)] = tmp8;
2023-01-11T21:05:10.7092594Z                         }
2023-01-11T21:05:10.7092657Z                     }
2023-01-11T21:05:10.7092722Z                 }
2023-01-11T21:05:10.7092783Z             }
2023-01-11T21:05:10.7092845Z         }
2023-01-11T21:05:10.7092919Z         #pragma omp for 
2023-01-11T21:05:10.7092998Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7093048Z         {
2023-01-11T21:05:10.7093125Z             #pragma GCC ivdep
2023-01-11T21:05:10.7093209Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:05:10.7093272Z             {
2023-01-11T21:05:10.7093351Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7093437Z                 for(long i2=0; i2<74; i2+=1)
2023-01-11T21:05:10.7093501Z                 {
2023-01-11T21:05:10.7093552Z                     {
2023-01-11T21:05:10.7093618Z                         {
2023-01-11T21:05:10.7093723Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7093837Z                             auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:05:10.7093931Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7094068Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7094174Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7094277Z                             auto tmp5 = static_cast<float>(0.5135135135135135);
2023-01-11T21:05:10.7094372Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.7094476Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:05:10.7094590Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:05:10.7094691Z                             out_ptr3[i2 + (74*i1) + (3330*i0)] = tmp8;
2023-01-11T21:05:10.7094758Z                         }
2023-01-11T21:05:10.7094822Z                     }
2023-01-11T21:05:10.7094871Z                 }
2023-01-11T21:05:10.7094934Z             }
2023-01-11T21:05:10.7094995Z         }
2023-01-11T21:05:10.7095070Z         #pragma omp for 
2023-01-11T21:05:10.7095151Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7095214Z         {
2023-01-11T21:05:10.7095292Z             #pragma GCC ivdep
2023-01-11T21:05:10.7095362Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:05:10.7095423Z             {
2023-01-11T21:05:10.7095529Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7095617Z                 for(long i2=0; i2<39; i2+=1)
2023-01-11T21:05:10.7095681Z                 {
2023-01-11T21:05:10.7095746Z                     {
2023-01-11T21:05:10.7095813Z                         {
2023-01-11T21:05:10.7095905Z                             auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7096019Z                             auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:05:10.7096116Z                             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7096222Z                             auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7096326Z                             auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7096441Z                             auto tmp5 = static_cast<float>(0.9743589743589743);
2023-01-11T21:05:10.7096537Z                             auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.7096628Z                             auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:05:10.7096745Z                             auto tmp8 = in_ptr0[tmp7 + (38*tmp3) + (1406*i0)];
2023-01-11T21:05:10.7096849Z                             out_ptr4[i2 + (39*i1) + (1404*i0)] = tmp8;
2023-01-11T21:05:10.7096918Z                         }
2023-01-11T21:05:10.7096983Z                     }
2023-01-11T21:05:10.7097047Z                 }
2023-01-11T21:05:10.7097109Z             }
2023-01-11T21:05:10.7097157Z         }
2023-01-11T21:05:10.7097218Z     }
2023-01-11T21:05:10.7097276Z }
2023-01-11T21:05:10.7097359Z ''')
2023-01-11T21:05:10.7097365Z 
2023-01-11T21:05:10.7097369Z 
2023-01-11T21:05:10.7097459Z async_compile.wait(globals())
2023-01-11T21:05:10.7097531Z del async_compile
2023-01-11T21:05:10.7097535Z 
2023-01-11T21:05:10.7097605Z def call(args):
2023-01-11T21:05:10.7097675Z     arg0_1, = args
2023-01-11T21:05:10.7097735Z     args.clear()
2023-01-11T21:05:10.7097961Z     buf0 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7098179Z     buf4 = empty_strided((2, 4, 74, 76), (22496, 5624, 76, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7098396Z     buf1 = empty_strided((2, 4, 70, 75), (21000, 5250, 75, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7098704Z     buf2 = empty_strided((2, 4, 45, 74), (13320, 3330, 74, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7098927Z     buf3 = empty_strided((2, 4, 36, 39), (5616, 1404, 39, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7099164Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.7099233Z     del arg0_1
2023-01-11T21:05:10.7099317Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.7099358Z 
2023-01-11T21:05:10.7099376Z 
2023-01-11T21:05:10.7099439Z if __name__ == "__main__":
2023-01-11T21:05:10.7099555Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7099681Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7099901Z     arg0_1 = rand_strided((2, 4, 37, 38), (5624, 1406, 38, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7100010Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7100015Z 
2023-01-11T21:05:10.7100083Z ok (3.721s)
2023-01-11T21:05:10.7100533Z   test_upsample_nearest3d_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7100664Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7100915Z [2023-01-11 21:01:46,049] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 458
2023-01-11T21:05:10.7101208Z [2023-01-11 21:01:48,868] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 458
2023-01-11T21:05:10.7101214Z 
2023-01-11T21:05:10.7101308Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7101377Z import torch
2023-01-11T21:05:10.7101447Z import random
2023-01-11T21:05:10.7101562Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7101680Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7101685Z 
2023-01-11T21:05:10.7101761Z aten = torch.ops.aten
2023-01-11T21:05:10.7101879Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7101969Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7101974Z 
2023-01-11T21:05:10.7101978Z 
2023-01-11T21:05:10.7102112Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7102319Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7102434Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7102534Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.7102631Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.7102724Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.7102802Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.7102894Z                        float* __restrict__ out_ptr4)
2023-01-11T21:05:10.7102954Z {
2023-01-11T21:05:10.7103049Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7103109Z     {
2023-01-11T21:05:10.7103185Z         #pragma omp for 
2023-01-11T21:05:10.7103264Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7103312Z         {
2023-01-11T21:05:10.7103389Z             #pragma GCC ivdep
2023-01-11T21:05:10.7103475Z             for(long i1=0; i1<74; i1+=1)
2023-01-11T21:05:10.7103537Z             {
2023-01-11T21:05:10.7103615Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7103702Z                 for(long i2=0; i2<76; i2+=1)
2023-01-11T21:05:10.7103767Z                 {
2023-01-11T21:05:10.7103836Z                     #pragma GCC ivdep
2023-01-11T21:05:10.7103924Z                     for(long i3=0; i3<78; i3+=1)
2023-01-11T21:05:10.7103988Z                     {
2023-01-11T21:05:10.7104055Z                         {
2023-01-11T21:05:10.7104123Z                             {
2023-01-11T21:05:10.7104231Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7104342Z                                 auto tmp1 = static_cast<float>(0.5);
2023-01-11T21:05:10.7104426Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7104534Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7104641Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7104765Z                                 auto tmp5 = tmp4 * tmp1;
2023-01-11T21:05:10.7104872Z                                 auto tmp6 = static_cast<int>(tmp5);
2023-01-11T21:05:10.7104979Z                                 auto tmp7 = static_cast<float>(i3);
2023-01-11T21:05:10.7105075Z                                 auto tmp8 = tmp7 * tmp1;
2023-01-11T21:05:10.7105170Z                                 auto tmp9 = static_cast<int>(tmp8);
2023-01-11T21:05:10.7105298Z                                 auto tmp10 = in_ptr0[tmp9 + (39*tmp6) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:05:10.7105410Z                                 out_ptr0[i3 + (78*i2) + (5928*i1) + (438672*i0)] = tmp10;
2023-01-11T21:05:10.7105521Z                                 out_ptr1[i3 + (78*i2) + (5928*i1) + (438672*i0)] = tmp10;
2023-01-11T21:05:10.7105590Z                             }
2023-01-11T21:05:10.7105656Z                         }
2023-01-11T21:05:10.7105720Z                     }
2023-01-11T21:05:10.7105785Z                 }
2023-01-11T21:05:10.7105834Z             }
2023-01-11T21:05:10.7105894Z         }
2023-01-11T21:05:10.7105969Z         #pragma omp for 
2023-01-11T21:05:10.7106049Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7106183Z         {
2023-01-11T21:05:10.7106263Z             #pragma GCC ivdep
2023-01-11T21:05:10.7106334Z             for(long i1=0; i1<70; i1+=1)
2023-01-11T21:05:10.7106399Z             {
2023-01-11T21:05:10.7106480Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7106569Z                 for(long i2=0; i2<75; i2+=1)
2023-01-11T21:05:10.7106633Z                 {
2023-01-11T21:05:10.7106715Z                     #pragma GCC ivdep
2023-01-11T21:05:10.7106806Z                     for(long i3=0; i3<80; i3+=1)
2023-01-11T21:05:10.7106858Z                     {
2023-01-11T21:05:10.7106924Z                         {
2023-01-11T21:05:10.7106993Z                             {
2023-01-11T21:05:10.7107102Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7107222Z                                 auto tmp1 = static_cast<float>(0.5285714285714286);
2023-01-11T21:05:10.7107321Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7107434Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7107541Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7107646Z                                 auto tmp5 = static_cast<float>(0.5066666666666667);
2023-01-11T21:05:10.7107746Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.7107856Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:05:10.7107961Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:05:10.7108073Z                                 auto tmp9 = static_cast<float>(0.4875);
2023-01-11T21:05:10.7108173Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.7108285Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:05:10.7108399Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:05:10.7108512Z                                 out_ptr2[i3 + (80*i2) + (6000*i1) + (420000*i0)] = tmp12;
2023-01-11T21:05:10.7108582Z                             }
2023-01-11T21:05:10.7108648Z                         }
2023-01-11T21:05:10.7108712Z                     }
2023-01-11T21:05:10.7108774Z                 }
2023-01-11T21:05:10.7108836Z             }
2023-01-11T21:05:10.7108883Z         }
2023-01-11T21:05:10.7108958Z         #pragma omp for 
2023-01-11T21:05:10.7109036Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7109097Z         {
2023-01-11T21:05:10.7109174Z             #pragma GCC ivdep
2023-01-11T21:05:10.7109256Z             for(long i1=0; i1<45; i1+=1)
2023-01-11T21:05:10.7109318Z             {
2023-01-11T21:05:10.7109385Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7109501Z                 for(long i2=0; i2<74; i2+=1)
2023-01-11T21:05:10.7109564Z                 {
2023-01-11T21:05:10.7109645Z                     #pragma GCC ivdep
2023-01-11T21:05:10.7109734Z                     for(long i3=0; i3<103; i3+=1)
2023-01-11T21:05:10.7109800Z                     {
2023-01-11T21:05:10.7109865Z                         {
2023-01-11T21:05:10.7109919Z                             {
2023-01-11T21:05:10.7110027Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7110142Z                                 auto tmp1 = static_cast<float>(0.8222222222222222);
2023-01-11T21:05:10.7110239Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7110345Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7110453Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7110567Z                                 auto tmp5 = static_cast<float>(0.5135135135135135);
2023-01-11T21:05:10.7110665Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.7110758Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:05:10.7110888Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:05:10.7111003Z                                 auto tmp9 = static_cast<float>(0.3786407766990291);
2023-01-11T21:05:10.7111100Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.7111210Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:05:10.7111337Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:05:10.7111450Z                                 out_ptr3[i3 + (103*i2) + (7622*i1) + (342990*i0)] = tmp12;
2023-01-11T21:05:10.7111518Z                             }
2023-01-11T21:05:10.7111572Z                         }
2023-01-11T21:05:10.7111637Z                     }
2023-01-11T21:05:10.7111698Z                 }
2023-01-11T21:05:10.7111762Z             }
2023-01-11T21:05:10.7111822Z         }
2023-01-11T21:05:10.7111897Z         #pragma omp for 
2023-01-11T21:05:10.7111963Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7112026Z         {
2023-01-11T21:05:10.7112101Z             #pragma GCC ivdep
2023-01-11T21:05:10.7112188Z             for(long i1=0; i1<36; i1+=1)
2023-01-11T21:05:10.7112250Z             {
2023-01-11T21:05:10.7112328Z                 #pragma GCC ivdep
2023-01-11T21:05:10.7112415Z                 for(long i2=0; i2<39; i2+=1)
2023-01-11T21:05:10.7112465Z                 {
2023-01-11T21:05:10.7112546Z                     #pragma GCC ivdep
2023-01-11T21:05:10.7112634Z                     for(long i3=0; i3<40; i3+=1)
2023-01-11T21:05:10.7112698Z                     {
2023-01-11T21:05:10.7112765Z                         {
2023-01-11T21:05:10.7112832Z                             {
2023-01-11T21:05:10.7112942Z                                 auto tmp0 = static_cast<float>(i1);
2023-01-11T21:05:10.7113049Z                                 auto tmp1 = static_cast<float>(1.0277777777777777);
2023-01-11T21:05:10.7113144Z                                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7113254Z                                 auto tmp3 = static_cast<int>(tmp2);
2023-01-11T21:05:10.7113360Z                                 auto tmp4 = static_cast<float>(i2);
2023-01-11T21:05:10.7113475Z                                 auto tmp5 = static_cast<float>(0.9743589743589743);
2023-01-11T21:05:10.7113571Z                                 auto tmp6 = tmp4 * tmp5;
2023-01-11T21:05:10.7113678Z                                 auto tmp7 = static_cast<int>(tmp6);
2023-01-11T21:05:10.7113782Z                                 auto tmp8 = static_cast<float>(i3);
2023-01-11T21:05:10.7113880Z                                 auto tmp9 = static_cast<float>(0.975);
2023-01-11T21:05:10.7113976Z                                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.7114087Z                                 auto tmp11 = static_cast<int>(tmp10);
2023-01-11T21:05:10.7114243Z                                 auto tmp12 = in_ptr0[tmp11 + (39*tmp7) + (1482*tmp3) + (54834*i0)];
2023-01-11T21:05:10.7114358Z                                 out_ptr4[i3 + (40*i2) + (1560*i1) + (56160*i0)] = tmp12;
2023-01-11T21:05:10.7114431Z                             }
2023-01-11T21:05:10.7114497Z                         }
2023-01-11T21:05:10.7114549Z                     }
2023-01-11T21:05:10.7114610Z                 }
2023-01-11T21:05:10.7114671Z             }
2023-01-11T21:05:10.7114732Z         }
2023-01-11T21:05:10.7114793Z     }
2023-01-11T21:05:10.7114851Z }
2023-01-11T21:05:10.7114934Z ''')
2023-01-11T21:05:10.7114940Z 
2023-01-11T21:05:10.7114945Z 
2023-01-11T21:05:10.7115020Z async_compile.wait(globals())
2023-01-11T21:05:10.7115089Z del async_compile
2023-01-11T21:05:10.7115094Z 
2023-01-11T21:05:10.7115161Z def call(args):
2023-01-11T21:05:10.7115228Z     arg0_1, = args
2023-01-11T21:05:10.7115297Z     args.clear()
2023-01-11T21:05:10.7115534Z     buf0 = empty_strided((2, 4, 74, 76, 78), (1754688, 438672, 5928, 78, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7115800Z     buf4 = empty_strided((2, 4, 74, 76, 78), (1754688, 438672, 5928, 78, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7116034Z     buf1 = empty_strided((2, 4, 70, 75, 80), (1680000, 420000, 6000, 80, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7116257Z     buf2 = empty_strided((2, 4, 45, 74, 103), (1371960, 342990, 7622, 103, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7116487Z     buf3 = empty_strided((2, 4, 36, 39, 40), (224640, 56160, 1560, 40, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7116720Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()))
2023-01-11T21:05:10.7116792Z     del arg0_1
2023-01-11T21:05:10.7116888Z     return (buf0, buf1, buf2, buf3, buf4, )
2023-01-11T21:05:10.7116895Z 
2023-01-11T21:05:10.7116900Z 
2023-01-11T21:05:10.7116974Z if __name__ == "__main__":
2023-01-11T21:05:10.7117089Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7117211Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7117429Z     arg0_1 = rand_strided((2, 4, 37, 38, 39), (219336, 54834, 1482, 39, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7117535Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7117540Z 
2023-01-11T21:05:10.7117604Z ok (6.146s)
2023-01-11T21:05:10.7118037Z   test_var_mean_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7118161Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7118423Z [2023-01-11 21:01:51,017] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 459
2023-01-11T21:05:10.7118687Z [2023-01-11 21:01:53,941] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 459
2023-01-11T21:05:10.7118693Z 
2023-01-11T21:05:10.7118784Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7118852Z import torch
2023-01-11T21:05:10.7118920Z import random
2023-01-11T21:05:10.7119020Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7119141Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7119146Z 
2023-01-11T21:05:10.7119223Z aten = torch.ops.aten
2023-01-11T21:05:10.7119359Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7119447Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7119452Z 
2023-01-11T21:05:10.7119457Z 
2023-01-11T21:05:10.7119589Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7119821Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7119939Z extern "C" void kernel(float* __restrict__ in_out_ptr0,
2023-01-11T21:05:10.7120029Z                        float* __restrict__ in_out_ptr1,
2023-01-11T21:05:10.7120127Z                        float* __restrict__ in_out_ptr2,
2023-01-11T21:05:10.7120224Z                        float* __restrict__ in_out_ptr3,
2023-01-11T21:05:10.7120325Z                        const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7120424Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.7120518Z                        float* __restrict__ out_ptr3)
2023-01-11T21:05:10.7120576Z {
2023-01-11T21:05:10.7120760Z     auto out_ptr1 = in_out_ptr0;
2023-01-11T21:05:10.7120842Z     auto out_ptr2 = in_out_ptr1;
2023-01-11T21:05:10.7120924Z     auto out_ptr4 = in_out_ptr2;
2023-01-11T21:05:10.7121007Z     auto out_ptr5 = in_out_ptr3;
2023-01-11T21:05:10.7121107Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7121169Z     {
2023-01-11T21:05:10.7121246Z         #pragma omp for 
2023-01-11T21:05:10.7121314Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7121376Z         {
2023-01-11T21:05:10.7121492Z             {
2023-01-11T21:05:10.7121559Z                 {
2023-01-11T21:05:10.7121642Z                     float tmp1 = 0;
2023-01-11T21:05:10.7121734Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.7121786Z                     {
2023-01-11T21:05:10.7121856Z                         {
2023-01-11T21:05:10.7121960Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.7122044Z                             tmp1 += tmp0;
2023-01-11T21:05:10.7122112Z                         }
2023-01-11T21:05:10.7122179Z                     }
2023-01-11T21:05:10.7122264Z                     out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.7122315Z                 }
2023-01-11T21:05:10.7122377Z             }
2023-01-11T21:05:10.7122441Z         }
2023-01-11T21:05:10.7122517Z         #pragma omp for 
2023-01-11T21:05:10.7122597Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7122661Z         {
2023-01-11T21:05:10.7122722Z             {
2023-01-11T21:05:10.7122774Z                 {
2023-01-11T21:05:10.7122853Z                     float tmp6 = 0;
2023-01-11T21:05:10.7122931Z                     float tmp7 = 0;
2023-01-11T21:05:10.7123021Z                     for(long i1=0; i1<8; i1+=1)
2023-01-11T21:05:10.7123087Z                     {
2023-01-11T21:05:10.7123153Z                         {
2023-01-11T21:05:10.7123253Z                             auto tmp0 = in_ptr0[i1 + (8*i0)];
2023-01-11T21:05:10.7123337Z                             auto tmp1 = out_ptr0[i0];
2023-01-11T21:05:10.7123442Z                             auto tmp2 = static_cast<float>(8);
2023-01-11T21:05:10.7123538Z                             auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.7123683Z                             auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.7123776Z                             auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.7123858Z                             tmp6 += tmp5;
2023-01-11T21:05:10.7123937Z                             tmp7 += tmp0;
2023-01-11T21:05:10.7123993Z                         }
2023-01-11T21:05:10.7124057Z                     }
2023-01-11T21:05:10.7124139Z                     out_ptr1[i0] = tmp6;
2023-01-11T21:05:10.7124221Z                     out_ptr2[i0] = tmp7;
2023-01-11T21:05:10.7124285Z                 }
2023-01-11T21:05:10.7124346Z             }
2023-01-11T21:05:10.7124405Z         }
2023-01-11T21:05:10.7124467Z         #pragma omp for 
2023-01-11T21:05:10.7124544Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7124603Z         {
2023-01-11T21:05:10.7124664Z             {
2023-01-11T21:05:10.7124726Z                 {
2023-01-11T21:05:10.7124817Z                     auto tmp0 = out_ptr1[i0];
2023-01-11T21:05:10.7124907Z                     auto tmp1 = static_cast<float>(7);
2023-01-11T21:05:10.7124996Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.7125121Z                     in_out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7125184Z                 }
2023-01-11T21:05:10.7125245Z             }
2023-01-11T21:05:10.7125306Z         }
2023-01-11T21:05:10.7125383Z         #pragma omp for 
2023-01-11T21:05:10.7125450Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7125510Z         {
2023-01-11T21:05:10.7125571Z             {
2023-01-11T21:05:10.7125635Z                 {
2023-01-11T21:05:10.7125727Z                     auto tmp0 = out_ptr2[i0];
2023-01-11T21:05:10.7125828Z                     auto tmp1 = static_cast<float>(8);
2023-01-11T21:05:10.7125917Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.7125991Z                     in_out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.7126053Z                 }
2023-01-11T21:05:10.7126113Z             }
2023-01-11T21:05:10.7126173Z         }
2023-01-11T21:05:10.7126247Z         #pragma omp for 
2023-01-11T21:05:10.7126326Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.7126390Z         {
2023-01-11T21:05:10.7126438Z             {
2023-01-11T21:05:10.7126500Z                 {
2023-01-11T21:05:10.7126579Z                     float tmp1 = 0;
2023-01-11T21:05:10.7126696Z                     for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.7126763Z                     {
2023-01-11T21:05:10.7126854Z                         for(long i2=0; i2<8; i2+=1)
2023-01-11T21:05:10.7126907Z                         {
2023-01-11T21:05:10.7126976Z                             {
2023-01-11T21:05:10.7127086Z                                 auto tmp0 = in_ptr0[i2 + (8*i0) + (32*i1)];
2023-01-11T21:05:10.7127171Z                                 tmp1 += tmp0;
2023-01-11T21:05:10.7127241Z                             }
2023-01-11T21:05:10.7127306Z                         }
2023-01-11T21:05:10.7127372Z                     }
2023-01-11T21:05:10.7127442Z                     out_ptr3[i0] = tmp1;
2023-01-11T21:05:10.7127505Z                 }
2023-01-11T21:05:10.7127573Z             }
2023-01-11T21:05:10.7127633Z         }
2023-01-11T21:05:10.7127707Z         #pragma omp for 
2023-01-11T21:05:10.7127787Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.7127847Z         {
2023-01-11T21:05:10.7127898Z             {
2023-01-11T21:05:10.7127961Z                 {
2023-01-11T21:05:10.7128039Z                     float tmp6 = 0;
2023-01-11T21:05:10.7128116Z                     float tmp7 = 0;
2023-01-11T21:05:10.7128206Z                     for(long i1=0; i1<2; i1+=1)
2023-01-11T21:05:10.7128272Z                     {
2023-01-11T21:05:10.7128363Z                         for(long i2=0; i2<8; i2+=1)
2023-01-11T21:05:10.7128416Z                         {
2023-01-11T21:05:10.7128485Z                             {
2023-01-11T21:05:10.7128595Z                                 auto tmp0 = in_ptr0[i2 + (8*i0) + (32*i1)];
2023-01-11T21:05:10.7128693Z                                 auto tmp1 = out_ptr3[i0];
2023-01-11T21:05:10.7128802Z                                 auto tmp2 = static_cast<float>(16);
2023-01-11T21:05:10.7128902Z                                 auto tmp3 = tmp1 / tmp2;
2023-01-11T21:05:10.7129053Z                                 auto tmp4 = tmp0 - tmp3;
2023-01-11T21:05:10.7129138Z                                 auto tmp5 = tmp4 * tmp4;
2023-01-11T21:05:10.7129222Z                                 tmp6 += tmp5;
2023-01-11T21:05:10.7129307Z                                 tmp7 += tmp0;
2023-01-11T21:05:10.7129376Z                             }
2023-01-11T21:05:10.7129444Z                         }
2023-01-11T21:05:10.7129510Z                     }
2023-01-11T21:05:10.7129592Z                     out_ptr4[i0] = tmp6;
2023-01-11T21:05:10.7129662Z                     out_ptr5[i0] = tmp7;
2023-01-11T21:05:10.7129724Z                 }
2023-01-11T21:05:10.7129785Z             }
2023-01-11T21:05:10.7129847Z         }
2023-01-11T21:05:10.7129923Z         #pragma omp for 
2023-01-11T21:05:10.7130002Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.7130063Z         {
2023-01-11T21:05:10.7130143Z             {
2023-01-11T21:05:10.7130206Z                 {
2023-01-11T21:05:10.7130294Z                     auto tmp0 = out_ptr4[i0];
2023-01-11T21:05:10.7130396Z                     auto tmp1 = static_cast<float>(15);
2023-01-11T21:05:10.7130489Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.7130576Z                     in_out_ptr2[i0] = tmp2;
2023-01-11T21:05:10.7130638Z                 }
2023-01-11T21:05:10.7130687Z             }
2023-01-11T21:05:10.7130749Z         }
2023-01-11T21:05:10.7130823Z         #pragma omp for 
2023-01-11T21:05:10.7130901Z         for(long i0=0; i0<4; i0+=1)
2023-01-11T21:05:10.7130961Z         {
2023-01-11T21:05:10.7131021Z             {
2023-01-11T21:05:10.7131071Z                 {
2023-01-11T21:05:10.7131160Z                     auto tmp0 = out_ptr5[i0];
2023-01-11T21:05:10.7131260Z                     auto tmp1 = static_cast<float>(16);
2023-01-11T21:05:10.7131348Z                     auto tmp2 = tmp0 / tmp1;
2023-01-11T21:05:10.7131437Z                     in_out_ptr3[i0] = tmp2;
2023-01-11T21:05:10.7131499Z                 }
2023-01-11T21:05:10.7131562Z             }
2023-01-11T21:05:10.7131609Z         }
2023-01-11T21:05:10.7131669Z     }
2023-01-11T21:05:10.7131728Z }
2023-01-11T21:05:10.7131834Z ''')
2023-01-11T21:05:10.7131840Z 
2023-01-11T21:05:10.7131844Z 
2023-01-11T21:05:10.7131935Z async_compile.wait(globals())
2023-01-11T21:05:10.7132006Z del async_compile
2023-01-11T21:05:10.7132010Z 
2023-01-11T21:05:10.7132080Z def call(args):
2023-01-11T21:05:10.7132134Z     arg0_1, = args
2023-01-11T21:05:10.7132204Z     args.clear()
2023-01-11T21:05:10.7132414Z     buf0 = empty_strided((1, 2, 4, 1), (8, 4, 1, 8), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7132618Z     buf1 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7132814Z     buf2 = empty_strided((1, 2, 4), (8, 4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7132900Z     buf3 = buf1; del buf1  # reuse
2023-01-11T21:05:10.7132983Z     buf4 = buf2; del buf2  # reuse
2023-01-11T21:05:10.7133187Z     buf5 = empty_strided((1, 1, 4, 1), (4, 4, 1, 4), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7133366Z     buf6 = empty_strided((1, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7133553Z     buf7 = empty_strided((1, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7133633Z     buf8 = buf6; del buf6  # reuse
2023-01-11T21:05:10.7133712Z     buf9 = buf7; del buf7  # reuse
2023-01-11T21:05:10.7133967Z     kernel_cpp_0(c_void_p(buf3.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf8.data_ptr()), c_void_p(buf9.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:05:10.7134035Z     del arg0_1
2023-01-11T21:05:10.7134120Z     return (buf3, buf4, buf8, buf9, )
2023-01-11T21:05:10.7134125Z 
2023-01-11T21:05:10.7134129Z 
2023-01-11T21:05:10.7134202Z if __name__ == "__main__":
2023-01-11T21:05:10.7134304Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7134426Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7134636Z     arg0_1 = rand_strided((1, 2, 4, 8), (64, 32, 8, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7134743Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7134748Z 
2023-01-11T21:05:10.7134813Z ok (2.970s)
2023-01-11T21:05:10.7135249Z   test_vdd_clamp_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7135374Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7135634Z [2023-01-11 21:01:54,012] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 460
2023-01-11T21:05:10.7135927Z [2023-01-11 21:01:56,710] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 460
2023-01-11T21:05:10.7135932Z 
2023-01-11T21:05:10.7136024Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7136082Z import torch
2023-01-11T21:05:10.7136150Z import random
2023-01-11T21:05:10.7136263Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7136382Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7136386Z 
2023-01-11T21:05:10.7136463Z aten = torch.ops.aten
2023-01-11T21:05:10.7136595Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7136685Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7136690Z 
2023-01-11T21:05:10.7136694Z 
2023-01-11T21:05:10.7136813Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7137015Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7137130Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7137231Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.7137326Z                        bool* __restrict__ out_ptr1)
2023-01-11T21:05:10.7137385Z {
2023-01-11T21:05:10.7137508Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7137557Z     {
2023-01-11T21:05:10.7137631Z         #pragma omp for 
2023-01-11T21:05:10.7137711Z         for(long i0=0; i0<16; i0+=1)
2023-01-11T21:05:10.7137772Z         {
2023-01-11T21:05:10.7137836Z             {
2023-01-11T21:05:10.7137898Z                 {
2023-01-11T21:05:10.7137989Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7138081Z                     auto tmp1 = static_cast<float>(3.0);
2023-01-11T21:05:10.7138216Z                     auto tmp2 = (tmp1 != tmp1) ? tmp1 : std::max(tmp0, tmp1);
2023-01-11T21:05:10.7138317Z                     auto tmp3 = static_cast<float>(3);
2023-01-11T21:05:10.7138407Z                     auto tmp4 = tmp0 >= tmp3;
2023-01-11T21:05:10.7138578Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7138665Z                     out_ptr1[i0] = tmp4;
2023-01-11T21:05:10.7138729Z                 }
2023-01-11T21:05:10.7138779Z             }
2023-01-11T21:05:10.7138842Z         }
2023-01-11T21:05:10.7138903Z     }
2023-01-11T21:05:10.7138963Z }
2023-01-11T21:05:10.7139043Z ''')
2023-01-11T21:05:10.7139048Z 
2023-01-11T21:05:10.7139052Z 
2023-01-11T21:05:10.7139144Z async_compile.wait(globals())
2023-01-11T21:05:10.7139215Z del async_compile
2023-01-11T21:05:10.7139220Z 
2023-01-11T21:05:10.7139289Z def call(args):
2023-01-11T21:05:10.7139350Z     primals_1, = args
2023-01-11T21:05:10.7139419Z     args.clear()
2023-01-11T21:05:10.7139618Z     buf0 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7139803Z     buf1 = empty_strided((16, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.7139971Z     kernel_cpp_0(c_void_p(primals_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf1.data_ptr()))
2023-01-11T21:05:10.7140042Z     del primals_1
2023-01-11T21:05:10.7140117Z     return (buf0, buf1, )
2023-01-11T21:05:10.7140122Z 
2023-01-11T21:05:10.7140126Z 
2023-01-11T21:05:10.7140188Z if __name__ == "__main__":
2023-01-11T21:05:10.7140300Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7140419Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7140615Z     primals_1 = rand_strided((16, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7140726Z     print_performance(lambda: call([primals_1]))
2023-01-11T21:05:10.7140731Z 
2023-01-11T21:05:10.7140796Z ok (2.767s)
2023-01-11T21:05:10.7141241Z   test_vertical_fusion1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7141403Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7141663Z [2023-01-11 21:01:56,935] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 461
2023-01-11T21:05:10.7141916Z [2023-01-11 21:01:59,613] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 461
2023-01-11T21:05:10.7141934Z 
2023-01-11T21:05:10.7142014Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7142081Z import torch
2023-01-11T21:05:10.7142151Z import random
2023-01-11T21:05:10.7142269Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7142388Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7142393Z 
2023-01-11T21:05:10.7142468Z aten = torch.ops.aten
2023-01-11T21:05:10.7142600Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7142677Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7142682Z 
2023-01-11T21:05:10.7142701Z 
2023-01-11T21:05:10.7142820Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7143022Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7143183Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7143288Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7143387Z                        const float* __restrict__ in_ptr2,
2023-01-11T21:05:10.7143486Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7143545Z {
2023-01-11T21:05:10.7143628Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7143688Z     {
2023-01-11T21:05:10.7143762Z         #pragma omp for 
2023-01-11T21:05:10.7143844Z         for(long i0=0; i0<41616; i0+=1)
2023-01-11T21:05:10.7143906Z         {
2023-01-11T21:05:10.7143986Z             for(long i1=0; i1<1; i1+=1)
2023-01-11T21:05:10.7144049Z             {
2023-01-11T21:05:10.7144179Z                 auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + (16*i1) + (26*i0));
2023-01-11T21:05:10.7144318Z                 auto tmp8 = at::vec::Vectorized<float>::loadu(in_ptr1 + (16*i1) + (26*i0));
2023-01-11T21:05:10.7144454Z                 auto tmp15 = at::vec::Vectorized<float>::loadu(in_ptr2 + 16*i1);
2023-01-11T21:05:10.7144684Z                 auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(-1.061519070296458e-11));
2023-01-11T21:05:10.7144771Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7144994Z                 auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(-1.988366587925593e-08));
2023-01-11T21:05:10.7145083Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7145172Z                 auto tmp5 = tmp0 * tmp4;
2023-01-11T21:05:10.7145382Z                 auto tmp6 = at::vec::Vectorized<float>(static_cast<float>(-3.087032500374211e-07));
2023-01-11T21:05:10.7145468Z                 auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.7145689Z                 auto tmp9 = at::vec::Vectorized<float>(static_cast<float>(1.55093272922008e-10));
2023-01-11T21:05:10.7145778Z                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.7145866Z                 auto tmp11 = tmp7 + tmp10;
2023-01-11T21:05:10.7145968Z                 auto tmp12 = tmp11.reciprocal();
2023-01-11T21:05:10.7146102Z                 auto tmp13 = at::vec::Vectorized<float>(static_cast<float>(1.0));
2023-01-11T21:05:10.7146192Z                 auto tmp14 = tmp12 * tmp13;
2023-01-11T21:05:10.7146268Z                 auto tmp16 = tmp11 * tmp15;
2023-01-11T21:05:10.7146355Z                 auto tmp17 = tmp14 + tmp16;
2023-01-11T21:05:10.7146458Z                 tmp17.store(out_ptr0 + (16*i1) + (26*i0));
2023-01-11T21:05:10.7146521Z             }
2023-01-11T21:05:10.7146612Z             #pragma omp simd simdlen(8) 
2023-01-11T21:05:10.7146696Z             for(long i1=16; i1<26; i1+=1)
2023-01-11T21:05:10.7146758Z             {
2023-01-11T21:05:10.7146840Z                 auto tmp0 = in_ptr0[i1 + (26*i0)];
2023-01-11T21:05:10.7146963Z                 auto tmp8 = in_ptr1[i1 + (26*i0)];
2023-01-11T21:05:10.7147049Z                 auto tmp15 = in_ptr2[i1];
2023-01-11T21:05:10.7147226Z                 auto tmp1 = static_cast<float>(-1.061519070296458e-11);
2023-01-11T21:05:10.7147314Z                 auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7147490Z                 auto tmp3 = static_cast<float>(-1.988366587925593e-08);
2023-01-11T21:05:10.7147576Z                 auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7147648Z                 auto tmp5 = tmp0 * tmp4;
2023-01-11T21:05:10.7147824Z                 auto tmp6 = static_cast<float>(-3.087032500374211e-07);
2023-01-11T21:05:10.7147906Z                 auto tmp7 = tmp5 + tmp6;
2023-01-11T21:05:10.7148080Z                 auto tmp9 = static_cast<float>(1.55093272922008e-10);
2023-01-11T21:05:10.7148165Z                 auto tmp10 = tmp8 * tmp9;
2023-01-11T21:05:10.7148252Z                 auto tmp11 = tmp7 + tmp10;
2023-01-11T21:05:10.7148333Z                 auto tmp12 = 1 / tmp11;
2023-01-11T21:05:10.7148438Z                 auto tmp13 = static_cast<float>(1.0);
2023-01-11T21:05:10.7148514Z                 auto tmp14 = tmp12 * tmp13;
2023-01-11T21:05:10.7148602Z                 auto tmp16 = tmp11 * tmp15;
2023-01-11T21:05:10.7148720Z                 auto tmp17 = tmp14 + tmp16;
2023-01-11T21:05:10.7148811Z                 out_ptr0[i1 + (26*i0)] = tmp17;
2023-01-11T21:05:10.7148874Z             }
2023-01-11T21:05:10.7148936Z         }
2023-01-11T21:05:10.7148996Z     }
2023-01-11T21:05:10.7149042Z }
2023-01-11T21:05:10.7149118Z ''')
2023-01-11T21:05:10.7149124Z 
2023-01-11T21:05:10.7149129Z 
2023-01-11T21:05:10.7149218Z async_compile.wait(globals())
2023-01-11T21:05:10.7149289Z del async_compile
2023-01-11T21:05:10.7149294Z 
2023-01-11T21:05:10.7149362Z def call(args):
2023-01-11T21:05:10.7149442Z     arg0_1, arg1_1, arg2_1 = args
2023-01-11T21:05:10.7149513Z     args.clear()
2023-01-11T21:05:10.7149714Z     buf0 = empty_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7149903Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(arg2_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7149970Z     del arg0_1
2023-01-11T21:05:10.7150037Z     del arg1_1
2023-01-11T21:05:10.7150102Z     del arg2_1
2023-01-11T21:05:10.7150171Z     return (buf0, )
2023-01-11T21:05:10.7150177Z 
2023-01-11T21:05:10.7150181Z 
2023-01-11T21:05:10.7150255Z if __name__ == "__main__":
2023-01-11T21:05:10.7150367Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7150476Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7150688Z     arg0_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7150898Z     arg1_1 = rand_strided((204, 204, 26), (5304, 26, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7151088Z     arg2_1 = rand_strided((26, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7151209Z     print_performance(lambda: call([arg0_1, arg1_1, arg2_1]))
2023-01-11T21:05:10.7151215Z 
2023-01-11T21:05:10.7151280Z ok (3.473s)
2023-01-11T21:05:10.7151719Z   test_views1_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7151847Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7152106Z [2023-01-11 21:02:00,218] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 462
2023-01-11T21:05:10.7152356Z [2023-01-11 21:02:02,840] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 462
2023-01-11T21:05:10.7152757Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7152913Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7153168Z [2023-01-11 21:02:02,880] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 463
2023-01-11T21:05:10.7153429Z [2023-01-11 21:02:05,517] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 463
2023-01-11T21:05:10.7153826Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7153950Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7154204Z [2023-01-11 21:02:05,555] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 464
2023-01-11T21:05:10.7154491Z [2023-01-11 21:02:08,213] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 464
2023-01-11T21:05:10.7154887Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7155011Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7155266Z [2023-01-11 21:02:08,264] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 465
2023-01-11T21:05:10.7155512Z [2023-01-11 21:02:10,957] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 465
2023-01-11T21:05:10.7155911Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7156036Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7156287Z [2023-01-11 21:02:11,003] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 466
2023-01-11T21:05:10.7156293Z 
2023-01-11T21:05:10.7156385Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7156454Z import torch
2023-01-11T21:05:10.7156523Z import random
2023-01-11T21:05:10.7156636Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7156743Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7156760Z 
2023-01-11T21:05:10.7156828Z aten = torch.ops.aten
2023-01-11T21:05:10.7156960Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7157051Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7157056Z 
2023-01-11T21:05:10.7157060Z 
2023-01-11T21:05:10.7157195Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7157397Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7157513Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7157616Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7157714Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7157761Z {
2023-01-11T21:05:10.7157856Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7157918Z     {
2023-01-11T21:05:10.7157992Z         #pragma omp for 
2023-01-11T21:05:10.7158073Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7158136Z         {
2023-01-11T21:05:10.7158256Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7158416Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7158502Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7158594Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7158657Z         }
2023-01-11T21:05:10.7158752Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7158834Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:05:10.7158895Z         {
2023-01-11T21:05:10.7158963Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7159042Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7159122Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7159201Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7159262Z         }
2023-01-11T21:05:10.7159322Z     }
2023-01-11T21:05:10.7159368Z }
2023-01-11T21:05:10.7159445Z ''')
2023-01-11T21:05:10.7159450Z 
2023-01-11T21:05:10.7159454Z 
2023-01-11T21:05:10.7159545Z async_compile.wait(globals())
2023-01-11T21:05:10.7159619Z del async_compile
2023-01-11T21:05:10.7159624Z 
2023-01-11T21:05:10.7159694Z def call(args):
2023-01-11T21:05:10.7159768Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7159837Z     args.clear()
2023-01-11T21:05:10.7160058Z     buf0 = empty_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7160207Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7160274Z     del arg0_1
2023-01-11T21:05:10.7160339Z     del arg1_1
2023-01-11T21:05:10.7160409Z     return (buf0, )
2023-01-11T21:05:10.7160414Z 
2023-01-11T21:05:10.7160419Z 
2023-01-11T21:05:10.7160494Z if __name__ == "__main__":
2023-01-11T21:05:10.7160728Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7160854Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7161035Z     arg0_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7161234Z     arg1_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7161348Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7161353Z 
2023-01-11T21:05:10.7161357Z 
2023-01-11T21:05:10.7161451Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7161523Z import torch
2023-01-11T21:05:10.7161591Z import random
2023-01-11T21:05:10.7161704Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7161824Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7161829Z 
2023-01-11T21:05:10.7161893Z aten = torch.ops.aten
2023-01-11T21:05:10.7162025Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7162115Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7162120Z 
2023-01-11T21:05:10.7162124Z 
2023-01-11T21:05:10.7162259Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7162463Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7162586Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7162690Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7162790Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7162838Z {
2023-01-11T21:05:10.7162937Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7162998Z     {
2023-01-11T21:05:10.7163075Z         #pragma omp for 
2023-01-11T21:05:10.7163156Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7163219Z         {
2023-01-11T21:05:10.7163351Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7163466Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7163596Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7163680Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7163759Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7163849Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7163965Z         }
2023-01-11T21:05:10.7164059Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7164140Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:05:10.7164188Z         {
2023-01-11T21:05:10.7164271Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7164350Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7164449Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7164530Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7164612Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7164677Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7164739Z         }
2023-01-11T21:05:10.7164800Z     }
2023-01-11T21:05:10.7164860Z }
2023-01-11T21:05:10.7164938Z ''')
2023-01-11T21:05:10.7164943Z 
2023-01-11T21:05:10.7164947Z 
2023-01-11T21:05:10.7165034Z async_compile.wait(globals())
2023-01-11T21:05:10.7165106Z del async_compile
2023-01-11T21:05:10.7165111Z 
2023-01-11T21:05:10.7165180Z def call(args):
2023-01-11T21:05:10.7165243Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7165312Z     args.clear()
2023-01-11T21:05:10.7165503Z     buf0 = empty_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7165700Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7165767Z     del arg0_1
2023-01-11T21:05:10.7165830Z     del arg1_1
2023-01-11T21:05:10.7165898Z     return (buf0, )
2023-01-11T21:05:10.7165903Z 
2023-01-11T21:05:10.7165907Z 
2023-01-11T21:05:10.7165968Z if __name__ == "__main__":
2023-01-11T21:05:10.7166079Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7166200Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7166394Z     arg0_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7166586Z     arg1_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7166699Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7166706Z 
2023-01-11T21:05:10.7166710Z 
2023-01-11T21:05:10.7166801Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7166868Z import torch
2023-01-11T21:05:10.7166924Z import random
2023-01-11T21:05:10.7167038Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7167157Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7167162Z 
2023-01-11T21:05:10.7167523Z aten = torch.ops.aten
2023-01-11T21:05:10.7167847Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7167952Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7167960Z 
2023-01-11T21:05:10.7167965Z 
2023-01-11T21:05:10.7168175Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7168387Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7168495Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7168598Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7168710Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7168773Z {
2023-01-11T21:05:10.7168873Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7168940Z     {
2023-01-11T21:05:10.7169016Z         #pragma omp for 
2023-01-11T21:05:10.7169086Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7169148Z         {
2023-01-11T21:05:10.7169296Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7169426Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7169510Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7169601Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7169663Z         }
2023-01-11T21:05:10.7169743Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7169831Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7169892Z         {
2023-01-11T21:05:10.7169974Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7170212Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7170294Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7170376Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7170426Z         }
2023-01-11T21:05:10.7170486Z     }
2023-01-11T21:05:10.7170547Z }
2023-01-11T21:05:10.7170632Z ''')
2023-01-11T21:05:10.7170637Z 
2023-01-11T21:05:10.7170642Z 
2023-01-11T21:05:10.7170730Z async_compile.wait(globals())
2023-01-11T21:05:10.7170802Z del async_compile
2023-01-11T21:05:10.7170807Z 
2023-01-11T21:05:10.7170878Z def call(args):
2023-01-11T21:05:10.7170939Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7171012Z     args.clear()
2023-01-11T21:05:10.7171374Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7171540Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7171608Z     del arg0_1
2023-01-11T21:05:10.7171677Z     del arg1_1
2023-01-11T21:05:10.7171748Z     return (buf0, )
2023-01-11T21:05:10.7171753Z 
2023-01-11T21:05:10.7171757Z 
2023-01-11T21:05:10.7171832Z if __name__ == "__main__":
2023-01-11T21:05:10.7171981Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7172107Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7172308Z     arg0_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7172543Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7172656Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7172661Z 
2023-01-11T21:05:10.7172666Z 
2023-01-11T21:05:10.7172757Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7172825Z import torch
2023-01-11T21:05:10.7172893Z import random
2023-01-11T21:05:10.7172995Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7173111Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7173119Z 
2023-01-11T21:05:10.7173195Z aten = torch.ops.aten
2023-01-11T21:05:10.7173328Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7173420Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7173425Z 
2023-01-11T21:05:10.7173429Z 
2023-01-11T21:05:10.7173559Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7173763Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7173879Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7173971Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7174069Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7174128Z {
2023-01-11T21:05:10.7174225Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7174284Z     {
2023-01-11T21:05:10.7174358Z         #pragma omp for 
2023-01-11T21:05:10.7174438Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7174490Z         {
2023-01-11T21:05:10.7174626Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7174760Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7174892Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7174975Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7175055Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7175145Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7175206Z         }
2023-01-11T21:05:10.7175286Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7175368Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7175427Z         {
2023-01-11T21:05:10.7175510Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7175588Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7175685Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7175754Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7175871Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7175947Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7176008Z         }
2023-01-11T21:05:10.7176067Z     }
2023-01-11T21:05:10.7176127Z }
2023-01-11T21:05:10.7176206Z ''')
2023-01-11T21:05:10.7176212Z 
2023-01-11T21:05:10.7176216Z 
2023-01-11T21:05:10.7176304Z async_compile.wait(globals())
2023-01-11T21:05:10.7176362Z del async_compile
2023-01-11T21:05:10.7176366Z 
2023-01-11T21:05:10.7176434Z def call(args):
2023-01-11T21:05:10.7176507Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7176577Z     args.clear()
2023-01-11T21:05:10.7176810Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7176970Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7177036Z     del arg0_1
2023-01-11T21:05:10.7177087Z     del arg1_1
2023-01-11T21:05:10.7177160Z     return (buf0, )
2023-01-11T21:05:10.7177166Z 
2023-01-11T21:05:10.7177170Z 
2023-01-11T21:05:10.7177245Z if __name__ == "__main__":
2023-01-11T21:05:10.7177358Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7177510Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7177796Z     arg0_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7178089Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7178262Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7178271Z 
2023-01-11T21:05:10.7178277Z 
2023-01-11T21:05:10.7178409Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7178537Z import torch
2023-01-11T21:05:10.7178611Z import random
2023-01-11T21:05:10.7178727Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7178849Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7178857Z 
2023-01-11T21:05:10.7178936Z aten = torch.ops.aten
2023-01-11T21:05:10.7179070Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7179163Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7179169Z 
2023-01-11T21:05:10.7179175Z 
2023-01-11T21:05:10.7179300Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7179506Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7179627Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7179730Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7179829Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7179890Z {
2023-01-11T21:05:10.7179987Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7180034Z     {
2023-01-11T21:05:10.7180112Z         #pragma omp for 
2023-01-11T21:05:10.7180195Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7180257Z         {
2023-01-11T21:05:10.7180393Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7180522Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7180608Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7180698Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7180746Z         }
2023-01-11T21:05:10.7180840Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7180923Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7180982Z         {
2023-01-11T21:05:10.7181064Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7181143Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7181210Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7181288Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7181347Z         }
2023-01-11T21:05:10.7181407Z     }
2023-01-11T21:05:10.7181465Z }
2023-01-11T21:05:10.7181541Z ''')
2023-01-11T21:05:10.7181546Z 
2023-01-11T21:05:10.7181550Z 
2023-01-11T21:05:10.7181699Z async_compile.wait(globals())
2023-01-11T21:05:10.7181757Z del async_compile
2023-01-11T21:05:10.7181774Z 
2023-01-11T21:05:10.7181830Z def call(args):
2023-01-11T21:05:10.7181903Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7181974Z     args.clear()
2023-01-11T21:05:10.7182212Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7182375Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7182442Z     del arg0_1
2023-01-11T21:05:10.7182507Z     del arg1_1
2023-01-11T21:05:10.7182564Z     return (buf0, )
2023-01-11T21:05:10.7182569Z 
2023-01-11T21:05:10.7182573Z 
2023-01-11T21:05:10.7182648Z if __name__ == "__main__":
2023-01-11T21:05:10.7182763Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7182887Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7183102Z     arg0_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7183339Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7183487Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7183764Z [2023-01-11 21:02:11,019] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 466
2023-01-11T21:05:10.7184166Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7184280Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7184540Z [2023-01-11 21:02:11,062] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 467
2023-01-11T21:05:10.7184808Z [2023-01-11 21:02:11,081] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 467
2023-01-11T21:05:10.7185205Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7185328Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7185584Z [2023-01-11 21:02:11,122] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 468
2023-01-11T21:05:10.7185846Z [2023-01-11 21:02:13,781] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 468
2023-01-11T21:05:10.7186245Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7186375Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7186629Z [2023-01-11 21:02:13,825] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 469
2023-01-11T21:05:10.7186890Z [2023-01-11 21:02:16,472] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 469
2023-01-11T21:05:10.7187276Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7187399Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7187688Z [2023-01-11 21:02:16,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 470
2023-01-11T21:05:10.7187950Z [2023-01-11 21:02:19,160] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 470
2023-01-11T21:05:10.7188346Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7188470Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7188722Z [2023-01-11 21:02:19,202] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 471
2023-01-11T21:05:10.7188728Z 
2023-01-11T21:05:10.7188732Z 
2023-01-11T21:05:10.7188827Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7188899Z import torch
2023-01-11T21:05:10.7188967Z import random
2023-01-11T21:05:10.7189070Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7189191Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7189234Z 
2023-01-11T21:05:10.7189314Z aten = torch.ops.aten
2023-01-11T21:05:10.7189448Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7189539Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7189543Z 
2023-01-11T21:05:10.7189548Z 
2023-01-11T21:05:10.7189681Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7189883Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7190001Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7190094Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7190192Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7190258Z {
2023-01-11T21:05:10.7190357Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7190422Z     {
2023-01-11T21:05:10.7190497Z         #pragma omp for 
2023-01-11T21:05:10.7190579Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7190627Z         {
2023-01-11T21:05:10.7190765Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7190895Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7191025Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7191109Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7191190Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7191281Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7191342Z         }
2023-01-11T21:05:10.7191423Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7191507Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7191567Z         {
2023-01-11T21:05:10.7191651Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7191736Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7191835Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7191918Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7191989Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7192066Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7192129Z         }
2023-01-11T21:05:10.7192189Z     }
2023-01-11T21:05:10.7192249Z }
2023-01-11T21:05:10.7192330Z ''')
2023-01-11T21:05:10.7192335Z 
2023-01-11T21:05:10.7192339Z 
2023-01-11T21:05:10.7192428Z async_compile.wait(globals())
2023-01-11T21:05:10.7192486Z del async_compile
2023-01-11T21:05:10.7192491Z 
2023-01-11T21:05:10.7192559Z def call(args):
2023-01-11T21:05:10.7192632Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7192703Z     args.clear()
2023-01-11T21:05:10.7192942Z     buf0 = empty_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7193104Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7193226Z     del arg0_1
2023-01-11T21:05:10.7193278Z     del arg1_1
2023-01-11T21:05:10.7193348Z     return (buf0, )
2023-01-11T21:05:10.7193355Z 
2023-01-11T21:05:10.7193359Z 
2023-01-11T21:05:10.7193432Z if __name__ == "__main__":
2023-01-11T21:05:10.7193547Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7193671Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7193894Z     arg0_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7194126Z     arg1_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7194241Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7194246Z 
2023-01-11T21:05:10.7194250Z 
2023-01-11T21:05:10.7194343Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7194398Z import torch
2023-01-11T21:05:10.7194473Z import random
2023-01-11T21:05:10.7194588Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7194710Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7194714Z 
2023-01-11T21:05:10.7194825Z aten = torch.ops.aten
2023-01-11T21:05:10.7194961Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7195049Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7195054Z 
2023-01-11T21:05:10.7195058Z 
2023-01-11T21:05:10.7195178Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7195379Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7195498Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7195601Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7195700Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7195761Z {
2023-01-11T21:05:10.7195856Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7195906Z     {
2023-01-11T21:05:10.7195981Z         #pragma omp for 
2023-01-11T21:05:10.7196063Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7196125Z         {
2023-01-11T21:05:10.7196262Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7196393Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7196478Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7196569Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7196618Z         }
2023-01-11T21:05:10.7196712Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7196794Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7196857Z         {
2023-01-11T21:05:10.7196940Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7197024Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7197106Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7197171Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7197234Z         }
2023-01-11T21:05:10.7197294Z     }
2023-01-11T21:05:10.7197352Z }
2023-01-11T21:05:10.7197431Z ''')
2023-01-11T21:05:10.7197435Z 
2023-01-11T21:05:10.7197440Z 
2023-01-11T21:05:10.7197529Z async_compile.wait(globals())
2023-01-11T21:05:10.7197599Z del async_compile
2023-01-11T21:05:10.7197604Z 
2023-01-11T21:05:10.7197662Z def call(args):
2023-01-11T21:05:10.7197735Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7197808Z     args.clear()
2023-01-11T21:05:10.7198015Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7198177Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7198243Z     del arg0_1
2023-01-11T21:05:10.7198307Z     del arg1_1
2023-01-11T21:05:10.7198365Z     return (buf0, )
2023-01-11T21:05:10.7198371Z 
2023-01-11T21:05:10.7198375Z 
2023-01-11T21:05:10.7198450Z if __name__ == "__main__":
2023-01-11T21:05:10.7198563Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7198720Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7198920Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7199130Z     arg1_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7199244Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7199249Z 
2023-01-11T21:05:10.7199253Z 
2023-01-11T21:05:10.7199345Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7199401Z import torch
2023-01-11T21:05:10.7199469Z import random
2023-01-11T21:05:10.7199581Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7199699Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7199703Z 
2023-01-11T21:05:10.7199780Z aten = torch.ops.aten
2023-01-11T21:05:10.7199909Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7199998Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7200006Z 
2023-01-11T21:05:10.7200010Z 
2023-01-11T21:05:10.7200141Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7200364Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7200487Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7200589Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7200983Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7201045Z {
2023-01-11T21:05:10.7201142Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7201204Z     {
2023-01-11T21:05:10.7201270Z         #pragma omp for 
2023-01-11T21:05:10.7201353Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7201416Z         {
2023-01-11T21:05:10.7201554Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7201685Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7201818Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7201905Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7201988Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7202069Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7202130Z         }
2023-01-11T21:05:10.7202224Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7202306Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7202366Z         {
2023-01-11T21:05:10.7202449Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7202528Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7202613Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7202694Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7202775Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7202854Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7202913Z         }
2023-01-11T21:05:10.7202972Z     }
2023-01-11T21:05:10.7203020Z }
2023-01-11T21:05:10.7203101Z ''')
2023-01-11T21:05:10.7203106Z 
2023-01-11T21:05:10.7203110Z 
2023-01-11T21:05:10.7203199Z async_compile.wait(globals())
2023-01-11T21:05:10.7203271Z del async_compile
2023-01-11T21:05:10.7203275Z 
2023-01-11T21:05:10.7203346Z def call(args):
2023-01-11T21:05:10.7203420Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7203490Z     args.clear()
2023-01-11T21:05:10.7203700Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7203850Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7203915Z     del arg0_1
2023-01-11T21:05:10.7203980Z     del arg1_1
2023-01-11T21:05:10.7204048Z     return (buf0, )
2023-01-11T21:05:10.7204054Z 
2023-01-11T21:05:10.7204057Z 
2023-01-11T21:05:10.7204130Z if __name__ == "__main__":
2023-01-11T21:05:10.7204242Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7204364Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7204654Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7204852Z     arg1_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7204968Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7204973Z 
2023-01-11T21:05:10.7204977Z 
2023-01-11T21:05:10.7205069Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7205139Z import torch
2023-01-11T21:05:10.7205208Z import random
2023-01-11T21:05:10.7205322Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7205442Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7205446Z 
2023-01-11T21:05:10.7205526Z aten = torch.ops.aten
2023-01-11T21:05:10.7205650Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7205744Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7205750Z 
2023-01-11T21:05:10.7205754Z 
2023-01-11T21:05:10.7205895Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7206099Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7206264Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7206369Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7206467Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7206517Z {
2023-01-11T21:05:10.7206615Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7206675Z     {
2023-01-11T21:05:10.7206750Z         #pragma omp for 
2023-01-11T21:05:10.7206832Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7206893Z         {
2023-01-11T21:05:10.7206955Z             {
2023-01-11T21:05:10.7207006Z                 {
2023-01-11T21:05:10.7207100Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7207188Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7207275Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7207363Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7207427Z                 }
2023-01-11T21:05:10.7207490Z             }
2023-01-11T21:05:10.7207537Z         }
2023-01-11T21:05:10.7207596Z     }
2023-01-11T21:05:10.7207658Z }
2023-01-11T21:05:10.7207740Z ''')
2023-01-11T21:05:10.7207746Z 
2023-01-11T21:05:10.7207750Z 
2023-01-11T21:05:10.7207839Z async_compile.wait(globals())
2023-01-11T21:05:10.7207907Z del async_compile
2023-01-11T21:05:10.7207912Z 
2023-01-11T21:05:10.7207979Z def call(args):
2023-01-11T21:05:10.7208039Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7208108Z     args.clear()
2023-01-11T21:05:10.7208305Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7208468Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7208537Z     del arg0_1
2023-01-11T21:05:10.7208604Z     del arg1_1
2023-01-11T21:05:10.7208674Z     return (buf0, )
2023-01-11T21:05:10.7208682Z 
2023-01-11T21:05:10.7208686Z 
2023-01-11T21:05:10.7208759Z if __name__ == "__main__":
2023-01-11T21:05:10.7208860Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7208984Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7209194Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7209391Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7209507Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7209513Z 
2023-01-11T21:05:10.7209517Z 
2023-01-11T21:05:10.7209608Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7209675Z import torch
2023-01-11T21:05:10.7209731Z import random
2023-01-11T21:05:10.7209845Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7209966Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7209971Z 
2023-01-11T21:05:10.7210050Z aten = torch.ops.aten
2023-01-11T21:05:10.7210223Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7210312Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7210317Z 
2023-01-11T21:05:10.7210321Z 
2023-01-11T21:05:10.7210458Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7210663Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7210779Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7210869Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7210968Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7211028Z {
2023-01-11T21:05:10.7211123Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7211188Z     {
2023-01-11T21:05:10.7211261Z         #pragma omp for 
2023-01-11T21:05:10.7211329Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7211392Z         {
2023-01-11T21:05:10.7211455Z             {
2023-01-11T21:05:10.7211520Z                 {
2023-01-11T21:05:10.7211613Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7211704Z                     auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7211840Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7211918Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7212006Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7212089Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7212150Z                 }
2023-01-11T21:05:10.7212211Z             }
2023-01-11T21:05:10.7212271Z         }
2023-01-11T21:05:10.7212330Z     }
2023-01-11T21:05:10.7212376Z }
2023-01-11T21:05:10.7212453Z ''')
2023-01-11T21:05:10.7212457Z 
2023-01-11T21:05:10.7212461Z 
2023-01-11T21:05:10.7212550Z async_compile.wait(globals())
2023-01-11T21:05:10.7212620Z del async_compile
2023-01-11T21:05:10.7212625Z 
2023-01-11T21:05:10.7212694Z def call(args):
2023-01-11T21:05:10.7212767Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7212839Z     args.clear()
2023-01-11T21:05:10.7213019Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7213181Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7213250Z     del arg0_1
2023-01-11T21:05:10.7213316Z     del arg1_1
2023-01-11T21:05:10.7213387Z     return (buf0, )
2023-01-11T21:05:10.7213391Z 
2023-01-11T21:05:10.7213395Z 
2023-01-11T21:05:10.7213470Z if __name__ == "__main__":
2023-01-11T21:05:10.7213582Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7213703Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7213899Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7214094Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7214209Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7214477Z [2023-01-11 21:02:21,805] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 471
2023-01-11T21:05:10.7214880Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7215005Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7215262Z [2023-01-11 21:02:21,841] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 472
2023-01-11T21:05:10.7215527Z [2023-01-11 21:02:21,856] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 472
2023-01-11T21:05:10.7215926Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7216089Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7216334Z [2023-01-11 21:02:21,891] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 473
2023-01-11T21:05:10.7216595Z [2023-01-11 21:02:21,923] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 473
2023-01-11T21:05:10.7216997Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7217121Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7217379Z [2023-01-11 21:02:21,953] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 474
2023-01-11T21:05:10.7217677Z [2023-01-11 21:02:24,594] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 474
2023-01-11T21:05:10.7218074Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7218198Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7218453Z [2023-01-11 21:02:24,643] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 475
2023-01-11T21:05:10.7218802Z [2023-01-11 21:02:27,339] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 475
2023-01-11T21:05:10.7219207Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7219333Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7219575Z [2023-01-11 21:02:27,375] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 476
2023-01-11T21:05:10.7219595Z 
2023-01-11T21:05:10.7219599Z 
2023-01-11T21:05:10.7219679Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7219751Z import torch
2023-01-11T21:05:10.7219824Z import random
2023-01-11T21:05:10.7219939Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7220062Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7220067Z 
2023-01-11T21:05:10.7220144Z aten = torch.ops.aten
2023-01-11T21:05:10.7220281Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7220360Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7220365Z 
2023-01-11T21:05:10.7220383Z 
2023-01-11T21:05:10.7220504Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7220708Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7220829Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7220932Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7221031Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7221091Z {
2023-01-11T21:05:10.7221188Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7221236Z     {
2023-01-11T21:05:10.7221310Z         #pragma omp for 
2023-01-11T21:05:10.7221391Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7221452Z         {
2023-01-11T21:05:10.7221590Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7221762Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7221844Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7221924Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7221985Z         }
2023-01-11T21:05:10.7222078Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7222160Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7222222Z         {
2023-01-11T21:05:10.7222307Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7222385Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7222452Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7222530Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7222589Z         }
2023-01-11T21:05:10.7222650Z     }
2023-01-11T21:05:10.7222710Z }
2023-01-11T21:05:10.7222792Z ''')
2023-01-11T21:05:10.7222797Z 
2023-01-11T21:05:10.7222801Z 
2023-01-11T21:05:10.7222888Z async_compile.wait(globals())
2023-01-11T21:05:10.7222949Z del async_compile
2023-01-11T21:05:10.7222954Z 
2023-01-11T21:05:10.7223023Z def call(args):
2023-01-11T21:05:10.7223096Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7223166Z     args.clear()
2023-01-11T21:05:10.7223432Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7223596Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7223667Z     del arg0_1
2023-01-11T21:05:10.7223721Z     del arg1_1
2023-01-11T21:05:10.7223793Z     return (buf0, )
2023-01-11T21:05:10.7223797Z 
2023-01-11T21:05:10.7223801Z 
2023-01-11T21:05:10.7223875Z if __name__ == "__main__":
2023-01-11T21:05:10.7223990Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7224115Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7224343Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7224545Z     arg1_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7224659Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7224664Z 
2023-01-11T21:05:10.7224668Z 
2023-01-11T21:05:10.7224762Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7224817Z import torch
2023-01-11T21:05:10.7224887Z import random
2023-01-11T21:05:10.7225000Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7225118Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7225123Z 
2023-01-11T21:05:10.7225202Z aten = torch.ops.aten
2023-01-11T21:05:10.7225336Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7225425Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7225430Z 
2023-01-11T21:05:10.7225434Z 
2023-01-11T21:05:10.7225555Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7225758Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7225878Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7225979Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7226080Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7226142Z {
2023-01-11T21:05:10.7226242Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7226304Z     {
2023-01-11T21:05:10.7226366Z         #pragma omp for 
2023-01-11T21:05:10.7226449Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7226510Z         {
2023-01-11T21:05:10.7226644Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7226774Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7226906Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7226989Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7227057Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7227179Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7227240Z         }
2023-01-11T21:05:10.7227335Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7227418Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7227482Z         {
2023-01-11T21:05:10.7227564Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7227632Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7227729Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7227811Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7227893Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7227973Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7228034Z         }
2023-01-11T21:05:10.7228093Z     }
2023-01-11T21:05:10.7228138Z }
2023-01-11T21:05:10.7228222Z ''')
2023-01-11T21:05:10.7228228Z 
2023-01-11T21:05:10.7228232Z 
2023-01-11T21:05:10.7228319Z async_compile.wait(globals())
2023-01-11T21:05:10.7228392Z del async_compile
2023-01-11T21:05:10.7228397Z 
2023-01-11T21:05:10.7228468Z def call(args):
2023-01-11T21:05:10.7228539Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7228608Z     args.clear()
2023-01-11T21:05:10.7228798Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7228990Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7229058Z     del arg0_1
2023-01-11T21:05:10.7229122Z     del arg1_1
2023-01-11T21:05:10.7229192Z     return (buf0, )
2023-01-11T21:05:10.7229197Z 
2023-01-11T21:05:10.7229200Z 
2023-01-11T21:05:10.7229274Z if __name__ == "__main__":
2023-01-11T21:05:10.7229390Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7229513Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7229729Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7229930Z     arg1_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7230046Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7230051Z 
2023-01-11T21:05:10.7230055Z 
2023-01-11T21:05:10.7230145Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7230218Z import torch
2023-01-11T21:05:10.7230291Z import random
2023-01-11T21:05:10.7230406Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7230525Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7230530Z 
2023-01-11T21:05:10.7230593Z aten = torch.ops.aten
2023-01-11T21:05:10.7230726Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7230819Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7230824Z 
2023-01-11T21:05:10.7230828Z 
2023-01-11T21:05:10.7230962Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7231165Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7231283Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7231392Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7231491Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7231537Z {
2023-01-11T21:05:10.7231635Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7231694Z     {
2023-01-11T21:05:10.7231769Z         #pragma omp for 
2023-01-11T21:05:10.7231848Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.7231908Z         {
2023-01-11T21:05:10.7232041Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7232157Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7232243Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7232332Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7232394Z         }
2023-01-11T21:05:10.7232489Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7232569Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.7232684Z         {
2023-01-11T21:05:10.7232753Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7232835Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7232917Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7232997Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7233058Z         }
2023-01-11T21:05:10.7233118Z     }
2023-01-11T21:05:10.7233164Z }
2023-01-11T21:05:10.7233245Z ''')
2023-01-11T21:05:10.7233250Z 
2023-01-11T21:05:10.7233253Z 
2023-01-11T21:05:10.7233341Z async_compile.wait(globals())
2023-01-11T21:05:10.7233412Z del async_compile
2023-01-11T21:05:10.7233416Z 
2023-01-11T21:05:10.7233486Z def call(args):
2023-01-11T21:05:10.7233564Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7233635Z     args.clear()
2023-01-11T21:05:10.7233832Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7233978Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7234046Z     del arg0_1
2023-01-11T21:05:10.7234119Z     del arg1_1
2023-01-11T21:05:10.7234189Z     return (buf0, )
2023-01-11T21:05:10.7234194Z 
2023-01-11T21:05:10.7234198Z 
2023-01-11T21:05:10.7234272Z if __name__ == "__main__":
2023-01-11T21:05:10.7234428Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7234553Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7234767Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7234950Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7235062Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7235068Z 
2023-01-11T21:05:10.7235072Z 
2023-01-11T21:05:10.7235166Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7235236Z import torch
2023-01-11T21:05:10.7235304Z import random
2023-01-11T21:05:10.7235416Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7235536Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7235544Z 
2023-01-11T21:05:10.7235619Z aten = torch.ops.aten
2023-01-11T21:05:10.7235740Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7235833Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7235838Z 
2023-01-11T21:05:10.7235842Z 
2023-01-11T21:05:10.7235974Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7236175Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7236294Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7236397Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7236498Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7236557Z {
2023-01-11T21:05:10.7236639Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7236699Z     {
2023-01-11T21:05:10.7236775Z         #pragma omp for 
2023-01-11T21:05:10.7236856Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.7236922Z         {
2023-01-11T21:05:10.7237055Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7237187Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7237306Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7237390Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7237470Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7237559Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7237619Z         }
2023-01-11T21:05:10.7237712Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7237792Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.7237840Z         {
2023-01-11T21:05:10.7237920Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7237999Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7238096Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7238179Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7238298Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7238379Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7238427Z         }
2023-01-11T21:05:10.7238487Z     }
2023-01-11T21:05:10.7238547Z }
2023-01-11T21:05:10.7238625Z ''')
2023-01-11T21:05:10.7238631Z 
2023-01-11T21:05:10.7238635Z 
2023-01-11T21:05:10.7238722Z async_compile.wait(globals())
2023-01-11T21:05:10.7238797Z del async_compile
2023-01-11T21:05:10.7238802Z 
2023-01-11T21:05:10.7238872Z def call(args):
2023-01-11T21:05:10.7238932Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7239003Z     args.clear()
2023-01-11T21:05:10.7239200Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7239362Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7239429Z     del arg0_1
2023-01-11T21:05:10.7239492Z     del arg1_1
2023-01-11T21:05:10.7239561Z     return (buf0, )
2023-01-11T21:05:10.7239569Z 
2023-01-11T21:05:10.7239573Z 
2023-01-11T21:05:10.7239636Z if __name__ == "__main__":
2023-01-11T21:05:10.7239747Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7239908Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7240121Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7240316Z     arg1_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7240430Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7240435Z 
2023-01-11T21:05:10.7240439Z 
2023-01-11T21:05:10.7240532Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7240722Z import torch
2023-01-11T21:05:10.7240780Z import random
2023-01-11T21:05:10.7240895Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7241016Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7241020Z 
2023-01-11T21:05:10.7241097Z aten = torch.ops.aten
2023-01-11T21:05:10.7241232Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7241322Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7241328Z 
2023-01-11T21:05:10.7241332Z 
2023-01-11T21:05:10.7241469Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7241675Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7241780Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7241885Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7241983Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7242044Z {
2023-01-11T21:05:10.7242143Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7242205Z     {
2023-01-11T21:05:10.7242283Z         #pragma omp for 
2023-01-11T21:05:10.7242353Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7242416Z         {
2023-01-11T21:05:10.7242548Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7242681Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7242765Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7242856Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7242917Z         }
2023-01-11T21:05:10.7243011Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7243080Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:05:10.7243140Z         {
2023-01-11T21:05:10.7243223Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7243302Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7243382Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7243459Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7243507Z         }
2023-01-11T21:05:10.7243565Z     }
2023-01-11T21:05:10.7243623Z }
2023-01-11T21:05:10.7243705Z ''')
2023-01-11T21:05:10.7243711Z 
2023-01-11T21:05:10.7243715Z 
2023-01-11T21:05:10.7243804Z async_compile.wait(globals())
2023-01-11T21:05:10.7243945Z del async_compile
2023-01-11T21:05:10.7243950Z 
2023-01-11T21:05:10.7244020Z def call(args):
2023-01-11T21:05:10.7244096Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7244153Z     args.clear()
2023-01-11T21:05:10.7244351Z     buf0 = empty_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7244510Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7244578Z     del arg0_1
2023-01-11T21:05:10.7244643Z     del arg1_1
2023-01-11T21:05:10.7244714Z     return (buf0, )
2023-01-11T21:05:10.7244720Z 
2023-01-11T21:05:10.7244724Z 
2023-01-11T21:05:10.7244800Z if __name__ == "__main__":
2023-01-11T21:05:10.7244903Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7245024Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7245222Z     arg0_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7245412Z     arg1_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7245529Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7245840Z [2023-01-11 21:02:27,387] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 476
2023-01-11T21:05:10.7246246Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7246373Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7246635Z [2023-01-11 21:02:27,419] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 477
2023-01-11T21:05:10.7246885Z [2023-01-11 21:02:27,440] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 477
2023-01-11T21:05:10.7247286Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7247412Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7247669Z [2023-01-11 21:02:27,466] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 478
2023-01-11T21:05:10.7247929Z [2023-01-11 21:02:27,477] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 478
2023-01-11T21:05:10.7248325Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7248454Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7248711Z [2023-01-11 21:02:27,516] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 479
2023-01-11T21:05:10.7248978Z [2023-01-11 21:02:27,557] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 479
2023-01-11T21:05:10.7249375Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7249503Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7249754Z [2023-01-11 21:02:27,592] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 480
2023-01-11T21:05:10.7250036Z [2023-01-11 21:02:27,608] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 480
2023-01-11T21:05:10.7250432Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7250554Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7250803Z [2023-01-11 21:02:27,648] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 481
2023-01-11T21:05:10.7250809Z 
2023-01-11T21:05:10.7250814Z 
2023-01-11T21:05:10.7250906Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7250975Z import torch
2023-01-11T21:05:10.7251043Z import random
2023-01-11T21:05:10.7251158Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7251279Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7251284Z 
2023-01-11T21:05:10.7251348Z aten = torch.ops.aten
2023-01-11T21:05:10.7251510Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7251602Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7251607Z 
2023-01-11T21:05:10.7251611Z 
2023-01-11T21:05:10.7251745Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7251946Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7252063Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7252164Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7252263Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7252310Z {
2023-01-11T21:05:10.7252406Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7252465Z     {
2023-01-11T21:05:10.7252540Z         #pragma omp for 
2023-01-11T21:05:10.7252623Z         for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7252684Z         {
2023-01-11T21:05:10.7252817Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7252938Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7253067Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7253151Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7253231Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7253320Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7253381Z         }
2023-01-11T21:05:10.7253473Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7253542Z         for(long i0=32; i0<35; i0+=1)
2023-01-11T21:05:10.7253603Z         {
2023-01-11T21:05:10.7253685Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7253765Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7253864Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7253949Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7254028Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7254093Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7254152Z         }
2023-01-11T21:05:10.7254214Z     }
2023-01-11T21:05:10.7254271Z }
2023-01-11T21:05:10.7254351Z ''')
2023-01-11T21:05:10.7254355Z 
2023-01-11T21:05:10.7254360Z 
2023-01-11T21:05:10.7254447Z async_compile.wait(globals())
2023-01-11T21:05:10.7254519Z del async_compile
2023-01-11T21:05:10.7254523Z 
2023-01-11T21:05:10.7254590Z def call(args):
2023-01-11T21:05:10.7254651Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7254719Z     args.clear()
2023-01-11T21:05:10.7254914Z     buf0 = empty_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7255074Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7255142Z     del arg0_1
2023-01-11T21:05:10.7255205Z     del arg1_1
2023-01-11T21:05:10.7255298Z     return (buf0, )
2023-01-11T21:05:10.7255315Z 
2023-01-11T21:05:10.7255319Z 
2023-01-11T21:05:10.7255379Z if __name__ == "__main__":
2023-01-11T21:05:10.7255492Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7255614Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7255809Z     arg0_1 = rand_strided((5, 7), (7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7256000Z     arg1_1 = rand_strided((35, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7256112Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7256116Z 
2023-01-11T21:05:10.7256120Z 
2023-01-11T21:05:10.7256212Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7256280Z import torch
2023-01-11T21:05:10.7256335Z import random
2023-01-11T21:05:10.7256447Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7256565Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7256570Z 
2023-01-11T21:05:10.7256649Z aten = torch.ops.aten
2023-01-11T21:05:10.7256783Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7256873Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7256878Z 
2023-01-11T21:05:10.7256911Z 
2023-01-11T21:05:10.7257044Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7257249Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7257355Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7257457Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7257556Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7257616Z {
2023-01-11T21:05:10.7257714Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7257773Z     {
2023-01-11T21:05:10.7257848Z         #pragma omp for 
2023-01-11T21:05:10.7257917Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7257978Z         {
2023-01-11T21:05:10.7258112Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7258243Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7258328Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7258420Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7258573Z         }
2023-01-11T21:05:10.7258658Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7258742Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7258805Z         {
2023-01-11T21:05:10.7258889Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7258970Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7259050Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7259129Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7259176Z         }
2023-01-11T21:05:10.7259237Z     }
2023-01-11T21:05:10.7259297Z }
2023-01-11T21:05:10.7259382Z ''')
2023-01-11T21:05:10.7259387Z 
2023-01-11T21:05:10.7259392Z 
2023-01-11T21:05:10.7259485Z async_compile.wait(globals())
2023-01-11T21:05:10.7259555Z del async_compile
2023-01-11T21:05:10.7259559Z 
2023-01-11T21:05:10.7259629Z def call(args):
2023-01-11T21:05:10.7259690Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7259764Z     args.clear()
2023-01-11T21:05:10.7259964Z     buf0 = empty_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7260127Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7260196Z     del arg0_1
2023-01-11T21:05:10.7260263Z     del arg1_1
2023-01-11T21:05:10.7260333Z     return (buf0, )
2023-01-11T21:05:10.7260338Z 
2023-01-11T21:05:10.7260343Z 
2023-01-11T21:05:10.7260417Z if __name__ == "__main__":
2023-01-11T21:05:10.7260522Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7260645Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7260879Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7261114Z     arg1_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7261228Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7261233Z 
2023-01-11T21:05:10.7261239Z 
2023-01-11T21:05:10.7261331Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7261401Z import torch
2023-01-11T21:05:10.7261470Z import random
2023-01-11T21:05:10.7261569Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7261689Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7261694Z 
2023-01-11T21:05:10.7261769Z aten = torch.ops.aten
2023-01-11T21:05:10.7261899Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7261990Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7261995Z 
2023-01-11T21:05:10.7261999Z 
2023-01-11T21:05:10.7262132Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7262338Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7262456Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7262547Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7262670Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7262733Z {
2023-01-11T21:05:10.7262829Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7262888Z     {
2023-01-11T21:05:10.7262962Z         #pragma omp for 
2023-01-11T21:05:10.7263044Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7263092Z         {
2023-01-11T21:05:10.7263223Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7263352Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7263481Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7263565Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7263643Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7263734Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7263784Z         }
2023-01-11T21:05:10.7263876Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7263962Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7264023Z         {
2023-01-11T21:05:10.7264107Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7264186Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7264283Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7264352Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7264432Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7264511Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7264571Z         }
2023-01-11T21:05:10.7264630Z     }
2023-01-11T21:05:10.7264688Z }
2023-01-11T21:05:10.7264771Z ''')
2023-01-11T21:05:10.7264777Z 
2023-01-11T21:05:10.7264781Z 
2023-01-11T21:05:10.7264856Z async_compile.wait(globals())
2023-01-11T21:05:10.7264926Z del async_compile
2023-01-11T21:05:10.7264933Z 
2023-01-11T21:05:10.7265003Z def call(args):
2023-01-11T21:05:10.7265074Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7265145Z     args.clear()
2023-01-11T21:05:10.7265345Z     buf0 = empty_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7265505Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7265559Z     del arg0_1
2023-01-11T21:05:10.7265622Z     del arg1_1
2023-01-11T21:05:10.7265691Z     return (buf0, )
2023-01-11T21:05:10.7265696Z 
2023-01-11T21:05:10.7265700Z 
2023-01-11T21:05:10.7265775Z if __name__ == "__main__":
2023-01-11T21:05:10.7265887Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7266007Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7266242Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7266437Z     arg1_1 = rand_strided((5040, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7266569Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7266588Z 
2023-01-11T21:05:10.7266591Z 
2023-01-11T21:05:10.7266672Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7266741Z import torch
2023-01-11T21:05:10.7266808Z import random
2023-01-11T21:05:10.7266920Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7267037Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7267042Z 
2023-01-11T21:05:10.7267118Z aten = torch.ops.aten
2023-01-11T21:05:10.7267251Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7267328Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7267333Z 
2023-01-11T21:05:10.7267350Z 
2023-01-11T21:05:10.7267468Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7267669Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7267785Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7267889Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7267986Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7268046Z {
2023-01-11T21:05:10.7268172Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7268221Z     {
2023-01-11T21:05:10.7268297Z         #pragma omp for 
2023-01-11T21:05:10.7268378Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7268438Z         {
2023-01-11T21:05:10.7268570Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7268701Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7268785Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7268864Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7268924Z         }
2023-01-11T21:05:10.7269017Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7269100Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7269164Z         {
2023-01-11T21:05:10.7269246Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7269325Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7269394Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7269472Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7269532Z         }
2023-01-11T21:05:10.7269591Z     }
2023-01-11T21:05:10.7269649Z }
2023-01-11T21:05:10.7269727Z ''')
2023-01-11T21:05:10.7269732Z 
2023-01-11T21:05:10.7269736Z 
2023-01-11T21:05:10.7269824Z async_compile.wait(globals())
2023-01-11T21:05:10.7269882Z del async_compile
2023-01-11T21:05:10.7269887Z 
2023-01-11T21:05:10.7269954Z def call(args):
2023-01-11T21:05:10.7270026Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7270096Z     args.clear()
2023-01-11T21:05:10.7270312Z     buf0 = empty_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7270471Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7270539Z     del arg0_1
2023-01-11T21:05:10.7270591Z     del arg1_1
2023-01-11T21:05:10.7270660Z     return (buf0, )
2023-01-11T21:05:10.7270665Z 
2023-01-11T21:05:10.7270669Z 
2023-01-11T21:05:10.7270744Z if __name__ == "__main__":
2023-01-11T21:05:10.7270856Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7270980Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7271213Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7271426Z     arg1_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7271538Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7271543Z 
2023-01-11T21:05:10.7271547Z 
2023-01-11T21:05:10.7271641Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7271695Z import torch
2023-01-11T21:05:10.7271763Z import random
2023-01-11T21:05:10.7271929Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7272047Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7272051Z 
2023-01-11T21:05:10.7272128Z aten = torch.ops.aten
2023-01-11T21:05:10.7272262Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7272354Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7272359Z 
2023-01-11T21:05:10.7272363Z 
2023-01-11T21:05:10.7272494Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7272683Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7272800Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7272903Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7273006Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7273065Z {
2023-01-11T21:05:10.7273160Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7273223Z     {
2023-01-11T21:05:10.7273287Z         #pragma omp for 
2023-01-11T21:05:10.7273370Z         for(long i0=0; i0<315; i0+=1)
2023-01-11T21:05:10.7273432Z         {
2023-01-11T21:05:10.7273600Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7273733Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7273863Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7273947Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7274013Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7274104Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7274165Z         }
2023-01-11T21:05:10.7274260Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7274342Z         for(long i0=5040; i0<5040; i0+=1)
2023-01-11T21:05:10.7274404Z         {
2023-01-11T21:05:10.7274487Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7274556Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7274657Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7274739Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7274818Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7274898Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7274959Z         }
2023-01-11T21:05:10.7275017Z     }
2023-01-11T21:05:10.7275063Z }
2023-01-11T21:05:10.7275144Z ''')
2023-01-11T21:05:10.7275149Z 
2023-01-11T21:05:10.7275153Z 
2023-01-11T21:05:10.7275240Z async_compile.wait(globals())
2023-01-11T21:05:10.7275310Z del async_compile
2023-01-11T21:05:10.7275315Z 
2023-01-11T21:05:10.7275385Z def call(args):
2023-01-11T21:05:10.7275459Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7275527Z     args.clear()
2023-01-11T21:05:10.7275731Z     buf0 = empty_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7275889Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7275957Z     del arg0_1
2023-01-11T21:05:10.7276023Z     del arg1_1
2023-01-11T21:05:10.7276091Z     return (buf0, )
2023-01-11T21:05:10.7276096Z 
2023-01-11T21:05:10.7276100Z 
2023-01-11T21:05:10.7276175Z if __name__ == "__main__":
2023-01-11T21:05:10.7276290Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7276410Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7276631Z     arg0_1 = rand_strided((2, 3, 4, 5, 6, 7), (2520, 840, 210, 42, 7, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7276845Z     arg1_1 = rand_strided((6, 4, 5, 42), (840, 210, 42, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7276959Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7277227Z [2023-01-11 21:02:27,711] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 481
2023-01-11T21:05:10.7277627Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7277794Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7278055Z [2023-01-11 21:02:27,746] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 482
2023-01-11T21:05:10.7278320Z [2023-01-11 21:02:27,758] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 482
2023-01-11T21:05:10.7278719Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7278843Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7279105Z [2023-01-11 21:02:27,791] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 483
2023-01-11T21:05:10.7279386Z [2023-01-11 21:02:27,819] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 483
2023-01-11T21:05:10.7279787Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7279911Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7280168Z [2023-01-11 21:02:27,851] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 484
2023-01-11T21:05:10.7280427Z [2023-01-11 21:02:27,862] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 484
2023-01-11T21:05:10.7280935Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7281062Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7281318Z [2023-01-11 21:02:27,893] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 485
2023-01-11T21:05:10.7281581Z [2023-01-11 21:02:27,905] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 485
2023-01-11T21:05:10.7281979Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7282106Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7282350Z [2023-01-11 21:02:27,933] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 486
2023-01-11T21:05:10.7282372Z 
2023-01-11T21:05:10.7282376Z 
2023-01-11T21:05:10.7282457Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7282527Z import torch
2023-01-11T21:05:10.7282598Z import random
2023-01-11T21:05:10.7282713Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7282835Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7282840Z 
2023-01-11T21:05:10.7282916Z aten = torch.ops.aten
2023-01-11T21:05:10.7283049Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7283127Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7283144Z 
2023-01-11T21:05:10.7283148Z 
2023-01-11T21:05:10.7283270Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7283532Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7283651Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7283754Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7283857Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7283918Z {
2023-01-11T21:05:10.7284017Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7284064Z     {
2023-01-11T21:05:10.7284141Z         #pragma omp for 
2023-01-11T21:05:10.7284224Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7284288Z         {
2023-01-11T21:05:10.7284428Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7284562Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7284647Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7284723Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7284789Z         }
2023-01-11T21:05:10.7284885Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7284969Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7285031Z         {
2023-01-11T21:05:10.7285149Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7285234Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7285302Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7285385Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7285447Z         }
2023-01-11T21:05:10.7285508Z     }
2023-01-11T21:05:10.7285566Z }
2023-01-11T21:05:10.7285649Z ''')
2023-01-11T21:05:10.7285655Z 
2023-01-11T21:05:10.7285658Z 
2023-01-11T21:05:10.7285745Z async_compile.wait(globals())
2023-01-11T21:05:10.7285802Z del async_compile
2023-01-11T21:05:10.7285807Z 
2023-01-11T21:05:10.7285875Z def call(args):
2023-01-11T21:05:10.7285949Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7286018Z     args.clear()
2023-01-11T21:05:10.7286216Z     buf0 = empty_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7286379Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7286445Z     del arg0_1
2023-01-11T21:05:10.7286498Z     del arg1_1
2023-01-11T21:05:10.7286568Z     return (buf0, )
2023-01-11T21:05:10.7286573Z 
2023-01-11T21:05:10.7286577Z 
2023-01-11T21:05:10.7286651Z if __name__ == "__main__":
2023-01-11T21:05:10.7286764Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7286885Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7287092Z     arg0_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7287288Z     arg1_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7287403Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7287408Z 
2023-01-11T21:05:10.7287412Z 
2023-01-11T21:05:10.7287503Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7287561Z import torch
2023-01-11T21:05:10.7287628Z import random
2023-01-11T21:05:10.7287739Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7287859Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7287864Z 
2023-01-11T21:05:10.7287941Z aten = torch.ops.aten
2023-01-11T21:05:10.7288072Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7288161Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7288166Z 
2023-01-11T21:05:10.7288170Z 
2023-01-11T21:05:10.7288301Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7288490Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7288606Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7288707Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7288804Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7288894Z {
2023-01-11T21:05:10.7288989Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7289050Z     {
2023-01-11T21:05:10.7289112Z         #pragma omp for 
2023-01-11T21:05:10.7289193Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7289256Z         {
2023-01-11T21:05:10.7289390Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7289519Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7289651Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7289735Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7289805Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7289895Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7289957Z         }
2023-01-11T21:05:10.7290052Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7290134Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7290197Z         {
2023-01-11T21:05:10.7290281Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7290349Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7290451Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7290559Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7290641Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7290718Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7290779Z         }
2023-01-11T21:05:10.7290840Z     }
2023-01-11T21:05:10.7290885Z }
2023-01-11T21:05:10.7290967Z ''')
2023-01-11T21:05:10.7290971Z 
2023-01-11T21:05:10.7290976Z 
2023-01-11T21:05:10.7291065Z async_compile.wait(globals())
2023-01-11T21:05:10.7291136Z del async_compile
2023-01-11T21:05:10.7291141Z 
2023-01-11T21:05:10.7291209Z def call(args):
2023-01-11T21:05:10.7291284Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7291355Z     args.clear()
2023-01-11T21:05:10.7291541Z     buf0 = empty_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7291703Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7291773Z     del arg0_1
2023-01-11T21:05:10.7291840Z     del arg1_1
2023-01-11T21:05:10.7291909Z     return (buf0, )
2023-01-11T21:05:10.7291916Z 
2023-01-11T21:05:10.7291920Z 
2023-01-11T21:05:10.7291995Z if __name__ == "__main__":
2023-01-11T21:05:10.7292109Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7292231Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7292427Z     arg0_1 = rand_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7292626Z     arg1_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7292740Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7292745Z 
2023-01-11T21:05:10.7292749Z 
2023-01-11T21:05:10.7292841Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7292911Z import torch
2023-01-11T21:05:10.7292980Z import random
2023-01-11T21:05:10.7293096Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7293217Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7293221Z 
2023-01-11T21:05:10.7293287Z aten = torch.ops.aten
2023-01-11T21:05:10.7293418Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7293507Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7293511Z 
2023-01-11T21:05:10.7293515Z 
2023-01-11T21:05:10.7293648Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7293851Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7293973Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7294077Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7294177Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7294223Z {
2023-01-11T21:05:10.7294317Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7294405Z     {
2023-01-11T21:05:10.7294480Z         #pragma omp for 
2023-01-11T21:05:10.7294561Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7294623Z         {
2023-01-11T21:05:10.7294672Z             {
2023-01-11T21:05:10.7294737Z                 {
2023-01-11T21:05:10.7294828Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7294916Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7295003Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7295085Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7295148Z                 }
2023-01-11T21:05:10.7295195Z             }
2023-01-11T21:05:10.7295257Z         }
2023-01-11T21:05:10.7295315Z     }
2023-01-11T21:05:10.7295374Z }
2023-01-11T21:05:10.7295451Z ''')
2023-01-11T21:05:10.7295456Z 
2023-01-11T21:05:10.7295460Z 
2023-01-11T21:05:10.7295548Z async_compile.wait(globals())
2023-01-11T21:05:10.7295617Z del async_compile
2023-01-11T21:05:10.7295622Z 
2023-01-11T21:05:10.7295689Z def call(args):
2023-01-11T21:05:10.7295754Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7295822Z     args.clear()
2023-01-11T21:05:10.7296030Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7296221Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7296287Z     del arg0_1
2023-01-11T21:05:10.7296351Z     del arg1_1
2023-01-11T21:05:10.7296420Z     return (buf0, )
2023-01-11T21:05:10.7296424Z 
2023-01-11T21:05:10.7296428Z 
2023-01-11T21:05:10.7296491Z if __name__ == "__main__":
2023-01-11T21:05:10.7296604Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7296728Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7296922Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7297125Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7297238Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7297246Z 
2023-01-11T21:05:10.7297250Z 
2023-01-11T21:05:10.7297340Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7297409Z import torch
2023-01-11T21:05:10.7297467Z import random
2023-01-11T21:05:10.7297581Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7297698Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7297703Z 
2023-01-11T21:05:10.7297779Z aten = torch.ops.aten
2023-01-11T21:05:10.7297910Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7297999Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7298004Z 
2023-01-11T21:05:10.7298007Z 
2023-01-11T21:05:10.7298138Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7298340Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7298443Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7298674Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7298777Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7298838Z {
2023-01-11T21:05:10.7298935Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7298998Z     {
2023-01-11T21:05:10.7299076Z         #pragma omp for 
2023-01-11T21:05:10.7299144Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7299207Z         {
2023-01-11T21:05:10.7299269Z             {
2023-01-11T21:05:10.7299333Z                 {
2023-01-11T21:05:10.7299425Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7299516Z                     auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7299621Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7299697Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7299786Z                     auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7299870Z                     out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7299936Z                 }
2023-01-11T21:05:10.7300034Z             }
2023-01-11T21:05:10.7300095Z         }
2023-01-11T21:05:10.7300143Z     }
2023-01-11T21:05:10.7300201Z }
2023-01-11T21:05:10.7300282Z ''')
2023-01-11T21:05:10.7300288Z 
2023-01-11T21:05:10.7300292Z 
2023-01-11T21:05:10.7300381Z async_compile.wait(globals())
2023-01-11T21:05:10.7300451Z del async_compile
2023-01-11T21:05:10.7300456Z 
2023-01-11T21:05:10.7300524Z def call(args):
2023-01-11T21:05:10.7300590Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7305669Z     args.clear()
2023-01-11T21:05:10.7305930Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7306100Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7306168Z     del arg0_1
2023-01-11T21:05:10.7306232Z     del arg1_1
2023-01-11T21:05:10.7306302Z     return (buf0, )
2023-01-11T21:05:10.7306309Z 
2023-01-11T21:05:10.7306314Z 
2023-01-11T21:05:10.7306376Z if __name__ == "__main__":
2023-01-11T21:05:10.7306502Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7306626Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7306896Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7307108Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7307226Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7307231Z 
2023-01-11T21:05:10.7307235Z 
2023-01-11T21:05:10.7307327Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7307396Z import torch
2023-01-11T21:05:10.7307453Z import random
2023-01-11T21:05:10.7307567Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7307688Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7307693Z 
2023-01-11T21:05:10.7307768Z aten = torch.ops.aten
2023-01-11T21:05:10.7307901Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7307992Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7307998Z 
2023-01-11T21:05:10.7308002Z 
2023-01-11T21:05:10.7308137Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7308346Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7308452Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7308554Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7308652Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7308712Z {
2023-01-11T21:05:10.7308811Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7308870Z     {
2023-01-11T21:05:10.7308943Z         #pragma omp for 
2023-01-11T21:05:10.7309012Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7309073Z         {
2023-01-11T21:05:10.7309211Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7309340Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7309426Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7309515Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7309575Z         }
2023-01-11T21:05:10.7309670Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7309741Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7309800Z         {
2023-01-11T21:05:10.7309881Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7309959Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7310038Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7310115Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7310162Z         }
2023-01-11T21:05:10.7310220Z     }
2023-01-11T21:05:10.7310276Z }
2023-01-11T21:05:10.7310353Z ''')
2023-01-11T21:05:10.7310358Z 
2023-01-11T21:05:10.7310362Z 
2023-01-11T21:05:10.7310448Z async_compile.wait(globals())
2023-01-11T21:05:10.7310516Z del async_compile
2023-01-11T21:05:10.7310521Z 
2023-01-11T21:05:10.7310580Z def call(args):
2023-01-11T21:05:10.7310700Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7310772Z     args.clear()
2023-01-11T21:05:10.7311003Z     buf0 = empty_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7311153Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7311220Z     del arg0_1
2023-01-11T21:05:10.7311284Z     del arg1_1
2023-01-11T21:05:10.7311354Z     return (buf0, )
2023-01-11T21:05:10.7311359Z 
2023-01-11T21:05:10.7311364Z 
2023-01-11T21:05:10.7311440Z if __name__ == "__main__":
2023-01-11T21:05:10.7311552Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7311673Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7311863Z     arg0_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7312089Z     arg1_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7312208Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7312482Z [2023-01-11 21:02:27,947] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 486
2023-01-11T21:05:10.7312914Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7313042Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7313301Z [2023-01-11 21:02:27,981] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 487
2023-01-11T21:05:10.7313565Z [2023-01-11 21:02:27,996] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 487
2023-01-11T21:05:10.7313966Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7314093Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7314347Z [2023-01-11 21:02:28,025] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 488
2023-01-11T21:05:10.7314597Z [2023-01-11 21:02:28,070] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 488
2023-01-11T21:05:10.7314994Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7315120Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7315374Z [2023-01-11 21:02:28,115] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 489
2023-01-11T21:05:10.7315637Z [2023-01-11 21:02:28,137] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 489
2023-01-11T21:05:10.7315642Z 
2023-01-11T21:05:10.7315646Z 
2023-01-11T21:05:10.7315740Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7315807Z import torch
2023-01-11T21:05:10.7315876Z import random
2023-01-11T21:05:10.7315991Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7316098Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7316115Z 
2023-01-11T21:05:10.7316180Z aten = torch.ops.aten
2023-01-11T21:05:10.7316313Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7316404Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7316489Z 
2023-01-11T21:05:10.7316493Z 
2023-01-11T21:05:10.7316636Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7316840Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7316964Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7317069Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7317155Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7317215Z {
2023-01-11T21:05:10.7317313Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7317377Z     {
2023-01-11T21:05:10.7317453Z         #pragma omp for 
2023-01-11T21:05:10.7317535Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7317598Z         {
2023-01-11T21:05:10.7317720Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7317852Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7317984Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7318072Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7318156Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7318282Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7318346Z         }
2023-01-11T21:05:10.7318440Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7318512Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7318571Z         {
2023-01-11T21:05:10.7318651Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7318732Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7318830Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7318911Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7318991Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7319056Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7319116Z         }
2023-01-11T21:05:10.7319176Z     }
2023-01-11T21:05:10.7319235Z }
2023-01-11T21:05:10.7319315Z ''')
2023-01-11T21:05:10.7319321Z 
2023-01-11T21:05:10.7319325Z 
2023-01-11T21:05:10.7319413Z async_compile.wait(globals())
2023-01-11T21:05:10.7319483Z del async_compile
2023-01-11T21:05:10.7319488Z 
2023-01-11T21:05:10.7319544Z def call(args):
2023-01-11T21:05:10.7319620Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7319689Z     args.clear()
2023-01-11T21:05:10.7319919Z     buf0 = empty_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7320080Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7320146Z     del arg0_1
2023-01-11T21:05:10.7320211Z     del arg1_1
2023-01-11T21:05:10.7320268Z     return (buf0, )
2023-01-11T21:05:10.7320273Z 
2023-01-11T21:05:10.7320289Z 
2023-01-11T21:05:10.7320351Z if __name__ == "__main__":
2023-01-11T21:05:10.7320466Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7320588Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7320939Z     arg0_1 = rand_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7321169Z     arg1_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7321288Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7321294Z 
2023-01-11T21:05:10.7321298Z 
2023-01-11T21:05:10.7321391Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7321446Z import torch
2023-01-11T21:05:10.7321516Z import random
2023-01-11T21:05:10.7321631Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7321751Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7321756Z 
2023-01-11T21:05:10.7321834Z aten = torch.ops.aten
2023-01-11T21:05:10.7321968Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7322060Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7322065Z 
2023-01-11T21:05:10.7322069Z 
2023-01-11T21:05:10.7322267Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7322457Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7322577Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7322683Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7322786Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7322847Z {
2023-01-11T21:05:10.7322944Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7323006Z     {
2023-01-11T21:05:10.7323068Z         #pragma omp for 
2023-01-11T21:05:10.7323150Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.7323211Z         {
2023-01-11T21:05:10.7323346Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7323477Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7323562Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7323655Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7323720Z         }
2023-01-11T21:05:10.7323800Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7323881Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.7323942Z         {
2023-01-11T21:05:10.7324065Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7324148Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7324228Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7324309Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7324356Z         }
2023-01-11T21:05:10.7324415Z     }
2023-01-11T21:05:10.7324475Z }
2023-01-11T21:05:10.7324555Z ''')
2023-01-11T21:05:10.7324560Z 
2023-01-11T21:05:10.7324564Z 
2023-01-11T21:05:10.7324651Z async_compile.wait(globals())
2023-01-11T21:05:10.7324723Z del async_compile
2023-01-11T21:05:10.7324728Z 
2023-01-11T21:05:10.7324797Z def call(args):
2023-01-11T21:05:10.7324858Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7324927Z     args.clear()
2023-01-11T21:05:10.7325137Z     buf0 = empty_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7325304Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7325374Z     del arg0_1
2023-01-11T21:05:10.7325440Z     del arg1_1
2023-01-11T21:05:10.7325510Z     return (buf0, )
2023-01-11T21:05:10.7325514Z 
2023-01-11T21:05:10.7325518Z 
2023-01-11T21:05:10.7325580Z if __name__ == "__main__":
2023-01-11T21:05:10.7325695Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7325815Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7326009Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7326217Z     arg1_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7326332Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7326337Z 
2023-01-11T21:05:10.7326341Z 
2023-01-11T21:05:10.7326436Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7326505Z import torch
2023-01-11T21:05:10.7326561Z import random
2023-01-11T21:05:10.7326676Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7326798Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7326803Z 
2023-01-11T21:05:10.7326879Z aten = torch.ops.aten
2023-01-11T21:05:10.7327012Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7327104Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7327110Z 
2023-01-11T21:05:10.7327114Z 
2023-01-11T21:05:10.7327246Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7327449Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7327555Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7327658Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7327757Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7327850Z {
2023-01-11T21:05:10.7327947Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7328008Z     {
2023-01-11T21:05:10.7328083Z         #pragma omp for 
2023-01-11T21:05:10.7328152Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.7328214Z         {
2023-01-11T21:05:10.7328345Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7328474Z             auto tmp3 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7328604Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7328688Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7328769Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7328860Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7328909Z         }
2023-01-11T21:05:10.7329004Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7329083Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.7329143Z         {
2023-01-11T21:05:10.7329226Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7329309Z             auto tmp3 = in_ptr1[i0];
2023-01-11T21:05:10.7329406Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7329507Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7329590Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7329668Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7329728Z         }
2023-01-11T21:05:10.7329788Z     }
2023-01-11T21:05:10.7329846Z }
2023-01-11T21:05:10.7329911Z ''')
2023-01-11T21:05:10.7329916Z 
2023-01-11T21:05:10.7329934Z 
2023-01-11T21:05:10.7330009Z async_compile.wait(globals())
2023-01-11T21:05:10.7330078Z del async_compile
2023-01-11T21:05:10.7330083Z 
2023-01-11T21:05:10.7330152Z def call(args):
2023-01-11T21:05:10.7330225Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7330296Z     args.clear()
2023-01-11T21:05:10.7330504Z     buf0 = empty_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7330668Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7330723Z     del arg0_1
2023-01-11T21:05:10.7330788Z     del arg1_1
2023-01-11T21:05:10.7330857Z     return (buf0, )
2023-01-11T21:05:10.7330864Z 
2023-01-11T21:05:10.7330868Z 
2023-01-11T21:05:10.7330941Z if __name__ == "__main__":
2023-01-11T21:05:10.7331054Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7331176Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7331368Z     arg0_1 = rand_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7331572Z     arg1_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7331673Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7331678Z 
2023-01-11T21:05:10.7331744Z ok (27.955s)
2023-01-11T21:05:10.7332182Z   test_views2_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7332311Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7332571Z [2023-01-11 21:02:28,174] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 490
2023-01-11T21:05:10.7332832Z [2023-01-11 21:02:30,813] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 490
2023-01-11T21:05:10.7333231Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7333395Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7333650Z [2023-01-11 21:02:30,852] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 491
2023-01-11T21:05:10.7333913Z [2023-01-11 21:02:33,477] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 491
2023-01-11T21:05:10.7334310Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7334420Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7334674Z [2023-01-11 21:02:33,510] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 492
2023-01-11T21:05:10.7334935Z [2023-01-11 21:02:36,151] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 492
2023-01-11T21:05:10.7335363Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7335487Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7335739Z [2023-01-11 21:02:36,191] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 493
2023-01-11T21:05:10.7335998Z [2023-01-11 21:02:38,823] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 493
2023-01-11T21:05:10.7336392Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7336520Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7336774Z [2023-01-11 21:02:38,858] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 494
2023-01-11T21:05:10.7336779Z 
2023-01-11T21:05:10.7336872Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7336928Z import torch
2023-01-11T21:05:10.7336997Z import random
2023-01-11T21:05:10.7337111Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7337232Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7337237Z 
2023-01-11T21:05:10.7337315Z aten = torch.ops.aten
2023-01-11T21:05:10.7337448Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7337538Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7337543Z 
2023-01-11T21:05:10.7337550Z 
2023-01-11T21:05:10.7337683Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7337874Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7337993Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7338094Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7338153Z {
2023-01-11T21:05:10.7338250Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7338310Z     {
2023-01-11T21:05:10.7338384Z         #pragma omp for 
2023-01-11T21:05:10.7338453Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.7338606Z         {
2023-01-11T21:05:10.7338742Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7338876Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7338963Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7339058Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7339161Z         }
2023-01-11T21:05:10.7339243Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7339326Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.7339389Z         {
2023-01-11T21:05:10.7339474Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7339572Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7339658Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7339738Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7339786Z         }
2023-01-11T21:05:10.7339846Z     }
2023-01-11T21:05:10.7339904Z }
2023-01-11T21:05:10.7339983Z ''')
2023-01-11T21:05:10.7339988Z 
2023-01-11T21:05:10.7339993Z 
2023-01-11T21:05:10.7340079Z async_compile.wait(globals())
2023-01-11T21:05:10.7340149Z del async_compile
2023-01-11T21:05:10.7340154Z 
2023-01-11T21:05:10.7340223Z def call(args):
2023-01-11T21:05:10.7340278Z     arg0_1, = args
2023-01-11T21:05:10.7340347Z     args.clear()
2023-01-11T21:05:10.7340541Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7340677Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7340744Z     del arg0_1
2023-01-11T21:05:10.7340814Z     return (buf0, )
2023-01-11T21:05:10.7340819Z 
2023-01-11T21:05:10.7340854Z 
2023-01-11T21:05:10.7340929Z if __name__ == "__main__":
2023-01-11T21:05:10.7341041Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7341149Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7341360Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7341466Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7341472Z 
2023-01-11T21:05:10.7341476Z 
2023-01-11T21:05:10.7341567Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7341635Z import torch
2023-01-11T21:05:10.7341703Z import random
2023-01-11T21:05:10.7341815Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7341922Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7341941Z 
2023-01-11T21:05:10.7342006Z aten = torch.ops.aten
2023-01-11T21:05:10.7342137Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7342228Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7342233Z 
2023-01-11T21:05:10.7342237Z 
2023-01-11T21:05:10.7342369Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7342572Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7342689Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7342786Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7342845Z {
2023-01-11T21:05:10.7342928Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7342987Z     {
2023-01-11T21:05:10.7343062Z         #pragma omp for 
2023-01-11T21:05:10.7343142Z         for(long i0=0; i0<1; i0+=1)
2023-01-11T21:05:10.7343202Z         {
2023-01-11T21:05:10.7343335Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7343467Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.7343538Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7343669Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7343751Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7343840Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7343903Z         }
2023-01-11T21:05:10.7343995Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7344075Z         for(long i0=16; i0<16; i0+=1)
2023-01-11T21:05:10.7344123Z         {
2023-01-11T21:05:10.7344205Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7344301Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.7344383Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7344480Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:05:10.7344561Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7344673Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7344722Z         }
2023-01-11T21:05:10.7344781Z     }
2023-01-11T21:05:10.7344839Z }
2023-01-11T21:05:10.7344916Z ''')
2023-01-11T21:05:10.7344921Z 
2023-01-11T21:05:10.7344928Z 
2023-01-11T21:05:10.7345015Z async_compile.wait(globals())
2023-01-11T21:05:10.7345085Z del async_compile
2023-01-11T21:05:10.7345090Z 
2023-01-11T21:05:10.7345158Z def call(args):
2023-01-11T21:05:10.7345213Z     arg0_1, = args
2023-01-11T21:05:10.7345281Z     args.clear()
2023-01-11T21:05:10.7345479Z     buf0 = empty_strided((4, 4), (4, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7345610Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7345676Z     del arg0_1
2023-01-11T21:05:10.7345745Z     return (buf0, )
2023-01-11T21:05:10.7345750Z 
2023-01-11T21:05:10.7345754Z 
2023-01-11T21:05:10.7345827Z if __name__ == "__main__":
2023-01-11T21:05:10.7345926Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7346048Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7346258Z     arg0_1 = rand_strided((2, 2, 2, 2), (8, 4, 2, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7346392Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7346398Z 
2023-01-11T21:05:10.7346402Z 
2023-01-11T21:05:10.7346496Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7346564Z import torch
2023-01-11T21:05:10.7346632Z import random
2023-01-11T21:05:10.7346745Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7346851Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7346869Z 
2023-01-11T21:05:10.7346932Z aten = torch.ops.aten
2023-01-11T21:05:10.7347063Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7347153Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7347158Z 
2023-01-11T21:05:10.7347162Z 
2023-01-11T21:05:10.7347293Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7347500Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7347618Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7347718Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7347764Z {
2023-01-11T21:05:10.7347861Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7347921Z     {
2023-01-11T21:05:10.7347997Z         #pragma omp for 
2023-01-11T21:05:10.7348079Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7348145Z         {
2023-01-11T21:05:10.7348279Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7348397Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7348482Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7348573Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7348634Z         }
2023-01-11T21:05:10.7348727Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7348812Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7348877Z         {
2023-01-11T21:05:10.7348946Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7349045Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7349127Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7349204Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7349270Z         }
2023-01-11T21:05:10.7349357Z     }
2023-01-11T21:05:10.7349436Z }
2023-01-11T21:05:10.7349541Z ''')
2023-01-11T21:05:10.7349548Z 
2023-01-11T21:05:10.7349554Z 
2023-01-11T21:05:10.7349642Z async_compile.wait(globals())
2023-01-11T21:05:10.7349714Z del async_compile
2023-01-11T21:05:10.7349719Z 
2023-01-11T21:05:10.7349787Z def call(args):
2023-01-11T21:05:10.7349855Z     arg0_1, = args
2023-01-11T21:05:10.7349926Z     args.clear()
2023-01-11T21:05:10.7350128Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7350257Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7350349Z     del arg0_1
2023-01-11T21:05:10.7350419Z     return (buf0, )
2023-01-11T21:05:10.7350423Z 
2023-01-11T21:05:10.7350428Z 
2023-01-11T21:05:10.7350503Z if __name__ == "__main__":
2023-01-11T21:05:10.7350613Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7350734Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7350966Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7351073Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7351078Z 
2023-01-11T21:05:10.7351082Z 
2023-01-11T21:05:10.7351174Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7351229Z import torch
2023-01-11T21:05:10.7351298Z import random
2023-01-11T21:05:10.7351410Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7351528Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7351535Z 
2023-01-11T21:05:10.7351611Z aten = torch.ops.aten
2023-01-11T21:05:10.7351743Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7351833Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7351877Z 
2023-01-11T21:05:10.7351882Z 
2023-01-11T21:05:10.7352015Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7352207Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7352323Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7352421Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7352480Z {
2023-01-11T21:05:10.7352574Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7352634Z     {
2023-01-11T21:05:10.7352709Z         #pragma omp for 
2023-01-11T21:05:10.7352777Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7352838Z         {
2023-01-11T21:05:10.7352970Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7353105Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.7353188Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7353321Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7353405Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7353484Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7353545Z         }
2023-01-11T21:05:10.7353640Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7353723Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7353785Z         {
2023-01-11T21:05:10.7353868Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7353966Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.7354034Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7354134Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:05:10.7354215Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7354298Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7354357Z         }
2023-01-11T21:05:10.7354417Z     }
2023-01-11T21:05:10.7354475Z }
2023-01-11T21:05:10.7354539Z ''')
2023-01-11T21:05:10.7354543Z 
2023-01-11T21:05:10.7354548Z 
2023-01-11T21:05:10.7354637Z async_compile.wait(globals())
2023-01-11T21:05:10.7354708Z del async_compile
2023-01-11T21:05:10.7354713Z 
2023-01-11T21:05:10.7354783Z def call(args):
2023-01-11T21:05:10.7354850Z     arg0_1, = args
2023-01-11T21:05:10.7354919Z     args.clear()
2023-01-11T21:05:10.7355126Z     buf0 = empty_strided((10, 100), (100, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7355246Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7355312Z     del arg0_1
2023-01-11T21:05:10.7355381Z     return (buf0, )
2023-01-11T21:05:10.7355387Z 
2023-01-11T21:05:10.7355392Z 
2023-01-11T21:05:10.7355466Z if __name__ == "__main__":
2023-01-11T21:05:10.7355579Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7355749Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7355981Z     arg0_1 = rand_strided((10, 1, 10, 1, 10), (100, 100, 10, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7356090Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7356095Z 
2023-01-11T21:05:10.7356099Z 
2023-01-11T21:05:10.7356192Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7356247Z import torch
2023-01-11T21:05:10.7356317Z import random
2023-01-11T21:05:10.7356432Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7356551Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7356556Z 
2023-01-11T21:05:10.7356633Z aten = torch.ops.aten
2023-01-11T21:05:10.7356766Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7356856Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7356861Z 
2023-01-11T21:05:10.7356866Z 
2023-01-11T21:05:10.7356998Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7357191Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7357310Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7357440Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7357502Z {
2023-01-11T21:05:10.7357600Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7357661Z     {
2023-01-11T21:05:10.7357738Z         #pragma omp for 
2023-01-11T21:05:10.7357807Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7357868Z         {
2023-01-11T21:05:10.7358004Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7358137Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7358222Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7358313Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7358375Z         }
2023-01-11T21:05:10.7358455Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7358541Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7358602Z         {
2023-01-11T21:05:10.7358685Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7358787Z             auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7358873Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7358952Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7358999Z         }
2023-01-11T21:05:10.7359062Z     }
2023-01-11T21:05:10.7359121Z }
2023-01-11T21:05:10.7359200Z ''')
2023-01-11T21:05:10.7359206Z 
2023-01-11T21:05:10.7359210Z 
2023-01-11T21:05:10.7359299Z async_compile.wait(globals())
2023-01-11T21:05:10.7359371Z del async_compile
2023-01-11T21:05:10.7359376Z 
2023-01-11T21:05:10.7359445Z def call(args):
2023-01-11T21:05:10.7359501Z     arg0_1, = args
2023-01-11T21:05:10.7359570Z     args.clear()
2023-01-11T21:05:10.7359780Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7359915Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7360017Z     del arg0_1
2023-01-11T21:05:10.7360123Z     return (buf0, )
2023-01-11T21:05:10.7360129Z 
2023-01-11T21:05:10.7360136Z 
2023-01-11T21:05:10.7360219Z if __name__ == "__main__":
2023-01-11T21:05:10.7360320Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7360443Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7360761Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7360871Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7361144Z [2023-01-11 21:02:38,870] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 494
2023-01-11T21:05:10.7361557Z inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7361755Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7362022Z [2023-01-11 21:02:38,900] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 495
2023-01-11T21:05:10.7362291Z [2023-01-11 21:02:38,912] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 495
2023-01-11T21:05:10.7362296Z 
2023-01-11T21:05:10.7362300Z 
2023-01-11T21:05:10.7362398Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7362454Z import torch
2023-01-11T21:05:10.7362523Z import random
2023-01-11T21:05:10.7362639Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7362760Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7362765Z 
2023-01-11T21:05:10.7362844Z aten = torch.ops.aten
2023-01-11T21:05:10.7362979Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7363077Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7363082Z 
2023-01-11T21:05:10.7363086Z 
2023-01-11T21:05:10.7363222Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7363454Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7363578Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7363676Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7363738Z {
2023-01-11T21:05:10.7363834Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7363894Z     {
2023-01-11T21:05:10.7363968Z         #pragma omp for 
2023-01-11T21:05:10.7364037Z         for(long i0=0; i0<62; i0+=1)
2023-01-11T21:05:10.7364097Z         {
2023-01-11T21:05:10.7364234Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7364369Z             auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(2));
2023-01-11T21:05:10.7364453Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7364591Z             auto tmp3 = at::vec::Vectorized<float>(static_cast<float>(1));
2023-01-11T21:05:10.7364675Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7364756Z             tmp4.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7364818Z         }
2023-01-11T21:05:10.7364912Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7364997Z         for(long i0=992; i0<1000; i0+=1)
2023-01-11T21:05:10.7365057Z         {
2023-01-11T21:05:10.7365139Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7365238Z             auto tmp1 = static_cast<float>(2);
2023-01-11T21:05:10.7365307Z             auto tmp2 = tmp0 * tmp1;
2023-01-11T21:05:10.7365404Z             auto tmp3 = static_cast<float>(1);
2023-01-11T21:05:10.7365486Z             auto tmp4 = tmp2 + tmp3;
2023-01-11T21:05:10.7365565Z             out_ptr0[i0] = tmp4;
2023-01-11T21:05:10.7365625Z         }
2023-01-11T21:05:10.7365684Z     }
2023-01-11T21:05:10.7365740Z }
2023-01-11T21:05:10.7365810Z ''')
2023-01-11T21:05:10.7365814Z 
2023-01-11T21:05:10.7365819Z 
2023-01-11T21:05:10.7365905Z async_compile.wait(globals())
2023-01-11T21:05:10.7365976Z del async_compile
2023-01-11T21:05:10.7365981Z 
2023-01-11T21:05:10.7366049Z def call(args):
2023-01-11T21:05:10.7366117Z     arg0_1, = args
2023-01-11T21:05:10.7366187Z     args.clear()
2023-01-11T21:05:10.7366399Z     buf0 = empty_strided((10, 5, 20), (100, 20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7366535Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7366588Z     del arg0_1
2023-01-11T21:05:10.7366657Z     return (buf0, )
2023-01-11T21:05:10.7366662Z 
2023-01-11T21:05:10.7366667Z 
2023-01-11T21:05:10.7366740Z if __name__ == "__main__":
2023-01-11T21:05:10.7366853Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7366975Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7367175Z     arg0_1 = rand_strided((50, 20), (20, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7367320Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7367325Z 
2023-01-11T21:05:10.7367389Z ok (10.773s)
2023-01-11T21:05:10.7367825Z   test_views3_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7367952Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7368213Z [2023-01-11 21:02:39,024] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 496
2023-01-11T21:05:10.7368477Z [2023-01-11 21:02:41,710] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 496
2023-01-11T21:05:10.7368483Z 
2023-01-11T21:05:10.7368576Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7368646Z import torch
2023-01-11T21:05:10.7368715Z import random
2023-01-11T21:05:10.7368831Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7368967Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7368987Z 
2023-01-11T21:05:10.7369051Z aten = torch.ops.aten
2023-01-11T21:05:10.7369183Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7369273Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7369278Z 
2023-01-11T21:05:10.7369282Z 
2023-01-11T21:05:10.7369417Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7369621Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7369738Z extern "C" void kernel(const long* __restrict__ in_ptr0,
2023-01-11T21:05:10.7369842Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7369940Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7369989Z {
2023-01-11T21:05:10.7370085Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7370145Z     {
2023-01-11T21:05:10.7370220Z         #pragma omp for 
2023-01-11T21:05:10.7370302Z         for(long i0=0; i0<744; i0+=1)
2023-01-11T21:05:10.7370365Z         {
2023-01-11T21:05:10.7370432Z             #pragma GCC ivdep
2023-01-11T21:05:10.7370517Z             for(long i1=0; i1<192; i1+=1)
2023-01-11T21:05:10.7370578Z             {
2023-01-11T21:05:10.7370641Z                 {
2023-01-11T21:05:10.7370706Z                     {
2023-01-11T21:05:10.7370813Z                         auto tmp0 = in_ptr0[(3*i0) + (i1 / 64)];
2023-01-11T21:05:10.7370922Z                         auto tmp1 = in_ptr1[(64*tmp0) + (i1 % 64)];
2023-01-11T21:05:10.7371004Z                         out_ptr0[i1 + (192*i0)] = tmp1;
2023-01-11T21:05:10.7371068Z                     }
2023-01-11T21:05:10.7371130Z                 }
2023-01-11T21:05:10.7371192Z             }
2023-01-11T21:05:10.7371252Z         }
2023-01-11T21:05:10.7371314Z     }
2023-01-11T21:05:10.7371372Z }
2023-01-11T21:05:10.7371436Z ''')
2023-01-11T21:05:10.7371441Z 
2023-01-11T21:05:10.7371445Z 
2023-01-11T21:05:10.7371532Z async_compile.wait(globals())
2023-01-11T21:05:10.7371602Z del async_compile
2023-01-11T21:05:10.7371609Z 
2023-01-11T21:05:10.7371679Z def call(args):
2023-01-11T21:05:10.7371751Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7371823Z     args.clear()
2023-01-11T21:05:10.7372051Z     buf0 = empty_strided((1, 12, 62, 192), (142848, 11904, 192, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7372202Z     kernel_cpp_0(c_void_p(arg1_1.data_ptr()), c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7372269Z     del arg0_1
2023-01-11T21:05:10.7372335Z     del arg1_1
2023-01-11T21:05:10.7372404Z     return (buf0, )
2023-01-11T21:05:10.7372409Z 
2023-01-11T21:05:10.7372413Z 
2023-01-11T21:05:10.7372487Z if __name__ == "__main__":
2023-01-11T21:05:10.7372601Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7372754Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7372956Z     arg0_1 = rand_strided((64, 64), (64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7373138Z     arg1_1 = rand_strided((2232, ), (1, ), device='cpu', dtype=torch.int64)
2023-01-11T21:05:10.7373252Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7373257Z 
2023-01-11T21:05:10.7373322Z ok (2.836s)
2023-01-11T21:05:10.7373668Z   test_zero_dim_reductions_cpu (__main__.CpuTests) ... [2023-01-11 21:02:41,908] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 497
2023-01-11T21:05:10.7373935Z [2023-01-11 21:02:44,551] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 497
2023-01-11T21:05:10.7374186Z [2023-01-11 21:02:44,676] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 498
2023-01-11T21:05:10.7374446Z [2023-01-11 21:02:44,686] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 498
2023-01-11T21:05:10.7374454Z 
2023-01-11T21:05:10.7374546Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7374615Z import torch
2023-01-11T21:05:10.7374672Z import random
2023-01-11T21:05:10.7374815Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7374936Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7374942Z 
2023-01-11T21:05:10.7375017Z aten = torch.ops.aten
2023-01-11T21:05:10.7375149Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7375239Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7375244Z 
2023-01-11T21:05:10.7375247Z 
2023-01-11T21:05:10.7375382Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7375585Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7375681Z extern "C" void kernel(bool* __restrict__ out_ptr0)
2023-01-11T21:05:10.7375741Z {
2023-01-11T21:05:10.7375815Z     #pragma GCC ivdep
2023-01-11T21:05:10.7375896Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7375956Z     {
2023-01-11T21:05:10.7376017Z         {
2023-01-11T21:05:10.7376079Z             {
2023-01-11T21:05:10.7376173Z                 auto tmp0 = static_cast<bool>(false);
2023-01-11T21:05:10.7376255Z                 auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.7376335Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.7376397Z             }
2023-01-11T21:05:10.7376458Z         }
2023-01-11T21:05:10.7376517Z     }
2023-01-11T21:05:10.7376562Z }
2023-01-11T21:05:10.7376638Z ''')
2023-01-11T21:05:10.7376642Z 
2023-01-11T21:05:10.7376647Z 
2023-01-11T21:05:10.7376735Z async_compile.wait(globals())
2023-01-11T21:05:10.7376806Z del async_compile
2023-01-11T21:05:10.7376810Z 
2023-01-11T21:05:10.7376878Z def call(args):
2023-01-11T21:05:10.7376945Z     arg0_1, = args
2023-01-11T21:05:10.7377014Z     args.clear()
2023-01-11T21:05:10.7377203Z     buf0 = empty_strided((2, 1), (1, 1), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.7377295Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7377365Z     return (buf0, )
2023-01-11T21:05:10.7377369Z 
2023-01-11T21:05:10.7377374Z 
2023-01-11T21:05:10.7377447Z if __name__ == "__main__":
2023-01-11T21:05:10.7377564Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7377685Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7377878Z     arg0_1 = rand_strided((2, 0), (1, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.7377984Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7377989Z 
2023-01-11T21:05:10.7377994Z 
2023-01-11T21:05:10.7378085Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7378140Z import torch
2023-01-11T21:05:10.7378207Z import random
2023-01-11T21:05:10.7378318Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7378437Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7378442Z 
2023-01-11T21:05:10.7378608Z aten = torch.ops.aten
2023-01-11T21:05:10.7378778Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7378869Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7378874Z 
2023-01-11T21:05:10.7378879Z 
2023-01-11T21:05:10.7379014Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7379206Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7379317Z extern "C" void kernel(bool* __restrict__ out_ptr0)
2023-01-11T21:05:10.7379378Z {
2023-01-11T21:05:10.7379455Z     #pragma GCC ivdep
2023-01-11T21:05:10.7379536Z     for(long i0=0; i0<2; i0+=1)
2023-01-11T21:05:10.7379599Z     {
2023-01-11T21:05:10.7379661Z         {
2023-01-11T21:05:10.7379710Z             {
2023-01-11T21:05:10.7379813Z                 auto tmp0 = static_cast<bool>(false);
2023-01-11T21:05:10.7379895Z                 auto tmp1 = tmp0 == 0;
2023-01-11T21:05:10.7379975Z                 out_ptr0[i0] = tmp1;
2023-01-11T21:05:10.7380036Z             }
2023-01-11T21:05:10.7380099Z         }
2023-01-11T21:05:10.7380146Z     }
2023-01-11T21:05:10.7380204Z }
2023-01-11T21:05:10.7380281Z ''')
2023-01-11T21:05:10.7380286Z 
2023-01-11T21:05:10.7380290Z 
2023-01-11T21:05:10.7380415Z async_compile.wait(globals())
2023-01-11T21:05:10.7380487Z del async_compile
2023-01-11T21:05:10.7380492Z 
2023-01-11T21:05:10.7380563Z def call(args):
2023-01-11T21:05:10.7380633Z     arg0_1, = args
2023-01-11T21:05:10.7380702Z     args.clear()
2023-01-11T21:05:10.7380876Z     buf0 = empty_strided((2, ), (1, ), device='cpu', dtype=torch.bool)
2023-01-11T21:05:10.7380976Z     kernel_cpp_0(c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7381047Z     return (buf0, )
2023-01-11T21:05:10.7381052Z 
2023-01-11T21:05:10.7381057Z 
2023-01-11T21:05:10.7381130Z if __name__ == "__main__":
2023-01-11T21:05:10.7381239Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7381359Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7381553Z     arg0_1 = rand_strided((2, 0), (1, 1), device='cpu', dtype=torch.float16)
2023-01-11T21:05:10.7381650Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7381668Z 
2023-01-11T21:05:10.7381719Z ok (2.938s)
2023-01-11T21:05:10.7382156Z   test_zeros_cpu (__main__.CpuTests) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7382282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7382541Z [2023-01-11 21:02:44,883] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 499
2023-01-11T21:05:10.7382807Z [2023-01-11 21:02:47,605] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 499
2023-01-11T21:05:10.7382813Z 
2023-01-11T21:05:10.7382909Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7382979Z import torch
2023-01-11T21:05:10.7383048Z import random
2023-01-11T21:05:10.7383149Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7383270Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7383275Z 
2023-01-11T21:05:10.7383351Z aten = torch.ops.aten
2023-01-11T21:05:10.7383481Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7383571Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7383576Z 
2023-01-11T21:05:10.7383580Z 
2023-01-11T21:05:10.7383713Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7383918Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7384034Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7384133Z                        float* __restrict__ out_ptr0,
2023-01-11T21:05:10.7384215Z                        float* __restrict__ out_ptr1,
2023-01-11T21:05:10.7384339Z                        float* __restrict__ out_ptr2,
2023-01-11T21:05:10.7384432Z                        float* __restrict__ out_ptr3,
2023-01-11T21:05:10.7384526Z                        float* __restrict__ out_ptr4,
2023-01-11T21:05:10.7384616Z                        float* __restrict__ out_ptr5)
2023-01-11T21:05:10.7384675Z {
2023-01-11T21:05:10.7384770Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7384818Z     {
2023-01-11T21:05:10.7384892Z         #pragma omp for 
2023-01-11T21:05:10.7384974Z         for(long i0=0; i0<8; i0+=1)
2023-01-11T21:05:10.7385037Z         {
2023-01-11T21:05:10.7385099Z             {
2023-01-11T21:05:10.7385164Z                 {
2023-01-11T21:05:10.7385242Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7385345Z                     auto tmp1 = static_cast<float>(1);
2023-01-11T21:05:10.7385436Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7385520Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7385607Z                     out_ptr1[i0] = tmp2;
2023-01-11T21:05:10.7385671Z                 }
2023-01-11T21:05:10.7385733Z             }
2023-01-11T21:05:10.7385780Z         }
2023-01-11T21:05:10.7385882Z         #pragma omp for 
2023-01-11T21:05:10.7385965Z         for(long i0=0; i0<2048; i0+=1)
2023-01-11T21:05:10.7386026Z         {
2023-01-11T21:05:10.7386163Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:05:10.7386255Z             tmp0.store(out_ptr2 + 16*i0);
2023-01-11T21:05:10.7386316Z         }
2023-01-11T21:05:10.7386396Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7386483Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:05:10.7386544Z         {
2023-01-11T21:05:10.7386642Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:05:10.7386722Z             out_ptr2[i0] = tmp0;
2023-01-11T21:05:10.7386781Z         }
2023-01-11T21:05:10.7386854Z         #pragma omp for 
2023-01-11T21:05:10.7386922Z         for(long i0=0; i0<2048; i0+=1)
2023-01-11T21:05:10.7386985Z         {
2023-01-11T21:05:10.7387118Z             auto tmp0 = at::vec::Vectorized<float>(static_cast<float>(0));
2023-01-11T21:05:10.7387210Z             tmp0.store(out_ptr3 + 16*i0);
2023-01-11T21:05:10.7387275Z         }
2023-01-11T21:05:10.7387368Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7387456Z         for(long i0=32768; i0<32768; i0+=1)
2023-01-11T21:05:10.7387503Z         {
2023-01-11T21:05:10.7387600Z             auto tmp0 = static_cast<float>(0);
2023-01-11T21:05:10.7387679Z             out_ptr3[i0] = tmp0;
2023-01-11T21:05:10.7387741Z         }
2023-01-11T21:05:10.7387816Z         #pragma omp for 
2023-01-11T21:05:10.7387896Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7387957Z         {
2023-01-11T21:05:10.7388005Z             {
2023-01-11T21:05:10.7388068Z                 {
2023-01-11T21:05:10.7388171Z                     auto tmp0 = static_cast<float>(0);
2023-01-11T21:05:10.7388256Z                     out_ptr4[i0] = tmp0;
2023-01-11T21:05:10.7388325Z                 }
2023-01-11T21:05:10.7388389Z             }
2023-01-11T21:05:10.7388437Z         }
2023-01-11T21:05:10.7388511Z         #pragma omp for 
2023-01-11T21:05:10.7388593Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7388654Z         {
2023-01-11T21:05:10.7388715Z             {
2023-01-11T21:05:10.7388778Z                 {
2023-01-11T21:05:10.7388887Z                     auto tmp0 = static_cast<float>(3.1416);
2023-01-11T21:05:10.7388956Z                     out_ptr5[i0] = tmp0;
2023-01-11T21:05:10.7389021Z                 }
2023-01-11T21:05:10.7389083Z             }
2023-01-11T21:05:10.7389144Z         }
2023-01-11T21:05:10.7389204Z     }
2023-01-11T21:05:10.7389263Z }
2023-01-11T21:05:10.7389330Z ''')
2023-01-11T21:05:10.7389349Z 
2023-01-11T21:05:10.7389353Z 
2023-01-11T21:05:10.7389428Z async_compile.wait(globals())
2023-01-11T21:05:10.7389501Z del async_compile
2023-01-11T21:05:10.7389506Z 
2023-01-11T21:05:10.7389575Z def call(args):
2023-01-11T21:05:10.7389675Z     arg0_1, = args
2023-01-11T21:05:10.7389744Z     args.clear()
2023-01-11T21:05:10.7389938Z     buf0 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7390134Z     buf4 = empty_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7390339Z     buf1 = empty_strided((1, 8, 64, 64), (32768, 4096, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7390556Z     buf2 = empty_strided((1, 8, 64, 64), (32768, 4096, 64, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7390747Z     buf3 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7390935Z     buf5 = empty_strided((2, 3), (3, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7391192Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()), c_void_p(buf4.data_ptr()), c_void_p(buf1.data_ptr()), c_void_p(buf2.data_ptr()), c_void_p(buf3.data_ptr()), c_void_p(buf5.data_ptr()))
2023-01-11T21:05:10.7391262Z     del arg0_1
2023-01-11T21:05:10.7391363Z     return (buf0, buf1, buf2, buf3, buf4, buf5, )
2023-01-11T21:05:10.7391369Z 
2023-01-11T21:05:10.7391373Z 
2023-01-11T21:05:10.7391449Z if __name__ == "__main__":
2023-01-11T21:05:10.7391594Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7391705Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7391897Z     arg0_1 = rand_strided((8, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7392003Z     print_performance(lambda: call([arg0_1]))
2023-01-11T21:05:10.7392008Z 
2023-01-11T21:05:10.7392073Z ok (2.984s)
2023-01-11T21:05:10.7392195Z   test_print_pow (__main__.ExprPrinterTests) ... ok (0.010s)
2023-01-11T21:05:10.7392660Z   test_cpu_broadcast1_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7392786Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7393049Z [2023-01-11 21:02:47,716] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 500
2023-01-11T21:05:10.7393309Z [2023-01-11 21:02:47,732] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 500
2023-01-11T21:05:10.7393315Z 
2023-01-11T21:05:10.7393395Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7393464Z import torch
2023-01-11T21:05:10.7393533Z import random
2023-01-11T21:05:10.7393646Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7393765Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7393770Z 
2023-01-11T21:05:10.7393847Z aten = torch.ops.aten
2023-01-11T21:05:10.7393979Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7394070Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7394076Z 
2023-01-11T21:05:10.7394080Z 
2023-01-11T21:05:10.7394199Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7394404Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7394523Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7394625Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7394723Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7394784Z {
2023-01-11T21:05:10.7394880Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7394927Z     {
2023-01-11T21:05:10.7395002Z         #pragma omp for 
2023-01-11T21:05:10.7395083Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7395144Z         {
2023-01-11T21:05:10.7395207Z             {
2023-01-11T21:05:10.7395270Z                 {
2023-01-11T21:05:10.7395361Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7395483Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7395572Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7395656Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7395721Z                 }
2023-01-11T21:05:10.7395782Z             }
2023-01-11T21:05:10.7395843Z         }
2023-01-11T21:05:10.7395901Z     }
2023-01-11T21:05:10.7395947Z }
2023-01-11T21:05:10.7396024Z ''')
2023-01-11T21:05:10.7396029Z 
2023-01-11T21:05:10.7396033Z 
2023-01-11T21:05:10.7396121Z async_compile.wait(globals())
2023-01-11T21:05:10.7396190Z del async_compile
2023-01-11T21:05:10.7396195Z 
2023-01-11T21:05:10.7396264Z def call(args):
2023-01-11T21:05:10.7396340Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7396412Z     args.clear()
2023-01-11T21:05:10.7396592Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7396754Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7396825Z     del arg0_1
2023-01-11T21:05:10.7396891Z     del arg1_1
2023-01-11T21:05:10.7396963Z     return (buf0, )
2023-01-11T21:05:10.7396968Z 
2023-01-11T21:05:10.7396972Z 
2023-01-11T21:05:10.7397047Z if __name__ == "__main__":
2023-01-11T21:05:10.7397189Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7397311Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7397494Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7397684Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7397797Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7397802Z 
2023-01-11T21:05:10.7397865Z ok (0.051s)
2023-01-11T21:05:10.7398333Z   test_cpu_broadcast1_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7398462Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7398721Z [2023-01-11 21:02:47,756] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 501
2023-01-11T21:05:10.7398985Z [2023-01-11 21:02:50,431] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 501
2023-01-11T21:05:10.7398991Z 
2023-01-11T21:05:10.7399081Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7399137Z import torch
2023-01-11T21:05:10.7399206Z import random
2023-01-11T21:05:10.7399318Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7399436Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7399441Z 
2023-01-11T21:05:10.7399517Z aten = torch.ops.aten
2023-01-11T21:05:10.7399649Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7399744Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7399749Z 
2023-01-11T21:05:10.7399753Z 
2023-01-11T21:05:10.7399887Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7400079Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7400197Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7400298Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7400397Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7400456Z {
2023-01-11T21:05:10.7400551Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7400734Z     {
2023-01-11T21:05:10.7400829Z         #pragma omp for 
2023-01-11T21:05:10.7400952Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7401025Z         {
2023-01-11T21:05:10.7401107Z             #pragma GCC ivdep
2023-01-11T21:05:10.7401192Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7401315Z             {
2023-01-11T21:05:10.7401378Z                 {
2023-01-11T21:05:10.7401430Z                     {
2023-01-11T21:05:10.7401526Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7401623Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7401715Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7401814Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7401880Z                     }
2023-01-11T21:05:10.7401944Z                 }
2023-01-11T21:05:10.7401992Z             }
2023-01-11T21:05:10.7402054Z         }
2023-01-11T21:05:10.7402115Z     }
2023-01-11T21:05:10.7402175Z }
2023-01-11T21:05:10.7402259Z ''')
2023-01-11T21:05:10.7402265Z 
2023-01-11T21:05:10.7402269Z 
2023-01-11T21:05:10.7402357Z async_compile.wait(globals())
2023-01-11T21:05:10.7402426Z del async_compile
2023-01-11T21:05:10.7402432Z 
2023-01-11T21:05:10.7402486Z def call(args):
2023-01-11T21:05:10.7402560Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7402630Z     args.clear()
2023-01-11T21:05:10.7402839Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7403073Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7403142Z     del arg0_1
2023-01-11T21:05:10.7403207Z     del arg1_1
2023-01-11T21:05:10.7403263Z     return (buf0, )
2023-01-11T21:05:10.7403269Z 
2023-01-11T21:05:10.7403287Z 
2023-01-11T21:05:10.7403348Z if __name__ == "__main__":
2023-01-11T21:05:10.7403463Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7403586Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7403782Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7403988Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7404104Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7404111Z 
2023-01-11T21:05:10.7404177Z ok (2.701s)
2023-01-11T21:05:10.7404646Z   test_cpu_broadcast1_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7404759Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7405016Z [2023-01-11 21:02:50,462] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 502
2023-01-11T21:05:10.7405281Z [2023-01-11 21:02:53,142] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 502
2023-01-11T21:05:10.7405286Z 
2023-01-11T21:05:10.7405379Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7405450Z import torch
2023-01-11T21:05:10.7405520Z import random
2023-01-11T21:05:10.7405633Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7405751Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7405756Z 
2023-01-11T21:05:10.7405834Z aten = torch.ops.aten
2023-01-11T21:05:10.7405954Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7406046Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7406051Z 
2023-01-11T21:05:10.7406055Z 
2023-01-11T21:05:10.7406190Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7406394Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7406511Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7406613Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7406712Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7406759Z {
2023-01-11T21:05:10.7406855Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7406957Z     {
2023-01-11T21:05:10.7407032Z         #pragma omp for 
2023-01-11T21:05:10.7407114Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7407175Z         {
2023-01-11T21:05:10.7407240Z             {
2023-01-11T21:05:10.7407289Z                 {
2023-01-11T21:05:10.7407380Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7407468Z                     auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.7407557Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7407640Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7407703Z                 }
2023-01-11T21:05:10.7407763Z             }
2023-01-11T21:05:10.7407811Z         }
2023-01-11T21:05:10.7407870Z     }
2023-01-11T21:05:10.7407927Z }
2023-01-11T21:05:10.7408004Z ''')
2023-01-11T21:05:10.7408009Z 
2023-01-11T21:05:10.7408014Z 
2023-01-11T21:05:10.7408102Z async_compile.wait(globals())
2023-01-11T21:05:10.7408173Z del async_compile
2023-01-11T21:05:10.7408178Z 
2023-01-11T21:05:10.7408251Z def call(args):
2023-01-11T21:05:10.7408312Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7408382Z     args.clear()
2023-01-11T21:05:10.7408576Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7408770Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7408841Z     del arg0_1
2023-01-11T21:05:10.7408906Z     del arg1_1
2023-01-11T21:05:10.7408975Z     return (buf0, )
2023-01-11T21:05:10.7408981Z 
2023-01-11T21:05:10.7408985Z 
2023-01-11T21:05:10.7409058Z if __name__ == "__main__":
2023-01-11T21:05:10.7409158Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7409277Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7409468Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7409657Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7409773Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7409778Z 
2023-01-11T21:05:10.7409841Z ok (2.712s)
2023-01-11T21:05:10.7410307Z   test_cpu_broadcast1_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7410432Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7410690Z [2023-01-11 21:02:53,180] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 503
2023-01-11T21:05:10.7410941Z [2023-01-11 21:02:55,855] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 503
2023-01-11T21:05:10.7410946Z 
2023-01-11T21:05:10.7411038Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7411108Z import torch
2023-01-11T21:05:10.7411176Z import random
2023-01-11T21:05:10.7411288Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7411410Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7411416Z 
2023-01-11T21:05:10.7411491Z aten = torch.ops.aten
2023-01-11T21:05:10.7411612Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7411702Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7411707Z 
2023-01-11T21:05:10.7411712Z 
2023-01-11T21:05:10.7411844Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7412050Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7412166Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7412269Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7412366Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7412425Z {
2023-01-11T21:05:10.7412538Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7412600Z     {
2023-01-11T21:05:10.7412675Z         #pragma omp for 
2023-01-11T21:05:10.7412755Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7412818Z         {
2023-01-11T21:05:10.7412897Z             #pragma GCC ivdep
2023-01-11T21:05:10.7412981Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7413029Z             {
2023-01-11T21:05:10.7413091Z                 {
2023-01-11T21:05:10.7413154Z                     {
2023-01-11T21:05:10.7413252Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7413352Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7413444Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7413539Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7413591Z                     }
2023-01-11T21:05:10.7413652Z                 }
2023-01-11T21:05:10.7413715Z             }
2023-01-11T21:05:10.7413778Z         }
2023-01-11T21:05:10.7413837Z     }
2023-01-11T21:05:10.7413895Z }
2023-01-11T21:05:10.7413959Z ''')
2023-01-11T21:05:10.7413976Z 
2023-01-11T21:05:10.7413980Z 
2023-01-11T21:05:10.7414055Z async_compile.wait(globals())
2023-01-11T21:05:10.7414150Z del async_compile
2023-01-11T21:05:10.7414156Z 
2023-01-11T21:05:10.7414226Z def call(args):
2023-01-11T21:05:10.7414300Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7414369Z     args.clear()
2023-01-11T21:05:10.7414567Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7414729Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7414782Z     del arg0_1
2023-01-11T21:05:10.7414845Z     del arg1_1
2023-01-11T21:05:10.7414914Z     return (buf0, )
2023-01-11T21:05:10.7414919Z 
2023-01-11T21:05:10.7414923Z 
2023-01-11T21:05:10.7414998Z if __name__ == "__main__":
2023-01-11T21:05:10.7415110Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7415233Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7415424Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7415620Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7415721Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7415727Z 
2023-01-11T21:05:10.7415791Z ok (2.713s)
2023-01-11T21:05:10.7416255Z   test_cpu_broadcast1_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7416379Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7416637Z [2023-01-11 21:02:55,895] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 504
2023-01-11T21:05:10.7416901Z [2023-01-11 21:02:58,539] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 504
2023-01-11T21:05:10.7416909Z 
2023-01-11T21:05:10.7417001Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7417069Z import torch
2023-01-11T21:05:10.7417136Z import random
2023-01-11T21:05:10.7417236Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7417355Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7417360Z 
2023-01-11T21:05:10.7417435Z aten = torch.ops.aten
2023-01-11T21:05:10.7417566Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7417657Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7417662Z 
2023-01-11T21:05:10.7417666Z 
2023-01-11T21:05:10.7417798Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7417999Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7418148Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7418241Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7418340Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7418400Z {
2023-01-11T21:05:10.7418592Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7418657Z     {
2023-01-11T21:05:10.7418736Z         #pragma omp for 
2023-01-11T21:05:10.7418817Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7418866Z         {
2023-01-11T21:05:10.7418946Z             #pragma GCC ivdep
2023-01-11T21:05:10.7419032Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7419095Z             {
2023-01-11T21:05:10.7419160Z                 {
2023-01-11T21:05:10.7419225Z                     {
2023-01-11T21:05:10.7419321Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7419411Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7419525Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.7419617Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7419745Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7419812Z                     }
2023-01-11T21:05:10.7419874Z                 }
2023-01-11T21:05:10.7419936Z             }
2023-01-11T21:05:10.7419983Z         }
2023-01-11T21:05:10.7420043Z     }
2023-01-11T21:05:10.7420103Z }
2023-01-11T21:05:10.7420186Z ''')
2023-01-11T21:05:10.7420191Z 
2023-01-11T21:05:10.7420195Z 
2023-01-11T21:05:10.7420285Z async_compile.wait(globals())
2023-01-11T21:05:10.7420357Z del async_compile
2023-01-11T21:05:10.7420362Z 
2023-01-11T21:05:10.7420431Z def call(args):
2023-01-11T21:05:10.7420491Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7420560Z     args.clear()
2023-01-11T21:05:10.7420760Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7420919Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7420992Z     del arg0_1
2023-01-11T21:05:10.7421057Z     del arg1_1
2023-01-11T21:05:10.7421127Z     return (buf0, )
2023-01-11T21:05:10.7421135Z 
2023-01-11T21:05:10.7421139Z 
2023-01-11T21:05:10.7421215Z if __name__ == "__main__":
2023-01-11T21:05:10.7421315Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7421438Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7421628Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7421825Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7421939Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7421944Z 
2023-01-11T21:05:10.7422010Z ok (2.683s)
2023-01-11T21:05:10.7422473Z   test_cpu_broadcast1_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7422601Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7422847Z [2023-01-11 21:02:58,573] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 505
2023-01-11T21:05:10.7423109Z [2023-01-11 21:03:01,216] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 505
2023-01-11T21:05:10.7423114Z 
2023-01-11T21:05:10.7423207Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7423276Z import torch
2023-01-11T21:05:10.7423347Z import random
2023-01-11T21:05:10.7423460Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7423579Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7423584Z 
2023-01-11T21:05:10.7423689Z aten = torch.ops.aten
2023-01-11T21:05:10.7423808Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7423897Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7423902Z 
2023-01-11T21:05:10.7423909Z 
2023-01-11T21:05:10.7424043Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7424246Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7424366Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7424467Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7424564Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7424625Z {
2023-01-11T21:05:10.7424708Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7424767Z     {
2023-01-11T21:05:10.7424842Z         #pragma omp for 
2023-01-11T21:05:10.7424924Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7424989Z         {
2023-01-11T21:05:10.7425054Z             {
2023-01-11T21:05:10.7425118Z                 {
2023-01-11T21:05:10.7425197Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7425289Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7425425Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.7425517Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7425601Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7425664Z                 }
2023-01-11T21:05:10.7425725Z             }
2023-01-11T21:05:10.7425772Z         }
2023-01-11T21:05:10.7425830Z     }
2023-01-11T21:05:10.7425888Z }
2023-01-11T21:05:10.7425965Z ''')
2023-01-11T21:05:10.7425971Z 
2023-01-11T21:05:10.7425975Z 
2023-01-11T21:05:10.7426062Z async_compile.wait(globals())
2023-01-11T21:05:10.7426132Z del async_compile
2023-01-11T21:05:10.7426137Z 
2023-01-11T21:05:10.7426205Z def call(args):
2023-01-11T21:05:10.7426266Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7426335Z     args.clear()
2023-01-11T21:05:10.7426533Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7426696Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7426767Z     del arg0_1
2023-01-11T21:05:10.7426833Z     del arg1_1
2023-01-11T21:05:10.7426903Z     return (buf0, )
2023-01-11T21:05:10.7426908Z 
2023-01-11T21:05:10.7426912Z 
2023-01-11T21:05:10.7426974Z if __name__ == "__main__":
2023-01-11T21:05:10.7427092Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7427216Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7427412Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7427603Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7427717Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7427722Z 
2023-01-11T21:05:10.7427787Z ok (2.677s)
2023-01-11T21:05:10.7428266Z   test_cpu_broadcast1_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7428391Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7428637Z [2023-01-11 21:03:01,256] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 506
2023-01-11T21:05:10.7428900Z [2023-01-11 21:03:03,937] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 506
2023-01-11T21:05:10.7428905Z 
2023-01-11T21:05:10.7429000Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7429067Z import torch
2023-01-11T21:05:10.7429135Z import random
2023-01-11T21:05:10.7429248Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7429396Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7429401Z 
2023-01-11T21:05:10.7429478Z aten = torch.ops.aten
2023-01-11T21:05:10.7429600Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7429690Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7429695Z 
2023-01-11T21:05:10.7429700Z 
2023-01-11T21:05:10.7429832Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7430034Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7430153Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7430255Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7430353Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7430412Z {
2023-01-11T21:05:10.7430497Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7430555Z     {
2023-01-11T21:05:10.7430630Z         #pragma omp for 
2023-01-11T21:05:10.7430714Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7430776Z         {
2023-01-11T21:05:10.7430854Z             #pragma GCC ivdep
2023-01-11T21:05:10.7430938Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7431015Z             {
2023-01-11T21:05:10.7431080Z                 {
2023-01-11T21:05:10.7431144Z                     {
2023-01-11T21:05:10.7431238Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7431342Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7431435Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7431531Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7431583Z                     }
2023-01-11T21:05:10.7431646Z                 }
2023-01-11T21:05:10.7431707Z             }
2023-01-11T21:05:10.7431767Z         }
2023-01-11T21:05:10.7431826Z     }
2023-01-11T21:05:10.7431884Z }
2023-01-11T21:05:10.7431948Z ''')
2023-01-11T21:05:10.7431955Z 
2023-01-11T21:05:10.7431972Z 
2023-01-11T21:05:10.7432049Z async_compile.wait(globals())
2023-01-11T21:05:10.7432120Z del async_compile
2023-01-11T21:05:10.7432124Z 
2023-01-11T21:05:10.7432194Z def call(args):
2023-01-11T21:05:10.7432269Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7432338Z     args.clear()
2023-01-11T21:05:10.7432538Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7432701Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7432755Z     del arg0_1
2023-01-11T21:05:10.7432818Z     del arg1_1
2023-01-11T21:05:10.7432888Z     return (buf0, )
2023-01-11T21:05:10.7432893Z 
2023-01-11T21:05:10.7432897Z 
2023-01-11T21:05:10.7432970Z if __name__ == "__main__":
2023-01-11T21:05:10.7433084Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7433205Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7433399Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7433599Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7433701Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7433708Z 
2023-01-11T21:05:10.7433772Z ok (2.720s)
2023-01-11T21:05:10.7434251Z   test_cpu_broadcast1_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7434376Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7434636Z [2023-01-11 21:03:03,970] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 507
2023-01-11T21:05:10.7434898Z [2023-01-11 21:03:06,633] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 507
2023-01-11T21:05:10.7434953Z 
2023-01-11T21:05:10.7435047Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7435115Z import torch
2023-01-11T21:05:10.7435184Z import random
2023-01-11T21:05:10.7435287Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7435405Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7435409Z 
2023-01-11T21:05:10.7435485Z aten = torch.ops.aten
2023-01-11T21:05:10.7435618Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7435708Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7435713Z 
2023-01-11T21:05:10.7435717Z 
2023-01-11T21:05:10.7435849Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7436054Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7436175Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7436268Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7436368Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7436427Z {
2023-01-11T21:05:10.7436555Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7436617Z     {
2023-01-11T21:05:10.7436694Z         #pragma omp for 
2023-01-11T21:05:10.7436776Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7436824Z         {
2023-01-11T21:05:10.7436901Z             #pragma GCC ivdep
2023-01-11T21:05:10.7436985Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7437046Z             {
2023-01-11T21:05:10.7437109Z                 {
2023-01-11T21:05:10.7437173Z                     {
2023-01-11T21:05:10.7437268Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7437358Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7437450Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7437544Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7437611Z                     }
2023-01-11T21:05:10.7437673Z                 }
2023-01-11T21:05:10.7437733Z             }
2023-01-11T21:05:10.7437781Z         }
2023-01-11T21:05:10.7437840Z     }
2023-01-11T21:05:10.7437900Z }
2023-01-11T21:05:10.7437978Z ''')
2023-01-11T21:05:10.7437983Z 
2023-01-11T21:05:10.7437987Z 
2023-01-11T21:05:10.7438076Z async_compile.wait(globals())
2023-01-11T21:05:10.7438147Z del async_compile
2023-01-11T21:05:10.7438152Z 
2023-01-11T21:05:10.7438220Z def call(args):
2023-01-11T21:05:10.7438291Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7438347Z     args.clear()
2023-01-11T21:05:10.7438548Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7438711Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7438776Z     del arg0_1
2023-01-11T21:05:10.7438844Z     del arg1_1
2023-01-11T21:05:10.7438913Z     return (buf0, )
2023-01-11T21:05:10.7438920Z 
2023-01-11T21:05:10.7438924Z 
2023-01-11T21:05:10.7438998Z if __name__ == "__main__":
2023-01-11T21:05:10.7439099Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7439223Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7439421Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7439620Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7439734Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7439740Z 
2023-01-11T21:05:10.7439805Z ok (2.696s)
2023-01-11T21:05:10.7440279Z   test_cpu_broadcast2_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7440444Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7440824Z [2023-01-11 21:03:06,666] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 508
2023-01-11T21:05:10.7441091Z [2023-01-11 21:03:09,279] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 508
2023-01-11T21:05:10.7441096Z 
2023-01-11T21:05:10.7441178Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7441251Z import torch
2023-01-11T21:05:10.7441321Z import random
2023-01-11T21:05:10.7441438Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7441559Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7441564Z 
2023-01-11T21:05:10.7441641Z aten = torch.ops.aten
2023-01-11T21:05:10.7441778Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7441856Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7441877Z 
2023-01-11T21:05:10.7441881Z 
2023-01-11T21:05:10.7442001Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7442205Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7442379Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7442485Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7442589Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7442649Z {
2023-01-11T21:05:10.7442748Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7442794Z     {
2023-01-11T21:05:10.7442872Z         #pragma omp for 
2023-01-11T21:05:10.7442959Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7443021Z         {
2023-01-11T21:05:10.7443100Z             #pragma GCC ivdep
2023-01-11T21:05:10.7443189Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7443252Z             {
2023-01-11T21:05:10.7443302Z                 {
2023-01-11T21:05:10.7443370Z                     {
2023-01-11T21:05:10.7443467Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7443563Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7443659Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7443758Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7443825Z                     }
2023-01-11T21:05:10.7443876Z                 }
2023-01-11T21:05:10.7443937Z             }
2023-01-11T21:05:10.7443998Z         }
2023-01-11T21:05:10.7444058Z     }
2023-01-11T21:05:10.7444116Z }
2023-01-11T21:05:10.7444193Z ''')
2023-01-11T21:05:10.7444198Z 
2023-01-11T21:05:10.7444202Z 
2023-01-11T21:05:10.7444289Z async_compile.wait(globals())
2023-01-11T21:05:10.7444347Z del async_compile
2023-01-11T21:05:10.7444351Z 
2023-01-11T21:05:10.7444419Z def call(args):
2023-01-11T21:05:10.7444492Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7444560Z     args.clear()
2023-01-11T21:05:10.7444770Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7444936Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7445002Z     del arg0_1
2023-01-11T21:05:10.7445057Z     del arg1_1
2023-01-11T21:05:10.7445127Z     return (buf0, )
2023-01-11T21:05:10.7445133Z 
2023-01-11T21:05:10.7445137Z 
2023-01-11T21:05:10.7445210Z if __name__ == "__main__":
2023-01-11T21:05:10.7445324Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7445445Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7445652Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7445844Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7445958Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7445964Z 
2023-01-11T21:05:10.7446015Z ok (2.649s)
2023-01-11T21:05:10.7446490Z   test_cpu_broadcast2_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7446655Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7446919Z [2023-01-11 21:03:09,315] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 509
2023-01-11T21:05:10.7447182Z [2023-01-11 21:03:09,328] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 509
2023-01-11T21:05:10.7447187Z 
2023-01-11T21:05:10.7447280Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7447349Z import torch
2023-01-11T21:05:10.7447418Z import random
2023-01-11T21:05:10.7447532Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7447641Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7447646Z 
2023-01-11T21:05:10.7447722Z aten = torch.ops.aten
2023-01-11T21:05:10.7447855Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7447972Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7447978Z 
2023-01-11T21:05:10.7447982Z 
2023-01-11T21:05:10.7448116Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7448322Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7448443Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7448547Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7448634Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7448694Z {
2023-01-11T21:05:10.7448791Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7448851Z     {
2023-01-11T21:05:10.7448926Z         #pragma omp for 
2023-01-11T21:05:10.7449009Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7449070Z         {
2023-01-11T21:05:10.7449120Z             {
2023-01-11T21:05:10.7449182Z                 {
2023-01-11T21:05:10.7449273Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7449367Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7449456Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7449539Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7449603Z                 }
2023-01-11T21:05:10.7449651Z             }
2023-01-11T21:05:10.7449710Z         }
2023-01-11T21:05:10.7449768Z     }
2023-01-11T21:05:10.7449825Z }
2023-01-11T21:05:10.7449901Z ''')
2023-01-11T21:05:10.7449906Z 
2023-01-11T21:05:10.7449910Z 
2023-01-11T21:05:10.7449998Z async_compile.wait(globals())
2023-01-11T21:05:10.7450056Z del async_compile
2023-01-11T21:05:10.7450071Z 
2023-01-11T21:05:10.7450128Z def call(args):
2023-01-11T21:05:10.7450199Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7450267Z     args.clear()
2023-01-11T21:05:10.7450476Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7450641Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7450708Z     del arg0_1
2023-01-11T21:05:10.7450773Z     del arg1_1
2023-01-11T21:05:10.7450830Z     return (buf0, )
2023-01-11T21:05:10.7450835Z 
2023-01-11T21:05:10.7450839Z 
2023-01-11T21:05:10.7450912Z if __name__ == "__main__":
2023-01-11T21:05:10.7451023Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7451147Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7451355Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7451556Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7451670Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7451676Z 
2023-01-11T21:05:10.7451769Z ok (0.042s)
2023-01-11T21:05:10.7452239Z   test_cpu_broadcast2_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7452366Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7452626Z [2023-01-11 21:03:09,351] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 510
2023-01-11T21:05:10.7452892Z [2023-01-11 21:03:09,365] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 510
2023-01-11T21:05:10.7452897Z 
2023-01-11T21:05:10.7452989Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7453059Z import torch
2023-01-11T21:05:10.7453126Z import random
2023-01-11T21:05:10.7453241Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7453360Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7453365Z 
2023-01-11T21:05:10.7453429Z aten = torch.ops.aten
2023-01-11T21:05:10.7453591Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7453682Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7453687Z 
2023-01-11T21:05:10.7453692Z 
2023-01-11T21:05:10.7453825Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7454031Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7454149Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7454252Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7454353Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7454399Z {
2023-01-11T21:05:10.7454496Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7454559Z     {
2023-01-11T21:05:10.7454634Z         #pragma omp for 
2023-01-11T21:05:10.7454715Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7454776Z         {
2023-01-11T21:05:10.7454837Z             {
2023-01-11T21:05:10.7454889Z                 {
2023-01-11T21:05:10.7454982Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7455071Z                     auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.7455161Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7455245Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7455309Z                 }
2023-01-11T21:05:10.7455358Z             }
2023-01-11T21:05:10.7455420Z         }
2023-01-11T21:05:10.7455478Z     }
2023-01-11T21:05:10.7455535Z }
2023-01-11T21:05:10.7455611Z ''')
2023-01-11T21:05:10.7455616Z 
2023-01-11T21:05:10.7455621Z 
2023-01-11T21:05:10.7455710Z async_compile.wait(globals())
2023-01-11T21:05:10.7455778Z del async_compile
2023-01-11T21:05:10.7455783Z 
2023-01-11T21:05:10.7455849Z def call(args):
2023-01-11T21:05:10.7455912Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7455978Z     args.clear()
2023-01-11T21:05:10.7456186Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7456349Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7456415Z     del arg0_1
2023-01-11T21:05:10.7456479Z     del arg1_1
2023-01-11T21:05:10.7456547Z     return (buf0, )
2023-01-11T21:05:10.7456552Z 
2023-01-11T21:05:10.7456556Z 
2023-01-11T21:05:10.7456617Z if __name__ == "__main__":
2023-01-11T21:05:10.7456729Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7456851Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7457055Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7457243Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7457358Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7457394Z 
2023-01-11T21:05:10.7457458Z ok (0.037s)
2023-01-11T21:05:10.7457931Z   test_cpu_broadcast2_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7458057Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7458304Z [2023-01-11 21:03:09,388] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 511
2023-01-11T21:05:10.7458642Z [2023-01-11 21:03:09,401] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 511
2023-01-11T21:05:10.7458649Z 
2023-01-11T21:05:10.7458743Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7458814Z import torch
2023-01-11T21:05:10.7458881Z import random
2023-01-11T21:05:10.7458996Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7459115Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7459120Z 
2023-01-11T21:05:10.7459233Z aten = torch.ops.aten
2023-01-11T21:05:10.7459356Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7459446Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7459450Z 
2023-01-11T21:05:10.7459454Z 
2023-01-11T21:05:10.7459590Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7459789Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7459906Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7460006Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7460104Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7460163Z {
2023-01-11T21:05:10.7460247Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7460307Z     {
2023-01-11T21:05:10.7460381Z         #pragma omp for 
2023-01-11T21:05:10.7460462Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7460521Z         {
2023-01-11T21:05:10.7460600Z             #pragma GCC ivdep
2023-01-11T21:05:10.7460685Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7460734Z             {
2023-01-11T21:05:10.7460795Z                 {
2023-01-11T21:05:10.7460859Z                     {
2023-01-11T21:05:10.7460955Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7461056Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7461150Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7461244Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7461296Z                     }
2023-01-11T21:05:10.7461357Z                 }
2023-01-11T21:05:10.7461418Z             }
2023-01-11T21:05:10.7461478Z         }
2023-01-11T21:05:10.7461540Z     }
2023-01-11T21:05:10.7461598Z }
2023-01-11T21:05:10.7461661Z ''')
2023-01-11T21:05:10.7461666Z 
2023-01-11T21:05:10.7461683Z 
2023-01-11T21:05:10.7461759Z async_compile.wait(globals())
2023-01-11T21:05:10.7461829Z del async_compile
2023-01-11T21:05:10.7461836Z 
2023-01-11T21:05:10.7461905Z def call(args):
2023-01-11T21:05:10.7461978Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7462047Z     args.clear()
2023-01-11T21:05:10.7462257Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7462420Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7462473Z     del arg0_1
2023-01-11T21:05:10.7462538Z     del arg1_1
2023-01-11T21:05:10.7462608Z     return (buf0, )
2023-01-11T21:05:10.7462612Z 
2023-01-11T21:05:10.7462616Z 
2023-01-11T21:05:10.7462691Z if __name__ == "__main__":
2023-01-11T21:05:10.7462805Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7462959Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7463165Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7463355Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7463471Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7463476Z 
2023-01-11T21:05:10.7463540Z ok (0.036s)
2023-01-11T21:05:10.7464016Z   test_cpu_broadcast2_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7464143Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7464401Z [2023-01-11 21:03:09,424] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 512
2023-01-11T21:05:10.7464669Z [2023-01-11 21:03:12,099] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 512
2023-01-11T21:05:10.7464675Z 
2023-01-11T21:05:10.7464795Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7464865Z import torch
2023-01-11T21:05:10.7464933Z import random
2023-01-11T21:05:10.7465036Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7465155Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7465159Z 
2023-01-11T21:05:10.7465235Z aten = torch.ops.aten
2023-01-11T21:05:10.7465368Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7465459Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7465464Z 
2023-01-11T21:05:10.7465468Z 
2023-01-11T21:05:10.7465603Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7465807Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7465929Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7466024Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7466129Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7466187Z {
2023-01-11T21:05:10.7466284Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7466343Z     {
2023-01-11T21:05:10.7466419Z         #pragma omp for 
2023-01-11T21:05:10.7466500Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7466549Z         {
2023-01-11T21:05:10.7466627Z             #pragma GCC ivdep
2023-01-11T21:05:10.7466713Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7466776Z             {
2023-01-11T21:05:10.7466839Z                 {
2023-01-11T21:05:10.7466904Z                     {
2023-01-11T21:05:10.7466986Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7467089Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7467203Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.7467293Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7467388Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7467455Z                     }
2023-01-11T21:05:10.7467518Z                 }
2023-01-11T21:05:10.7467568Z             }
2023-01-11T21:05:10.7467628Z         }
2023-01-11T21:05:10.7467687Z     }
2023-01-11T21:05:10.7467746Z }
2023-01-11T21:05:10.7467822Z ''')
2023-01-11T21:05:10.7467827Z 
2023-01-11T21:05:10.7467831Z 
2023-01-11T21:05:10.7467920Z async_compile.wait(globals())
2023-01-11T21:05:10.7467990Z del async_compile
2023-01-11T21:05:10.7467995Z 
2023-01-11T21:05:10.7468063Z def call(args):
2023-01-11T21:05:10.7468125Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7468195Z     args.clear()
2023-01-11T21:05:10.7468405Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7468566Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7468664Z     del arg0_1
2023-01-11T21:05:10.7468731Z     del arg1_1
2023-01-11T21:05:10.7468799Z     return (buf0, )
2023-01-11T21:05:10.7468807Z 
2023-01-11T21:05:10.7468811Z 
2023-01-11T21:05:10.7468874Z if __name__ == "__main__":
2023-01-11T21:05:10.7468987Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7469108Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7469315Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7469514Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7469629Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7469634Z 
2023-01-11T21:05:10.7469699Z ok (2.702s)
2023-01-11T21:05:10.7470196Z   test_cpu_broadcast2_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7470326Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7470574Z [2023-01-11 21:03:12,139] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 513
2023-01-11T21:05:10.7470839Z [2023-01-11 21:03:14,791] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 513
2023-01-11T21:05:10.7470845Z 
2023-01-11T21:05:10.7470938Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7471006Z import torch
2023-01-11T21:05:10.7471075Z import random
2023-01-11T21:05:10.7471191Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7471311Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7471318Z 
2023-01-11T21:05:10.7471393Z aten = torch.ops.aten
2023-01-11T21:05:10.7471514Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7471604Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7471609Z 
2023-01-11T21:05:10.7471615Z 
2023-01-11T21:05:10.7471748Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7471949Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7472069Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7472172Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7472272Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7472331Z {
2023-01-11T21:05:10.7472415Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7472474Z     {
2023-01-11T21:05:10.7472549Z         #pragma omp for 
2023-01-11T21:05:10.7472630Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7472691Z         {
2023-01-11T21:05:10.7472772Z             #pragma GCC ivdep
2023-01-11T21:05:10.7472857Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7472906Z             {
2023-01-11T21:05:10.7472970Z                 {
2023-01-11T21:05:10.7473034Z                     {
2023-01-11T21:05:10.7473132Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7473229Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7473338Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.7473430Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7473513Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7473579Z                     }
2023-01-11T21:05:10.7473642Z                 }
2023-01-11T21:05:10.7473703Z             }
2023-01-11T21:05:10.7473766Z         }
2023-01-11T21:05:10.7473824Z     }
2023-01-11T21:05:10.7473869Z }
2023-01-11T21:05:10.7473945Z ''')
2023-01-11T21:05:10.7473950Z 
2023-01-11T21:05:10.7473954Z 
2023-01-11T21:05:10.7474089Z async_compile.wait(globals())
2023-01-11T21:05:10.7474160Z del async_compile
2023-01-11T21:05:10.7474165Z 
2023-01-11T21:05:10.7474234Z def call(args):
2023-01-11T21:05:10.7474308Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7474379Z     args.clear()
2023-01-11T21:05:10.7474591Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7474741Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7474808Z     del arg0_1
2023-01-11T21:05:10.7474872Z     del arg1_1
2023-01-11T21:05:10.7474941Z     return (buf0, )
2023-01-11T21:05:10.7474946Z 
2023-01-11T21:05:10.7474950Z 
2023-01-11T21:05:10.7475024Z if __name__ == "__main__":
2023-01-11T21:05:10.7475138Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7475259Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7475450Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7475642Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7475757Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7475762Z 
2023-01-11T21:05:10.7475860Z ok (2.692s)
2023-01-11T21:05:10.7476337Z   test_cpu_broadcast2_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7476466Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7476726Z [2023-01-11 21:03:14,832] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 514
2023-01-11T21:05:10.7476990Z [2023-01-11 21:03:17,485] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 514
2023-01-11T21:05:10.7476997Z 
2023-01-11T21:05:10.7477090Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7477158Z import torch
2023-01-11T21:05:10.7477213Z import random
2023-01-11T21:05:10.7477331Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7477450Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7477454Z 
2023-01-11T21:05:10.7477530Z aten = torch.ops.aten
2023-01-11T21:05:10.7477662Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7477752Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7477757Z 
2023-01-11T21:05:10.7477761Z 
2023-01-11T21:05:10.7477892Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7478095Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7478201Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7478305Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7478407Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7478467Z {
2023-01-11T21:05:10.7478564Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7478627Z     {
2023-01-11T21:05:10.7478705Z         #pragma omp for 
2023-01-11T21:05:10.7478774Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7478836Z         {
2023-01-11T21:05:10.7478914Z             #pragma GCC ivdep
2023-01-11T21:05:10.7478998Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7479061Z             {
2023-01-11T21:05:10.7479123Z                 {
2023-01-11T21:05:10.7479175Z                     {
2023-01-11T21:05:10.7479271Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7479376Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7479470Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7479564Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7479660Z                     }
2023-01-11T21:05:10.7479723Z                 }
2023-01-11T21:05:10.7479772Z             }
2023-01-11T21:05:10.7479833Z         }
2023-01-11T21:05:10.7479893Z     }
2023-01-11T21:05:10.7479953Z }
2023-01-11T21:05:10.7480033Z ''')
2023-01-11T21:05:10.7480038Z 
2023-01-11T21:05:10.7480042Z 
2023-01-11T21:05:10.7480130Z async_compile.wait(globals())
2023-01-11T21:05:10.7480202Z del async_compile
2023-01-11T21:05:10.7480207Z 
2023-01-11T21:05:10.7480276Z def call(args):
2023-01-11T21:05:10.7480337Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7480406Z     args.clear()
2023-01-11T21:05:10.7480736Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7480898Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7480965Z     del arg0_1
2023-01-11T21:05:10.7481031Z     del arg1_1
2023-01-11T21:05:10.7481103Z     return (buf0, )
2023-01-11T21:05:10.7481108Z 
2023-01-11T21:05:10.7481115Z 
2023-01-11T21:05:10.7481176Z if __name__ == "__main__":
2023-01-11T21:05:10.7481290Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7481413Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7481671Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7481874Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7481988Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7481993Z 
2023-01-11T21:05:10.7482061Z ok (2.695s)
2023-01-11T21:05:10.7482528Z   test_cpu_broadcast2_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7482657Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7482901Z [2023-01-11 21:03:17,528] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 515
2023-01-11T21:05:10.7483167Z [2023-01-11 21:03:17,549] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 515
2023-01-11T21:05:10.7483172Z 
2023-01-11T21:05:10.7483266Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7483335Z import torch
2023-01-11T21:05:10.7483405Z import random
2023-01-11T21:05:10.7483521Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7483641Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7483647Z 
2023-01-11T21:05:10.7483724Z aten = torch.ops.aten
2023-01-11T21:05:10.7483843Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7483935Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7483940Z 
2023-01-11T21:05:10.7483946Z 
2023-01-11T21:05:10.7484079Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7484282Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7484403Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7484508Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7484608Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7484668Z {
2023-01-11T21:05:10.7484751Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7484813Z     {
2023-01-11T21:05:10.7484889Z         #pragma omp for 
2023-01-11T21:05:10.7484972Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7485034Z         {
2023-01-11T21:05:10.7485112Z             #pragma GCC ivdep
2023-01-11T21:05:10.7485197Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7485247Z             {
2023-01-11T21:05:10.7485310Z                 {
2023-01-11T21:05:10.7485375Z                     {
2023-01-11T21:05:10.7485512Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7485614Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7485707Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7485807Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7485859Z                     }
2023-01-11T21:05:10.7485924Z                 }
2023-01-11T21:05:10.7485986Z             }
2023-01-11T21:05:10.7486048Z         }
2023-01-11T21:05:10.7486109Z     }
2023-01-11T21:05:10.7486167Z }
2023-01-11T21:05:10.7486232Z ''')
2023-01-11T21:05:10.7486237Z 
2023-01-11T21:05:10.7486254Z 
2023-01-11T21:05:10.7486330Z async_compile.wait(globals())
2023-01-11T21:05:10.7486402Z del async_compile
2023-01-11T21:05:10.7486406Z 
2023-01-11T21:05:10.7486477Z def call(args):
2023-01-11T21:05:10.7486549Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7486619Z     args.clear()
2023-01-11T21:05:10.7486828Z     buf0 = empty_strided((1, 10, 10), (100, 1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7486990Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7487045Z     del arg0_1
2023-01-11T21:05:10.7487137Z     del arg1_1
2023-01-11T21:05:10.7487211Z     return (buf0, )
2023-01-11T21:05:10.7487216Z 
2023-01-11T21:05:10.7487220Z 
2023-01-11T21:05:10.7487294Z if __name__ == "__main__":
2023-01-11T21:05:10.7487407Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7487530Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7487737Z     arg0_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7487922Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7488035Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7488040Z 
2023-01-11T21:05:10.7488106Z ok (0.060s)
2023-01-11T21:05:10.7488570Z   test_cpu_broadcast3_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7488698Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7488958Z [2023-01-11 21:03:17,584] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 516
2023-01-11T21:05:10.7489216Z [2023-01-11 21:03:20,271] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 516
2023-01-11T21:05:10.7489222Z 
2023-01-11T21:05:10.7489313Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7489381Z import torch
2023-01-11T21:05:10.7489454Z import random
2023-01-11T21:05:10.7489555Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7489678Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7489683Z 
2023-01-11T21:05:10.7489759Z aten = torch.ops.aten
2023-01-11T21:05:10.7489892Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7489985Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7489990Z 
2023-01-11T21:05:10.7489995Z 
2023-01-11T21:05:10.7490129Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7490332Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7490454Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7490543Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7490642Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7490701Z {
2023-01-11T21:05:10.7490798Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7490858Z     {
2023-01-11T21:05:10.7490934Z         #pragma omp for 
2023-01-11T21:05:10.7491014Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7491132Z         {
2023-01-11T21:05:10.7491195Z             {
2023-01-11T21:05:10.7491257Z                 {
2023-01-11T21:05:10.7491348Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7491442Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7491534Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7491618Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7491668Z                 }
2023-01-11T21:05:10.7491730Z             }
2023-01-11T21:05:10.7491791Z         }
2023-01-11T21:05:10.7491852Z     }
2023-01-11T21:05:10.7491912Z }
2023-01-11T21:05:10.7491992Z ''')
2023-01-11T21:05:10.7491997Z 
2023-01-11T21:05:10.7492001Z 
2023-01-11T21:05:10.7492076Z async_compile.wait(globals())
2023-01-11T21:05:10.7492148Z del async_compile
2023-01-11T21:05:10.7492153Z 
2023-01-11T21:05:10.7492224Z def call(args):
2023-01-11T21:05:10.7492300Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7492370Z     args.clear()
2023-01-11T21:05:10.7492569Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7492735Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7492832Z     del arg0_1
2023-01-11T21:05:10.7492886Z     del arg1_1
2023-01-11T21:05:10.7492956Z     return (buf0, )
2023-01-11T21:05:10.7492962Z 
2023-01-11T21:05:10.7492966Z 
2023-01-11T21:05:10.7493040Z if __name__ == "__main__":
2023-01-11T21:05:10.7493152Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7493274Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7493466Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7493658Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7493759Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7493776Z 
2023-01-11T21:05:10.7493828Z ok (2.724s)
2023-01-11T21:05:10.7494311Z   test_cpu_broadcast3_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7494437Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7494694Z [2023-01-11 21:03:20,304] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 517
2023-01-11T21:05:10.7494959Z [2023-01-11 21:03:20,317] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 517
2023-01-11T21:05:10.7494964Z 
2023-01-11T21:05:10.7495057Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7495126Z import torch
2023-01-11T21:05:10.7495195Z import random
2023-01-11T21:05:10.7495309Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7495418Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7495423Z 
2023-01-11T21:05:10.7495500Z aten = torch.ops.aten
2023-01-11T21:05:10.7495633Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7495723Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7495727Z 
2023-01-11T21:05:10.7495732Z 
2023-01-11T21:05:10.7495863Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7496065Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7496183Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7496284Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7496371Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7496431Z {
2023-01-11T21:05:10.7496528Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7496588Z     {
2023-01-11T21:05:10.7496664Z         #pragma omp for 
2023-01-11T21:05:10.7496776Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7496835Z         {
2023-01-11T21:05:10.7496884Z             {
2023-01-11T21:05:10.7496947Z                 {
2023-01-11T21:05:10.7497039Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7497131Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7497220Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7497302Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7497352Z                 }
2023-01-11T21:05:10.7497414Z             }
2023-01-11T21:05:10.7497475Z         }
2023-01-11T21:05:10.7497533Z     }
2023-01-11T21:05:10.7497591Z }
2023-01-11T21:05:10.7497668Z ''')
2023-01-11T21:05:10.7497674Z 
2023-01-11T21:05:10.7497678Z 
2023-01-11T21:05:10.7497766Z async_compile.wait(globals())
2023-01-11T21:05:10.7497825Z del async_compile
2023-01-11T21:05:10.7497843Z 
2023-01-11T21:05:10.7497899Z def call(args):
2023-01-11T21:05:10.7497974Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7498045Z     args.clear()
2023-01-11T21:05:10.7498252Z     buf0 = empty_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7498442Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7498603Z     del arg0_1
2023-01-11T21:05:10.7498672Z     del arg1_1
2023-01-11T21:05:10.7498730Z     return (buf0, )
2023-01-11T21:05:10.7498735Z 
2023-01-11T21:05:10.7498739Z 
2023-01-11T21:05:10.7498816Z if __name__ == "__main__":
2023-01-11T21:05:10.7498932Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7499055Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7499252Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7499461Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7499578Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7499585Z 
2023-01-11T21:05:10.7499652Z ok (0.042s)
2023-01-11T21:05:10.7500119Z   test_cpu_broadcast3_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7500247Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7500508Z [2023-01-11 21:03:20,340] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 518
2023-01-11T21:05:10.7500771Z [2023-01-11 21:03:22,976] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 518
2023-01-11T21:05:10.7500776Z 
2023-01-11T21:05:10.7500870Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7500939Z import torch
2023-01-11T21:05:10.7501011Z import random
2023-01-11T21:05:10.7501127Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7501245Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7501251Z 
2023-01-11T21:05:10.7501317Z aten = torch.ops.aten
2023-01-11T21:05:10.7501450Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7501541Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7501546Z 
2023-01-11T21:05:10.7501550Z 
2023-01-11T21:05:10.7501681Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7501887Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7502005Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7502110Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7502208Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7502255Z {
2023-01-11T21:05:10.7502315Z     {
2023-01-11T21:05:10.7502376Z         {
2023-01-11T21:05:10.7502495Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7502576Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.7502660Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7502739Z             out_ptr0[0] = tmp2;
2023-01-11T21:05:10.7502787Z         }
2023-01-11T21:05:10.7502847Z     }
2023-01-11T21:05:10.7502905Z }
2023-01-11T21:05:10.7502982Z ''')
2023-01-11T21:05:10.7502987Z 
2023-01-11T21:05:10.7502991Z 
2023-01-11T21:05:10.7503078Z async_compile.wait(globals())
2023-01-11T21:05:10.7503149Z del async_compile
2023-01-11T21:05:10.7503154Z 
2023-01-11T21:05:10.7503224Z def call(args):
2023-01-11T21:05:10.7503286Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7503354Z     args.clear()
2023-01-11T21:05:10.7503546Z     buf0 = empty_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7503707Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7503773Z     del arg0_1
2023-01-11T21:05:10.7503838Z     del arg1_1
2023-01-11T21:05:10.7503908Z     return (buf0, )
2023-01-11T21:05:10.7503913Z 
2023-01-11T21:05:10.7503917Z 
2023-01-11T21:05:10.7503978Z if __name__ == "__main__":
2023-01-11T21:05:10.7504120Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7504244Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7504436Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7504625Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7504739Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7504744Z 
2023-01-11T21:05:10.7504809Z ok (2.662s)
2023-01-11T21:05:10.7505282Z   test_cpu_broadcast3_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7505410Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7505659Z [2023-01-11 21:03:23,011] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 519
2023-01-11T21:05:10.7505925Z [2023-01-11 21:03:25,736] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 519
2023-01-11T21:05:10.7505930Z 
2023-01-11T21:05:10.7506024Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7506092Z import torch
2023-01-11T21:05:10.7506161Z import random
2023-01-11T21:05:10.7506276Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7506396Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7506401Z 
2023-01-11T21:05:10.7506478Z aten = torch.ops.aten
2023-01-11T21:05:10.7506599Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7506691Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7506696Z 
2023-01-11T21:05:10.7506700Z 
2023-01-11T21:05:10.7506834Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7507038Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7507157Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7507260Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7507359Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7507419Z {
2023-01-11T21:05:10.7507503Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7507563Z     {
2023-01-11T21:05:10.7507636Z         #pragma omp for 
2023-01-11T21:05:10.7507721Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7507783Z         {
2023-01-11T21:05:10.7507914Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:05:10.7508047Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7508149Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7508241Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7508301Z         }
2023-01-11T21:05:10.7508398Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7508479Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.7508539Z         {
2023-01-11T21:05:10.7508620Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7508691Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7508772Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7508851Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7508912Z         }
2023-01-11T21:05:10.7508971Z     }
2023-01-11T21:05:10.7509030Z }
2023-01-11T21:05:10.7509095Z ''')
2023-01-11T21:05:10.7509112Z 
2023-01-11T21:05:10.7509116Z 
2023-01-11T21:05:10.7509191Z async_compile.wait(globals())
2023-01-11T21:05:10.7509262Z del async_compile
2023-01-11T21:05:10.7509267Z 
2023-01-11T21:05:10.7509336Z def call(args):
2023-01-11T21:05:10.7509411Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7509480Z     args.clear()
2023-01-11T21:05:10.7509679Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7509871Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7509926Z     del arg0_1
2023-01-11T21:05:10.7509991Z     del arg1_1
2023-01-11T21:05:10.7510061Z     return (buf0, )
2023-01-11T21:05:10.7510066Z 
2023-01-11T21:05:10.7510070Z 
2023-01-11T21:05:10.7510143Z if __name__ == "__main__":
2023-01-11T21:05:10.7510257Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7510378Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7510570Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7510768Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7510872Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7510879Z 
2023-01-11T21:05:10.7510944Z ok (2.760s)
2023-01-11T21:05:10.7511417Z   test_cpu_broadcast3_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7511543Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7511800Z [2023-01-11 21:03:25,769] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 520
2023-01-11T21:05:10.7512064Z [2023-01-11 21:03:28,500] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 520
2023-01-11T21:05:10.7512070Z 
2023-01-11T21:05:10.7512163Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7512235Z import torch
2023-01-11T21:05:10.7512304Z import random
2023-01-11T21:05:10.7512406Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7512525Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7512530Z 
2023-01-11T21:05:10.7512608Z aten = torch.ops.aten
2023-01-11T21:05:10.7512740Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7512829Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7512834Z 
2023-01-11T21:05:10.7512838Z 
2023-01-11T21:05:10.7512970Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7513171Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7513290Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7513383Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7513482Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7513541Z {
2023-01-11T21:05:10.7513638Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7513750Z     {
2023-01-11T21:05:10.7513826Z         #pragma omp for 
2023-01-11T21:05:10.7513908Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:05:10.7513956Z         {
2023-01-11T21:05:10.7514020Z             {
2023-01-11T21:05:10.7514082Z                 {
2023-01-11T21:05:10.7514173Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7514264Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.7514371Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.7514461Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7514533Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7514597Z                 }
2023-01-11T21:05:10.7514660Z             }
2023-01-11T21:05:10.7514723Z         }
2023-01-11T21:05:10.7514783Z     }
2023-01-11T21:05:10.7514843Z }
2023-01-11T21:05:10.7514908Z ''')
2023-01-11T21:05:10.7514927Z 
2023-01-11T21:05:10.7514931Z 
2023-01-11T21:05:10.7515007Z async_compile.wait(globals())
2023-01-11T21:05:10.7515084Z del async_compile
2023-01-11T21:05:10.7515090Z 
2023-01-11T21:05:10.7515157Z def call(args):
2023-01-11T21:05:10.7515232Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7515302Z     args.clear()
2023-01-11T21:05:10.7515532Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7515699Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7515753Z     del arg0_1
2023-01-11T21:05:10.7515817Z     del arg1_1
2023-01-11T21:05:10.7515888Z     return (buf0, )
2023-01-11T21:05:10.7515893Z 
2023-01-11T21:05:10.7515897Z 
2023-01-11T21:05:10.7515973Z if __name__ == "__main__":
2023-01-11T21:05:10.7516090Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7516216Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7516411Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7516612Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7516714Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7516719Z 
2023-01-11T21:05:10.7516788Z ok (2.764s)
2023-01-11T21:05:10.7517262Z   test_cpu_broadcast3_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7517391Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7517653Z [2023-01-11 21:03:28,533] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 521
2023-01-11T21:05:10.7517920Z [2023-01-11 21:03:31,274] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 521
2023-01-11T21:05:10.7517927Z 
2023-01-11T21:05:10.7518022Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7518094Z import torch
2023-01-11T21:05:10.7518164Z import random
2023-01-11T21:05:10.7518269Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7518393Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7518399Z 
2023-01-11T21:05:10.7518477Z aten = torch.ops.aten
2023-01-11T21:05:10.7518611Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7518703Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7518709Z 
2023-01-11T21:05:10.7518713Z 
2023-01-11T21:05:10.7518846Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7519052Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7519170Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7519259Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7519391Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7519451Z {
2023-01-11T21:05:10.7519548Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7519610Z     {
2023-01-11T21:05:10.7519689Z         #pragma omp for 
2023-01-11T21:05:10.7519771Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7519820Z         {
2023-01-11T21:05:10.7519881Z             {
2023-01-11T21:05:10.7519945Z                 {
2023-01-11T21:05:10.7520034Z                     auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7520126Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7520235Z                     auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.7520325Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7520398Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7520461Z                 }
2023-01-11T21:05:10.7520523Z             }
2023-01-11T21:05:10.7520584Z         }
2023-01-11T21:05:10.7520767Z     }
2023-01-11T21:05:10.7520830Z }
2023-01-11T21:05:10.7520896Z ''')
2023-01-11T21:05:10.7520915Z 
2023-01-11T21:05:10.7520919Z 
2023-01-11T21:05:10.7520993Z async_compile.wait(globals())
2023-01-11T21:05:10.7521065Z del async_compile
2023-01-11T21:05:10.7521069Z 
2023-01-11T21:05:10.7521196Z def call(args):
2023-01-11T21:05:10.7521284Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7521388Z     args.clear()
2023-01-11T21:05:10.7521631Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7521793Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7521847Z     del arg0_1
2023-01-11T21:05:10.7521914Z     del arg1_1
2023-01-11T21:05:10.7521985Z     return (buf0, )
2023-01-11T21:05:10.7521990Z 
2023-01-11T21:05:10.7521994Z 
2023-01-11T21:05:10.7522071Z if __name__ == "__main__":
2023-01-11T21:05:10.7522185Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7522309Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7522503Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7522693Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7522796Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7522801Z 
2023-01-11T21:05:10.7522867Z ok (2.773s)
2023-01-11T21:05:10.7523330Z   test_cpu_broadcast3_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7523456Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7523713Z [2023-01-11 21:03:31,308] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 522
2023-01-11T21:05:10.7523977Z [2023-01-11 21:03:34,075] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 522
2023-01-11T21:05:10.7523982Z 
2023-01-11T21:05:10.7524074Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7524145Z import torch
2023-01-11T21:05:10.7524212Z import random
2023-01-11T21:05:10.7524313Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7524430Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7524435Z 
2023-01-11T21:05:10.7524513Z aten = torch.ops.aten
2023-01-11T21:05:10.7524645Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7524735Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7524740Z 
2023-01-11T21:05:10.7524744Z 
2023-01-11T21:05:10.7524877Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7525079Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7525195Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7525329Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7525428Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7525488Z {
2023-01-11T21:05:10.7525584Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7525645Z     {
2023-01-11T21:05:10.7525719Z         #pragma omp for 
2023-01-11T21:05:10.7525800Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7525849Z         {
2023-01-11T21:05:10.7525927Z             #pragma GCC ivdep
2023-01-11T21:05:10.7526011Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7526073Z             {
2023-01-11T21:05:10.7526135Z                 {
2023-01-11T21:05:10.7526200Z                     {
2023-01-11T21:05:10.7526293Z                         auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7526385Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7526477Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7526577Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7526644Z                     }
2023-01-11T21:05:10.7526708Z                 }
2023-01-11T21:05:10.7526770Z             }
2023-01-11T21:05:10.7526860Z         }
2023-01-11T21:05:10.7526908Z     }
2023-01-11T21:05:10.7526966Z }
2023-01-11T21:05:10.7527042Z ''')
2023-01-11T21:05:10.7527048Z 
2023-01-11T21:05:10.7527052Z 
2023-01-11T21:05:10.7527141Z async_compile.wait(globals())
2023-01-11T21:05:10.7527211Z del async_compile
2023-01-11T21:05:10.7527216Z 
2023-01-11T21:05:10.7527286Z def call(args):
2023-01-11T21:05:10.7527359Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7527415Z     args.clear()
2023-01-11T21:05:10.7527615Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7527774Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7527841Z     del arg0_1
2023-01-11T21:05:10.7527906Z     del arg1_1
2023-01-11T21:05:10.7527978Z     return (buf0, )
2023-01-11T21:05:10.7527983Z 
2023-01-11T21:05:10.7527987Z 
2023-01-11T21:05:10.7528059Z if __name__ == "__main__":
2023-01-11T21:05:10.7528173Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7528283Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7528472Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7528668Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7528787Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7528792Z 
2023-01-11T21:05:10.7528856Z ok (2.801s)
2023-01-11T21:05:10.7529327Z   test_cpu_broadcast3_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7529455Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7529715Z [2023-01-11 21:03:34,108] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 523
2023-01-11T21:05:10.7529978Z [2023-01-11 21:03:34,122] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 523
2023-01-11T21:05:10.7529983Z 
2023-01-11T21:05:10.7530063Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7530132Z import torch
2023-01-11T21:05:10.7530201Z import random
2023-01-11T21:05:10.7530314Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7530432Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7530437Z 
2023-01-11T21:05:10.7530513Z aten = torch.ops.aten
2023-01-11T21:05:10.7530648Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7530752Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7530770Z 
2023-01-11T21:05:10.7530774Z 
2023-01-11T21:05:10.7530894Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7531100Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7531217Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7531318Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7531417Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7531478Z {
2023-01-11T21:05:10.7531573Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7531620Z     {
2023-01-11T21:05:10.7531694Z         #pragma omp for 
2023-01-11T21:05:10.7531774Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7531835Z         {
2023-01-11T21:05:10.7531961Z             auto tmp0 = at::vec::Vectorized<float>(in_ptr0[0]);
2023-01-11T21:05:10.7532093Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7532178Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7532258Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7532319Z         }
2023-01-11T21:05:10.7532441Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7532524Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.7532584Z         {
2023-01-11T21:05:10.7532664Z             auto tmp0 = in_ptr0[0];
2023-01-11T21:05:10.7532746Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7532815Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7532893Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7532953Z         }
2023-01-11T21:05:10.7533011Z     }
2023-01-11T21:05:10.7533070Z }
2023-01-11T21:05:10.7533146Z ''')
2023-01-11T21:05:10.7533151Z 
2023-01-11T21:05:10.7533155Z 
2023-01-11T21:05:10.7533240Z async_compile.wait(globals())
2023-01-11T21:05:10.7533299Z del async_compile
2023-01-11T21:05:10.7533315Z 
2023-01-11T21:05:10.7533372Z def call(args):
2023-01-11T21:05:10.7533447Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7533516Z     args.clear()
2023-01-11T21:05:10.7533714Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7533876Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7533942Z     del arg0_1
2023-01-11T21:05:10.7533995Z     del arg1_1
2023-01-11T21:05:10.7534065Z     return (buf0, )
2023-01-11T21:05:10.7534070Z 
2023-01-11T21:05:10.7534074Z 
2023-01-11T21:05:10.7534148Z if __name__ == "__main__":
2023-01-11T21:05:10.7534260Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7534380Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7534570Z     arg0_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7534768Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7534881Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7534889Z 
2023-01-11T21:05:10.7534952Z ok (0.044s)
2023-01-11T21:05:10.7535401Z   test_cpu_dense_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7535527Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7535783Z [2023-01-11 21:03:34,146] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 524
2023-01-11T21:05:10.7536045Z [2023-01-11 21:03:36,883] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 524
2023-01-11T21:05:10.7536049Z 
2023-01-11T21:05:10.7536140Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7536207Z import torch
2023-01-11T21:05:10.7536307Z import random
2023-01-11T21:05:10.7536420Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7536526Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7536543Z 
2023-01-11T21:05:10.7536609Z aten = torch.ops.aten
2023-01-11T21:05:10.7536742Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7536832Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7536837Z 
2023-01-11T21:05:10.7536841Z 
2023-01-11T21:05:10.7536974Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7537177Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7537295Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7537397Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7537494Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7537541Z {
2023-01-11T21:05:10.7537634Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7537697Z     {
2023-01-11T21:05:10.7537771Z         #pragma omp for 
2023-01-11T21:05:10.7537850Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7537911Z         {
2023-01-11T21:05:10.7538003Z             #pragma GCC ivdep
2023-01-11T21:05:10.7538088Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7538150Z             {
2023-01-11T21:05:10.7538212Z                 {
2023-01-11T21:05:10.7538276Z                     {
2023-01-11T21:05:10.7538378Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7538539Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7538622Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7538719Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7538784Z                     }
2023-01-11T21:05:10.7538847Z                 }
2023-01-11T21:05:10.7538909Z             }
2023-01-11T21:05:10.7538971Z         }
2023-01-11T21:05:10.7539032Z     }
2023-01-11T21:05:10.7539079Z }
2023-01-11T21:05:10.7539160Z ''')
2023-01-11T21:05:10.7539165Z 
2023-01-11T21:05:10.7539169Z 
2023-01-11T21:05:10.7539259Z async_compile.wait(globals())
2023-01-11T21:05:10.7539331Z del async_compile
2023-01-11T21:05:10.7539338Z 
2023-01-11T21:05:10.7539408Z def call(args):
2023-01-11T21:05:10.7539483Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7539553Z     args.clear()
2023-01-11T21:05:10.7539741Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7539902Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7539970Z     del arg0_1
2023-01-11T21:05:10.7540035Z     del arg1_1
2023-01-11T21:05:10.7540106Z     return (buf0, )
2023-01-11T21:05:10.7540110Z 
2023-01-11T21:05:10.7540114Z 
2023-01-11T21:05:10.7540189Z if __name__ == "__main__":
2023-01-11T21:05:10.7540302Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7540423Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7540609Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7540801Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7540915Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7540920Z 
2023-01-11T21:05:10.7540986Z ok (2.764s)
2023-01-11T21:05:10.7541445Z   test_cpu_dense_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7541571Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7541828Z [2023-01-11 21:03:36,915] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 525
2023-01-11T21:05:10.7542124Z [2023-01-11 21:03:39,626] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 525
2023-01-11T21:05:10.7542129Z 
2023-01-11T21:05:10.7542223Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7542278Z import torch
2023-01-11T21:05:10.7542345Z import random
2023-01-11T21:05:10.7542459Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7542579Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7542584Z 
2023-01-11T21:05:10.7542660Z aten = torch.ops.aten
2023-01-11T21:05:10.7542794Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7542883Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7542888Z 
2023-01-11T21:05:10.7542892Z 
2023-01-11T21:05:10.7543025Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7543215Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7543336Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7543440Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7543539Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7543628Z {
2023-01-11T21:05:10.7543728Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7543789Z     {
2023-01-11T21:05:10.7543852Z         #pragma omp for 
2023-01-11T21:05:10.7543933Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7543994Z         {
2023-01-11T21:05:10.7544074Z             #pragma GCC ivdep
2023-01-11T21:05:10.7544157Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7544220Z             {
2023-01-11T21:05:10.7544283Z                 {
2023-01-11T21:05:10.7544335Z                     {
2023-01-11T21:05:10.7544437Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7544532Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7544624Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7544724Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7544788Z                     }
2023-01-11T21:05:10.7544850Z                 }
2023-01-11T21:05:10.7544898Z             }
2023-01-11T21:05:10.7544960Z         }
2023-01-11T21:05:10.7545020Z     }
2023-01-11T21:05:10.7545081Z }
2023-01-11T21:05:10.7545157Z ''')
2023-01-11T21:05:10.7545163Z 
2023-01-11T21:05:10.7545167Z 
2023-01-11T21:05:10.7545255Z async_compile.wait(globals())
2023-01-11T21:05:10.7545325Z del async_compile
2023-01-11T21:05:10.7545330Z 
2023-01-11T21:05:10.7545385Z def call(args):
2023-01-11T21:05:10.7545459Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7545528Z     args.clear()
2023-01-11T21:05:10.7545736Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7545896Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7545962Z     del arg0_1
2023-01-11T21:05:10.7546029Z     del arg1_1
2023-01-11T21:05:10.7546085Z     return (buf0, )
2023-01-11T21:05:10.7546103Z 
2023-01-11T21:05:10.7546107Z 
2023-01-11T21:05:10.7546168Z if __name__ == "__main__":
2023-01-11T21:05:10.7546282Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7546404Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7546604Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7546810Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7546924Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7546929Z 
2023-01-11T21:05:10.7546997Z ok (2.743s)
2023-01-11T21:05:10.7547463Z   test_cpu_dense_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7547630Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7547878Z [2023-01-11 21:03:39,658] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 526
2023-01-11T21:05:10.7548143Z [2023-01-11 21:03:42,387] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 526
2023-01-11T21:05:10.7548148Z 
2023-01-11T21:05:10.7548242Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7548310Z import torch
2023-01-11T21:05:10.7548379Z import random
2023-01-11T21:05:10.7548497Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7548618Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7548622Z 
2023-01-11T21:05:10.7548700Z aten = torch.ops.aten
2023-01-11T21:05:10.7548822Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7548916Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7548921Z 
2023-01-11T21:05:10.7548925Z 
2023-01-11T21:05:10.7549060Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7549296Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7549420Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7549526Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7549626Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7549687Z {
2023-01-11T21:05:10.7549771Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7549832Z     {
2023-01-11T21:05:10.7549908Z         #pragma omp for 
2023-01-11T21:05:10.7549989Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7550052Z         {
2023-01-11T21:05:10.7550191Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7550311Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:05:10.7550385Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7550477Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7550537Z         }
2023-01-11T21:05:10.7550633Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7550718Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.7550779Z         {
2023-01-11T21:05:10.7550849Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7550930Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.7551012Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7551092Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7551152Z         }
2023-01-11T21:05:10.7551214Z     }
2023-01-11T21:05:10.7551272Z }
2023-01-11T21:05:10.7551336Z ''')
2023-01-11T21:05:10.7551340Z 
2023-01-11T21:05:10.7551357Z 
2023-01-11T21:05:10.7551431Z async_compile.wait(globals())
2023-01-11T21:05:10.7551503Z del async_compile
2023-01-11T21:05:10.7551508Z 
2023-01-11T21:05:10.7551578Z def call(args):
2023-01-11T21:05:10.7551655Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7551724Z     args.clear()
2023-01-11T21:05:10.7551923Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7552090Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7552145Z     del arg0_1
2023-01-11T21:05:10.7552210Z     del arg1_1
2023-01-11T21:05:10.7552283Z     return (buf0, )
2023-01-11T21:05:10.7552288Z 
2023-01-11T21:05:10.7552292Z 
2023-01-11T21:05:10.7552366Z if __name__ == "__main__":
2023-01-11T21:05:10.7552480Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7552602Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7552802Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7552981Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7553096Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7553147Z 
2023-01-11T21:05:10.7553215Z ok (2.762s)
2023-01-11T21:05:10.7553685Z   test_cpu_dense_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7553813Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7554072Z [2023-01-11 21:03:42,421] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 527
2023-01-11T21:05:10.7554335Z [2023-01-11 21:03:42,434] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 527
2023-01-11T21:05:10.7554341Z 
2023-01-11T21:05:10.7554434Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7554506Z import torch
2023-01-11T21:05:10.7554574Z import random
2023-01-11T21:05:10.7554676Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7554797Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7554840Z 
2023-01-11T21:05:10.7554919Z aten = torch.ops.aten
2023-01-11T21:05:10.7555051Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7555143Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7555147Z 
2023-01-11T21:05:10.7555151Z 
2023-01-11T21:05:10.7555284Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7555492Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7555610Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7555700Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7555798Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7555858Z {
2023-01-11T21:05:10.7555954Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7556017Z     {
2023-01-11T21:05:10.7556092Z         #pragma omp for 
2023-01-11T21:05:10.7556172Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7556220Z         {
2023-01-11T21:05:10.7556356Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7556488Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7556574Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7556667Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7556730Z         }
2023-01-11T21:05:10.7556827Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7556895Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.7556960Z         {
2023-01-11T21:05:10.7557044Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7557125Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7557205Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7557282Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7557348Z         }
2023-01-11T21:05:10.7557395Z     }
2023-01-11T21:05:10.7557452Z }
2023-01-11T21:05:10.7557532Z ''')
2023-01-11T21:05:10.7557537Z 
2023-01-11T21:05:10.7557541Z 
2023-01-11T21:05:10.7557630Z async_compile.wait(globals())
2023-01-11T21:05:10.7557702Z del async_compile
2023-01-11T21:05:10.7557707Z 
2023-01-11T21:05:10.7557775Z def call(args):
2023-01-11T21:05:10.7557848Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7557905Z     args.clear()
2023-01-11T21:05:10.7558106Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7558265Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7558334Z     del arg0_1
2023-01-11T21:05:10.7558398Z     del arg1_1
2023-01-11T21:05:10.7558467Z     return (buf0, )
2023-01-11T21:05:10.7558472Z 
2023-01-11T21:05:10.7558476Z 
2023-01-11T21:05:10.7558553Z if __name__ == "__main__":
2023-01-11T21:05:10.7558700Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7558809Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7559012Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7559212Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7559326Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7559331Z 
2023-01-11T21:05:10.7559397Z ok (0.043s)
2023-01-11T21:05:10.7559867Z   test_cpu_dense_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7559993Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7560254Z [2023-01-11 21:03:42,458] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 528
2023-01-11T21:05:10.7560549Z [2023-01-11 21:03:45,183] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 528
2023-01-11T21:05:10.7560555Z 
2023-01-11T21:05:10.7560762Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7560832Z import torch
2023-01-11T21:05:10.7560901Z import random
2023-01-11T21:05:10.7561017Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7561140Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7561145Z 
2023-01-11T21:05:10.7561224Z aten = torch.ops.aten
2023-01-11T21:05:10.7561359Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7561435Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7561455Z 
2023-01-11T21:05:10.7561460Z 
2023-01-11T21:05:10.7561582Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7561786Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7561909Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7562020Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7562122Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7562183Z {
2023-01-11T21:05:10.7562281Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7562329Z     {
2023-01-11T21:05:10.7562406Z         #pragma omp for 
2023-01-11T21:05:10.7562488Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:05:10.7562550Z         {
2023-01-11T21:05:10.7562613Z             {
2023-01-11T21:05:10.7562676Z                 {
2023-01-11T21:05:10.7562767Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7562845Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.7562955Z                     auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.7563044Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7563131Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7563195Z                 }
2023-01-11T21:05:10.7563256Z             }
2023-01-11T21:05:10.7563315Z         }
2023-01-11T21:05:10.7563364Z     }
2023-01-11T21:05:10.7563421Z }
2023-01-11T21:05:10.7563497Z ''')
2023-01-11T21:05:10.7563502Z 
2023-01-11T21:05:10.7563506Z 
2023-01-11T21:05:10.7563595Z async_compile.wait(globals())
2023-01-11T21:05:10.7563666Z del async_compile
2023-01-11T21:05:10.7563671Z 
2023-01-11T21:05:10.7563739Z def call(args):
2023-01-11T21:05:10.7563813Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7563869Z     args.clear()
2023-01-11T21:05:10.7564069Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7564232Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7564299Z     del arg0_1
2023-01-11T21:05:10.7564365Z     del arg1_1
2023-01-11T21:05:10.7564435Z     return (buf0, )
2023-01-11T21:05:10.7564498Z 
2023-01-11T21:05:10.7564502Z 
2023-01-11T21:05:10.7564578Z if __name__ == "__main__":
2023-01-11T21:05:10.7564694Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7564806Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7565009Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7565208Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7565323Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7565329Z 
2023-01-11T21:05:10.7565395Z ok (2.751s)
2023-01-11T21:05:10.7565860Z   test_cpu_dense_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7565991Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7566286Z [2023-01-11 21:03:45,214] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 529
2023-01-11T21:05:10.7566555Z [2023-01-11 21:03:47,986] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 529
2023-01-11T21:05:10.7566560Z 
2023-01-11T21:05:10.7566641Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7566710Z import torch
2023-01-11T21:05:10.7566780Z import random
2023-01-11T21:05:10.7566893Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7567014Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7567020Z 
2023-01-11T21:05:10.7567096Z aten = torch.ops.aten
2023-01-11T21:05:10.7567229Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7567306Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7567326Z 
2023-01-11T21:05:10.7567330Z 
2023-01-11T21:05:10.7567449Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7567653Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7567774Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7567878Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7567977Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7568035Z {
2023-01-11T21:05:10.7568134Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7568181Z     {
2023-01-11T21:05:10.7568257Z         #pragma omp for 
2023-01-11T21:05:10.7568340Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7568403Z         {
2023-01-11T21:05:10.7568483Z             #pragma GCC ivdep
2023-01-11T21:05:10.7568569Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7568631Z             {
2023-01-11T21:05:10.7568681Z                 {
2023-01-11T21:05:10.7568748Z                     {
2023-01-11T21:05:10.7568851Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7568945Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7569058Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.7569152Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7569247Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7569300Z                     }
2023-01-11T21:05:10.7569365Z                 }
2023-01-11T21:05:10.7569426Z             }
2023-01-11T21:05:10.7569487Z         }
2023-01-11T21:05:10.7569549Z     }
2023-01-11T21:05:10.7569606Z }
2023-01-11T21:05:10.7569669Z ''')
2023-01-11T21:05:10.7569687Z 
2023-01-11T21:05:10.7569691Z 
2023-01-11T21:05:10.7569767Z async_compile.wait(globals())
2023-01-11T21:05:10.7569838Z del async_compile
2023-01-11T21:05:10.7569843Z 
2023-01-11T21:05:10.7569912Z def call(args):
2023-01-11T21:05:10.7569987Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7570088Z     args.clear()
2023-01-11T21:05:10.7570287Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7570453Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7570507Z     del arg0_1
2023-01-11T21:05:10.7570572Z     del arg1_1
2023-01-11T21:05:10.7570643Z     return (buf0, )
2023-01-11T21:05:10.7570647Z 
2023-01-11T21:05:10.7570652Z 
2023-01-11T21:05:10.7570725Z if __name__ == "__main__":
2023-01-11T21:05:10.7570837Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7570959Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7571160Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7571351Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7571452Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7571460Z 
2023-01-11T21:05:10.7571524Z ok (2.804s)
2023-01-11T21:05:10.7572021Z   test_cpu_dense_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7572147Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7572407Z [2023-01-11 21:03:48,027] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 530
2023-01-11T21:05:10.7572669Z [2023-01-11 21:03:50,795] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 530
2023-01-11T21:05:10.7572674Z 
2023-01-11T21:05:10.7572768Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7572841Z import torch
2023-01-11T21:05:10.7572910Z import random
2023-01-11T21:05:10.7573015Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7573132Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7573137Z 
2023-01-11T21:05:10.7573213Z aten = torch.ops.aten
2023-01-11T21:05:10.7573348Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7573438Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7573443Z 
2023-01-11T21:05:10.7573447Z 
2023-01-11T21:05:10.7573579Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7573782Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7573902Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7573992Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7574092Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7574151Z {
2023-01-11T21:05:10.7574249Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7574311Z     {
2023-01-11T21:05:10.7574386Z         #pragma omp for 
2023-01-11T21:05:10.7574468Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7574517Z         {
2023-01-11T21:05:10.7574595Z             #pragma GCC ivdep
2023-01-11T21:05:10.7574682Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7574746Z             {
2023-01-11T21:05:10.7574809Z                 {
2023-01-11T21:05:10.7574875Z                     {
2023-01-11T21:05:10.7574978Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7575070Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7575164Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7575260Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7575324Z                     }
2023-01-11T21:05:10.7575386Z                 }
2023-01-11T21:05:10.7575448Z             }
2023-01-11T21:05:10.7575508Z         }
2023-01-11T21:05:10.7575555Z     }
2023-01-11T21:05:10.7575641Z }
2023-01-11T21:05:10.7575719Z ''')
2023-01-11T21:05:10.7575724Z 
2023-01-11T21:05:10.7575728Z 
2023-01-11T21:05:10.7575817Z async_compile.wait(globals())
2023-01-11T21:05:10.7575888Z del async_compile
2023-01-11T21:05:10.7575893Z 
2023-01-11T21:05:10.7575963Z def call(args):
2023-01-11T21:05:10.7576037Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7576093Z     args.clear()
2023-01-11T21:05:10.7576293Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7576458Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7576525Z     del arg0_1
2023-01-11T21:05:10.7576590Z     del arg1_1
2023-01-11T21:05:10.7576659Z     return (buf0, )
2023-01-11T21:05:10.7576664Z 
2023-01-11T21:05:10.7576668Z 
2023-01-11T21:05:10.7576742Z if __name__ == "__main__":
2023-01-11T21:05:10.7576855Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7576964Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7577165Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7577362Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7577542Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7577547Z 
2023-01-11T21:05:10.7577615Z ok (2.810s)
2023-01-11T21:05:10.7578090Z   test_cpu_dense_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7578218Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7578575Z [2023-01-11 21:03:50,833] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 531
2023-01-11T21:05:10.7578848Z [2023-01-11 21:03:53,502] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 531
2023-01-11T21:05:10.7578853Z 
2023-01-11T21:05:10.7578935Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7579005Z import torch
2023-01-11T21:05:10.7579074Z import random
2023-01-11T21:05:10.7579192Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7579313Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7579318Z 
2023-01-11T21:05:10.7579397Z aten = torch.ops.aten
2023-01-11T21:05:10.7579533Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7579610Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7579629Z 
2023-01-11T21:05:10.7579633Z 
2023-01-11T21:05:10.7579752Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7579959Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7580077Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7580183Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7580284Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7580344Z {
2023-01-11T21:05:10.7580442Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7580491Z     {
2023-01-11T21:05:10.7580566Z         #pragma omp for 
2023-01-11T21:05:10.7580648Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7580711Z         {
2023-01-11T21:05:10.7580791Z             #pragma GCC ivdep
2023-01-11T21:05:10.7580876Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7580939Z             {
2023-01-11T21:05:10.7580989Z                 {
2023-01-11T21:05:10.7581056Z                     {
2023-01-11T21:05:10.7581160Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7581261Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:05:10.7581353Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7581480Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7581547Z                     }
2023-01-11T21:05:10.7581597Z                 }
2023-01-11T21:05:10.7581659Z             }
2023-01-11T21:05:10.7581722Z         }
2023-01-11T21:05:10.7581783Z     }
2023-01-11T21:05:10.7581842Z }
2023-01-11T21:05:10.7581919Z ''')
2023-01-11T21:05:10.7581925Z 
2023-01-11T21:05:10.7581929Z 
2023-01-11T21:05:10.7582016Z async_compile.wait(globals())
2023-01-11T21:05:10.7582074Z del async_compile
2023-01-11T21:05:10.7582078Z 
2023-01-11T21:05:10.7582148Z def call(args):
2023-01-11T21:05:10.7582222Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7582292Z     args.clear()
2023-01-11T21:05:10.7582491Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7582651Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7582719Z     del arg0_1
2023-01-11T21:05:10.7582773Z     del arg1_1
2023-01-11T21:05:10.7582843Z     return (buf0, )
2023-01-11T21:05:10.7582848Z 
2023-01-11T21:05:10.7582852Z 
2023-01-11T21:05:10.7582927Z if __name__ == "__main__":
2023-01-11T21:05:10.7583067Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7583191Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7583388Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7583584Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7583699Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7583704Z 
2023-01-11T21:05:10.7583755Z ok (2.705s)
2023-01-11T21:05:10.7584216Z   test_cpu_double_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7584343Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7584603Z [2023-01-11 21:03:53,534] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 532
2023-01-11T21:05:10.7584865Z [2023-01-11 21:03:56,198] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 532
2023-01-11T21:05:10.7584870Z 
2023-01-11T21:05:10.7584963Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7585031Z import torch
2023-01-11T21:05:10.7585101Z import random
2023-01-11T21:05:10.7585215Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7585321Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7585326Z 
2023-01-11T21:05:10.7585402Z aten = torch.ops.aten
2023-01-11T21:05:10.7585533Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7585625Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7585631Z 
2023-01-11T21:05:10.7585635Z 
2023-01-11T21:05:10.7585767Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7585971Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7586091Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7586194Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7586280Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7586340Z {
2023-01-11T21:05:10.7586436Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7586495Z     {
2023-01-11T21:05:10.7586570Z         #pragma omp for 
2023-01-11T21:05:10.7586648Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7586709Z         {
2023-01-11T21:05:10.7586774Z             #pragma GCC ivdep
2023-01-11T21:05:10.7586858Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7586919Z             {
2023-01-11T21:05:10.7587011Z                 {
2023-01-11T21:05:10.7587076Z                     {
2023-01-11T21:05:10.7587178Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7587273Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7587372Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:05:10.7587463Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7587558Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7587622Z                     }
2023-01-11T21:05:10.7587684Z                 }
2023-01-11T21:05:10.7587746Z             }
2023-01-11T21:05:10.7587805Z         }
2023-01-11T21:05:10.7587852Z     }
2023-01-11T21:05:10.7587910Z }
2023-01-11T21:05:10.7587986Z ''')
2023-01-11T21:05:10.7587991Z 
2023-01-11T21:05:10.7587995Z 
2023-01-11T21:05:10.7588083Z async_compile.wait(globals())
2023-01-11T21:05:10.7588153Z del async_compile
2023-01-11T21:05:10.7588158Z 
2023-01-11T21:05:10.7588226Z def call(args):
2023-01-11T21:05:10.7588302Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7588357Z     args.clear()
2023-01-11T21:05:10.7588553Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7588741Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7588809Z     del arg0_1
2023-01-11T21:05:10.7588873Z     del arg1_1
2023-01-11T21:05:10.7588942Z     return (buf0, )
2023-01-11T21:05:10.7588948Z 
2023-01-11T21:05:10.7588951Z 
2023-01-11T21:05:10.7589024Z if __name__ == "__main__":
2023-01-11T21:05:10.7589136Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7589245Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7589442Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7589635Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7589750Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7589757Z 
2023-01-11T21:05:10.7589821Z ok (2.696s)
2023-01-11T21:05:10.7590285Z   test_cpu_double_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7590409Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7590666Z [2023-01-11 21:03:56,230] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 533
2023-01-11T21:05:10.7590925Z [2023-01-11 21:03:58,873] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 533
2023-01-11T21:05:10.7590930Z 
2023-01-11T21:05:10.7591009Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7591079Z import torch
2023-01-11T21:05:10.7591147Z import random
2023-01-11T21:05:10.7591259Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7591376Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7591383Z 
2023-01-11T21:05:10.7591459Z aten = torch.ops.aten
2023-01-11T21:05:10.7591590Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7591667Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7591684Z 
2023-01-11T21:05:10.7591688Z 
2023-01-11T21:05:10.7591806Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7592008Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7592127Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7592228Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7592328Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7592387Z {
2023-01-11T21:05:10.7592525Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7592572Z     {
2023-01-11T21:05:10.7592647Z         #pragma omp for 
2023-01-11T21:05:10.7592726Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7592787Z         {
2023-01-11T21:05:10.7592867Z             #pragma GCC ivdep
2023-01-11T21:05:10.7592951Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7593013Z             {
2023-01-11T21:05:10.7593063Z                 {
2023-01-11T21:05:10.7597941Z                     {
2023-01-11T21:05:10.7598082Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7598180Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7598294Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:05:10.7598375Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7598470Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7598536Z                     }
2023-01-11T21:05:10.7598601Z                 }
2023-01-11T21:05:10.7598667Z             }
2023-01-11T21:05:10.7598727Z         }
2023-01-11T21:05:10.7598787Z     }
2023-01-11T21:05:10.7598834Z }
2023-01-11T21:05:10.7598938Z ''')
2023-01-11T21:05:10.7598945Z 
2023-01-11T21:05:10.7599022Z 
2023-01-11T21:05:10.7599114Z async_compile.wait(globals())
2023-01-11T21:05:10.7599186Z del async_compile
2023-01-11T21:05:10.7599191Z 
2023-01-11T21:05:10.7599260Z def call(args):
2023-01-11T21:05:10.7599333Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7599403Z     args.clear()
2023-01-11T21:05:10.7599607Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7599771Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7599836Z     del arg0_1
2023-01-11T21:05:10.7599901Z     del arg1_1
2023-01-11T21:05:10.7599969Z     return (buf0, )
2023-01-11T21:05:10.7599974Z 
2023-01-11T21:05:10.7599978Z 
2023-01-11T21:05:10.7600052Z if __name__ == "__main__":
2023-01-11T21:05:10.7600168Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7600293Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7600483Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7600819Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7600933Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7600939Z 
2023-01-11T21:05:10.7601004Z ok (2.675s)
2023-01-11T21:05:10.7601470Z   test_cpu_double_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7601594Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7601862Z [2023-01-11 21:03:58,905] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 534
2023-01-11T21:05:10.7602131Z [2023-01-11 21:04:01,584] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 534
2023-01-11T21:05:10.7602137Z 
2023-01-11T21:05:10.7602229Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7602284Z import torch
2023-01-11T21:05:10.7602353Z import random
2023-01-11T21:05:10.7602466Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7602586Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7602591Z 
2023-01-11T21:05:10.7602669Z aten = torch.ops.aten
2023-01-11T21:05:10.7602804Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7602893Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7602898Z 
2023-01-11T21:05:10.7602902Z 
2023-01-11T21:05:10.7603033Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7603290Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7603410Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7603516Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7603614Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7603674Z {
2023-01-11T21:05:10.7603768Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7603821Z     {
2023-01-11T21:05:10.7603900Z         #pragma omp for 
2023-01-11T21:05:10.7603984Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:05:10.7604033Z         {
2023-01-11T21:05:10.7604097Z             {
2023-01-11T21:05:10.7604161Z                 {
2023-01-11T21:05:10.7604255Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7604345Z                     auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.7604454Z                     auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:05:10.7604548Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7604620Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7604685Z                 }
2023-01-11T21:05:10.7604747Z             }
2023-01-11T21:05:10.7604848Z         }
2023-01-11T21:05:10.7604909Z     }
2023-01-11T21:05:10.7604967Z }
2023-01-11T21:05:10.7605033Z ''')
2023-01-11T21:05:10.7605054Z 
2023-01-11T21:05:10.7605058Z 
2023-01-11T21:05:10.7605133Z async_compile.wait(globals())
2023-01-11T21:05:10.7605203Z del async_compile
2023-01-11T21:05:10.7605209Z 
2023-01-11T21:05:10.7605276Z def call(args):
2023-01-11T21:05:10.7605348Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7605418Z     args.clear()
2023-01-11T21:05:10.7605617Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7605777Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7605832Z     del arg0_1
2023-01-11T21:05:10.7605897Z     del arg1_1
2023-01-11T21:05:10.7605969Z     return (buf0, )
2023-01-11T21:05:10.7605974Z 
2023-01-11T21:05:10.7605979Z 
2023-01-11T21:05:10.7606054Z if __name__ == "__main__":
2023-01-11T21:05:10.7606168Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7606292Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7606493Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7606687Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7606787Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7606792Z 
2023-01-11T21:05:10.7606858Z ok (2.711s)
2023-01-11T21:05:10.7607311Z   test_cpu_double_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7607439Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7607698Z [2023-01-11 21:04:01,615] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 535
2023-01-11T21:05:10.7607962Z [2023-01-11 21:04:04,272] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 535
2023-01-11T21:05:10.7607967Z 
2023-01-11T21:05:10.7608060Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7608127Z import torch
2023-01-11T21:05:10.7608195Z import random
2023-01-11T21:05:10.7608296Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7608413Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7608418Z 
2023-01-11T21:05:10.7608495Z aten = torch.ops.aten
2023-01-11T21:05:10.7608627Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7608716Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7608755Z 
2023-01-11T21:05:10.7608759Z 
2023-01-11T21:05:10.7608890Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7609095Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7609216Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7609307Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7609405Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7609465Z {
2023-01-11T21:05:10.7609559Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7609619Z     {
2023-01-11T21:05:10.7609695Z         #pragma omp for 
2023-01-11T21:05:10.7609776Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:05:10.7609825Z         {
2023-01-11T21:05:10.7609886Z             {
2023-01-11T21:05:10.7609949Z                 {
2023-01-11T21:05:10.7610043Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7610133Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7610243Z                     auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:05:10.7610331Z                     auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7610429Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7610493Z                 }
2023-01-11T21:05:10.7610554Z             }
2023-01-11T21:05:10.7610615Z         }
2023-01-11T21:05:10.7610674Z     }
2023-01-11T21:05:10.7610735Z }
2023-01-11T21:05:10.7610800Z ''')
2023-01-11T21:05:10.7610817Z 
2023-01-11T21:05:10.7610821Z 
2023-01-11T21:05:10.7610897Z async_compile.wait(globals())
2023-01-11T21:05:10.7610966Z del async_compile
2023-01-11T21:05:10.7610971Z 
2023-01-11T21:05:10.7611040Z def call(args):
2023-01-11T21:05:10.7611113Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7611183Z     args.clear()
2023-01-11T21:05:10.7611382Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7611541Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7611598Z     del arg0_1
2023-01-11T21:05:10.7611664Z     del arg1_1
2023-01-11T21:05:10.7611733Z     return (buf0, )
2023-01-11T21:05:10.7611737Z 
2023-01-11T21:05:10.7611743Z 
2023-01-11T21:05:10.7611817Z if __name__ == "__main__":
2023-01-11T21:05:10.7611928Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7612049Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7612248Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7612443Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7612545Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7612550Z 
2023-01-11T21:05:10.7612615Z ok (2.687s)
2023-01-11T21:05:10.7613080Z   test_cpu_double_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7613210Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7613468Z [2023-01-11 21:04:04,302] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 536
2023-01-11T21:05:10.7613728Z [2023-01-11 21:04:06,921] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 536
2023-01-11T21:05:10.7613733Z 
2023-01-11T21:05:10.7613826Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7613894Z import torch
2023-01-11T21:05:10.7613962Z import random
2023-01-11T21:05:10.7614063Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7614181Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7614186Z 
2023-01-11T21:05:10.7614262Z aten = torch.ops.aten
2023-01-11T21:05:10.7614429Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7614519Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7614524Z 
2023-01-11T21:05:10.7614528Z 
2023-01-11T21:05:10.7614662Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7614865Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7614985Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7615077Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7615176Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7615236Z {
2023-01-11T21:05:10.7615331Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7615391Z     {
2023-01-11T21:05:10.7615469Z         #pragma omp for 
2023-01-11T21:05:10.7615550Z         for(long i0=0; i0<100; i0+=1)
2023-01-11T21:05:10.7615600Z         {
2023-01-11T21:05:10.7615662Z             {
2023-01-11T21:05:10.7615727Z                 {
2023-01-11T21:05:10.7615819Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7615909Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7616027Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7616112Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7616163Z                 }
2023-01-11T21:05:10.7616224Z             }
2023-01-11T21:05:10.7616288Z         }
2023-01-11T21:05:10.7616348Z     }
2023-01-11T21:05:10.7616407Z }
2023-01-11T21:05:10.7616484Z ''')
2023-01-11T21:05:10.7616489Z 
2023-01-11T21:05:10.7616493Z 
2023-01-11T21:05:10.7616581Z async_compile.wait(globals())
2023-01-11T21:05:10.7616639Z del async_compile
2023-01-11T21:05:10.7616644Z 
2023-01-11T21:05:10.7616713Z def call(args):
2023-01-11T21:05:10.7616785Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7616855Z     args.clear()
2023-01-11T21:05:10.7617056Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7617218Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7617287Z     del arg0_1
2023-01-11T21:05:10.7617338Z     del arg1_1
2023-01-11T21:05:10.7617411Z     return (buf0, )
2023-01-11T21:05:10.7617416Z 
2023-01-11T21:05:10.7617420Z 
2023-01-11T21:05:10.7617494Z if __name__ == "__main__":
2023-01-11T21:05:10.7617607Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7617728Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7617927Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7618123Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7618238Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7618243Z 
2023-01-11T21:05:10.7618296Z ok (2.649s)
2023-01-11T21:05:10.7618846Z   test_cpu_double_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7618977Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7619241Z [2023-01-11 21:04:06,952] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 537
2023-01-11T21:05:10.7619506Z [2023-01-11 21:04:09,600] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 537
2023-01-11T21:05:10.7619510Z 
2023-01-11T21:05:10.7619604Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7619674Z import torch
2023-01-11T21:05:10.7619745Z import random
2023-01-11T21:05:10.7619860Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7619967Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7620020Z 
2023-01-11T21:05:10.7620085Z aten = torch.ops.aten
2023-01-11T21:05:10.7620220Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7620309Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7620316Z 
2023-01-11T21:05:10.7620321Z 
2023-01-11T21:05:10.7620456Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7620660Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7620780Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7620880Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7620967Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7621027Z {
2023-01-11T21:05:10.7621123Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7621184Z     {
2023-01-11T21:05:10.7621261Z         #pragma omp for 
2023-01-11T21:05:10.7621341Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7621401Z         {
2023-01-11T21:05:10.7621468Z             #pragma GCC ivdep
2023-01-11T21:05:10.7621553Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7621617Z             {
2023-01-11T21:05:10.7621680Z                 {
2023-01-11T21:05:10.7621776Z                     {
2023-01-11T21:05:10.7621880Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7621972Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7622071Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:05:10.7622163Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7622257Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7622323Z                     }
2023-01-11T21:05:10.7622386Z                 }
2023-01-11T21:05:10.7622448Z             }
2023-01-11T21:05:10.7622508Z         }
2023-01-11T21:05:10.7622555Z     }
2023-01-11T21:05:10.7622613Z }
2023-01-11T21:05:10.7622688Z ''')
2023-01-11T21:05:10.7622693Z 
2023-01-11T21:05:10.7622699Z 
2023-01-11T21:05:10.7622787Z async_compile.wait(globals())
2023-01-11T21:05:10.7622857Z del async_compile
2023-01-11T21:05:10.7622863Z 
2023-01-11T21:05:10.7622931Z def call(args):
2023-01-11T21:05:10.7623004Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7623063Z     args.clear()
2023-01-11T21:05:10.7623259Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7623418Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7623484Z     del arg0_1
2023-01-11T21:05:10.7623550Z     del arg1_1
2023-01-11T21:05:10.7623620Z     return (buf0, )
2023-01-11T21:05:10.7623625Z 
2023-01-11T21:05:10.7623629Z 
2023-01-11T21:05:10.7623702Z if __name__ == "__main__":
2023-01-11T21:05:10.7623815Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7623923Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7624119Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7624312Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7624424Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7624431Z 
2023-01-11T21:05:10.7624495Z ok (2.679s)
2023-01-11T21:05:10.7624957Z   test_cpu_double_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7625082Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7625341Z [2023-01-11 21:04:09,632] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 538
2023-01-11T21:05:10.7625599Z [2023-01-11 21:04:12,286] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 538
2023-01-11T21:05:10.7625641Z 
2023-01-11T21:05:10.7625722Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7625791Z import torch
2023-01-11T21:05:10.7625859Z import random
2023-01-11T21:05:10.7625974Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7626092Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7626097Z 
2023-01-11T21:05:10.7626173Z aten = torch.ops.aten
2023-01-11T21:05:10.7626304Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7626381Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7626398Z 
2023-01-11T21:05:10.7626402Z 
2023-01-11T21:05:10.7626521Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7626722Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7626842Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7626947Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7627047Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7627110Z {
2023-01-11T21:05:10.7627205Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7627284Z     {
2023-01-11T21:05:10.7627359Z         #pragma omp for 
2023-01-11T21:05:10.7627439Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7627500Z         {
2023-01-11T21:05:10.7627577Z             #pragma GCC ivdep
2023-01-11T21:05:10.7627661Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7627723Z             {
2023-01-11T21:05:10.7627773Z                 {
2023-01-11T21:05:10.7627838Z                     {
2023-01-11T21:05:10.7627939Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7628041Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7628151Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:05:10.7628243Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7628338Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7628390Z                     }
2023-01-11T21:05:10.7628454Z                 }
2023-01-11T21:05:10.7628516Z             }
2023-01-11T21:05:10.7628579Z         }
2023-01-11T21:05:10.7628639Z     }
2023-01-11T21:05:10.7628697Z }
2023-01-11T21:05:10.7628761Z ''')
2023-01-11T21:05:10.7628778Z 
2023-01-11T21:05:10.7628782Z 
2023-01-11T21:05:10.7628857Z async_compile.wait(globals())
2023-01-11T21:05:10.7628927Z del async_compile
2023-01-11T21:05:10.7628933Z 
2023-01-11T21:05:10.7629001Z def call(args):
2023-01-11T21:05:10.7629075Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7629144Z     args.clear()
2023-01-11T21:05:10.7629345Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7629504Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7629558Z     del arg0_1
2023-01-11T21:05:10.7629623Z     del arg1_1
2023-01-11T21:05:10.7629692Z     return (buf0, )
2023-01-11T21:05:10.7629697Z 
2023-01-11T21:05:10.7629701Z 
2023-01-11T21:05:10.7629774Z if __name__ == "__main__":
2023-01-11T21:05:10.7629888Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7630009Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7630209Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7630405Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7630505Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7630510Z 
2023-01-11T21:05:10.7630573Z ok (2.686s)
2023-01-11T21:05:10.7631034Z   test_cpu_double_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7631186Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7631446Z [2023-01-11 21:04:12,318] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 539
2023-01-11T21:05:10.7631708Z [2023-01-11 21:04:14,953] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 539
2023-01-11T21:05:10.7631713Z 
2023-01-11T21:05:10.7631808Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7631877Z import torch
2023-01-11T21:05:10.7631946Z import random
2023-01-11T21:05:10.7632047Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7632164Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7632169Z 
2023-01-11T21:05:10.7632245Z aten = torch.ops.aten
2023-01-11T21:05:10.7632376Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7632467Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7632472Z 
2023-01-11T21:05:10.7632477Z 
2023-01-11T21:05:10.7632608Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7632843Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7632964Z extern "C" void kernel(const double* __restrict__ in_ptr0,
2023-01-11T21:05:10.7633056Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7633155Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7633213Z {
2023-01-11T21:05:10.7633310Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7633369Z     {
2023-01-11T21:05:10.7633444Z         #pragma omp for 
2023-01-11T21:05:10.7633524Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7633574Z         {
2023-01-11T21:05:10.7633653Z             #pragma GCC ivdep
2023-01-11T21:05:10.7633736Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7633798Z             {
2023-01-11T21:05:10.7633863Z                 {
2023-01-11T21:05:10.7633927Z                     {
2023-01-11T21:05:10.7634029Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7634118Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:05:10.7634232Z                         auto tmp2 = static_cast<double>(tmp1);
2023-01-11T21:05:10.7634324Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7634421Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7634485Z                     }
2023-01-11T21:05:10.7634548Z                 }
2023-01-11T21:05:10.7634610Z             }
2023-01-11T21:05:10.7634658Z         }
2023-01-11T21:05:10.7634717Z     }
2023-01-11T21:05:10.7634774Z }
2023-01-11T21:05:10.7634851Z ''')
2023-01-11T21:05:10.7634856Z 
2023-01-11T21:05:10.7634860Z 
2023-01-11T21:05:10.7634948Z async_compile.wait(globals())
2023-01-11T21:05:10.7635019Z del async_compile
2023-01-11T21:05:10.7635023Z 
2023-01-11T21:05:10.7635094Z def call(args):
2023-01-11T21:05:10.7635154Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7635223Z     args.clear()
2023-01-11T21:05:10.7635423Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7635584Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7635651Z     del arg0_1
2023-01-11T21:05:10.7635717Z     del arg1_1
2023-01-11T21:05:10.7635787Z     return (buf0, )
2023-01-11T21:05:10.7635791Z 
2023-01-11T21:05:10.7635795Z 
2023-01-11T21:05:10.7635870Z if __name__ == "__main__":
2023-01-11T21:05:10.7635972Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7636092Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7636291Z     arg0_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7636486Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7636648Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7636653Z 
2023-01-11T21:05:10.7636718Z ok (2.666s)
2023-01-11T21:05:10.7637178Z   test_cpu_int_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7637303Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7637565Z [2023-01-11 21:04:14,984] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 540
2023-01-11T21:05:10.7637816Z [2023-01-11 21:04:17,605] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 540
2023-01-11T21:05:10.7637832Z 
2023-01-11T21:05:10.7637911Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7637982Z import torch
2023-01-11T21:05:10.7638051Z import random
2023-01-11T21:05:10.7638164Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7638282Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7638314Z 
2023-01-11T21:05:10.7638392Z aten = torch.ops.aten
2023-01-11T21:05:10.7638524Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7638601Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7638606Z 
2023-01-11T21:05:10.7638610Z 
2023-01-11T21:05:10.7638744Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7638946Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7639063Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7639167Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7639265Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7639323Z {
2023-01-11T21:05:10.7639410Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7639469Z     {
2023-01-11T21:05:10.7639544Z         #pragma omp for 
2023-01-11T21:05:10.7639623Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7639687Z         {
2023-01-11T21:05:10.7639750Z             {
2023-01-11T21:05:10.7639815Z                 {
2023-01-11T21:05:10.7639894Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7639984Z                     auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.7640090Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.7640180Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7640263Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7640325Z                 }
2023-01-11T21:05:10.7640385Z             }
2023-01-11T21:05:10.7640433Z         }
2023-01-11T21:05:10.7640490Z     }
2023-01-11T21:05:10.7640549Z }
2023-01-11T21:05:10.7640729Z ''')
2023-01-11T21:05:10.7640735Z 
2023-01-11T21:05:10.7640739Z 
2023-01-11T21:05:10.7640832Z async_compile.wait(globals())
2023-01-11T21:05:10.7640902Z del async_compile
2023-01-11T21:05:10.7640908Z 
2023-01-11T21:05:10.7640978Z def call(args):
2023-01-11T21:05:10.7641039Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7641111Z     args.clear()
2023-01-11T21:05:10.7641307Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7641471Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7641538Z     del arg0_1
2023-01-11T21:05:10.7641603Z     del arg1_1
2023-01-11T21:05:10.7641674Z     return (buf0, )
2023-01-11T21:05:10.7641680Z 
2023-01-11T21:05:10.7641684Z 
2023-01-11T21:05:10.7641759Z if __name__ == "__main__":
2023-01-11T21:05:10.7641861Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7641986Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7642177Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7642432Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7642545Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7642550Z 
2023-01-11T21:05:10.7642619Z ok (2.653s)
2023-01-11T21:05:10.7643076Z   test_cpu_int_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7643202Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7643460Z [2023-01-11 21:04:17,645] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 541
2023-01-11T21:05:10.7643710Z [2023-01-11 21:04:20,300] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 541
2023-01-11T21:05:10.7643718Z 
2023-01-11T21:05:10.7643813Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7643882Z import torch
2023-01-11T21:05:10.7643952Z import random
2023-01-11T21:05:10.7644105Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7644227Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7644232Z 
2023-01-11T21:05:10.7644309Z aten = torch.ops.aten
2023-01-11T21:05:10.7644442Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7644519Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7644524Z 
2023-01-11T21:05:10.7644528Z 
2023-01-11T21:05:10.7644660Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7644863Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7644980Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7645083Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7645183Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7645242Z {
2023-01-11T21:05:10.7645324Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7645384Z     {
2023-01-11T21:05:10.7645460Z         #pragma omp for 
2023-01-11T21:05:10.7645544Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7645605Z         {
2023-01-11T21:05:10.7645684Z             #pragma GCC ivdep
2023-01-11T21:05:10.7645768Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7645817Z             {
2023-01-11T21:05:10.7645881Z                 {
2023-01-11T21:05:10.7645947Z                     {
2023-01-11T21:05:10.7646041Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7646133Z                         auto tmp2 = in_ptr1[i0];
2023-01-11T21:05:10.7646243Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.7646332Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7646415Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7646482Z                     }
2023-01-11T21:05:10.7646544Z                 }
2023-01-11T21:05:10.7646605Z             }
2023-01-11T21:05:10.7646665Z         }
2023-01-11T21:05:10.7646726Z     }
2023-01-11T21:05:10.7646783Z }
2023-01-11T21:05:10.7646848Z ''')
2023-01-11T21:05:10.7646852Z 
2023-01-11T21:05:10.7646856Z 
2023-01-11T21:05:10.7646945Z async_compile.wait(globals())
2023-01-11T21:05:10.7647014Z del async_compile
2023-01-11T21:05:10.7647019Z 
2023-01-11T21:05:10.7647089Z def call(args):
2023-01-11T21:05:10.7647161Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7647232Z     args.clear()
2023-01-11T21:05:10.7647439Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7647589Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7647656Z     del arg0_1
2023-01-11T21:05:10.7647725Z     del arg1_1
2023-01-11T21:05:10.7647836Z     return (buf0, )
2023-01-11T21:05:10.7647841Z 
2023-01-11T21:05:10.7647845Z 
2023-01-11T21:05:10.7647919Z if __name__ == "__main__":
2023-01-11T21:05:10.7648036Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7648160Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7648349Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7648540Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7648655Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7648660Z 
2023-01-11T21:05:10.7648725Z ok (2.694s)
2023-01-11T21:05:10.7649189Z   test_cpu_int_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7649317Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7649603Z [2023-01-11 21:04:20,331] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 542
2023-01-11T21:05:10.7649869Z [2023-01-11 21:04:22,971] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 542
2023-01-11T21:05:10.7649874Z 
2023-01-11T21:05:10.7649967Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7650036Z import torch
2023-01-11T21:05:10.7650092Z import random
2023-01-11T21:05:10.7650208Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7650329Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7650334Z 
2023-01-11T21:05:10.7650411Z aten = torch.ops.aten
2023-01-11T21:05:10.7650545Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7650636Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7650643Z 
2023-01-11T21:05:10.7650647Z 
2023-01-11T21:05:10.7650779Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7650983Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7651088Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7651192Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7651289Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7651349Z {
2023-01-11T21:05:10.7651444Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7651506Z     {
2023-01-11T21:05:10.7651583Z         #pragma omp for 
2023-01-11T21:05:10.7651651Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7651714Z         {
2023-01-11T21:05:10.7651776Z             {
2023-01-11T21:05:10.7651840Z                 {
2023-01-11T21:05:10.7651932Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7652023Z                     auto tmp2 = in_ptr1[0];
2023-01-11T21:05:10.7652133Z                     auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.7652211Z                     auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7652294Z                     out_ptr0[i0] = tmp3;
2023-01-11T21:05:10.7652359Z                 }
2023-01-11T21:05:10.7652421Z             }
2023-01-11T21:05:10.7652484Z         }
2023-01-11T21:05:10.7652545Z     }
2023-01-11T21:05:10.7652604Z }
2023-01-11T21:05:10.7652667Z ''')
2023-01-11T21:05:10.7652672Z 
2023-01-11T21:05:10.7652676Z 
2023-01-11T21:05:10.7652763Z async_compile.wait(globals())
2023-01-11T21:05:10.7652832Z del async_compile
2023-01-11T21:05:10.7652837Z 
2023-01-11T21:05:10.7652905Z def call(args):
2023-01-11T21:05:10.7652978Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7653047Z     args.clear()
2023-01-11T21:05:10.7653242Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7653387Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7653488Z     del arg0_1
2023-01-11T21:05:10.7653552Z     del arg1_1
2023-01-11T21:05:10.7653621Z     return (buf0, )
2023-01-11T21:05:10.7653626Z 
2023-01-11T21:05:10.7653631Z 
2023-01-11T21:05:10.7653706Z if __name__ == "__main__":
2023-01-11T21:05:10.7653818Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7653938Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7654126Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7654303Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7654416Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7654421Z 
2023-01-11T21:05:10.7654485Z ok (2.671s)
2023-01-11T21:05:10.7654938Z   test_cpu_int_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7655092Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7655351Z [2023-01-11 21:04:23,002] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 543
2023-01-11T21:05:10.7655614Z [2023-01-11 21:04:25,716] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 543
2023-01-11T21:05:10.7655620Z 
2023-01-11T21:05:10.7655712Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7655781Z import torch
2023-01-11T21:05:10.7655837Z import random
2023-01-11T21:05:10.7655949Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7656067Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7656072Z 
2023-01-11T21:05:10.7656148Z aten = torch.ops.aten
2023-01-11T21:05:10.7656281Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7656372Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7656377Z 
2023-01-11T21:05:10.7656381Z 
2023-01-11T21:05:10.7656515Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7656718Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7656821Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7656924Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7657022Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7657082Z {
2023-01-11T21:05:10.7657178Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7657237Z     {
2023-01-11T21:05:10.7657311Z         #pragma omp for 
2023-01-11T21:05:10.7657380Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7657440Z         {
2023-01-11T21:05:10.7657518Z             #pragma GCC ivdep
2023-01-11T21:05:10.7657603Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7657666Z             {
2023-01-11T21:05:10.7657728Z                 {
2023-01-11T21:05:10.7657792Z                     {
2023-01-11T21:05:10.7657875Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7657977Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7658084Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.7658174Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7658269Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7658333Z                     }
2023-01-11T21:05:10.7658395Z                 }
2023-01-11T21:05:10.7658444Z             }
2023-01-11T21:05:10.7658601Z         }
2023-01-11T21:05:10.7658668Z     }
2023-01-11T21:05:10.7658727Z }
2023-01-11T21:05:10.7658807Z ''')
2023-01-11T21:05:10.7658811Z 
2023-01-11T21:05:10.7658816Z 
2023-01-11T21:05:10.7658906Z async_compile.wait(globals())
2023-01-11T21:05:10.7658978Z del async_compile
2023-01-11T21:05:10.7659018Z 
2023-01-11T21:05:10.7659074Z def call(args):
2023-01-11T21:05:10.7659151Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7659221Z     args.clear()
2023-01-11T21:05:10.7659423Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7659585Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7659652Z     del arg0_1
2023-01-11T21:05:10.7659717Z     del arg1_1
2023-01-11T21:05:10.7659774Z     return (buf0, )
2023-01-11T21:05:10.7659779Z 
2023-01-11T21:05:10.7659795Z 
2023-01-11T21:05:10.7659857Z if __name__ == "__main__":
2023-01-11T21:05:10.7659968Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7660090Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7660280Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7660477Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7660592Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7660597Z 
2023-01-11T21:05:10.7660661Z ok (2.745s)
2023-01-11T21:05:10.7661141Z   test_cpu_int_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7661256Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7661516Z [2023-01-11 21:04:25,747] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 544
2023-01-11T21:05:10.7661778Z [2023-01-11 21:04:28,389] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 544
2023-01-11T21:05:10.7661784Z 
2023-01-11T21:05:10.7661879Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7661948Z import torch
2023-01-11T21:05:10.7662019Z import random
2023-01-11T21:05:10.7662131Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7662251Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7662256Z 
2023-01-11T21:05:10.7662320Z aten = torch.ops.aten
2023-01-11T21:05:10.7662450Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7662539Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7662544Z 
2023-01-11T21:05:10.7662549Z 
2023-01-11T21:05:10.7662679Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7662881Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7662998Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7663103Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7663201Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7663251Z {
2023-01-11T21:05:10.7663346Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7663406Z     {
2023-01-11T21:05:10.7663480Z         #pragma omp for 
2023-01-11T21:05:10.7663564Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7663625Z         {
2023-01-11T21:05:10.7663704Z             #pragma GCC ivdep
2023-01-11T21:05:10.7663775Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7663838Z             {
2023-01-11T21:05:10.7663901Z                 {
2023-01-11T21:05:10.7663966Z                     {
2023-01-11T21:05:10.7664061Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7664162Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7664274Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.7664352Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7664447Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7664542Z                     }
2023-01-11T21:05:10.7664603Z                 }
2023-01-11T21:05:10.7664666Z             }
2023-01-11T21:05:10.7664725Z         }
2023-01-11T21:05:10.7664785Z     }
2023-01-11T21:05:10.7664830Z }
2023-01-11T21:05:10.7664910Z ''')
2023-01-11T21:05:10.7664916Z 
2023-01-11T21:05:10.7664920Z 
2023-01-11T21:05:10.7665009Z async_compile.wait(globals())
2023-01-11T21:05:10.7665078Z del async_compile
2023-01-11T21:05:10.7665083Z 
2023-01-11T21:05:10.7665150Z def call(args):
2023-01-11T21:05:10.7665222Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7665290Z     args.clear()
2023-01-11T21:05:10.7665473Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7665633Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7665699Z     del arg0_1
2023-01-11T21:05:10.7665763Z     del arg1_1
2023-01-11T21:05:10.7665832Z     return (buf0, )
2023-01-11T21:05:10.7665837Z 
2023-01-11T21:05:10.7665843Z 
2023-01-11T21:05:10.7665918Z if __name__ == "__main__":
2023-01-11T21:05:10.7666030Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7666152Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7666355Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7666552Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7666666Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7666671Z 
2023-01-11T21:05:10.7666738Z ok (2.672s)
2023-01-11T21:05:10.7667184Z   test_cpu_int_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7667311Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7667570Z [2023-01-11 21:04:28,418] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 545
2023-01-11T21:05:10.7667834Z [2023-01-11 21:04:31,009] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 545
2023-01-11T21:05:10.7667840Z 
2023-01-11T21:05:10.7667932Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7667987Z import torch
2023-01-11T21:05:10.7668057Z import random
2023-01-11T21:05:10.7668169Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7668287Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7668293Z 
2023-01-11T21:05:10.7668367Z aten = torch.ops.aten
2023-01-11T21:05:10.7668498Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7668587Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7668592Z 
2023-01-11T21:05:10.7668596Z 
2023-01-11T21:05:10.7668731Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7668921Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7669035Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7669138Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7669232Z                        int* __restrict__ out_ptr0)
2023-01-11T21:05:10.7669290Z {
2023-01-11T21:05:10.7669386Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7669444Z     {
2023-01-11T21:05:10.7669506Z         #pragma omp for 
2023-01-11T21:05:10.7669587Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7669648Z         {
2023-01-11T21:05:10.7669708Z             {
2023-01-11T21:05:10.7669770Z                 {
2023-01-11T21:05:10.7669861Z                     auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7669951Z                     auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7670026Z                     auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7670176Z                     out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7670239Z                 }
2023-01-11T21:05:10.7670301Z             }
2023-01-11T21:05:10.7670363Z         }
2023-01-11T21:05:10.7670423Z     }
2023-01-11T21:05:10.7670472Z }
2023-01-11T21:05:10.7670549Z ''')
2023-01-11T21:05:10.7670554Z 
2023-01-11T21:05:10.7670559Z 
2023-01-11T21:05:10.7670647Z async_compile.wait(globals())
2023-01-11T21:05:10.7670718Z del async_compile
2023-01-11T21:05:10.7670722Z 
2023-01-11T21:05:10.7670791Z def call(args):
2023-01-11T21:05:10.7670866Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7670937Z     args.clear()
2023-01-11T21:05:10.7671126Z     buf0 = empty_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7671274Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7671342Z     del arg0_1
2023-01-11T21:05:10.7671408Z     del arg1_1
2023-01-11T21:05:10.7671478Z     return (buf0, )
2023-01-11T21:05:10.7671485Z 
2023-01-11T21:05:10.7671489Z 
2023-01-11T21:05:10.7671564Z if __name__ == "__main__":
2023-01-11T21:05:10.7671676Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7671831Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7672022Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7672197Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7672310Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7672315Z 
2023-01-11T21:05:10.7672379Z ok (2.619s)
2023-01-11T21:05:10.7672831Z   test_cpu_int_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7672960Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7673216Z [2023-01-11 21:04:31,040] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 546
2023-01-11T21:05:10.7673479Z [2023-01-11 21:04:33,718] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 546
2023-01-11T21:05:10.7673484Z 
2023-01-11T21:05:10.7673574Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7673642Z import torch
2023-01-11T21:05:10.7673697Z import random
2023-01-11T21:05:10.7673809Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7673928Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7673933Z 
2023-01-11T21:05:10.7674009Z aten = torch.ops.aten
2023-01-11T21:05:10.7674140Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7674231Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7674236Z 
2023-01-11T21:05:10.7674243Z 
2023-01-11T21:05:10.7674374Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7674575Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7674679Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7674783Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7674883Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7674942Z {
2023-01-11T21:05:10.7675035Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7675095Z     {
2023-01-11T21:05:10.7675170Z         #pragma omp for 
2023-01-11T21:05:10.7675237Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7675297Z         {
2023-01-11T21:05:10.7675375Z             #pragma GCC ivdep
2023-01-11T21:05:10.7675459Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7675520Z             {
2023-01-11T21:05:10.7675583Z                 {
2023-01-11T21:05:10.7675646Z                     {
2023-01-11T21:05:10.7675769Z                         auto tmp0 = in_ptr0[i1];
2023-01-11T21:05:10.7675874Z                         auto tmp2 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7675981Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.7676073Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7676169Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7676235Z                     }
2023-01-11T21:05:10.7676297Z                 }
2023-01-11T21:05:10.7676347Z             }
2023-01-11T21:05:10.7676408Z         }
2023-01-11T21:05:10.7676469Z     }
2023-01-11T21:05:10.7676526Z }
2023-01-11T21:05:10.7676605Z ''')
2023-01-11T21:05:10.7676610Z 
2023-01-11T21:05:10.7676614Z 
2023-01-11T21:05:10.7676701Z async_compile.wait(globals())
2023-01-11T21:05:10.7676771Z del async_compile
2023-01-11T21:05:10.7676775Z 
2023-01-11T21:05:10.7676831Z def call(args):
2023-01-11T21:05:10.7676903Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7676973Z     args.clear()
2023-01-11T21:05:10.7677173Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7677333Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7677426Z     del arg0_1
2023-01-11T21:05:10.7677493Z     del arg1_1
2023-01-11T21:05:10.7677550Z     return (buf0, )
2023-01-11T21:05:10.7677555Z 
2023-01-11T21:05:10.7677559Z 
2023-01-11T21:05:10.7677632Z if __name__ == "__main__":
2023-01-11T21:05:10.7677744Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7677865Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7678056Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7678252Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7678364Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7678369Z 
2023-01-11T21:05:10.7678432Z ok (2.710s)
2023-01-11T21:05:10.7678903Z   test_cpu_int_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7679016Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7679275Z [2023-01-11 21:04:33,750] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 547
2023-01-11T21:05:10.7679538Z [2023-01-11 21:04:36,400] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 547
2023-01-11T21:05:10.7679542Z 
2023-01-11T21:05:10.7679635Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7679704Z import torch
2023-01-11T21:05:10.7679773Z import random
2023-01-11T21:05:10.7679886Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7680009Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7680014Z 
2023-01-11T21:05:10.7680077Z aten = torch.ops.aten
2023-01-11T21:05:10.7680214Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7680304Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7680308Z 
2023-01-11T21:05:10.7680313Z 
2023-01-11T21:05:10.7680446Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7680764Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7680881Z extern "C" void kernel(const int* __restrict__ in_ptr0,
2023-01-11T21:05:10.7680989Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7681090Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7681137Z {
2023-01-11T21:05:10.7681233Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7681294Z     {
2023-01-11T21:05:10.7681422Z         #pragma omp for 
2023-01-11T21:05:10.7681504Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7681568Z         {
2023-01-11T21:05:10.7681647Z             #pragma GCC ivdep
2023-01-11T21:05:10.7681720Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7681783Z             {
2023-01-11T21:05:10.7681847Z                 {
2023-01-11T21:05:10.7681913Z                     {
2023-01-11T21:05:10.7682009Z                         auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7682112Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7682221Z                         auto tmp1 = static_cast<float>(tmp0);
2023-01-11T21:05:10.7682303Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7682400Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7682466Z                     }
2023-01-11T21:05:10.7682531Z                 }
2023-01-11T21:05:10.7682594Z             }
2023-01-11T21:05:10.7682655Z         }
2023-01-11T21:05:10.7682704Z     }
2023-01-11T21:05:10.7682762Z }
2023-01-11T21:05:10.7682845Z ''')
2023-01-11T21:05:10.7682850Z 
2023-01-11T21:05:10.7682854Z 
2023-01-11T21:05:10.7682943Z async_compile.wait(globals())
2023-01-11T21:05:10.7683014Z del async_compile
2023-01-11T21:05:10.7683054Z 
2023-01-11T21:05:10.7683125Z def call(args):
2023-01-11T21:05:10.7683198Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7683266Z     args.clear()
2023-01-11T21:05:10.7683453Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7683612Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7683680Z     del arg0_1
2023-01-11T21:05:10.7683745Z     del arg1_1
2023-01-11T21:05:10.7683816Z     return (buf0, )
2023-01-11T21:05:10.7683821Z 
2023-01-11T21:05:10.7683825Z 
2023-01-11T21:05:10.7683898Z if __name__ == "__main__":
2023-01-11T21:05:10.7684011Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7684119Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7684310Z     arg0_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7684512Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7684624Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7684629Z 
2023-01-11T21:05:10.7684694Z ok (2.682s)
2023-01-11T21:05:10.7685158Z   test_cpu_strided_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7685282Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7685540Z [2023-01-11 21:04:36,432] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 548
2023-01-11T21:05:10.7685802Z [2023-01-11 21:04:39,093] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 548
2023-01-11T21:05:10.7685807Z 
2023-01-11T21:05:10.7685899Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7685955Z import torch
2023-01-11T21:05:10.7686022Z import random
2023-01-11T21:05:10.7686133Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7686251Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7686256Z 
2023-01-11T21:05:10.7686331Z aten = torch.ops.aten
2023-01-11T21:05:10.7686462Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7686551Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7686556Z 
2023-01-11T21:05:10.7686560Z 
2023-01-11T21:05:10.7686692Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7686882Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7687029Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7687131Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7687230Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7687289Z {
2023-01-11T21:05:10.7687385Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7687444Z     {
2023-01-11T21:05:10.7687507Z         #pragma omp for 
2023-01-11T21:05:10.7687586Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7687646Z         {
2023-01-11T21:05:10.7687725Z             #pragma GCC ivdep
2023-01-11T21:05:10.7687809Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7687872Z             {
2023-01-11T21:05:10.7687933Z                 {
2023-01-11T21:05:10.7687985Z                     {
2023-01-11T21:05:10.7688088Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7688181Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7688271Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7688370Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7688436Z                     }
2023-01-11T21:05:10.7688496Z                 }
2023-01-11T21:05:10.7688572Z             }
2023-01-11T21:05:10.7688635Z         }
2023-01-11T21:05:10.7688693Z     }
2023-01-11T21:05:10.7688749Z }
2023-01-11T21:05:10.7688826Z ''')
2023-01-11T21:05:10.7688831Z 
2023-01-11T21:05:10.7688835Z 
2023-01-11T21:05:10.7688923Z async_compile.wait(globals())
2023-01-11T21:05:10.7688993Z del async_compile
2023-01-11T21:05:10.7688997Z 
2023-01-11T21:05:10.7689053Z def call(args):
2023-01-11T21:05:10.7689124Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7689193Z     args.clear()
2023-01-11T21:05:10.7689391Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7689551Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7689616Z     del arg0_1
2023-01-11T21:05:10.7689687Z     del arg1_1
2023-01-11T21:05:10.7689744Z     return (buf0, )
2023-01-11T21:05:10.7689749Z 
2023-01-11T21:05:10.7689753Z 
2023-01-11T21:05:10.7689826Z if __name__ == "__main__":
2023-01-11T21:05:10.7689939Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7690059Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7690256Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7690449Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7690561Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7690566Z 
2023-01-11T21:05:10.7690629Z ok (2.693s)
2023-01-11T21:05:10.7691078Z   test_cpu_strided_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7691203Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7691464Z [2023-01-11 21:04:39,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 549
2023-01-11T21:05:10.7691724Z [2023-01-11 21:04:41,787] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 549
2023-01-11T21:05:10.7691729Z 
2023-01-11T21:05:10.7691820Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7691890Z import torch
2023-01-11T21:05:10.7691958Z import random
2023-01-11T21:05:10.7692071Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7692191Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7692195Z 
2023-01-11T21:05:10.7692258Z aten = torch.ops.aten
2023-01-11T21:05:10.7692389Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7692511Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7692516Z 
2023-01-11T21:05:10.7692520Z 
2023-01-11T21:05:10.7692652Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7692858Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7692976Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7693077Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7693174Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7693222Z {
2023-01-11T21:05:10.7693316Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7693374Z     {
2023-01-11T21:05:10.7693450Z         #pragma omp for 
2023-01-11T21:05:10.7693531Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7693590Z         {
2023-01-11T21:05:10.7693666Z             #pragma GCC ivdep
2023-01-11T21:05:10.7693738Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7693801Z             {
2023-01-11T21:05:10.7693862Z                 {
2023-01-11T21:05:10.7693926Z                     {
2023-01-11T21:05:10.7694030Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7694153Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7694246Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7694330Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7694392Z                     }
2023-01-11T21:05:10.7694454Z                 }
2023-01-11T21:05:10.7694514Z             }
2023-01-11T21:05:10.7694574Z         }
2023-01-11T21:05:10.7694632Z     }
2023-01-11T21:05:10.7694677Z }
2023-01-11T21:05:10.7694755Z ''')
2023-01-11T21:05:10.7694760Z 
2023-01-11T21:05:10.7694764Z 
2023-01-11T21:05:10.7694850Z async_compile.wait(globals())
2023-01-11T21:05:10.7694921Z del async_compile
2023-01-11T21:05:10.7694926Z 
2023-01-11T21:05:10.7694993Z def call(args):
2023-01-11T21:05:10.7695071Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7695141Z     args.clear()
2023-01-11T21:05:10.7695348Z     buf0 = empty_strided((1, 10, 10), (100, 10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7695498Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7695566Z     del arg0_1
2023-01-11T21:05:10.7695630Z     del arg1_1
2023-01-11T21:05:10.7695700Z     return (buf0, )
2023-01-11T21:05:10.7695704Z 
2023-01-11T21:05:10.7695709Z 
2023-01-11T21:05:10.7695781Z if __name__ == "__main__":
2023-01-11T21:05:10.7695895Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7696015Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7696200Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7696406Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7696518Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7696525Z 
2023-01-11T21:05:10.7696589Z ok (2.694s)
2023-01-11T21:05:10.7697055Z   test_cpu_strided_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7697181Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7697439Z [2023-01-11 21:04:41,822] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 550
2023-01-11T21:05:10.7697699Z [2023-01-11 21:04:44,443] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 550
2023-01-11T21:05:10.7697704Z 
2023-01-11T21:05:10.7697796Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7697861Z import torch
2023-01-11T21:05:10.7697947Z import random
2023-01-11T21:05:10.7698060Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7698178Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7698183Z 
2023-01-11T21:05:10.7698262Z aten = torch.ops.aten
2023-01-11T21:05:10.7698396Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7698574Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7698580Z 
2023-01-11T21:05:10.7698584Z 
2023-01-11T21:05:10.7698724Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7698927Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7699033Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7699136Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7699238Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7699299Z {
2023-01-11T21:05:10.7699395Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7699459Z     {
2023-01-11T21:05:10.7699539Z         #pragma omp for 
2023-01-11T21:05:10.7699607Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7699670Z         {
2023-01-11T21:05:10.7699784Z             #pragma GCC ivdep
2023-01-11T21:05:10.7699872Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7699936Z             {
2023-01-11T21:05:10.7700001Z                 {
2023-01-11T21:05:10.7700054Z                     {
2023-01-11T21:05:10.7700158Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7700251Z                         auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.7700344Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7700438Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7700503Z                     }
2023-01-11T21:05:10.7700566Z                 }
2023-01-11T21:05:10.7700614Z             }
2023-01-11T21:05:10.7700675Z         }
2023-01-11T21:05:10.7700733Z     }
2023-01-11T21:05:10.7700793Z }
2023-01-11T21:05:10.7700870Z ''')
2023-01-11T21:05:10.7700875Z 
2023-01-11T21:05:10.7700879Z 
2023-01-11T21:05:10.7700967Z async_compile.wait(globals())
2023-01-11T21:05:10.7701037Z del async_compile
2023-01-11T21:05:10.7701044Z 
2023-01-11T21:05:10.7701112Z def call(args):
2023-01-11T21:05:10.7701173Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7701242Z     args.clear()
2023-01-11T21:05:10.7701439Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7701598Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7701665Z     del arg0_1
2023-01-11T21:05:10.7701728Z     del arg1_1
2023-01-11T21:05:10.7701798Z     return (buf0, )
2023-01-11T21:05:10.7701803Z 
2023-01-11T21:05:10.7701806Z 
2023-01-11T21:05:10.7701867Z if __name__ == "__main__":
2023-01-11T21:05:10.7701978Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7702098Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7702296Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7702484Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7702599Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7702603Z 
2023-01-11T21:05:10.7702668Z ok (2.655s)
2023-01-11T21:05:10.7703127Z   test_cpu_strided_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7703251Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7703495Z [2023-01-11 21:04:44,474] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 551
2023-01-11T21:05:10.7703787Z [2023-01-11 21:04:47,135] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 551
2023-01-11T21:05:10.7703792Z 
2023-01-11T21:05:10.7703886Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7703958Z import torch
2023-01-11T21:05:10.7704027Z import random
2023-01-11T21:05:10.7704139Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7704259Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7704264Z 
2023-01-11T21:05:10.7704341Z aten = torch.ops.aten
2023-01-11T21:05:10.7704458Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7704547Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7704552Z 
2023-01-11T21:05:10.7704556Z 
2023-01-11T21:05:10.7704685Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7704888Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7705009Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7705109Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7705207Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7705300Z {
2023-01-11T21:05:10.7705385Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7705447Z     {
2023-01-11T21:05:10.7705523Z         #pragma omp for 
2023-01-11T21:05:10.7705603Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7705665Z         {
2023-01-11T21:05:10.7705742Z             #pragma GCC ivdep
2023-01-11T21:05:10.7705826Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7705876Z             {
2023-01-11T21:05:10.7705937Z                 {
2023-01-11T21:05:10.7706002Z                     {
2023-01-11T21:05:10.7706106Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7706207Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7706301Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7706398Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7706451Z                     }
2023-01-11T21:05:10.7706513Z                 }
2023-01-11T21:05:10.7706576Z             }
2023-01-11T21:05:10.7706637Z         }
2023-01-11T21:05:10.7706696Z     }
2023-01-11T21:05:10.7706754Z }
2023-01-11T21:05:10.7706818Z ''')
2023-01-11T21:05:10.7706823Z 
2023-01-11T21:05:10.7706839Z 
2023-01-11T21:05:10.7706914Z async_compile.wait(globals())
2023-01-11T21:05:10.7706983Z del async_compile
2023-01-11T21:05:10.7706988Z 
2023-01-11T21:05:10.7707056Z def call(args):
2023-01-11T21:05:10.7707128Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7707197Z     args.clear()
2023-01-11T21:05:10.7707394Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7707552Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7707606Z     del arg0_1
2023-01-11T21:05:10.7707670Z     del arg1_1
2023-01-11T21:05:10.7707740Z     return (buf0, )
2023-01-11T21:05:10.7707745Z 
2023-01-11T21:05:10.7707749Z 
2023-01-11T21:05:10.7707822Z if __name__ == "__main__":
2023-01-11T21:05:10.7707940Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7708061Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7708259Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7708443Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7708556Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7708561Z 
2023-01-11T21:05:10.7708627Z ok (2.692s)
2023-01-11T21:05:10.7709086Z   test_cpu_strided_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7709246Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7709507Z [2023-01-11 21:04:47,167] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 552
2023-01-11T21:05:10.7709773Z [2023-01-11 21:04:49,837] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 552
2023-01-11T21:05:10.7709777Z 
2023-01-11T21:05:10.7709873Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7709939Z import torch
2023-01-11T21:05:10.7710006Z import random
2023-01-11T21:05:10.7710106Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7710224Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7710229Z 
2023-01-11T21:05:10.7710307Z aten = torch.ops.aten
2023-01-11T21:05:10.7710436Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7710528Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7710533Z 
2023-01-11T21:05:10.7710538Z 
2023-01-11T21:05:10.7710668Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7710897Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7711016Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7711110Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7711209Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7711269Z {
2023-01-11T21:05:10.7711366Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7711427Z     {
2023-01-11T21:05:10.7711503Z         #pragma omp for 
2023-01-11T21:05:10.7711583Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7711633Z         {
2023-01-11T21:05:10.7711713Z             #pragma GCC ivdep
2023-01-11T21:05:10.7711797Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7711860Z             {
2023-01-11T21:05:10.7711927Z                 {
2023-01-11T21:05:10.7711991Z                     {
2023-01-11T21:05:10.7712083Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7712185Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7712295Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.7712388Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7712484Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7712550Z                     }
2023-01-11T21:05:10.7712614Z                 }
2023-01-11T21:05:10.7712662Z             }
2023-01-11T21:05:10.7712723Z         }
2023-01-11T21:05:10.7712783Z     }
2023-01-11T21:05:10.7712841Z }
2023-01-11T21:05:10.7712921Z ''')
2023-01-11T21:05:10.7712927Z 
2023-01-11T21:05:10.7712931Z 
2023-01-11T21:05:10.7713021Z async_compile.wait(globals())
2023-01-11T21:05:10.7713092Z del async_compile
2023-01-11T21:05:10.7713096Z 
2023-01-11T21:05:10.7713167Z def call(args):
2023-01-11T21:05:10.7713227Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7713296Z     args.clear()
2023-01-11T21:05:10.7713496Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7713657Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7713724Z     del arg0_1
2023-01-11T21:05:10.7713789Z     del arg1_1
2023-01-11T21:05:10.7713860Z     return (buf0, )
2023-01-11T21:05:10.7713865Z 
2023-01-11T21:05:10.7713869Z 
2023-01-11T21:05:10.7713930Z if __name__ == "__main__":
2023-01-11T21:05:10.7714042Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7714164Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7714364Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7714563Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7714723Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7714727Z 
2023-01-11T21:05:10.7714791Z ok (2.702s)
2023-01-11T21:05:10.7715249Z   test_cpu_strided_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7715375Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7715622Z [2023-01-11 21:04:49,869] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 553
2023-01-11T21:05:10.7715884Z [2023-01-11 21:04:52,528] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 553
2023-01-11T21:05:10.7715889Z 
2023-01-11T21:05:10.7715981Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7716051Z import torch
2023-01-11T21:05:10.7716120Z import random
2023-01-11T21:05:10.7716234Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7716383Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7716389Z 
2023-01-11T21:05:10.7716467Z aten = torch.ops.aten
2023-01-11T21:05:10.7716586Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7716676Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7716681Z 
2023-01-11T21:05:10.7716686Z 
2023-01-11T21:05:10.7716819Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7717023Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7717142Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7717242Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7717341Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7717406Z {
2023-01-11T21:05:10.7717489Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7717549Z     {
2023-01-11T21:05:10.7717625Z         #pragma omp for 
2023-01-11T21:05:10.7717706Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7717769Z         {
2023-01-11T21:05:10.7717847Z             #pragma GCC ivdep
2023-01-11T21:05:10.7717932Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7717982Z             {
2023-01-11T21:05:10.7718045Z                 {
2023-01-11T21:05:10.7718111Z                     {
2023-01-11T21:05:10.7718214Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7718307Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7718415Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.7718505Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7718587Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7718651Z                     }
2023-01-11T21:05:10.7718716Z                 }
2023-01-11T21:05:10.7718778Z             }
2023-01-11T21:05:10.7718839Z         }
2023-01-11T21:05:10.7718898Z     }
2023-01-11T21:05:10.7718944Z }
2023-01-11T21:05:10.7719021Z ''')
2023-01-11T21:05:10.7719026Z 
2023-01-11T21:05:10.7719032Z 
2023-01-11T21:05:10.7719119Z async_compile.wait(globals())
2023-01-11T21:05:10.7719189Z del async_compile
2023-01-11T21:05:10.7719194Z 
2023-01-11T21:05:10.7719262Z def call(args):
2023-01-11T21:05:10.7719335Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7719404Z     args.clear()
2023-01-11T21:05:10.7719601Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7719748Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7719815Z     del arg0_1
2023-01-11T21:05:10.7719879Z     del arg1_1
2023-01-11T21:05:10.7719948Z     return (buf0, )
2023-01-11T21:05:10.7719953Z 
2023-01-11T21:05:10.7719957Z 
2023-01-11T21:05:10.7720061Z if __name__ == "__main__":
2023-01-11T21:05:10.7720174Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7720296Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7720484Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7720796Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7720913Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7720918Z 
2023-01-11T21:05:10.7720983Z ok (2.690s)
2023-01-11T21:05:10.7721442Z   test_cpu_strided_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7721569Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7721832Z [2023-01-11 21:04:52,561] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 554
2023-01-11T21:05:10.7722150Z [2023-01-11 21:04:55,259] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 554
2023-01-11T21:05:10.7722157Z 
2023-01-11T21:05:10.7722254Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7722324Z import torch
2023-01-11T21:05:10.7722380Z import random
2023-01-11T21:05:10.7722498Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7722619Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7722624Z 
2023-01-11T21:05:10.7722701Z aten = torch.ops.aten
2023-01-11T21:05:10.7722834Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7722925Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7722931Z 
2023-01-11T21:05:10.7722935Z 
2023-01-11T21:05:10.7723070Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7723278Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7723382Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7723487Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7723587Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7723647Z {
2023-01-11T21:05:10.7723743Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7723803Z     {
2023-01-11T21:05:10.7723878Z         #pragma omp for 
2023-01-11T21:05:10.7723947Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7724007Z         {
2023-01-11T21:05:10.7724084Z             #pragma GCC ivdep
2023-01-11T21:05:10.7724169Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7724231Z             {
2023-01-11T21:05:10.7724293Z                 {
2023-01-11T21:05:10.7724345Z                     {
2023-01-11T21:05:10.7724449Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7724553Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7724646Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7724743Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7724808Z                     }
2023-01-11T21:05:10.7724869Z                 }
2023-01-11T21:05:10.7724917Z             }
2023-01-11T21:05:10.7724978Z         }
2023-01-11T21:05:10.7725037Z     }
2023-01-11T21:05:10.7725095Z }
2023-01-11T21:05:10.7725171Z ''')
2023-01-11T21:05:10.7725176Z 
2023-01-11T21:05:10.7725180Z 
2023-01-11T21:05:10.7725268Z async_compile.wait(globals())
2023-01-11T21:05:10.7725337Z del async_compile
2023-01-11T21:05:10.7725342Z 
2023-01-11T21:05:10.7725409Z def call(args):
2023-01-11T21:05:10.7725469Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7725537Z     args.clear()
2023-01-11T21:05:10.7725735Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7725937Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7726004Z     del arg0_1
2023-01-11T21:05:10.7726067Z     del arg1_1
2023-01-11T21:05:10.7726136Z     return (buf0, )
2023-01-11T21:05:10.7726143Z 
2023-01-11T21:05:10.7726147Z 
2023-01-11T21:05:10.7726208Z if __name__ == "__main__":
2023-01-11T21:05:10.7726317Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7726437Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7726636Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7726832Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7726943Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7726948Z 
2023-01-11T21:05:10.7727012Z ok (2.732s)
2023-01-11T21:05:10.7727507Z   test_cpu_strided_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7727636Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7727881Z [2023-01-11 21:04:55,292] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 555
2023-01-11T21:05:10.7728145Z [2023-01-11 21:04:57,958] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 555
2023-01-11T21:05:10.7728150Z 
2023-01-11T21:05:10.7728243Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7728310Z import torch
2023-01-11T21:05:10.7728379Z import random
2023-01-11T21:05:10.7728491Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7728610Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7728617Z 
2023-01-11T21:05:10.7728694Z aten = torch.ops.aten
2023-01-11T21:05:10.7728813Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7728904Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7728911Z 
2023-01-11T21:05:10.7728915Z 
2023-01-11T21:05:10.7729047Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7729248Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7729365Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7729467Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7729564Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7729624Z {
2023-01-11T21:05:10.7729707Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7729766Z     {
2023-01-11T21:05:10.7729842Z         #pragma omp for 
2023-01-11T21:05:10.7729922Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7729984Z         {
2023-01-11T21:05:10.7730065Z             #pragma GCC ivdep
2023-01-11T21:05:10.7730148Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7730197Z             {
2023-01-11T21:05:10.7730261Z                 {
2023-01-11T21:05:10.7730327Z                     {
2023-01-11T21:05:10.7730432Z                         auto tmp0 = in_ptr0[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7730533Z                         auto tmp1 = in_ptr1[i0 + (10*i1)];
2023-01-11T21:05:10.7730626Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7730723Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7730777Z                     }
2023-01-11T21:05:10.7730838Z                 }
2023-01-11T21:05:10.7730899Z             }
2023-01-11T21:05:10.7730958Z         }
2023-01-11T21:05:10.7731017Z     }
2023-01-11T21:05:10.7731075Z }
2023-01-11T21:05:10.7731139Z ''')
2023-01-11T21:05:10.7731143Z 
2023-01-11T21:05:10.7731162Z 
2023-01-11T21:05:10.7731238Z async_compile.wait(globals())
2023-01-11T21:05:10.7731343Z del async_compile
2023-01-11T21:05:10.7731348Z 
2023-01-11T21:05:10.7731418Z def call(args):
2023-01-11T21:05:10.7731491Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7731560Z     args.clear()
2023-01-11T21:05:10.7731763Z     buf0 = empty_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7731925Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7731978Z     del arg0_1
2023-01-11T21:05:10.7732041Z     del arg1_1
2023-01-11T21:05:10.7732110Z     return (buf0, )
2023-01-11T21:05:10.7732114Z 
2023-01-11T21:05:10.7732118Z 
2023-01-11T21:05:10.7732192Z if __name__ == "__main__":
2023-01-11T21:05:10.7732303Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7732424Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7732623Z     arg0_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7732820Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7732923Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7732928Z 
2023-01-11T21:05:10.7732992Z ok (2.699s)
2023-01-11T21:05:10.7733489Z   test_cpu_transposed_broadcast1 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7733614Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7733872Z [2023-01-11 21:04:57,992] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 556
2023-01-11T21:05:10.7734136Z [2023-01-11 21:04:58,006] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 556
2023-01-11T21:05:10.7734143Z 
2023-01-11T21:05:10.7734239Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7734306Z import torch
2023-01-11T21:05:10.7734374Z import random
2023-01-11T21:05:10.7734478Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7734596Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7734601Z 
2023-01-11T21:05:10.7734677Z aten = torch.ops.aten
2023-01-11T21:05:10.7734810Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7734899Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7734904Z 
2023-01-11T21:05:10.7734908Z 
2023-01-11T21:05:10.7735040Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7735244Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7735362Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7735452Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7735553Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7735613Z {
2023-01-11T21:05:10.7735708Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7735769Z     {
2023-01-11T21:05:10.7735844Z         #pragma omp for 
2023-01-11T21:05:10.7735926Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7735975Z         {
2023-01-11T21:05:10.7736052Z             #pragma GCC ivdep
2023-01-11T21:05:10.7736135Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7736198Z             {
2023-01-11T21:05:10.7736261Z                 {
2023-01-11T21:05:10.7736325Z                     {
2023-01-11T21:05:10.7736414Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7736505Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7736597Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7736692Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7736758Z                     }
2023-01-11T21:05:10.7736851Z                 }
2023-01-11T21:05:10.7736912Z             }
2023-01-11T21:05:10.7736959Z         }
2023-01-11T21:05:10.7737018Z     }
2023-01-11T21:05:10.7737076Z }
2023-01-11T21:05:10.7737154Z ''')
2023-01-11T21:05:10.7737159Z 
2023-01-11T21:05:10.7737166Z 
2023-01-11T21:05:10.7737254Z async_compile.wait(globals())
2023-01-11T21:05:10.7737326Z del async_compile
2023-01-11T21:05:10.7737330Z 
2023-01-11T21:05:10.7737397Z def call(args):
2023-01-11T21:05:10.7737468Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7737526Z     args.clear()
2023-01-11T21:05:10.7737723Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7737883Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7737949Z     del arg0_1
2023-01-11T21:05:10.7738012Z     del arg1_1
2023-01-11T21:05:10.7738081Z     return (buf0, )
2023-01-11T21:05:10.7738086Z 
2023-01-11T21:05:10.7738091Z 
2023-01-11T21:05:10.7738164Z if __name__ == "__main__":
2023-01-11T21:05:10.7738267Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7738388Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7738707Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7738908Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7739020Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7739025Z 
2023-01-11T21:05:10.7739092Z ok (0.043s)
2023-01-11T21:05:10.7739560Z   test_cpu_transposed_broadcast2 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7739686Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7739947Z [2023-01-11 21:04:58,028] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 557
2023-01-11T21:05:10.7740201Z [2023-01-11 21:04:58,040] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 557
2023-01-11T21:05:10.7740220Z 
2023-01-11T21:05:10.7740299Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7740367Z import torch
2023-01-11T21:05:10.7740437Z import random
2023-01-11T21:05:10.7740551Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7740674Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7740679Z 
2023-01-11T21:05:10.7740755Z aten = torch.ops.aten
2023-01-11T21:05:10.7740892Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7740969Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7740974Z 
2023-01-11T21:05:10.7740993Z 
2023-01-11T21:05:10.7741111Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7741317Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7741435Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7741542Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7741642Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7741703Z {
2023-01-11T21:05:10.7741799Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7741847Z     {
2023-01-11T21:05:10.7741920Z         #pragma omp for 
2023-01-11T21:05:10.7742001Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7742062Z         {
2023-01-11T21:05:10.7742139Z             #pragma GCC ivdep
2023-01-11T21:05:10.7742222Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7742284Z             {
2023-01-11T21:05:10.7742334Z                 {
2023-01-11T21:05:10.7742396Z                     {
2023-01-11T21:05:10.7742498Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7742622Z                         auto tmp1 = in_ptr1[i1];
2023-01-11T21:05:10.7742716Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7742813Z                         out_ptr0[i1 + (10*i0)] = tmp2;
2023-01-11T21:05:10.7742878Z                     }
2023-01-11T21:05:10.7742928Z                 }
2023-01-11T21:05:10.7742990Z             }
2023-01-11T21:05:10.7743049Z         }
2023-01-11T21:05:10.7743108Z     }
2023-01-11T21:05:10.7743167Z }
2023-01-11T21:05:10.7743246Z ''')
2023-01-11T21:05:10.7743251Z 
2023-01-11T21:05:10.7743255Z 
2023-01-11T21:05:10.7743332Z async_compile.wait(globals())
2023-01-11T21:05:10.7743405Z del async_compile
2023-01-11T21:05:10.7743409Z 
2023-01-11T21:05:10.7743477Z def call(args):
2023-01-11T21:05:10.7743550Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7743621Z     args.clear()
2023-01-11T21:05:10.7743829Z     buf0 = empty_strided((1, 10, 10), (100, 1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7743994Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7744062Z     del arg0_1
2023-01-11T21:05:10.7744115Z     del arg1_1
2023-01-11T21:05:10.7744185Z     return (buf0, )
2023-01-11T21:05:10.7744219Z 
2023-01-11T21:05:10.7744223Z 
2023-01-11T21:05:10.7744298Z if __name__ == "__main__":
2023-01-11T21:05:10.7744410Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7744532Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7744729Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7744936Z     arg1_1 = rand_strided((1, 10, 1), (10, 1, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7745053Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7745057Z 
2023-01-11T21:05:10.7745109Z ok (0.034s)
2023-01-11T21:05:10.7745579Z   test_cpu_transposed_broadcast3 (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7745705Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7745964Z [2023-01-11 21:04:58,061] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 558
2023-01-11T21:05:10.7746226Z [2023-01-11 21:04:58,072] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 558
2023-01-11T21:05:10.7746231Z 
2023-01-11T21:05:10.7746326Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7746395Z import torch
2023-01-11T21:05:10.7746466Z import random
2023-01-11T21:05:10.7746581Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7746688Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7746696Z 
2023-01-11T21:05:10.7746773Z aten = torch.ops.aten
2023-01-11T21:05:10.7746906Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7746996Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7747004Z 
2023-01-11T21:05:10.7747008Z 
2023-01-11T21:05:10.7747145Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7747346Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7747465Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7747567Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7747653Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7747715Z {
2023-01-11T21:05:10.7747810Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7747871Z     {
2023-01-11T21:05:10.7747948Z         #pragma omp for 
2023-01-11T21:05:10.7748028Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7748088Z         {
2023-01-11T21:05:10.7748263Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7748384Z             auto tmp1 = at::vec::Vectorized<float>(in_ptr1[0]);
2023-01-11T21:05:10.7748470Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7748562Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7748623Z         }
2023-01-11T21:05:10.7748715Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7748797Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.7748846Z         {
2023-01-11T21:05:10.7748928Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7749006Z             auto tmp1 = in_ptr1[0];
2023-01-11T21:05:10.7749088Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7749164Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7749223Z         }
2023-01-11T21:05:10.7749280Z     }
2023-01-11T21:05:10.7749326Z }
2023-01-11T21:05:10.7749403Z ''')
2023-01-11T21:05:10.7749408Z 
2023-01-11T21:05:10.7749412Z 
2023-01-11T21:05:10.7749500Z async_compile.wait(globals())
2023-01-11T21:05:10.7749571Z del async_compile
2023-01-11T21:05:10.7749577Z 
2023-01-11T21:05:10.7749644Z def call(args):
2023-01-11T21:05:10.7749717Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7749812Z     args.clear()
2023-01-11T21:05:10.7750000Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7750158Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7750224Z     del arg0_1
2023-01-11T21:05:10.7750290Z     del arg1_1
2023-01-11T21:05:10.7750359Z     return (buf0, )
2023-01-11T21:05:10.7750364Z 
2023-01-11T21:05:10.7750368Z 
2023-01-11T21:05:10.7750442Z if __name__ == "__main__":
2023-01-11T21:05:10.7750554Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7750676Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7750859Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7751048Z     arg1_1 = rand_strided((1, ), (1, ), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7751158Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7751163Z 
2023-01-11T21:05:10.7751229Z ok (0.031s)
2023-01-11T21:05:10.7751690Z   test_cpu_transposed_dense (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7751815Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7752070Z [2023-01-11 21:04:58,092] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 559
2023-01-11T21:05:10.7752331Z [2023-01-11 21:05:00,760] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 559
2023-01-11T21:05:10.7752339Z 
2023-01-11T21:05:10.7752430Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7752487Z import torch
2023-01-11T21:05:10.7752555Z import random
2023-01-11T21:05:10.7752671Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7752792Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7752797Z 
2023-01-11T21:05:10.7752873Z aten = torch.ops.aten
2023-01-11T21:05:10.7753006Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7753095Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7753100Z 
2023-01-11T21:05:10.7753104Z 
2023-01-11T21:05:10.7753236Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7753426Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7753543Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7753645Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7753784Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7753844Z {
2023-01-11T21:05:10.7753939Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7753999Z     {
2023-01-11T21:05:10.7754063Z         #pragma omp for 
2023-01-11T21:05:10.7754145Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7754209Z         {
2023-01-11T21:05:10.7754286Z             #pragma GCC ivdep
2023-01-11T21:05:10.7754370Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7754431Z             {
2023-01-11T21:05:10.7754494Z                 {
2023-01-11T21:05:10.7754547Z                     {
2023-01-11T21:05:10.7754649Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:05:10.7754749Z                         auto tmp1 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7754842Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7754935Z                         out_ptr0[i0 + (10*i1)] = tmp2;
2023-01-11T21:05:10.7755001Z                     }
2023-01-11T21:05:10.7755063Z                 }
2023-01-11T21:05:10.7755112Z             }
2023-01-11T21:05:10.7755171Z         }
2023-01-11T21:05:10.7755231Z     }
2023-01-11T21:05:10.7755292Z }
2023-01-11T21:05:10.7755429Z ''')
2023-01-11T21:05:10.7755434Z 
2023-01-11T21:05:10.7755438Z 
2023-01-11T21:05:10.7755526Z async_compile.wait(globals())
2023-01-11T21:05:10.7755597Z del async_compile
2023-01-11T21:05:10.7755601Z 
2023-01-11T21:05:10.7755656Z def call(args):
2023-01-11T21:05:10.7755730Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7755801Z     args.clear()
2023-01-11T21:05:10.7756004Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7756165Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7756232Z     del arg0_1
2023-01-11T21:05:10.7756300Z     del arg1_1
2023-01-11T21:05:10.7756356Z     return (buf0, )
2023-01-11T21:05:10.7756361Z 
2023-01-11T21:05:10.7756382Z 
2023-01-11T21:05:10.7756443Z if __name__ == "__main__":
2023-01-11T21:05:10.7756556Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7756679Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7756880Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7757077Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7757192Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7757197Z 
2023-01-11T21:05:10.7757262Z ok (2.693s)
2023-01-11T21:05:10.7757724Z   test_cpu_transposed_double (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7757840Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7758096Z [2023-01-11 21:05:00,792] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 560
2023-01-11T21:05:10.7758360Z [2023-01-11 21:05:03,443] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 560
2023-01-11T21:05:10.7758365Z 
2023-01-11T21:05:10.7758458Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7758528Z import torch
2023-01-11T21:05:10.7758596Z import random
2023-01-11T21:05:10.7758715Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7758833Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7758838Z 
2023-01-11T21:05:10.7758901Z aten = torch.ops.aten
2023-01-11T21:05:10.7759034Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7759125Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7759130Z 
2023-01-11T21:05:10.7759134Z 
2023-01-11T21:05:10.7759301Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7759504Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7759624Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7759731Z                        const double* __restrict__ in_ptr1,
2023-01-11T21:05:10.7759831Z                        double* __restrict__ out_ptr0)
2023-01-11T21:05:10.7759879Z {
2023-01-11T21:05:10.7759973Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7760033Z     {
2023-01-11T21:05:10.7760109Z         #pragma omp for 
2023-01-11T21:05:10.7760189Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7760250Z         {
2023-01-11T21:05:10.7760327Z             #pragma GCC ivdep
2023-01-11T21:05:10.7760399Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7760460Z             {
2023-01-11T21:05:10.7760522Z                 {
2023-01-11T21:05:10.7760587Z                     {
2023-01-11T21:05:10.7760815Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:05:10.7760916Z                         auto tmp2 = in_ptr1[i1 + (10*i0)];
2023-01-11T21:05:10.7761084Z                         auto tmp1 = static_cast<double>(tmp0);
2023-01-11T21:05:10.7761167Z                         auto tmp3 = tmp1 + tmp2;
2023-01-11T21:05:10.7761262Z                         out_ptr0[i0 + (10*i1)] = tmp3;
2023-01-11T21:05:10.7761329Z                     }
2023-01-11T21:05:10.7761392Z                 }
2023-01-11T21:05:10.7761456Z             }
2023-01-11T21:05:10.7761517Z         }
2023-01-11T21:05:10.7761579Z     }
2023-01-11T21:05:10.7761624Z }
2023-01-11T21:05:10.7761704Z ''')
2023-01-11T21:05:10.7761709Z 
2023-01-11T21:05:10.7761713Z 
2023-01-11T21:05:10.7761802Z async_compile.wait(globals())
2023-01-11T21:05:10.7761874Z del async_compile
2023-01-11T21:05:10.7761878Z 
2023-01-11T21:05:10.7761949Z def call(args):
2023-01-11T21:05:10.7762022Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7762094Z     args.clear()
2023-01-11T21:05:10.7762284Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7762448Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7762519Z     del arg0_1
2023-01-11T21:05:10.7762585Z     del arg1_1
2023-01-11T21:05:10.7762654Z     return (buf0, )
2023-01-11T21:05:10.7762660Z 
2023-01-11T21:05:10.7762664Z 
2023-01-11T21:05:10.7762737Z if __name__ == "__main__":
2023-01-11T21:05:10.7762848Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7762968Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7763152Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7763346Z     arg1_1 = rand_strided((10, 10), (10, 1), device='cpu', dtype=torch.float64)
2023-01-11T21:05:10.7763459Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7763463Z 
2023-01-11T21:05:10.7763531Z ok (2.683s)
2023-01-11T21:05:10.7763993Z   test_cpu_transposed_int (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7764117Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7764376Z [2023-01-11 21:05:03,476] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 561
2023-01-11T21:05:10.7764638Z [2023-01-11 21:05:06,125] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 561
2023-01-11T21:05:10.7764644Z 
2023-01-11T21:05:10.7764735Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7764792Z import torch
2023-01-11T21:05:10.7764861Z import random
2023-01-11T21:05:10.7765014Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7765132Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7765137Z 
2023-01-11T21:05:10.7765214Z aten = torch.ops.aten
2023-01-11T21:05:10.7765348Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7765438Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7765443Z 
2023-01-11T21:05:10.7765448Z 
2023-01-11T21:05:10.7765582Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7765772Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7765889Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7765988Z                        const int* __restrict__ in_ptr1,
2023-01-11T21:05:10.7766087Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7766145Z {
2023-01-11T21:05:10.7766240Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7766301Z     {
2023-01-11T21:05:10.7766366Z         #pragma omp for 
2023-01-11T21:05:10.7766448Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7766510Z         {
2023-01-11T21:05:10.7766589Z             #pragma GCC ivdep
2023-01-11T21:05:10.7766704Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7766770Z             {
2023-01-11T21:05:10.7766830Z                 {
2023-01-11T21:05:10.7766883Z                     {
2023-01-11T21:05:10.7766986Z                         auto tmp0 = in_ptr0[i1 + (10*i0)];
2023-01-11T21:05:10.7767078Z                         auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7767187Z                         auto tmp2 = static_cast<float>(tmp1);
2023-01-11T21:05:10.7767279Z                         auto tmp3 = tmp0 + tmp2;
2023-01-11T21:05:10.7767373Z                         out_ptr0[i1 + (10*i0)] = tmp3;
2023-01-11T21:05:10.7767437Z                     }
2023-01-11T21:05:10.7767488Z                 }
2023-01-11T21:05:10.7767546Z             }
2023-01-11T21:05:10.7767605Z         }
2023-01-11T21:05:10.7767667Z     }
2023-01-11T21:05:10.7767725Z }
2023-01-11T21:05:10.7767801Z ''')
2023-01-11T21:05:10.7767806Z 
2023-01-11T21:05:10.7767810Z 
2023-01-11T21:05:10.7767898Z async_compile.wait(globals())
2023-01-11T21:05:10.7767958Z del async_compile
2023-01-11T21:05:10.7767963Z 
2023-01-11T21:05:10.7768030Z def call(args):
2023-01-11T21:05:10.7768105Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7768174Z     args.clear()
2023-01-11T21:05:10.7768370Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7768532Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7768598Z     del arg0_1
2023-01-11T21:05:10.7768650Z     del arg1_1
2023-01-11T21:05:10.7768720Z     return (buf0, )
2023-01-11T21:05:10.7768724Z 
2023-01-11T21:05:10.7768728Z 
2023-01-11T21:05:10.7768802Z if __name__ == "__main__":
2023-01-11T21:05:10.7768915Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7769037Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7769232Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7769422Z     arg1_1 = rand_strided((10, ), (1, ), device='cpu', dtype=torch.int32)
2023-01-11T21:05:10.7769533Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7769538Z 
2023-01-11T21:05:10.7769590Z ok (2.682s)
2023-01-11T21:05:10.7770053Z   test_cpu_transposed_strided (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7770180Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7770435Z [2023-01-11 21:05:06,312] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 562
2023-01-11T21:05:10.7770739Z [2023-01-11 21:05:08,894] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 562
2023-01-11T21:05:10.7770744Z 
2023-01-11T21:05:10.7770838Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7770906Z import torch
2023-01-11T21:05:10.7770975Z import random
2023-01-11T21:05:10.7771089Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7771196Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7771215Z 
2023-01-11T21:05:10.7771277Z aten = torch.ops.aten
2023-01-11T21:05:10.7771409Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7771498Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7771503Z 
2023-01-11T21:05:10.7771507Z 
2023-01-11T21:05:10.7771640Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7771843Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7771965Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7772068Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7772182Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7772245Z {
2023-01-11T21:05:10.7772342Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7772404Z     {
2023-01-11T21:05:10.7772479Z         #pragma omp for 
2023-01-11T21:05:10.7772559Z         for(long i0=0; i0<10; i0+=1)
2023-01-11T21:05:10.7772620Z         {
2023-01-11T21:05:10.7772685Z             #pragma GCC ivdep
2023-01-11T21:05:10.7772769Z             for(long i1=0; i1<10; i1+=1)
2023-01-11T21:05:10.7772831Z             {
2023-01-11T21:05:10.7772892Z                 {
2023-01-11T21:05:10.7772957Z                     {
2023-01-11T21:05:10.7773060Z                         auto tmp0 = in_ptr0[i0 + (10*i1)];
2023-01-11T21:05:10.7773160Z                         auto tmp1 = in_ptr1[(2*i1) + (30*i0)];
2023-01-11T21:05:10.7773243Z                         auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7773336Z                         out_ptr0[i0 + (10*i1)] = tmp2;
2023-01-11T21:05:10.7773401Z                     }
2023-01-11T21:05:10.7773463Z                 }
2023-01-11T21:05:10.7773525Z             }
2023-01-11T21:05:10.7773585Z         }
2023-01-11T21:05:10.7773643Z     }
2023-01-11T21:05:10.7773689Z }
2023-01-11T21:05:10.7773764Z ''')
2023-01-11T21:05:10.7773769Z 
2023-01-11T21:05:10.7773774Z 
2023-01-11T21:05:10.7773860Z async_compile.wait(globals())
2023-01-11T21:05:10.7773933Z del async_compile
2023-01-11T21:05:10.7773938Z 
2023-01-11T21:05:10.7774005Z def call(args):
2023-01-11T21:05:10.7774080Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7774150Z     args.clear()
2023-01-11T21:05:10.7774336Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7774496Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7774565Z     del arg0_1
2023-01-11T21:05:10.7774630Z     del arg1_1
2023-01-11T21:05:10.7774700Z     return (buf0, )
2023-01-11T21:05:10.7774705Z 
2023-01-11T21:05:10.7774709Z 
2023-01-11T21:05:10.7774784Z if __name__ == "__main__":
2023-01-11T21:05:10.7774901Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7775022Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7775206Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7775403Z     arg1_1 = rand_strided((10, 10), (30, 2), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7775517Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7775522Z 
2023-01-11T21:05:10.7775586Z ok (2.769s)
2023-01-11T21:05:10.7776055Z   test_cpu_transposed_transposed (__main__.SweepInputsCpuTest) ... inductor/test_torchinductor.py:246: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:05:10.7776213Z   buffer = torch.as_strided(x, (x.storage().size(),), (1,), 0).clone()
2023-01-11T21:05:10.7776472Z [2023-01-11 21:05:08,926] torch._inductor.compile_fx: [INFO] Step 1: torchinductor compiling FORWARDS graph 563
2023-01-11T21:05:10.7776736Z [2023-01-11 21:05:08,939] torch._inductor.compile_fx: [INFO] Step 1: torchinductor done compiling FORWARDS graph 563
2023-01-11T21:05:10.7776741Z 
2023-01-11T21:05:10.7776835Z from ctypes import c_void_p, c_long
2023-01-11T21:05:10.7776891Z import torch
2023-01-11T21:05:10.7776960Z import random
2023-01-11T21:05:10.7777078Z from torch import empty_strided, as_strided, device
2023-01-11T21:05:10.7777197Z from torch._inductor.codecache import AsyncCompile
2023-01-11T21:05:10.7777202Z 
2023-01-11T21:05:10.7777279Z aten = torch.ops.aten
2023-01-11T21:05:10.7777413Z assert_size_stride = torch._C._dynamo.guards.assert_size_stride
2023-01-11T21:05:10.7777505Z async_compile = AsyncCompile()
2023-01-11T21:05:10.7777510Z 
2023-01-11T21:05:10.7777514Z 
2023-01-11T21:05:10.7777646Z kernel_cpp_0 = async_compile.cpp('''
2023-01-11T21:05:10.7777865Z #include "/tmp/torchinductor_jenkins/77/c7773nj5pwikpmm2pwa62rcudlf7p3if7eyqb5k4sjsvewwje4le.h"
2023-01-11T21:05:10.7777984Z extern "C" void kernel(const float* __restrict__ in_ptr0,
2023-01-11T21:05:10.7778089Z                        const float* __restrict__ in_ptr1,
2023-01-11T21:05:10.7778189Z                        float* __restrict__ out_ptr0)
2023-01-11T21:05:10.7778248Z {
2023-01-11T21:05:10.7778344Z     #pragma omp parallel num_threads(4)
2023-01-11T21:05:10.7778404Z     {
2023-01-11T21:05:10.7778561Z         #pragma omp for 
2023-01-11T21:05:10.7778645Z         for(long i0=0; i0<6; i0+=1)
2023-01-11T21:05:10.7778707Z         {
2023-01-11T21:05:10.7778848Z             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + 16*i0);
2023-01-11T21:05:10.7778983Z             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + 16*i0);
2023-01-11T21:05:10.7779066Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7779159Z             tmp2.store(out_ptr0 + 16*i0);
2023-01-11T21:05:10.7779210Z         }
2023-01-11T21:05:10.7779304Z         #pragma omp for simd simdlen(8) 
2023-01-11T21:05:10.7779387Z         for(long i0=96; i0<100; i0+=1)
2023-01-11T21:05:10.7779449Z         {
2023-01-11T21:05:10.7779531Z             auto tmp0 = in_ptr0[i0];
2023-01-11T21:05:10.7779612Z             auto tmp1 = in_ptr1[i0];
2023-01-11T21:05:10.7779693Z             auto tmp2 = tmp0 + tmp1;
2023-01-11T21:05:10.7779759Z             out_ptr0[i0] = tmp2;
2023-01-11T21:05:10.7779819Z         }
2023-01-11T21:05:10.7779879Z     }
2023-01-11T21:05:10.7779938Z }
2023-01-11T21:05:10.7780021Z ''')
2023-01-11T21:05:10.7780027Z 
2023-01-11T21:05:10.7780030Z 
2023-01-11T21:05:10.7780118Z async_compile.wait(globals())
2023-01-11T21:05:10.7780190Z del async_compile
2023-01-11T21:05:10.7780197Z 
2023-01-11T21:05:10.7780266Z def call(args):
2023-01-11T21:05:10.7780327Z     arg0_1, arg1_1 = args
2023-01-11T21:05:10.7780396Z     args.clear()
2023-01-11T21:05:10.7780597Z     buf0 = empty_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7780758Z     kernel_cpp_0(c_void_p(arg0_1.data_ptr()), c_void_p(arg1_1.data_ptr()), c_void_p(buf0.data_ptr()))
2023-01-11T21:05:10.7780827Z     del arg0_1
2023-01-11T21:05:10.7780890Z     del arg1_1
2023-01-11T21:05:10.7780948Z     return (buf0, )
2023-01-11T21:05:10.7780966Z 
2023-01-11T21:05:10.7780970Z 
2023-01-11T21:05:10.7781030Z if __name__ == "__main__":
2023-01-11T21:05:10.7781141Z     from torch._dynamo.testing import rand_strided
2023-01-11T21:05:10.7781261Z     from torch._inductor.utils import print_performance
2023-01-11T21:05:10.7781458Z     arg0_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7781653Z     arg1_1 = rand_strided((10, 10), (1, 10), device='cpu', dtype=torch.float32)
2023-01-11T21:05:10.7781806Z     print_performance(lambda: call([arg0_1, arg1_1]))
2023-01-11T21:05:10.7781811Z 
2023-01-11T21:05:10.7781877Z ok (0.041s)
2023-01-11T21:05:10.7782025Z   test_indexing_join (__main__.TestIndexingSimplification) ... ok (0.083s)
2023-01-11T21:05:10.7782175Z   test_indexing_simplification (__main__.TestIndexingSimplification) ... ok (0.088s)
2023-01-11T21:05:10.7782180Z 
2023-01-11T21:05:10.7782377Z ----------------------------------------------------------------------
2023-01-11T21:05:10.7782454Z Ran 362 tests in 1295.323s
2023-01-11T21:05:10.7782459Z 
2023-01-11T21:05:10.7782528Z OK (skipped=20)
2023-01-11T21:05:10.7782533Z 
2023-01-11T21:05:10.7782615Z Generating XML reports...
2023-01-11T21:05:10.7782924Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-CPUReproTests-20230111204333.xml
2023-01-11T21:05:10.7783210Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-CpuTests-20230111204333.xml
2023-01-11T21:05:10.7783525Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-ExprPrinterTests-20230111204333.xml
2023-01-11T21:05:10.7783867Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-SweepInputsCpuTest-20230111204333.xml
2023-01-11T21:05:10.7784201Z Generated XML report: test-reports/python-unittest/inductor.test_torchinductor/TEST-TestIndexingSimplification-20230111204333.xml
2023-01-11T21:05:10.7784207Z 
2023-01-11T21:05:10.7784575Z ##[endgroup]
2023-01-11T21:05:10.7784904Z FINISHED PRINTING LOG FILE of inductor/test_torchinductor (/var/lib/jenkins/workspace/test/test-reports/inductor-test_torchinductor_1hiiowhq)
2023-01-11T21:05:10.7784910Z 
2023-01-11T21:05:11.0725765Z Running test_sparse_csr ... [2023-01-11 21:05:11.072164]
2023-01-11T21:05:11.0729726Z Executing ['/opt/conda/bin/python', '-bb', 'test_sparse_csr.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:05:11.072708]
2023-01-11T21:22:05.4102520Z 
2023-01-11T21:22:05.4103095Z Expand the folded group to see the log file of test_sparse_csr
2023-01-11T21:22:05.4103946Z ##[group]PRINTING LOG FILE of test_sparse_csr (/var/lib/jenkins/workspace/test/test-reports/test_sparse_csr_1aons3sa)
2023-01-11T21:22:05.4166257Z Test results will be stored in test-reports/python-unittest/test_sparse_csr
2023-01-11T21:22:05.4166623Z 
2023-01-11T21:22:05.4168432Z Running tests...
2023-01-11T21:22:05.4169018Z ----------------------------------------------------------------------
2023-01-11T21:22:05.4170723Z   test_add_cpu_float32 (__main__.TestSparseCSRCPU) ... /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py:2430: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:56.)
2023-01-11T21:22:05.4171913Z   values, size=size, dtype=dtype, layout=layout, device=device)
2023-01-11T21:22:05.4172280Z ok (0.259s)
2023-01-11T21:22:05.4172583Z   test_add_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.206s)
2023-01-11T21:22:05.4173074Z   test_addmm_all_sparse_csr_SparseCSC_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.178s)
2023-01-11T21:22:05.4173577Z   test_addmm_all_sparse_csr_SparseCSC_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.157s)
2023-01-11T21:22:05.4174020Z   test_addmm_all_sparse_csr_SparseCSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.147s)
2023-01-11T21:22:05.4174518Z   test_addmm_all_sparse_csr_SparseCSC_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.151s)
2023-01-11T21:22:05.4175012Z   test_addmm_all_sparse_csr_SparseCSR_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.135s)
2023-01-11T21:22:05.4261022Z   test_addmm_all_sparse_csr_SparseCSR_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.117s)
2023-01-11T21:22:05.4261633Z   test_addmm_all_sparse_csr_SparseCSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.107s)
2023-01-11T21:22:05.4262443Z   test_addmm_all_sparse_csr_SparseCSR_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.116s)
2023-01-11T21:22:05.4262961Z   test_addmm_dense_result_SparseCSC_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.127s)
2023-01-11T21:22:05.4263512Z   test_addmm_dense_result_SparseCSC_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.129s)
2023-01-11T21:22:05.4264029Z   test_addmm_dense_result_SparseCSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.120s)
2023-01-11T21:22:05.4264573Z   test_addmm_dense_result_SparseCSC_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.118s)
2023-01-11T21:22:05.4265117Z   test_addmm_dense_result_SparseCSR_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.116s)
2023-01-11T21:22:05.4265704Z   test_addmm_dense_result_SparseCSR_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.093s)
2023-01-11T21:22:05.4266291Z   test_addmm_dense_result_SparseCSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.085s)
2023-01-11T21:22:05.4266912Z   test_addmm_dense_result_SparseCSR_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.083s)
2023-01-11T21:22:05.4267475Z   test_addmm_errors_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.088s)
2023-01-11T21:22:05.4268178Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4268799Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4269408Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4269994Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4270473Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4271021Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4271596Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4272150Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4272687Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4273237Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4273821Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4274337Z   test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4274874Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4275409Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4275974Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4276505Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4277003Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4277509Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4278010Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.046s)
2023-01-11T21:22:05.4278536Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4279090Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4279603Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4280175Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4281285Z   test_addmm_sizes_all_sparse_csr_k_0_n_10_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4281803Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4282358Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4282859Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4283369Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4283859Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4284373Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4284833Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4285458Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4285957Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4286433Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4286895Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4287408Z   test_addmm_sizes_all_sparse_csr_k_0_n_1_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4287935Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4288451Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4288948Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4289455Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4289907Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4290455Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4290940Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4291438Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4291936Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4292476Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4292982Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4293505Z   test_addmm_sizes_all_sparse_csr_k_1_n_0_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.039s)
2023-01-11T21:22:05.4294039Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4294520Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4294965Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4295422Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4295880Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4296470Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4296928Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4297536Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4298096Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.039s)
2023-01-11T21:22:05.4298603Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.039s)
2023-01-11T21:22:05.4299131Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.039s)
2023-01-11T21:22:05.4299716Z   test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4300260Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.039s)
2023-01-11T21:22:05.4300820Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4301321Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4301789Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4302251Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4302695Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4303157Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4303619Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4304081Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4304532Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4304998Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4305464Z   test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4305936Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4306393Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4306869Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4307326Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.043s)
2023-01-11T21:22:05.4307832Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4308306Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4308771Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4309229Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4309684Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4310124Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4310574Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4311099Z   test_addmm_sizes_all_sparse_csr_k_8_n_0_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4311544Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4311986Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4312439Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4312884Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4313316Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4313768Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4314224Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4314675Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4315177Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4315641Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4316092Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.052s)
2023-01-11T21:22:05.4316545Z   test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4316976Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4317427Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4317888Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4318319Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4318772Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_1_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.039s)
2023-01-11T21:22:05.4319230Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_1_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4319680Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4320105Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4320547Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.045s)
2023-01-11T21:22:05.4321138Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.038s)
2023-01-11T21:22:05.4321591Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4322029Z   test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4322476Z   test_autograd_dense_output_addmm_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.127s)
2023-01-11T21:22:05.4322914Z   test_autograd_dense_output_addmv_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.100s)
2023-01-11T21:22:05.4323327Z   test_autograd_dense_output_mm_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.015s)
2023-01-11T21:22:05.4323765Z   test_autograd_dense_output_mv_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.014s)
2023-01-11T21:22:05.4324206Z   test_autograd_sparse_csr_unary_abs_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4324653Z   test_autograd_sparse_csr_unary_abs_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4325194Z   test_autograd_sparse_csr_unary_angle_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op angle not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4325952Z   test_autograd_sparse_csr_unary_angle_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op angle not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4326593Z   test_autograd_sparse_csr_unary_asin_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op asin not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4327233Z   test_autograd_sparse_csr_unary_asin_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op asin not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4327851Z   test_autograd_sparse_csr_unary_asinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op asinh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4328492Z   test_autograd_sparse_csr_unary_asinh_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op asinh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4329189Z   test_autograd_sparse_csr_unary_atan_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op atan not supported with CSR input and autograd (0.006s)
2023-01-11T21:22:05.4329817Z   test_autograd_sparse_csr_unary_atan_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op atan not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4330446Z   test_autograd_sparse_csr_unary_atanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op atanh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4331069Z   test_autograd_sparse_csr_unary_atanh_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op atanh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4331692Z   test_autograd_sparse_csr_unary_ceil_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op ceil not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4332247Z   test_autograd_sparse_csr_unary_conj_physical_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4332730Z   test_autograd_sparse_csr_unary_conj_physical_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4333190Z   test_autograd_sparse_csr_unary_deg2rad_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4333741Z   test_autograd_sparse_csr_unary_erf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op erf not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4334366Z   test_autograd_sparse_csr_unary_erfinv_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op erfinv not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4335002Z   test_autograd_sparse_csr_unary_expm1_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op expm1 not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4335615Z   test_autograd_sparse_csr_unary_floor_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op floor not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4336152Z   test_autograd_sparse_csr_unary_frac_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4336701Z   test_autograd_sparse_csr_unary_isinf_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op isinf not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4337438Z   test_autograd_sparse_csr_unary_isinf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op isinf not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4338059Z   test_autograd_sparse_csr_unary_isnan_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op isnan not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4338710Z   test_autograd_sparse_csr_unary_isnan_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op isnan not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4339412Z   test_autograd_sparse_csr_unary_isneginf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op isneginf not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4340065Z   test_autograd_sparse_csr_unary_isposinf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op isposinf not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4341487Z   test_autograd_sparse_csr_unary_log1p_cpu_complex128 (__main__.TestSparseCSRCPU) ... /opt/conda/lib/python3.7/site-packages/torch/autograd/__init__.py:199: UserWarning: log1p_backward: received self with sparse layout, but backward requires materialization of a dense tensor with this shape (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/autograd/FunctionsManual.cpp:4679.)
2023-01-11T21:22:05.4342394Z   allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass
2023-01-11T21:22:05.4342717Z ok (0.010s)
2023-01-11T21:22:05.4343065Z   test_autograd_sparse_csr_unary_log1p_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4343535Z   test_autograd_sparse_csr_unary_neg_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4344042Z   test_autograd_sparse_csr_unary_neg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4344496Z   test_autograd_sparse_csr_unary_nn_functional_relu_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4344973Z   test_autograd_sparse_csr_unary_positive_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4345436Z   test_autograd_sparse_csr_unary_positive_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4345878Z   test_autograd_sparse_csr_unary_rad2deg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4346433Z   test_autograd_sparse_csr_unary_round_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op round not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4347073Z   test_autograd_sparse_csr_unary_sgn_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sgn not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4347713Z   test_autograd_sparse_csr_unary_sgn_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sgn not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4348329Z   test_autograd_sparse_csr_unary_sign_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sign not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4348935Z   test_autograd_sparse_csr_unary_signbit_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op signbit not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4349560Z   test_autograd_sparse_csr_unary_sin_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sin not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4350189Z   test_autograd_sparse_csr_unary_sin_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sin not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4350816Z   test_autograd_sparse_csr_unary_sinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sinh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4351418Z   test_autograd_sparse_csr_unary_sinh_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sinh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4352051Z   test_autograd_sparse_csr_unary_sqrt_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sqrt not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4352679Z   test_autograd_sparse_csr_unary_sqrt_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op sqrt not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4353300Z   test_autograd_sparse_csr_unary_tan_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op tan not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4354022Z   test_autograd_sparse_csr_unary_tan_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op tan not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4354642Z   test_autograd_sparse_csr_unary_tanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op tanh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4355286Z   test_autograd_sparse_csr_unary_tanh_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op tanh not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4355921Z   test_autograd_sparse_csr_unary_trunc_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Unary op trunc not supported with CSR input and autograd (0.002s)
2023-01-11T21:22:05.4356468Z   test_baddbmm_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.003s)
2023-01-11T21:22:05.4356936Z   test_baddbmm_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.003s)
2023-01-11T21:22:05.4357406Z   test_baddbmm_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.003s)
2023-01-11T21:22:05.4357926Z   test_baddbmm_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.003s)
2023-01-11T21:22:05.4358403Z   test_block_addmm_block_size_2_int32_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.155s)
2023-01-11T21:22:05.4358913Z   test_block_addmm_block_size_2_int32_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.155s)
2023-01-11T21:22:05.4359414Z   test_block_addmm_block_size_2_int32_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.135s)
2023-01-11T21:22:05.4359915Z   test_block_addmm_block_size_2_int32_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.155s)
2023-01-11T21:22:05.4360406Z   test_block_addmm_block_size_2_int32_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.165s)
2023-01-11T21:22:05.4361027Z   test_block_addmm_block_size_2_int32_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.171s)
2023-01-11T21:22:05.4361526Z   test_block_addmm_block_size_2_int32_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.147s)
2023-01-11T21:22:05.4362012Z   test_block_addmm_block_size_2_int32_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.145s)
2023-01-11T21:22:05.4362488Z   test_block_addmm_block_size_2_int64_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.166s)
2023-01-11T21:22:05.4362992Z   test_block_addmm_block_size_2_int64_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.175s)
2023-01-11T21:22:05.4363483Z   test_block_addmm_block_size_2_int64_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.154s)
2023-01-11T21:22:05.4363965Z   test_block_addmm_block_size_2_int64_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.144s)
2023-01-11T21:22:05.4364448Z   test_block_addmm_block_size_2_int64_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.184s)
2023-01-11T21:22:05.4364942Z   test_block_addmm_block_size_2_int64_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.176s)
2023-01-11T21:22:05.4365433Z   test_block_addmm_block_size_2_int64_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.155s)
2023-01-11T21:22:05.4365915Z   test_block_addmm_block_size_2_int64_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.150s)
2023-01-11T21:22:05.4366394Z   test_block_addmm_block_size_3_int32_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.185s)
2023-01-11T21:22:05.4366899Z   test_block_addmm_block_size_3_int32_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.162s)
2023-01-11T21:22:05.4367394Z   test_block_addmm_block_size_3_int32_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.146s)
2023-01-11T21:22:05.4367993Z   test_block_addmm_block_size_3_int32_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.145s)
2023-01-11T21:22:05.4368475Z   test_block_addmm_block_size_3_int32_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.176s)
2023-01-11T21:22:05.4368979Z   test_block_addmm_block_size_3_int32_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.179s)
2023-01-11T21:22:05.4369483Z   test_block_addmm_block_size_3_int32_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.177s)
2023-01-11T21:22:05.4369947Z   test_block_addmm_block_size_3_int32_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.152s)
2023-01-11T21:22:05.4370448Z   test_block_addmm_block_size_3_int64_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.166s)
2023-01-11T21:22:05.4370945Z   test_block_addmm_block_size_3_int64_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.174s)
2023-01-11T21:22:05.4371442Z   test_block_addmm_block_size_3_int64_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.165s)
2023-01-11T21:22:05.4371982Z   test_block_addmm_block_size_3_int64_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.143s)
2023-01-11T21:22:05.4372488Z   test_block_addmm_block_size_3_int64_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.205s)
2023-01-11T21:22:05.4372998Z   test_block_addmm_block_size_3_int64_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.192s)
2023-01-11T21:22:05.4373492Z   test_block_addmm_block_size_3_int64_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.160s)
2023-01-11T21:22:05.4373964Z   test_block_addmm_block_size_3_int64_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.154s)
2023-01-11T21:22:05.4374454Z   test_block_addmv_block_size_2_int32_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.019s)
2023-01-11T21:22:05.4374962Z   test_block_addmv_block_size_2_int32_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4375470Z   test_block_addmv_block_size_2_int32_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4375939Z   test_block_addmv_block_size_2_int32_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4376435Z   test_block_addmv_block_size_2_int32_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4376940Z   test_block_addmv_block_size_2_int32_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4377511Z   test_block_addmv_block_size_2_int32_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4377997Z   test_block_addmv_block_size_2_int32_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4378493Z   test_block_addmv_block_size_2_int64_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4378998Z   test_block_addmv_block_size_2_int64_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4379510Z   test_block_addmv_block_size_2_int64_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4379984Z   test_block_addmv_block_size_2_int64_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4380477Z   test_block_addmv_block_size_2_int64_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4380971Z   test_block_addmv_block_size_2_int64_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4381460Z   test_block_addmv_block_size_2_int64_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4381950Z   test_block_addmv_block_size_2_int64_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4382532Z   test_block_addmv_block_size_3_int32_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4383040Z   test_block_addmv_block_size_3_int32_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4383527Z   test_block_addmv_block_size_3_int32_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4384012Z   test_block_addmv_block_size_3_int32_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4384513Z   test_block_addmv_block_size_3_int32_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4385015Z   test_block_addmv_block_size_3_int32_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4385505Z   test_block_addmv_block_size_3_int32_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4385997Z   test_block_addmv_block_size_3_int32_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.016s)
2023-01-11T21:22:05.4386548Z   test_block_addmv_block_size_3_int64_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4387056Z   test_block_addmv_block_size_3_int64_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.042s)
2023-01-11T21:22:05.4387539Z   test_block_addmv_block_size_3_int64_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4388035Z   test_block_addmv_block_size_3_int64_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4388535Z   test_block_addmv_block_size_3_int64_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4389043Z   test_block_addmv_block_size_3_int64_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4389520Z   test_block_addmv_block_size_3_int64_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4390009Z   test_block_addmv_block_size_3_int64_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4390750Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... test_sparse_csr.py:1621: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.
2023-01-11T21:22:05.4391487Z torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.
2023-01-11T21:22:05.4391869Z X = torch.triangular_solve(B, A).solution
2023-01-11T21:22:05.4392155Z should be replaced with
2023-01-11T21:22:05.4392614Z X = torch.linalg.solve_triangular(A, B). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:2225.)
2023-01-11T21:22:05.4393058Z   unitriangular=unitriangular)
2023-01-11T21:22:05.4393315Z ok (0.031s)
2023-01-11T21:22:05.4393718Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.026s)
2023-01-11T21:22:05.4394253Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.024s)
2023-01-11T21:22:05.4394777Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.023s)
2023-01-11T21:22:05.4395310Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4395833Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4396359Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.024s)
2023-01-11T21:22:05.4396934Z   test_block_triangular_solve_block_size_2_int32_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4397465Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4397998Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4398526Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4399031Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4399564Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4400097Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4400824Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4401337Z   test_block_triangular_solve_block_size_2_int64_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4401861Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4402396Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4402930Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4403456Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4403991Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4404532Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4405040Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4405569Z   test_block_triangular_solve_block_size_3_int32_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4406085Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4406612Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4407129Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4407661Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.028s)
2023-01-11T21:22:05.4408249Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4408799Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4409353Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4409886Z   test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4410379Z   test_bmm_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.002s)
2023-01-11T21:22:05.4410925Z   test_bmm_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.002s)
2023-01-11T21:22:05.4411370Z   test_bmm_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.002s)
2023-01-11T21:22:05.4411821Z   test_bmm_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.002s)
2023-01-11T21:22:05.4412295Z   test_compressed_layout_conversions_coverage_SparseBSC_SparseBSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4412760Z This test performs a smoke test for covered conversion and verifies ... ok (0.074s)
2023-01-11T21:22:05.4413213Z   test_compressed_layout_conversions_coverage_SparseBSC_SparseBSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4413669Z This test performs a smoke test for covered conversion and verifies ... ok (0.077s)
2023-01-11T21:22:05.4414133Z   test_compressed_layout_conversions_coverage_SparseBSC_SparseCSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4414598Z This test performs a smoke test for covered conversion and verifies ... ok (0.036s)
2023-01-11T21:22:05.4415066Z   test_compressed_layout_conversions_coverage_SparseBSC_SparseCSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4415568Z This test performs a smoke test for covered conversion and verifies ... ok (0.035s)
2023-01-11T21:22:05.4416029Z   test_compressed_layout_conversions_coverage_SparseBSR_SparseBSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4416476Z This test performs a smoke test for covered conversion and verifies ... ok (0.077s)
2023-01-11T21:22:05.4416930Z   test_compressed_layout_conversions_coverage_SparseBSR_SparseBSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4417495Z This test performs a smoke test for covered conversion and verifies ... ok (0.071s)
2023-01-11T21:22:05.4417950Z   test_compressed_layout_conversions_coverage_SparseBSR_SparseCSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4418391Z This test performs a smoke test for covered conversion and verifies ... ok (0.035s)
2023-01-11T21:22:05.4418852Z   test_compressed_layout_conversions_coverage_SparseBSR_SparseCSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4419309Z This test performs a smoke test for covered conversion and verifies ... ok (0.029s)
2023-01-11T21:22:05.4419753Z   test_compressed_layout_conversions_coverage_SparseCSC_SparseBSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4420197Z This test performs a smoke test for covered conversion and verifies ... ok (0.037s)
2023-01-11T21:22:05.4420645Z   test_compressed_layout_conversions_coverage_SparseCSC_SparseBSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4421095Z This test performs a smoke test for covered conversion and verifies ... ok (0.036s)
2023-01-11T21:22:05.4421535Z   test_compressed_layout_conversions_coverage_SparseCSC_SparseCSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4421986Z This test performs a smoke test for covered conversion and verifies ... ok (0.010s)
2023-01-11T21:22:05.4422439Z   test_compressed_layout_conversions_coverage_SparseCSC_SparseCSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4422886Z This test performs a smoke test for covered conversion and verifies ... ok (0.014s)
2023-01-11T21:22:05.4423346Z   test_compressed_layout_conversions_coverage_SparseCSR_SparseBSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4423797Z This test performs a smoke test for covered conversion and verifies ... ok (0.037s)
2023-01-11T21:22:05.4424250Z   test_compressed_layout_conversions_coverage_SparseCSR_SparseBSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4424688Z This test performs a smoke test for covered conversion and verifies ... ok (0.036s)
2023-01-11T21:22:05.4425139Z   test_compressed_layout_conversions_coverage_SparseCSR_SparseCSC_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4425589Z This test performs a smoke test for covered conversion and verifies ... ok (0.014s)
2023-01-11T21:22:05.4426033Z   test_compressed_layout_conversions_coverage_SparseCSR_SparseCSR_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4426566Z This test performs a smoke test for covered conversion and verifies ... ok (0.009s)
2023-01-11T21:22:05.4426989Z   test_coo_csr_conversion_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4427405Z   test_coo_csr_conversion_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4427816Z   test_coo_csr_conversion_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.016s)
2023-01-11T21:22:05.4428231Z   test_coo_csr_conversion_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4428643Z   test_coo_csr_conversion_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4429055Z   test_coo_csr_conversion_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4429447Z   test_coo_csr_conversion_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4429869Z   test_coo_csr_conversion_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4430278Z   test_coo_csr_conversion_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4430666Z   test_coo_csr_conversion_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4431115Z   test_coo_csr_conversion_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4431518Z   test_coo_csr_conversion_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4431949Z   test_coo_to_csr_convert_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.031s)
2023-01-11T21:22:05.4432342Z   test_csr_coo_conversion_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4432746Z   test_csr_coo_conversion_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4433165Z   test_csr_coo_conversion_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4433570Z   test_csr_coo_conversion_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4433987Z   test_csr_coo_conversion_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4434392Z   test_csr_coo_conversion_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4434798Z   test_csr_coo_conversion_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4435185Z   test_csr_coo_conversion_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4435580Z   test_csr_coo_conversion_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4435979Z   test_csr_coo_conversion_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4436365Z   test_csr_coo_conversion_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4436762Z   test_csr_coo_conversion_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4437157Z   test_csr_double_to_sparse_csr_cpu (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4437536Z   test_csr_is_contiguous_cpu (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4437935Z   test_csr_matvec_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.081s)
2023-01-11T21:22:05.4438332Z   test_csr_matvec_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.079s)
2023-01-11T21:22:05.4438726Z   test_csr_matvec_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.078s)
2023-01-11T21:22:05.4439093Z   test_csr_matvec_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.079s)
2023-01-11T21:22:05.4439874Z   test_csr_storage_cpu (__main__.TestSparseCSRCPU) ... test_sparse_csr.py:924: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:22:05.4440541Z   a.storage()
2023-01-11T21:22:05.4440908Z ok (0.006s)
2023-01-11T21:22:05.4441194Z   test_csr_stride_cpu (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4441698Z   test_csr_to_block_csr_blocksize_2_cpu_float64_int32 (__main__.TestSparseCSRCPU) ... ok (0.027s)
2023-01-11T21:22:05.4442142Z   test_csr_to_block_csr_blocksize_2_cpu_float64_int64 (__main__.TestSparseCSRCPU) ... ok (0.024s)
2023-01-11T21:22:05.4442569Z   test_csr_to_block_csr_blocksize_4_cpu_float64_int32 (__main__.TestSparseCSRCPU) ... ok (0.041s)
2023-01-11T21:22:05.4443007Z   test_csr_to_block_csr_blocksize_4_cpu_float64_int64 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4443435Z   test_csr_to_block_csr_errors_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.042s)
2023-01-11T21:22:05.4443888Z   test_dense_to_from_sparse_compressed_SparseBSC_Batched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4444305Z This test tests conversion from dense to/from CSR and CSC ... ok (0.023s)
2023-01-11T21:22:05.4444746Z   test_dense_to_from_sparse_compressed_SparseBSC_Batched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4445176Z This test tests conversion from dense to/from CSR and CSC ... ok (0.856s)
2023-01-11T21:22:05.4445614Z   test_dense_to_from_sparse_compressed_SparseBSC_NonBatched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4446092Z This test tests conversion from dense to/from CSR and CSC ... ok (0.025s)
2023-01-11T21:22:05.4446533Z   test_dense_to_from_sparse_compressed_SparseBSC_NonBatched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4446966Z This test tests conversion from dense to/from CSR and CSC ... ok (0.041s)
2023-01-11T21:22:05.4447376Z   test_dense_to_from_sparse_compressed_SparseBSR_Batched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4447804Z This test tests conversion from dense to/from CSR and CSC ... ok (0.021s)
2023-01-11T21:22:05.4448233Z   test_dense_to_from_sparse_compressed_SparseBSR_Batched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4448652Z This test tests conversion from dense to/from CSR and CSC ... ok (0.773s)
2023-01-11T21:22:05.4449075Z   test_dense_to_from_sparse_compressed_SparseBSR_NonBatched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4449505Z This test tests conversion from dense to/from CSR and CSC ... ok (0.019s)
2023-01-11T21:22:05.4449957Z   test_dense_to_from_sparse_compressed_SparseBSR_NonBatched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4450378Z This test tests conversion from dense to/from CSR and CSC ... ok (0.041s)
2023-01-11T21:22:05.4450804Z   test_dense_to_from_sparse_compressed_SparseCSC_Batched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4451222Z This test tests conversion from dense to/from CSR and CSC ... ok (0.026s)
2023-01-11T21:22:05.4451653Z   test_dense_to_from_sparse_compressed_SparseCSC_Batched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4452063Z This test tests conversion from dense to/from CSR and CSC ... ok (0.179s)
2023-01-11T21:22:05.4452491Z   test_dense_to_from_sparse_compressed_SparseCSC_NonBatched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4452922Z This test tests conversion from dense to/from CSR and CSC ... ok (0.042s)
2023-01-11T21:22:05.4453346Z   test_dense_to_from_sparse_compressed_SparseCSC_NonBatched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4453782Z This test tests conversion from dense to/from CSR and CSC ... ok (0.019s)
2023-01-11T21:22:05.4454211Z   test_dense_to_from_sparse_compressed_SparseCSR_Batched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4454635Z This test tests conversion from dense to/from CSR and CSC ... ok (0.023s)
2023-01-11T21:22:05.4455050Z   test_dense_to_from_sparse_compressed_SparseCSR_Batched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4455474Z This test tests conversion from dense to/from CSR and CSC ... ok (0.177s)
2023-01-11T21:22:05.4455900Z   test_dense_to_from_sparse_compressed_SparseCSR_NonBatched_Hybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4456315Z This test tests conversion from dense to/from CSR and CSC ... ok (0.019s)
2023-01-11T21:22:05.4456750Z   test_dense_to_from_sparse_compressed_SparseCSR_NonBatched_NonHybrid_cpu (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4457347Z This test tests conversion from dense to/from CSR and CSC ... ok (0.018s)
2023-01-11T21:22:05.4457771Z   test_direct_coo_csr_conversion_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4458200Z   test_direct_coo_csr_conversion_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4458631Z   test_direct_coo_csr_conversion_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4459058Z   test_direct_coo_csr_conversion_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4459467Z   test_direct_coo_csr_conversion_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4459885Z   test_direct_coo_csr_conversion_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4460305Z   test_direct_coo_csr_conversion_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4460726Z   test_direct_coo_csr_conversion_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4461135Z   test_direct_coo_csr_conversion_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4461615Z   test_exercise_detach_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4462018Z   test_exercise_detach_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4462427Z   test_exercise_detach_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4462820Z   test_exercise_detach_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4463224Z   test_exercise_detach_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4463627Z   test_exercise_detach_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4464009Z   test_exercise_detach_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4464403Z   test_exercise_detach_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4464798Z   test_exercise_detach_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4465181Z   test_exercise_detach_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4465581Z   test_exercise_detach_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4465972Z   test_exercise_detach_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4466414Z   test_matmul_device_mismatch_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:22:05.4466855Z   test_mm_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Only CUDA 11+ is supported (0.006s)
2023-01-11T21:22:05.4467267Z   test_mm_errors_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.049s)
2023-01-11T21:22:05.4467643Z   test_mul_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.491s)
2023-01-11T21:22:05.4467995Z   test_mul_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.513s)
2023-01-11T21:22:05.4468426Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (13.962s)
2023-01-11T21:22:05.4468911Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_bool (__main__.TestSparseCSRCPU) ... ok (8.059s)
2023-01-11T21:22:05.4469414Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (15.431s)
2023-01-11T21:22:05.4469883Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (15.304s)
2023-01-11T21:22:05.4470359Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (12.463s)
2023-01-11T21:22:05.4470829Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (13.853s)
2023-01-11T21:22:05.4471302Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (13.714s)
2023-01-11T21:22:05.4471752Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (11.117s)
2023-01-11T21:22:05.4472283Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (11.129s)
2023-01-11T21:22:05.4472750Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (11.239s)
2023-01-11T21:22:05.4473214Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (11.162s)
2023-01-11T21:22:05.4473671Z   test_mul_scalar_enable_hybrid_False_SparseBSC_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (11.220s)
2023-01-11T21:22:05.4474140Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (11.940s)
2023-01-11T21:22:05.4474609Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_bool (__main__.TestSparseCSRCPU) ... ok (7.039s)
2023-01-11T21:22:05.4475067Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (13.435s)
2023-01-11T21:22:05.4475570Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (13.662s)
2023-01-11T21:22:05.4476045Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (10.981s)
2023-01-11T21:22:05.4476576Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (12.009s)
2023-01-11T21:22:05.4477046Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (11.790s)
2023-01-11T21:22:05.4477513Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (9.582s)
2023-01-11T21:22:05.4477987Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (9.571s)
2023-01-11T21:22:05.4478445Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (9.382s)
2023-01-11T21:22:05.4478924Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (9.536s)
2023-01-11T21:22:05.4479420Z   test_mul_scalar_enable_hybrid_False_SparseBSR_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (9.551s)
2023-01-11T21:22:05.4479934Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (3.990s)
2023-01-11T21:22:05.4480400Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_bool (__main__.TestSparseCSRCPU) ... ok (2.252s)
2023-01-11T21:22:05.4481031Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (4.457s)
2023-01-11T21:22:05.4481516Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (4.564s)
2023-01-11T21:22:05.4481995Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (3.634s)
2023-01-11T21:22:05.4482452Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (3.942s)
2023-01-11T21:22:05.4482921Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (3.895s)
2023-01-11T21:22:05.4483389Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (3.032s)
2023-01-11T21:22:05.4483866Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (2.976s)
2023-01-11T21:22:05.4484363Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (2.934s)
2023-01-11T21:22:05.4484833Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (2.952s)
2023-01-11T21:22:05.4485292Z   test_mul_scalar_enable_hybrid_False_SparseCSC_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (2.967s)
2023-01-11T21:22:05.4485758Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (3.419s)
2023-01-11T21:22:05.4486208Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_bool (__main__.TestSparseCSRCPU) ... ok (1.909s)
2023-01-11T21:22:05.4486694Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (3.925s)
2023-01-11T21:22:05.4487394Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (3.980s)
2023-01-11T21:22:05.4487966Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (3.140s)
2023-01-11T21:22:05.4488469Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (3.408s)
2023-01-11T21:22:05.4488823Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (3.298s)
2023-01-11T21:22:05.4489168Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (2.562s)
2023-01-11T21:22:05.4489499Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (2.559s)
2023-01-11T21:22:05.4489839Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (2.506s)
2023-01-11T21:22:05.4490184Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (2.605s)
2023-01-11T21:22:05.4490525Z   test_mul_scalar_enable_hybrid_False_SparseCSR_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (2.594s)
2023-01-11T21:22:05.4491469Z   test_resize_as_sparse_compressed_SparseBSC_cpu_bool (__main__.TestSparseCSRCPU) ... /opt/conda/lib/python3.7/site-packages/torch/testing/_creation.py:167: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:22:05.4492011Z   result = torch.empty(shape, device=device, dtype=dtype)
2023-01-11T21:22:05.4492229Z ok (0.138s)
2023-01-11T21:22:05.4492496Z   test_resize_as_sparse_compressed_SparseBSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.138s)
2023-01-11T21:22:05.4492832Z   test_resize_as_sparse_compressed_SparseBSR_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.134s)
2023-01-11T21:22:05.4493190Z   test_resize_as_sparse_compressed_SparseBSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.133s)
2023-01-11T21:22:05.4493539Z   test_resize_as_sparse_compressed_SparseCSC_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.150s)
2023-01-11T21:22:05.4493877Z   test_resize_as_sparse_compressed_SparseCSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.151s)
2023-01-11T21:22:05.4494227Z   test_resize_as_sparse_compressed_SparseCSR_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.168s)
2023-01-11T21:22:05.4494578Z   test_resize_as_sparse_compressed_SparseCSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.168s)
2023-01-11T21:22:05.4494891Z   test_resize_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.044s)
2023-01-11T21:22:05.4495162Z   test_resize_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4495446Z   test_resize_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4495732Z   test_resize_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4496005Z   test_resize_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4496285Z   test_resize_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4496565Z   test_resize_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4496842Z   test_resize_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4497103Z   test_resize_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4497469Z   test_resize_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4497745Z   test_resize_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4498007Z   test_resize_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4498298Z   test_resize_errors_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.034s)
2023-01-11T21:22:05.4498596Z   test_resize_errors_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.034s)
2023-01-11T21:22:05.4498894Z   test_resize_errors_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4499221Z   test_resize_errors_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4499526Z   test_resize_errors_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.034s)
2023-01-11T21:22:05.4499823Z   test_resize_errors_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.034s)
2023-01-11T21:22:05.4500101Z   test_resize_errors_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.034s)
2023-01-11T21:22:05.4500389Z   test_resize_errors_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4500679Z   test_resize_errors_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4500949Z   test_resize_errors_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4501230Z   test_resize_errors_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4501515Z   test_resize_errors_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.035s)
2023-01-11T21:22:05.4501821Z   test_sampled_addmm_autograd_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4502136Z   test_sampled_addmm_autograd_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4502487Z   test_sampled_addmm_autograd_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.016s)
2023-01-11T21:22:05.4502806Z   test_sampled_addmm_autograd_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.015s)
2023-01-11T21:22:05.4503104Z   test_sampled_addmm_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.582s)
2023-01-11T21:22:05.4503409Z   test_sampled_addmm_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.575s)
2023-01-11T21:22:05.4503712Z   test_sampled_addmm_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.549s)
2023-01-11T21:22:05.4504009Z   test_sampled_addmm_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.508s)
2023-01-11T21:22:05.4504329Z   test_sampled_addmm_errors_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:22:05.4504694Z   test_sampled_addmm_errors_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:22:05.4505051Z   test_sampled_addmm_errors_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:22:05.4505404Z   test_sampled_addmm_errors_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:22:05.4505751Z   test_sampled_addmm_zero_sized_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:22:05.4506117Z   test_sampled_addmm_zero_sized_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:22:05.4506476Z   test_sampled_addmm_zero_sized_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.017s)
2023-01-11T21:22:05.4506822Z   test_sampled_addmm_zero_sized_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:22:05.4507155Z   test_select_SparseBSC_int32_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.063s)
2023-01-11T21:22:05.4507472Z   test_select_SparseBSC_int32_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4507793Z   test_select_SparseBSC_int32_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.061s)
2023-01-11T21:22:05.4508104Z   test_select_SparseBSC_int32_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.061s)
2023-01-11T21:22:05.4508420Z   test_select_SparseBSC_int32_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.061s)
2023-01-11T21:22:05.4508731Z   test_select_SparseBSC_int32_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.062s)
2023-01-11T21:22:05.4509033Z   test_select_SparseBSC_int32_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4509323Z   test_select_SparseBSC_int32_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4509634Z   test_select_SparseBSC_int32_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4509941Z   test_select_SparseBSC_int32_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4510265Z   test_select_SparseBSC_int32_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4510575Z   test_select_SparseBSC_int32_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4510891Z   test_select_SparseBSC_int64_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.059s)
2023-01-11T21:22:05.4511204Z   test_select_SparseBSC_int64_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4511506Z   test_select_SparseBSC_int64_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.059s)
2023-01-11T21:22:05.4511827Z   test_select_SparseBSC_int64_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4512144Z   test_select_SparseBSC_int64_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.075s)
2023-01-11T21:22:05.4512442Z   test_select_SparseBSC_int64_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4512753Z   test_select_SparseBSC_int64_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.059s)
2023-01-11T21:22:05.4513063Z   test_select_SparseBSC_int64_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.058s)
2023-01-11T21:22:05.4513401Z   test_select_SparseBSC_int64_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4513696Z   test_select_SparseBSC_int64_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.059s)
2023-01-11T21:22:05.4514002Z   test_select_SparseBSC_int64_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4514314Z   test_select_SparseBSC_int64_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.058s)
2023-01-11T21:22:05.4514611Z   test_select_SparseBSR_int32_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4514925Z   test_select_SparseBSR_int32_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4515243Z   test_select_SparseBSR_int32_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4515567Z   test_select_SparseBSR_int32_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4515872Z   test_select_SparseBSR_int32_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4516192Z   test_select_SparseBSR_int32_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4516503Z   test_select_SparseBSR_int32_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4516794Z   test_select_SparseBSR_int32_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4517103Z   test_select_SparseBSR_int32_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4517405Z   test_select_SparseBSR_int32_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4517711Z   test_select_SparseBSR_int32_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4518000Z   test_select_SparseBSR_int32_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.073s)
2023-01-11T21:22:05.4518315Z   test_select_SparseBSR_int64_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4518626Z   test_select_SparseBSR_int64_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4518928Z   test_select_SparseBSR_int64_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4519247Z   test_select_SparseBSR_int64_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4519561Z   test_select_SparseBSR_int64_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4519869Z   test_select_SparseBSR_int64_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4520162Z   test_select_SparseBSR_int64_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4520466Z   test_select_SparseBSR_int64_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4520946Z   test_select_SparseBSR_int64_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4521242Z   test_select_SparseBSR_int64_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4521619Z   test_select_SparseBSR_int64_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4521931Z   test_select_SparseBSR_int64_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4522246Z   test_select_SparseCSC_int32_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.060s)
2023-01-11T21:22:05.4522545Z   test_select_SparseCSC_int32_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.058s)
2023-01-11T21:22:05.4522860Z   test_select_SparseCSC_int32_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.059s)
2023-01-11T21:22:05.4523183Z   test_select_SparseCSC_int32_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.059s)
2023-01-11T21:22:05.4523489Z   test_select_SparseCSC_int32_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.058s)
2023-01-11T21:22:05.4523803Z   test_select_SparseCSC_int32_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.090s)
2023-01-11T21:22:05.4524116Z   test_select_SparseCSC_int32_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.058s)
2023-01-11T21:22:05.4524423Z   test_select_SparseCSC_int32_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4524751Z   test_select_SparseCSC_int32_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4525059Z   test_select_SparseCSC_int32_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4525365Z   test_select_SparseCSC_int32_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4525671Z   test_select_SparseCSC_int32_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4525969Z   test_select_SparseCSC_int64_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4526274Z   test_select_SparseCSC_int64_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4526588Z   test_select_SparseCSC_int64_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4526895Z   test_select_SparseCSC_int64_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4527209Z   test_select_SparseCSC_int64_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4527529Z   test_select_SparseCSC_int64_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4527836Z   test_select_SparseCSC_int64_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4528124Z   test_select_SparseCSC_int64_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4528431Z   test_select_SparseCSC_int64_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4528735Z   test_select_SparseCSC_int64_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4529027Z   test_select_SparseCSC_int64_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4529336Z   test_select_SparseCSC_int64_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4529651Z   test_select_SparseCSR_int32_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.070s)
2023-01-11T21:22:05.4529962Z   test_select_SparseCSR_int32_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.051s)
2023-01-11T21:22:05.4530262Z   test_select_SparseCSR_int32_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4530578Z   test_select_SparseCSR_int32_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4530891Z   test_select_SparseCSR_int32_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4531188Z   test_select_SparseCSR_int32_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4531492Z   test_select_SparseCSR_int32_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4531799Z   test_select_SparseCSR_int32_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4532104Z   test_select_SparseCSR_int32_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4532430Z   test_select_SparseCSR_int32_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4532739Z   test_select_SparseCSR_int32_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4533047Z   test_select_SparseCSR_int32_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4533343Z   test_select_SparseCSR_int64_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4533650Z   test_select_SparseCSR_int64_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.052s)
2023-01-11T21:22:05.4533962Z   test_select_SparseCSR_int64_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4534282Z   test_select_SparseCSR_int64_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.055s)
2023-01-11T21:22:05.4534583Z   test_select_SparseCSR_int64_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4534894Z   test_select_SparseCSR_int64_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.054s)
2023-01-11T21:22:05.4535204Z   test_select_SparseCSR_int64_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4535492Z   test_select_SparseCSR_int64_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.052s)
2023-01-11T21:22:05.4535829Z   test_select_SparseCSR_int64_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.070s)
2023-01-11T21:22:05.4536133Z   test_select_SparseCSR_int64_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.052s)
2023-01-11T21:22:05.4536440Z   test_select_SparseCSR_int64_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4536731Z   test_select_SparseCSR_int64_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.053s)
2023-01-11T21:22:05.4537034Z   test_sparse_add_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.067s)
2023-01-11T21:22:05.4537433Z   test_sparse_add_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.061s)
2023-01-11T21:22:05.4537719Z   test_sparse_add_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.058s)
2023-01-11T21:22:05.4538011Z   test_sparse_add_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.058s)
2023-01-11T21:22:05.4538315Z   test_sparse_add_errors_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.031s)
2023-01-11T21:22:05.4538634Z   test_sparse_add_errors_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.031s)
2023-01-11T21:22:05.4538932Z   test_sparse_add_errors_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.030s)
2023-01-11T21:22:05.4539238Z   test_sparse_add_errors_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.031s)
2023-01-11T21:22:05.4539541Z   test_sparse_addmm_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.061s)
2023-01-11T21:22:05.4539829Z   test_sparse_addmm_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.056s)
2023-01-11T21:22:05.4540123Z   test_sparse_addmm_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.051s)
2023-01-11T21:22:05.4540414Z   test_sparse_addmm_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4540716Z   test_sparse_csc_to_dense_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4541013Z   test_sparse_csc_to_dense_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4541325Z   test_sparse_csc_to_dense_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.013s)
2023-01-11T21:22:05.4541644Z   test_sparse_csc_to_dense_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.013s)
2023-01-11T21:22:05.4541947Z   test_sparse_csc_to_dense_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4542252Z   test_sparse_csc_to_dense_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4542556Z   test_sparse_csc_to_dense_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4542858Z   test_sparse_csc_to_dense_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4543147Z   test_sparse_csc_to_dense_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4543443Z   test_sparse_csc_to_dense_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4543796Z   test_sparse_csc_to_dense_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4544083Z   test_sparse_csc_to_dense_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4544394Z   test_sparse_csr_from_dense_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4544708Z   test_sparse_csr_from_dense_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4545025Z   test_sparse_csr_from_dense_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4545330Z   test_sparse_csr_from_dense_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4545644Z   test_sparse_csr_from_dense_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4545960Z   test_sparse_csr_from_dense_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4546260Z   test_sparse_csr_from_dense_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4546573Z   test_sparse_csr_from_dense_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4546882Z   test_sparse_csr_from_dense_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4547214Z   test_sparse_csr_from_dense_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4547507Z   test_sparse_csr_from_dense_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4547815Z   test_sparse_csr_from_dense_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4548121Z   test_sparse_csr_to_dense_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4548415Z   test_sparse_csr_to_dense_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4548723Z   test_sparse_csr_to_dense_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.013s)
2023-01-11T21:22:05.4549038Z   test_sparse_csr_to_dense_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.013s)
2023-01-11T21:22:05.4549347Z   test_sparse_csr_to_dense_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4549638Z   test_sparse_csr_to_dense_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4549945Z   test_sparse_csr_to_dense_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4550252Z   test_sparse_csr_to_dense_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4550539Z   test_sparse_csr_to_dense_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4550836Z   test_sparse_csr_to_dense_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4551133Z   test_sparse_csr_to_dense_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4551432Z   test_sparse_csr_to_dense_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4551736Z   test_sparse_csr_unary_inplace_abs_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4552068Z   test_sparse_csr_unary_inplace_abs_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4552407Z   test_sparse_csr_unary_inplace_abs_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.016s)
2023-01-11T21:22:05.4552742Z   test_sparse_csr_unary_inplace_abs_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.016s)
2023-01-11T21:22:05.4553057Z   test_sparse_csr_unary_inplace_abs_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4553380Z   test_sparse_csr_unary_inplace_abs_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4553700Z   test_sparse_csr_unary_inplace_abs_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4554002Z   test_sparse_csr_unary_inplace_abs_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4554321Z   test_sparse_csr_unary_inplace_abs_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4554638Z   test_sparse_csr_unary_inplace_abs_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4554995Z   test_sparse_csr_unary_inplace_abs_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4555301Z   test_sparse_csr_unary_inplace_abs_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4555678Z   test_sparse_csr_unary_inplace_angle_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4556107Z   test_sparse_csr_unary_inplace_angle_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4556528Z   test_sparse_csr_unary_inplace_angle_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4556945Z   test_sparse_csr_unary_inplace_angle_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4557368Z   test_sparse_csr_unary_inplace_angle_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4557790Z   test_sparse_csr_unary_inplace_angle_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4558234Z   test_sparse_csr_unary_inplace_angle_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4558636Z   test_sparse_csr_unary_inplace_angle_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4559050Z   test_sparse_csr_unary_inplace_angle_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4559457Z   test_sparse_csr_unary_inplace_angle_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4559871Z   test_sparse_csr_unary_inplace_angle_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4560274Z   test_sparse_csr_unary_inplace_angle_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4560807Z   test_sparse_csr_unary_inplace_asin_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4561144Z   test_sparse_csr_unary_inplace_asin_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4561476Z   test_sparse_csr_unary_inplace_asin_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4561801Z   test_sparse_csr_unary_inplace_asin_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4562134Z   test_sparse_csr_unary_inplace_asin_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4562464Z   test_sparse_csr_unary_inplace_asin_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4562774Z   test_sparse_csr_unary_inplace_asin_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4563103Z   test_sparse_csr_unary_inplace_asin_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4563427Z   test_sparse_csr_unary_inplace_asin_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4563747Z   test_sparse_csr_unary_inplace_asin_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4564053Z   test_sparse_csr_unary_inplace_asin_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4564382Z   test_sparse_csr_unary_inplace_asinh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4564712Z   test_sparse_csr_unary_inplace_asinh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4565030Z   test_sparse_csr_unary_inplace_asinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4565369Z   test_sparse_csr_unary_inplace_asinh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4565762Z   test_sparse_csr_unary_inplace_asinh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4566094Z   test_sparse_csr_unary_inplace_asinh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4566412Z   test_sparse_csr_unary_inplace_asinh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4566737Z   test_sparse_csr_unary_inplace_asinh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4567059Z   test_sparse_csr_unary_inplace_asinh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4567375Z   test_sparse_csr_unary_inplace_asinh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4567682Z   test_sparse_csr_unary_inplace_asinh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4568014Z   test_sparse_csr_unary_inplace_atan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4568343Z   test_sparse_csr_unary_inplace_atan_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4568666Z   test_sparse_csr_unary_inplace_atan_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.007s)
2023-01-11T21:22:05.4569034Z   test_sparse_csr_unary_inplace_atan_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.019s)
2023-01-11T21:22:05.4569365Z   test_sparse_csr_unary_inplace_atan_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4569688Z   test_sparse_csr_unary_inplace_atan_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4569995Z   test_sparse_csr_unary_inplace_atan_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4570316Z   test_sparse_csr_unary_inplace_atan_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4570633Z   test_sparse_csr_unary_inplace_atan_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4570938Z   test_sparse_csr_unary_inplace_atan_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4571258Z   test_sparse_csr_unary_inplace_atan_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4571577Z   test_sparse_csr_unary_inplace_atanh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4571902Z   test_sparse_csr_unary_inplace_atanh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4572222Z   test_sparse_csr_unary_inplace_atanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4572561Z   test_sparse_csr_unary_inplace_atanh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4572891Z   test_sparse_csr_unary_inplace_atanh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4573219Z   test_sparse_csr_unary_inplace_atanh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4573528Z   test_sparse_csr_unary_inplace_atanh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4573852Z   test_sparse_csr_unary_inplace_atanh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4574171Z   test_sparse_csr_unary_inplace_atanh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4574477Z   test_sparse_csr_unary_inplace_atanh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4574797Z   test_sparse_csr_unary_inplace_atanh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4575124Z   test_sparse_csr_unary_inplace_ceil_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4575449Z   test_sparse_csr_unary_inplace_ceil_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4575759Z   test_sparse_csr_unary_inplace_ceil_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4576084Z   test_sparse_csr_unary_inplace_ceil_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4576402Z   test_sparse_csr_unary_inplace_ceil_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4576735Z   test_sparse_csr_unary_inplace_ceil_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4577051Z   test_sparse_csr_unary_inplace_ceil_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4577451Z   test_sparse_csr_unary_inplace_ceil_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4577791Z   test_sparse_csr_unary_inplace_conj_physical_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4578127Z   test_sparse_csr_unary_inplace_conj_physical_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4578483Z   test_sparse_csr_unary_inplace_conj_physical_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4578844Z   test_sparse_csr_unary_inplace_conj_physical_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4579201Z   test_sparse_csr_unary_inplace_conj_physical_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4579540Z   test_sparse_csr_unary_inplace_conj_physical_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4579928Z   test_sparse_csr_unary_inplace_conj_physical_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4580273Z   test_sparse_csr_unary_inplace_conj_physical_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4580600Z   test_sparse_csr_unary_inplace_conj_physical_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4580943Z   test_sparse_csr_unary_inplace_conj_physical_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4581284Z   test_sparse_csr_unary_inplace_conj_physical_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4581628Z   test_sparse_csr_unary_inplace_conj_physical_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4581957Z   test_sparse_csr_unary_inplace_conj_physical_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4582297Z   test_sparse_csr_unary_inplace_deg2rad_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4582633Z   test_sparse_csr_unary_inplace_deg2rad_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4582969Z   test_sparse_csr_unary_inplace_deg2rad_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4583288Z   test_sparse_csr_unary_inplace_deg2rad_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4583613Z   test_sparse_csr_unary_inplace_deg2rad_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4583944Z   test_sparse_csr_unary_inplace_deg2rad_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4584263Z   test_sparse_csr_unary_inplace_deg2rad_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4584592Z   test_sparse_csr_unary_inplace_deg2rad_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4584921Z   test_sparse_csr_unary_inplace_deg2rad_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4585249Z   test_sparse_csr_unary_inplace_deg2rad_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4585567Z   test_sparse_csr_unary_inplace_erf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4585888Z   test_sparse_csr_unary_inplace_erf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4586208Z   test_sparse_csr_unary_inplace_erf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4586532Z   test_sparse_csr_unary_inplace_erf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4586837Z   test_sparse_csr_unary_inplace_erf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4587156Z   test_sparse_csr_unary_inplace_erf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4587473Z   test_sparse_csr_unary_inplace_erf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4587824Z   test_sparse_csr_unary_inplace_erf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4588142Z   test_sparse_csr_unary_inplace_erf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4588472Z   test_sparse_csr_unary_inplace_erfinv_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4588798Z   test_sparse_csr_unary_inplace_erfinv_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4589115Z   test_sparse_csr_unary_inplace_erfinv_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4589442Z   test_sparse_csr_unary_inplace_erfinv_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4589770Z   test_sparse_csr_unary_inplace_erfinv_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4590080Z   test_sparse_csr_unary_inplace_erfinv_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4590403Z   test_sparse_csr_unary_inplace_erfinv_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4590759Z   test_sparse_csr_unary_inplace_erfinv_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4591081Z   test_sparse_csr_unary_inplace_erfinv_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4591396Z   test_sparse_csr_unary_inplace_expm1_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4591722Z   test_sparse_csr_unary_inplace_expm1_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4592048Z   test_sparse_csr_unary_inplace_expm1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4592376Z   test_sparse_csr_unary_inplace_expm1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4592687Z   test_sparse_csr_unary_inplace_expm1_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4593006Z   test_sparse_csr_unary_inplace_expm1_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.043s)
2023-01-11T21:22:05.4593326Z   test_sparse_csr_unary_inplace_expm1_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4593631Z   test_sparse_csr_unary_inplace_expm1_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4593954Z   test_sparse_csr_unary_inplace_expm1_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4594283Z   test_sparse_csr_unary_inplace_floor_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4594613Z   test_sparse_csr_unary_inplace_floor_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4594928Z   test_sparse_csr_unary_inplace_floor_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4595251Z   test_sparse_csr_unary_inplace_floor_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4595570Z   test_sparse_csr_unary_inplace_floor_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4595876Z   test_sparse_csr_unary_inplace_floor_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4596194Z   test_sparse_csr_unary_inplace_floor_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4596515Z   test_sparse_csr_unary_inplace_floor_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4596841Z   test_sparse_csr_unary_inplace_frac_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4597155Z   test_sparse_csr_unary_inplace_frac_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4597479Z   test_sparse_csr_unary_inplace_frac_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4597803Z   test_sparse_csr_unary_inplace_frac_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4598174Z   test_sparse_csr_unary_inplace_isinf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4598613Z   test_sparse_csr_unary_inplace_isinf_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4599037Z   test_sparse_csr_unary_inplace_isinf_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4601357Z   test_sparse_csr_unary_inplace_isinf_cpu_complex32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4601785Z   test_sparse_csr_unary_inplace_isinf_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4602189Z   test_sparse_csr_unary_inplace_isinf_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4602610Z   test_sparse_csr_unary_inplace_isinf_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4603030Z   test_sparse_csr_unary_inplace_isinf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4603517Z   test_sparse_csr_unary_inplace_isinf_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4603920Z   test_sparse_csr_unary_inplace_isinf_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4604333Z   test_sparse_csr_unary_inplace_isinf_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4604743Z   test_sparse_csr_unary_inplace_isinf_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4605154Z   test_sparse_csr_unary_inplace_isinf_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4605556Z   test_sparse_csr_unary_inplace_isnan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4605979Z   test_sparse_csr_unary_inplace_isnan_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4606402Z   test_sparse_csr_unary_inplace_isnan_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4606823Z   test_sparse_csr_unary_inplace_isnan_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4607225Z   test_sparse_csr_unary_inplace_isnan_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4607642Z   test_sparse_csr_unary_inplace_isnan_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4608052Z   test_sparse_csr_unary_inplace_isnan_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4608472Z   test_sparse_csr_unary_inplace_isnan_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4608873Z   test_sparse_csr_unary_inplace_isnan_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4609289Z   test_sparse_csr_unary_inplace_isnan_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4609699Z   test_sparse_csr_unary_inplace_isnan_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4610114Z   test_sparse_csr_unary_inplace_isnan_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4610516Z   test_sparse_csr_unary_inplace_isneginf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4610984Z   test_sparse_csr_unary_inplace_isneginf_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4611409Z   test_sparse_csr_unary_inplace_isneginf_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4611834Z   test_sparse_csr_unary_inplace_isneginf_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4612241Z   test_sparse_csr_unary_inplace_isneginf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4612654Z   test_sparse_csr_unary_inplace_isneginf_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4613071Z   test_sparse_csr_unary_inplace_isneginf_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4613494Z   test_sparse_csr_unary_inplace_isneginf_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4613936Z   test_sparse_csr_unary_inplace_isneginf_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4614351Z   test_sparse_csr_unary_inplace_isneginf_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4614767Z   test_sparse_csr_unary_inplace_isposinf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4615185Z   test_sparse_csr_unary_inplace_isposinf_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4615604Z   test_sparse_csr_unary_inplace_isposinf_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4616014Z   test_sparse_csr_unary_inplace_isposinf_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4616433Z   test_sparse_csr_unary_inplace_isposinf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4616846Z   test_sparse_csr_unary_inplace_isposinf_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4617328Z   test_sparse_csr_unary_inplace_isposinf_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4617729Z   test_sparse_csr_unary_inplace_isposinf_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4618140Z   test_sparse_csr_unary_inplace_isposinf_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4618557Z   test_sparse_csr_unary_inplace_isposinf_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4618932Z   test_sparse_csr_unary_inplace_log1p_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4619252Z   test_sparse_csr_unary_inplace_log1p_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4619592Z   test_sparse_csr_unary_inplace_log1p_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4619931Z   test_sparse_csr_unary_inplace_log1p_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4620253Z   test_sparse_csr_unary_inplace_log1p_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4620582Z   test_sparse_csr_unary_inplace_log1p_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4620909Z   test_sparse_csr_unary_inplace_log1p_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4621281Z   test_sparse_csr_unary_inplace_log1p_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4621592Z   test_sparse_csr_unary_inplace_log1p_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4621917Z   test_sparse_csr_unary_inplace_log1p_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4622239Z   test_sparse_csr_unary_inplace_log1p_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4622565Z   test_sparse_csr_unary_inplace_neg_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4622885Z   test_sparse_csr_unary_inplace_neg_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4623222Z   test_sparse_csr_unary_inplace_neg_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4623553Z   test_sparse_csr_unary_inplace_neg_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4623871Z   test_sparse_csr_unary_inplace_neg_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4624198Z   test_sparse_csr_unary_inplace_neg_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4624550Z   test_sparse_csr_unary_inplace_neg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4624872Z   test_sparse_csr_unary_inplace_neg_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4625176Z   test_sparse_csr_unary_inplace_neg_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4625486Z   test_sparse_csr_unary_inplace_neg_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4625805Z   test_sparse_csr_unary_inplace_neg_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4626112Z   test_sparse_csr_unary_inplace_neg_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4626498Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4626951Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4627391Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4627817Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4628248Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4628679Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4629112Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4629547Z   test_sparse_csr_unary_inplace_nn_functional_relu_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4629966Z   test_sparse_csr_unary_inplace_positive_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4630398Z   test_sparse_csr_unary_inplace_positive_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4630835Z   test_sparse_csr_unary_inplace_positive_cpu_complex32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4631264Z   test_sparse_csr_unary_inplace_positive_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4631675Z   test_sparse_csr_unary_inplace_positive_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4632136Z   test_sparse_csr_unary_inplace_positive_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4632555Z   test_sparse_csr_unary_inplace_positive_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4632966Z   test_sparse_csr_unary_inplace_positive_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4633365Z   test_sparse_csr_unary_inplace_positive_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4633769Z   test_sparse_csr_unary_inplace_positive_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4634188Z   test_sparse_csr_unary_inplace_positive_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4634630Z   test_sparse_csr_unary_inplace_positive_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4634994Z   test_sparse_csr_unary_inplace_rad2deg_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4635330Z   test_sparse_csr_unary_inplace_rad2deg_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4635677Z   test_sparse_csr_unary_inplace_rad2deg_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4636013Z   test_sparse_csr_unary_inplace_rad2deg_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4636329Z   test_sparse_csr_unary_inplace_rad2deg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4636655Z   test_sparse_csr_unary_inplace_rad2deg_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4636991Z   test_sparse_csr_unary_inplace_rad2deg_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4637308Z   test_sparse_csr_unary_inplace_rad2deg_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4637636Z   test_sparse_csr_unary_inplace_rad2deg_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4637962Z   test_sparse_csr_unary_inplace_rad2deg_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.022s)
2023-01-11T21:22:05.4638290Z   test_sparse_csr_unary_inplace_round_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4638604Z   test_sparse_csr_unary_inplace_round_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4638926Z   test_sparse_csr_unary_inplace_round_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4639249Z   test_sparse_csr_unary_inplace_round_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4639557Z   test_sparse_csr_unary_inplace_round_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4639875Z   test_sparse_csr_unary_inplace_round_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4640200Z   test_sparse_csr_unary_inplace_round_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4640523Z   test_sparse_csr_unary_inplace_round_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4641053Z   test_sparse_csr_unary_inplace_sgn_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4641634Z   test_sparse_csr_unary_inplace_sgn_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4642057Z   test_sparse_csr_unary_inplace_sgn_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4642395Z   test_sparse_csr_unary_inplace_sgn_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4642713Z   test_sparse_csr_unary_inplace_sgn_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4643126Z   test_sparse_csr_unary_inplace_sgn_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4643454Z   test_sparse_csr_unary_inplace_sgn_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4643763Z   test_sparse_csr_unary_inplace_sgn_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4644081Z   test_sparse_csr_unary_inplace_sgn_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4644400Z   test_sparse_csr_unary_inplace_sgn_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4644718Z   test_sparse_csr_unary_inplace_sgn_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4645026Z   test_sparse_csr_unary_inplace_sgn_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4645349Z   test_sparse_csr_unary_inplace_sgn_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4645672Z   test_sparse_csr_unary_inplace_sign_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4645983Z   test_sparse_csr_unary_inplace_sign_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4646348Z   test_sparse_csr_unary_inplace_sign_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4646675Z   test_sparse_csr_unary_inplace_sign_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4646997Z   test_sparse_csr_unary_inplace_sign_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4647298Z   test_sparse_csr_unary_inplace_sign_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4647621Z   test_sparse_csr_unary_inplace_sign_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4647937Z   test_sparse_csr_unary_inplace_sign_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4648253Z   test_sparse_csr_unary_inplace_sign_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4648561Z   test_sparse_csr_unary_inplace_sign_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4648937Z   test_sparse_csr_unary_inplace_signbit_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4649366Z   test_sparse_csr_unary_inplace_signbit_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4649786Z   test_sparse_csr_unary_inplace_signbit_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4650194Z   test_sparse_csr_unary_inplace_signbit_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4650609Z   test_sparse_csr_unary_inplace_signbit_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4651022Z   test_sparse_csr_unary_inplace_signbit_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4651440Z   test_sparse_csr_unary_inplace_signbit_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4651837Z   test_sparse_csr_unary_inplace_signbit_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4652245Z   test_sparse_csr_unary_inplace_signbit_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4652655Z   test_sparse_csr_unary_inplace_signbit_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Inplace variant not supported! (0.002s)
2023-01-11T21:22:05.4653020Z   test_sparse_csr_unary_inplace_sin_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.042s)
2023-01-11T21:22:05.4653331Z   test_sparse_csr_unary_inplace_sin_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4653690Z   test_sparse_csr_unary_inplace_sin_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4654027Z   test_sparse_csr_unary_inplace_sin_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4654343Z   test_sparse_csr_unary_inplace_sin_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4654667Z   test_sparse_csr_unary_inplace_sin_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4654988Z   test_sparse_csr_unary_inplace_sin_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4655306Z   test_sparse_csr_unary_inplace_sin_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4655609Z   test_sparse_csr_unary_inplace_sin_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4655921Z   test_sparse_csr_unary_inplace_sin_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4656235Z   test_sparse_csr_unary_inplace_sin_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4656543Z   test_sparse_csr_unary_inplace_sinh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4656893Z   test_sparse_csr_unary_inplace_sinh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4657298Z   test_sparse_csr_unary_inplace_sinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4657637Z   test_sparse_csr_unary_inplace_sinh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4657953Z   test_sparse_csr_unary_inplace_sinh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4658281Z   test_sparse_csr_unary_inplace_sinh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4658603Z   test_sparse_csr_unary_inplace_sinh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4658927Z   test_sparse_csr_unary_inplace_sinh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4659234Z   test_sparse_csr_unary_inplace_sinh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4659558Z   test_sparse_csr_unary_inplace_sinh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4659878Z   test_sparse_csr_unary_inplace_sinh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4660190Z   test_sparse_csr_unary_inplace_sqrt_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4660516Z   test_sparse_csr_unary_inplace_sqrt_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4660845Z   test_sparse_csr_unary_inplace_sqrt_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4661179Z   test_sparse_csr_unary_inplace_sqrt_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4661499Z   test_sparse_csr_unary_inplace_sqrt_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4661825Z   test_sparse_csr_unary_inplace_sqrt_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4662152Z   test_sparse_csr_unary_inplace_sqrt_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.042s)
2023-01-11T21:22:05.4662462Z   test_sparse_csr_unary_inplace_sqrt_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4662778Z   test_sparse_csr_unary_inplace_sqrt_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4663100Z   test_sparse_csr_unary_inplace_sqrt_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4663423Z   test_sparse_csr_unary_inplace_sqrt_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4663731Z   test_sparse_csr_unary_inplace_tan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4664055Z   test_sparse_csr_unary_inplace_tan_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4664379Z   test_sparse_csr_unary_inplace_tan_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.006s)
2023-01-11T21:22:05.4664756Z   test_sparse_csr_unary_inplace_tan_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4665076Z   test_sparse_csr_unary_inplace_tan_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4665399Z   test_sparse_csr_unary_inplace_tan_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4665718Z   test_sparse_csr_unary_inplace_tan_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4666026Z   test_sparse_csr_unary_inplace_tan_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4666342Z   test_sparse_csr_unary_inplace_tan_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4666659Z   test_sparse_csr_unary_inplace_tan_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4666977Z   test_sparse_csr_unary_inplace_tan_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4667290Z   test_sparse_csr_unary_inplace_tanh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4667613Z   test_sparse_csr_unary_inplace_tanh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4667974Z   test_sparse_csr_unary_inplace_tanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.005s)
2023-01-11T21:22:05.4668306Z   test_sparse_csr_unary_inplace_tanh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4668636Z   test_sparse_csr_unary_inplace_tanh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4668963Z   test_sparse_csr_unary_inplace_tanh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4669283Z   test_sparse_csr_unary_inplace_tanh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4669590Z   test_sparse_csr_unary_inplace_tanh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4669911Z   test_sparse_csr_unary_inplace_tanh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4670230Z   test_sparse_csr_unary_inplace_tanh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4670548Z   test_sparse_csr_unary_inplace_tanh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4670861Z   test_sparse_csr_unary_inplace_trunc_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4671188Z   test_sparse_csr_unary_inplace_trunc_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4671513Z   test_sparse_csr_unary_inplace_trunc_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4671823Z   test_sparse_csr_unary_inplace_trunc_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4672142Z   test_sparse_csr_unary_inplace_trunc_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4672460Z   test_sparse_csr_unary_inplace_trunc_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4672783Z   test_sparse_csr_unary_inplace_trunc_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4673090Z   test_sparse_csr_unary_inplace_trunc_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4673412Z   test_sparse_csr_unary_out_abs_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4674261Z   test_sparse_csr_unary_out_abs_cpu_complex128 (__main__.TestSparseCSRCPU) ... /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/opinfo/core.py:1068: UserWarning: Casting complex values to real discards the imaginary part (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Copy.cpp:276.)
2023-01-11T21:22:05.4674759Z   return self.op(*args, **kwargs)
2023-01-11T21:22:05.4674931Z ok (0.013s)
2023-01-11T21:22:05.4675177Z   test_sparse_csr_unary_out_abs_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4675499Z   test_sparse_csr_unary_out_abs_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4675841Z   test_sparse_csr_unary_out_abs_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4676157Z   test_sparse_csr_unary_out_abs_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4676476Z   test_sparse_csr_unary_out_abs_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4676788Z   test_sparse_csr_unary_out_abs_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4677085Z   test_sparse_csr_unary_out_abs_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4677393Z   test_sparse_csr_unary_out_abs_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4677539Z   test_sparse_csr_unary_out_abs_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4677684Z   test_sparse_csr_unary_out_abs_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4677837Z   test_sparse_csr_unary_out_angle_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4677985Z   test_sparse_csr_unary_out_angle_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4678155Z   test_sparse_csr_unary_out_angle_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4678309Z   test_sparse_csr_unary_out_angle_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4678458Z   test_sparse_csr_unary_out_angle_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4678606Z   test_sparse_csr_unary_out_angle_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4678749Z   test_sparse_csr_unary_out_angle_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4678897Z   test_sparse_csr_unary_out_angle_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4679041Z   test_sparse_csr_unary_out_angle_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4679182Z   test_sparse_csr_unary_out_angle_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4679318Z   test_sparse_csr_unary_out_angle_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4679461Z   test_sparse_csr_unary_out_angle_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4679607Z   test_sparse_csr_unary_out_asin_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4679751Z   test_sparse_csr_unary_out_asin_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4679903Z   test_sparse_csr_unary_out_asin_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4680054Z   test_sparse_csr_unary_out_asin_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4680201Z   test_sparse_csr_unary_out_asin_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4680346Z   test_sparse_csr_unary_out_asin_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4680491Z   test_sparse_csr_unary_out_asin_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4680770Z   test_sparse_csr_unary_out_asin_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4680925Z   test_sparse_csr_unary_out_asin_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4681073Z   test_sparse_csr_unary_out_asin_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4681224Z   test_sparse_csr_unary_out_asin_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4681376Z   test_sparse_csr_unary_out_asinh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4681522Z   test_sparse_csr_unary_out_asinh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4681680Z   test_sparse_csr_unary_out_asinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4681833Z   test_sparse_csr_unary_out_asinh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4682022Z   test_sparse_csr_unary_out_asinh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4682173Z   test_sparse_csr_unary_out_asinh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4682319Z   test_sparse_csr_unary_out_asinh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4682465Z   test_sparse_csr_unary_out_asinh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4682611Z   test_sparse_csr_unary_out_asinh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4682756Z   test_sparse_csr_unary_out_asinh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4682898Z   test_sparse_csr_unary_out_asinh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4683048Z   test_sparse_csr_unary_out_atan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4683196Z   test_sparse_csr_unary_out_atan_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4683334Z   test_sparse_csr_unary_out_atan_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4683526Z   test_sparse_csr_unary_out_atan_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4683677Z   test_sparse_csr_unary_out_atan_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4683824Z   test_sparse_csr_unary_out_atan_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4683966Z   test_sparse_csr_unary_out_atan_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4684109Z   test_sparse_csr_unary_out_atan_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4684248Z   test_sparse_csr_unary_out_atan_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4684390Z   test_sparse_csr_unary_out_atan_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4684526Z   test_sparse_csr_unary_out_atan_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4684674Z   test_sparse_csr_unary_out_atanh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4684818Z   test_sparse_csr_unary_out_atanh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4684971Z   test_sparse_csr_unary_out_atanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4685122Z   test_sparse_csr_unary_out_atanh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4685270Z   test_sparse_csr_unary_out_atanh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4685418Z   test_sparse_csr_unary_out_atanh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4685563Z   test_sparse_csr_unary_out_atanh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.037s)
2023-01-11T21:22:05.4685697Z   test_sparse_csr_unary_out_atanh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4685842Z   test_sparse_csr_unary_out_atanh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4685987Z   test_sparse_csr_unary_out_atanh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4686133Z   test_sparse_csr_unary_out_atanh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4686283Z   test_sparse_csr_unary_out_ceil_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4686430Z   test_sparse_csr_unary_out_ceil_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4686580Z   test_sparse_csr_unary_out_ceil_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4686725Z   test_sparse_csr_unary_out_ceil_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4686868Z   test_sparse_csr_unary_out_ceil_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4686999Z   test_sparse_csr_unary_out_ceil_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4687188Z   test_sparse_csr_unary_out_ceil_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4687331Z   test_sparse_csr_unary_out_ceil_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4687494Z   test_sparse_csr_unary_out_conj_physical_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4687652Z   test_sparse_csr_unary_out_conj_physical_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4687817Z   test_sparse_csr_unary_out_conj_physical_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4687981Z   test_sparse_csr_unary_out_conj_physical_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4688142Z   test_sparse_csr_unary_out_conj_physical_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4688291Z   test_sparse_csr_unary_out_conj_physical_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4688448Z   test_sparse_csr_unary_out_conj_physical_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4688635Z   test_sparse_csr_unary_out_conj_physical_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4688795Z   test_sparse_csr_unary_out_conj_physical_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4688947Z   test_sparse_csr_unary_out_conj_physical_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4689098Z   test_sparse_csr_unary_out_conj_physical_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4689255Z   test_sparse_csr_unary_out_conj_physical_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4689405Z   test_sparse_csr_unary_out_conj_physical_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4689558Z   test_sparse_csr_unary_out_deg2rad_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4689697Z   test_sparse_csr_unary_out_deg2rad_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4689847Z   test_sparse_csr_unary_out_deg2rad_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4690000Z   test_sparse_csr_unary_out_deg2rad_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4690150Z   test_sparse_csr_unary_out_deg2rad_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4690297Z   test_sparse_csr_unary_out_deg2rad_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4690443Z   test_sparse_csr_unary_out_deg2rad_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4690586Z   test_sparse_csr_unary_out_deg2rad_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4690734Z   test_sparse_csr_unary_out_deg2rad_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4690878Z   test_sparse_csr_unary_out_deg2rad_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4691013Z   test_sparse_csr_unary_out_erf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4691160Z   test_sparse_csr_unary_out_erf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4691307Z   test_sparse_csr_unary_out_erf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4691452Z   test_sparse_csr_unary_out_erf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4691594Z   test_sparse_csr_unary_out_erf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4691736Z   test_sparse_csr_unary_out_erf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4691878Z   test_sparse_csr_unary_out_erf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4692021Z   test_sparse_csr_unary_out_erf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4692150Z   test_sparse_csr_unary_out_erf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4692330Z   test_sparse_csr_unary_out_erfinv_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4692477Z   test_sparse_csr_unary_out_erfinv_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4692628Z   test_sparse_csr_unary_out_erfinv_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4692776Z   test_sparse_csr_unary_out_erfinv_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4692926Z   test_sparse_csr_unary_out_erfinv_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4693070Z   test_sparse_csr_unary_out_erfinv_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4693213Z   test_sparse_csr_unary_out_erfinv_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4693347Z   test_sparse_csr_unary_out_erfinv_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.020s)
2023-01-11T21:22:05.4693491Z   test_sparse_csr_unary_out_erfinv_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.008s)
2023-01-11T21:22:05.4693643Z   test_sparse_csr_unary_out_expm1_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4693816Z   test_sparse_csr_unary_out_expm1_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4693965Z   test_sparse_csr_unary_out_expm1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4694114Z   test_sparse_csr_unary_out_expm1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4694259Z   test_sparse_csr_unary_out_expm1_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4694404Z   test_sparse_csr_unary_out_expm1_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4694547Z   test_sparse_csr_unary_out_expm1_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4694678Z   test_sparse_csr_unary_out_expm1_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4694823Z   test_sparse_csr_unary_out_expm1_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4694975Z   test_sparse_csr_unary_out_floor_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4695122Z   test_sparse_csr_unary_out_floor_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4695269Z   test_sparse_csr_unary_out_floor_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4695413Z   test_sparse_csr_unary_out_floor_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4695558Z   test_sparse_csr_unary_out_floor_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4695700Z   test_sparse_csr_unary_out_floor_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4695832Z   test_sparse_csr_unary_out_floor_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4695972Z   test_sparse_csr_unary_out_floor_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4696121Z   test_sparse_csr_unary_out_frac_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4696272Z   test_sparse_csr_unary_out_frac_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4696420Z   test_sparse_csr_unary_out_frac_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4696563Z   test_sparse_csr_unary_out_frac_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4696750Z   test_sparse_csr_unary_out_isinf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4696933Z   test_sparse_csr_unary_out_isinf_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4697122Z   test_sparse_csr_unary_out_isinf_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4697383Z   test_sparse_csr_unary_out_isinf_cpu_complex32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4697609Z   test_sparse_csr_unary_out_isinf_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4697792Z   test_sparse_csr_unary_out_isinf_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4697976Z   test_sparse_csr_unary_out_isinf_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4698156Z   test_sparse_csr_unary_out_isinf_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4698337Z   test_sparse_csr_unary_out_isinf_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4698517Z   test_sparse_csr_unary_out_isinf_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4698699Z   test_sparse_csr_unary_out_isinf_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4698909Z   test_sparse_csr_unary_out_isinf_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4699092Z   test_sparse_csr_unary_out_isinf_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4699265Z   test_sparse_csr_unary_out_isnan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4699447Z   test_sparse_csr_unary_out_isnan_cpu_bool (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4699633Z   test_sparse_csr_unary_out_isnan_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4699817Z   test_sparse_csr_unary_out_isnan_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4700001Z   test_sparse_csr_unary_out_isnan_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4700183Z   test_sparse_csr_unary_out_isnan_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4700360Z   test_sparse_csr_unary_out_isnan_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4700537Z   test_sparse_csr_unary_out_isnan_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4700715Z   test_sparse_csr_unary_out_isnan_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4700880Z   test_sparse_csr_unary_out_isnan_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4701060Z   test_sparse_csr_unary_out_isnan_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4701238Z   test_sparse_csr_unary_out_isnan_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4701396Z   test_sparse_csr_unary_out_isneginf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.012s)
2023-01-11T21:22:05.4701546Z   test_sparse_csr_unary_out_isneginf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4701701Z   test_sparse_csr_unary_out_isneginf_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4701853Z   test_sparse_csr_unary_out_isneginf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4702005Z   test_sparse_csr_unary_out_isneginf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4702154Z   test_sparse_csr_unary_out_isneginf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4702294Z   test_sparse_csr_unary_out_isneginf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4702471Z   test_sparse_csr_unary_out_isneginf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4702619Z   test_sparse_csr_unary_out_isneginf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4702768Z   test_sparse_csr_unary_out_isneginf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4702919Z   test_sparse_csr_unary_out_isposinf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4703067Z   test_sparse_csr_unary_out_isposinf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4703221Z   test_sparse_csr_unary_out_isposinf_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4703371Z   test_sparse_csr_unary_out_isposinf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4703523Z   test_sparse_csr_unary_out_isposinf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4703659Z   test_sparse_csr_unary_out_isposinf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4703811Z   test_sparse_csr_unary_out_isposinf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4703985Z   test_sparse_csr_unary_out_isposinf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4704135Z   test_sparse_csr_unary_out_isposinf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4704281Z   test_sparse_csr_unary_out_isposinf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4704428Z   test_sparse_csr_unary_out_log1p_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4704576Z   test_sparse_csr_unary_out_log1p_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4704729Z   test_sparse_csr_unary_out_log1p_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4704869Z   test_sparse_csr_unary_out_log1p_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.021s)
2023-01-11T21:22:05.4705017Z   test_sparse_csr_unary_out_log1p_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4705167Z   test_sparse_csr_unary_out_log1p_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4705316Z   test_sparse_csr_unary_out_log1p_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.034s)
2023-01-11T21:22:05.4705463Z   test_sparse_csr_unary_out_log1p_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4705606Z   test_sparse_csr_unary_out_log1p_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4705751Z   test_sparse_csr_unary_out_log1p_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4705893Z   test_sparse_csr_unary_out_log1p_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4706028Z   test_sparse_csr_unary_out_neg_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4706177Z   test_sparse_csr_unary_out_neg_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4706327Z   test_sparse_csr_unary_out_neg_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4706474Z   test_sparse_csr_unary_out_neg_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4706621Z   test_sparse_csr_unary_out_neg_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4706763Z   test_sparse_csr_unary_out_neg_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4706907Z   test_sparse_csr_unary_out_neg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4707050Z   test_sparse_csr_unary_out_neg_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4707193Z   test_sparse_csr_unary_out_neg_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4707327Z   test_sparse_csr_unary_out_neg_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4707471Z   test_sparse_csr_unary_out_neg_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4707644Z   test_sparse_csr_unary_out_neg_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4707847Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.002s)
2023-01-11T21:22:05.4708051Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4708249Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4708448Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4708641Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4708836Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4709017Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4709238Z   test_sparse_csr_unary_out_nn_functional_relu_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4709430Z   test_sparse_csr_unary_out_positive_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4709619Z   test_sparse_csr_unary_out_positive_cpu_complex128 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4709811Z   test_sparse_csr_unary_out_positive_cpu_complex32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4710001Z   test_sparse_csr_unary_out_positive_cpu_complex64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4710190Z   test_sparse_csr_unary_out_positive_cpu_float16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4710380Z   test_sparse_csr_unary_out_positive_cpu_float32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4710566Z   test_sparse_csr_unary_out_positive_cpu_float64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4710753Z   test_sparse_csr_unary_out_positive_cpu_int16 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4710925Z   test_sparse_csr_unary_out_positive_cpu_int32 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4711107Z   test_sparse_csr_unary_out_positive_cpu_int64 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4711290Z   test_sparse_csr_unary_out_positive_cpu_int8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4711473Z   test_sparse_csr_unary_out_positive_cpu_uint8 (__main__.TestSparseCSRCPU) ... skip: Skipped! Out not supported (0.001s)
2023-01-11T21:22:05.4711631Z   test_sparse_csr_unary_out_rad2deg_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4711784Z   test_sparse_csr_unary_out_rad2deg_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4711939Z   test_sparse_csr_unary_out_rad2deg_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4712089Z   test_sparse_csr_unary_out_rad2deg_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4712241Z   test_sparse_csr_unary_out_rad2deg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4712378Z   test_sparse_csr_unary_out_rad2deg_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4712526Z   test_sparse_csr_unary_out_rad2deg_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4712701Z   test_sparse_csr_unary_out_rad2deg_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4712851Z   test_sparse_csr_unary_out_rad2deg_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4712997Z   test_sparse_csr_unary_out_rad2deg_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4713148Z   test_sparse_csr_unary_out_round_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4713292Z   test_sparse_csr_unary_out_round_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4713438Z   test_sparse_csr_unary_out_round_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4713586Z   test_sparse_csr_unary_out_round_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4713720Z   test_sparse_csr_unary_out_round_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4713863Z   test_sparse_csr_unary_out_round_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4714013Z   test_sparse_csr_unary_out_round_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4714184Z   test_sparse_csr_unary_out_round_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4714333Z   test_sparse_csr_unary_out_sgn_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4714475Z   test_sparse_csr_unary_out_sgn_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4714627Z   test_sparse_csr_unary_out_sgn_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4714776Z   test_sparse_csr_unary_out_sgn_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4714911Z   test_sparse_csr_unary_out_sgn_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4715053Z   test_sparse_csr_unary_out_sgn_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4715199Z   test_sparse_csr_unary_out_sgn_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4715344Z   test_sparse_csr_unary_out_sgn_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4715491Z   test_sparse_csr_unary_out_sgn_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4715634Z   test_sparse_csr_unary_out_sgn_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4715776Z   test_sparse_csr_unary_out_sgn_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.025s)
2023-01-11T21:22:05.4715922Z   test_sparse_csr_unary_out_sgn_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4716051Z   test_sparse_csr_unary_out_sgn_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4716201Z   test_sparse_csr_unary_out_sign_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4716346Z   test_sparse_csr_unary_out_sign_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4716494Z   test_sparse_csr_unary_out_sign_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4716644Z   test_sparse_csr_unary_out_sign_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4716789Z   test_sparse_csr_unary_out_sign_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4716933Z   test_sparse_csr_unary_out_sign_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4717078Z   test_sparse_csr_unary_out_sign_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4717217Z   test_sparse_csr_unary_out_sign_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4717347Z   test_sparse_csr_unary_out_sign_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4717496Z   test_sparse_csr_unary_out_sign_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4717647Z   test_sparse_csr_unary_out_signbit_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4717823Z   test_sparse_csr_unary_out_signbit_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4717975Z   test_sparse_csr_unary_out_signbit_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4718127Z   test_sparse_csr_unary_out_signbit_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4718277Z   test_sparse_csr_unary_out_signbit_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4718424Z   test_sparse_csr_unary_out_signbit_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4718558Z   test_sparse_csr_unary_out_signbit_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4718701Z   test_sparse_csr_unary_out_signbit_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4718848Z   test_sparse_csr_unary_out_signbit_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4718993Z   test_sparse_csr_unary_out_signbit_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4719141Z   test_sparse_csr_unary_out_sin_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4719314Z   test_sparse_csr_unary_out_sin_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4719467Z   test_sparse_csr_unary_out_sin_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4719612Z   test_sparse_csr_unary_out_sin_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4719757Z   test_sparse_csr_unary_out_sin_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4719889Z   test_sparse_csr_unary_out_sin_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4720031Z   test_sparse_csr_unary_out_sin_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4720171Z   test_sparse_csr_unary_out_sin_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4720312Z   test_sparse_csr_unary_out_sin_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4720456Z   test_sparse_csr_unary_out_sin_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4720703Z   test_sparse_csr_unary_out_sin_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4720878Z   test_sparse_csr_unary_out_sinh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4721023Z   test_sparse_csr_unary_out_sinh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4721164Z   test_sparse_csr_unary_out_sinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4721313Z   test_sparse_csr_unary_out_sinh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4721462Z   test_sparse_csr_unary_out_sinh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4721615Z   test_sparse_csr_unary_out_sinh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4721765Z   test_sparse_csr_unary_out_sinh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4721910Z   test_sparse_csr_unary_out_sinh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4722056Z   test_sparse_csr_unary_out_sinh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4722203Z   test_sparse_csr_unary_out_sinh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4722335Z   test_sparse_csr_unary_out_sinh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4722488Z   test_sparse_csr_unary_out_sqrt_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4722632Z   test_sparse_csr_unary_out_sqrt_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4722786Z   test_sparse_csr_unary_out_sqrt_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4722937Z   test_sparse_csr_unary_out_sqrt_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4723155Z   test_sparse_csr_unary_out_sqrt_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4723305Z   test_sparse_csr_unary_out_sqrt_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4723450Z   test_sparse_csr_unary_out_sqrt_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4723594Z   test_sparse_csr_unary_out_sqrt_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4723722Z   test_sparse_csr_unary_out_sqrt_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4723866Z   test_sparse_csr_unary_out_sqrt_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4724010Z   test_sparse_csr_unary_out_sqrt_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.036s)
2023-01-11T21:22:05.4724158Z   test_sparse_csr_unary_out_tan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4724304Z   test_sparse_csr_unary_out_tan_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4724459Z   test_sparse_csr_unary_out_tan_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4724647Z   test_sparse_csr_unary_out_tan_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4724795Z   test_sparse_csr_unary_out_tan_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4724927Z   test_sparse_csr_unary_out_tan_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4725070Z   test_sparse_csr_unary_out_tan_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4725212Z   test_sparse_csr_unary_out_tan_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4725355Z   test_sparse_csr_unary_out_tan_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4725500Z   test_sparse_csr_unary_out_tan_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4725642Z   test_sparse_csr_unary_out_tan_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.011s)
2023-01-11T21:22:05.4725793Z   test_sparse_csr_unary_out_tanh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4725938Z   test_sparse_csr_unary_out_tanh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4726090Z   test_sparse_csr_unary_out_tanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4726228Z   test_sparse_csr_unary_out_tanh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4726374Z   test_sparse_csr_unary_out_tanh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4726520Z   test_sparse_csr_unary_out_tanh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4726664Z   test_sparse_csr_unary_out_tanh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4726805Z   test_sparse_csr_unary_out_tanh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4726951Z   test_sparse_csr_unary_out_tanh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4727094Z   test_sparse_csr_unary_out_tanh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4727233Z   test_sparse_csr_unary_out_tanh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4748258Z   test_sparse_csr_unary_out_trunc_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4748517Z   test_sparse_csr_unary_out_trunc_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4748673Z   test_sparse_csr_unary_out_trunc_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4748818Z   test_sparse_csr_unary_out_trunc_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4748967Z   test_sparse_csr_unary_out_trunc_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.010s)
2023-01-11T21:22:05.4749103Z   test_sparse_csr_unary_out_trunc_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4749340Z   test_sparse_csr_unary_out_trunc_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4749493Z   test_sparse_csr_unary_out_trunc_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.009s)
2023-01-11T21:22:05.4749625Z   test_sparse_mm_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4749757Z   test_sparse_mm_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4749885Z   test_sparse_mm_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4750011Z   test_sparse_mm_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.019s)
2023-01-11T21:22:05.4750164Z   test_sparse_to_sparse_compressed_SparseBSC_cpu_float64 (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4750317Z This test tests conversion from COO to CSR and CSC and CSC to CSR and CSC ... skip: NOT IMPL (0.003s)
2023-01-11T21:22:05.4750473Z   test_sparse_to_sparse_compressed_SparseBSR_cpu_float64 (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4750630Z This test tests conversion from COO to CSR and CSC and CSC to CSR and CSC ... skip: NOT IMPL (0.003s)
2023-01-11T21:22:05.4750773Z   test_sparse_to_sparse_compressed_SparseCSC_cpu_float64 (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4750957Z This test tests conversion from COO to CSR and CSC and CSC to CSR and CSC ... ok (0.037s)
2023-01-11T21:22:05.4751104Z   test_sparse_to_sparse_compressed_SparseCSR_cpu_float64 (__main__.TestSparseCSRCPU)
2023-01-11T21:22:05.4751244Z This test tests conversion from COO to CSR and CSC and CSC to CSR and CSC ... ok (0.040s)
2023-01-11T21:22:05.4751394Z   test_sparse_triangular_solve_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.274s)
2023-01-11T21:22:05.4751542Z   test_sparse_triangular_solve_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.275s)
2023-01-11T21:22:05.4751679Z   test_sparse_triangular_solve_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.239s)
2023-01-11T21:22:05.4751822Z   test_sparse_triangular_solve_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.254s)
2023-01-11T21:22:05.4751947Z   test_sum_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.049s)
2023-01-11T21:22:05.4752066Z   test_sum_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.044s)
2023-01-11T21:22:05.4752192Z   test_sum_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.049s)
2023-01-11T21:22:05.4752312Z   test_sum_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.050s)
2023-01-11T21:22:05.4752433Z   test_sum_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.050s)
2023-01-11T21:22:05.4752541Z   test_sum_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.059s)
2023-01-11T21:22:05.4752656Z   test_sum_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.057s)
2023-01-11T21:22:05.4752774Z   test_sum_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.044s)
2023-01-11T21:22:05.4752889Z   test_sum_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.044s)
2023-01-11T21:22:05.4753002Z   test_sum_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.044s)
2023-01-11T21:22:05.4753123Z   test_sum_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.044s)
2023-01-11T21:22:05.4753236Z   test_sum_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.046s)
2023-01-11T21:22:05.4753380Z   test_transpose_SparseBSC_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (7.854s)
2023-01-11T21:22:05.4753511Z   test_transpose_SparseBSC_cpu_bool (__main__.TestSparseCSRCPU) ... ok (7.636s)
2023-01-11T21:22:05.4753659Z   test_transpose_SparseBSC_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (7.717s)
2023-01-11T21:22:05.4753801Z   test_transpose_SparseBSC_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (7.650s)
2023-01-11T21:22:05.4753942Z   test_transpose_SparseBSC_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (7.482s)
2023-01-11T21:22:05.4754082Z   test_transpose_SparseBSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (7.455s)
2023-01-11T21:22:05.4754221Z   test_transpose_SparseBSC_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (7.444s)
2023-01-11T21:22:05.4754359Z   test_transpose_SparseBSC_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (7.241s)
2023-01-11T21:22:05.4754527Z   test_transpose_SparseBSC_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (7.259s)
2023-01-11T21:22:05.4754659Z   test_transpose_SparseBSC_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (7.265s)
2023-01-11T21:22:05.4754793Z   test_transpose_SparseBSC_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (7.307s)
2023-01-11T21:22:05.4754929Z   test_transpose_SparseBSC_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (7.361s)
2023-01-11T21:22:05.4755069Z   test_transpose_SparseBSR_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (7.622s)
2023-01-11T21:22:05.4755207Z   test_transpose_SparseBSR_cpu_bool (__main__.TestSparseCSRCPU) ... ok (7.438s)
2023-01-11T21:22:05.4755349Z   test_transpose_SparseBSR_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (7.758s)
2023-01-11T21:22:05.4755487Z   test_transpose_SparseBSR_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (7.763s)
2023-01-11T21:22:05.4755628Z   test_transpose_SparseBSR_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (7.647s)
2023-01-11T21:22:05.4755767Z   test_transpose_SparseBSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (7.688s)
2023-01-11T21:22:05.4755895Z   test_transpose_SparseBSR_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (7.618s)
2023-01-11T21:22:05.4756058Z   test_transpose_SparseBSR_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (7.370s)
2023-01-11T21:22:05.4756198Z   test_transpose_SparseBSR_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (7.398s)
2023-01-11T21:22:05.4756331Z   test_transpose_SparseBSR_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (7.378s)
2023-01-11T21:22:05.4756468Z   test_transpose_SparseBSR_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (7.423s)
2023-01-11T21:22:05.4756600Z   test_transpose_SparseBSR_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (7.442s)
2023-01-11T21:22:05.4756738Z   test_transpose_SparseCSC_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (3.367s)
2023-01-11T21:22:05.4756874Z   test_transpose_SparseCSC_cpu_bool (__main__.TestSparseCSRCPU) ... ok (3.207s)
2023-01-11T21:22:05.4757012Z   test_transpose_SparseCSC_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (3.422s)
2023-01-11T21:22:05.4757151Z   test_transpose_SparseCSC_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (3.431s)
2023-01-11T21:22:05.4757295Z   test_transpose_SparseCSC_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (3.351s)
2023-01-11T21:22:05.4757431Z   test_transpose_SparseCSC_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (3.318s)
2023-01-11T21:22:05.4757567Z   test_transpose_SparseCSC_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (3.350s)
2023-01-11T21:22:05.4757701Z   test_transpose_SparseCSC_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (3.219s)
2023-01-11T21:22:05.4757837Z   test_transpose_SparseCSC_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (3.213s)
2023-01-11T21:22:05.4757972Z   test_transpose_SparseCSC_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (3.173s)
2023-01-11T21:22:05.4758100Z   test_transpose_SparseCSC_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (3.208s)
2023-01-11T21:22:05.4758230Z   test_transpose_SparseCSC_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (3.267s)
2023-01-11T21:22:05.4758373Z   test_transpose_SparseCSR_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (3.391s)
2023-01-11T21:22:05.4758507Z   test_transpose_SparseCSR_cpu_bool (__main__.TestSparseCSRCPU) ... ok (3.305s)
2023-01-11T21:22:05.4758653Z   test_transpose_SparseCSR_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (3.418s)
2023-01-11T21:22:05.4758798Z   test_transpose_SparseCSR_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (3.480s)
2023-01-11T21:22:05.4758934Z   test_transpose_SparseCSR_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (3.375s)
2023-01-11T21:22:05.4759074Z   test_transpose_SparseCSR_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (3.423s)
2023-01-11T21:22:05.4759199Z   test_transpose_SparseCSR_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (3.375s)
2023-01-11T21:22:05.4759330Z   test_transpose_SparseCSR_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (3.259s)
2023-01-11T21:22:05.4759465Z   test_transpose_SparseCSR_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (3.339s)
2023-01-11T21:22:05.4759628Z   test_transpose_SparseCSR_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (3.261s)
2023-01-11T21:22:05.4759769Z   test_transpose_SparseCSR_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (3.312s)
2023-01-11T21:22:05.4759901Z   test_transpose_SparseCSR_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (3.307s)
2023-01-11T21:22:05.4760065Z   test_zero_to_zero_correspondence_unary_abs_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4760227Z   test_zero_to_zero_correspondence_unary_abs_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4760391Z   test_zero_to_zero_correspondence_unary_abs_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4760542Z   test_zero_to_zero_correspondence_unary_abs_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4760876Z   test_zero_to_zero_correspondence_unary_abs_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4761040Z   test_zero_to_zero_correspondence_unary_abs_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4761245Z   test_zero_to_zero_correspondence_unary_abs_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4761408Z   test_zero_to_zero_correspondence_unary_abs_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4761565Z   test_zero_to_zero_correspondence_unary_abs_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4761720Z   test_zero_to_zero_correspondence_unary_abs_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4761874Z   test_zero_to_zero_correspondence_unary_abs_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4762022Z   test_zero_to_zero_correspondence_unary_abs_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4762186Z   test_zero_to_zero_correspondence_unary_angle_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4762345Z   test_zero_to_zero_correspondence_unary_angle_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4762514Z   test_zero_to_zero_correspondence_unary_angle_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4762679Z   test_zero_to_zero_correspondence_unary_angle_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4762838Z   test_zero_to_zero_correspondence_unary_angle_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.018s)
2023-01-11T21:22:05.4763003Z   test_zero_to_zero_correspondence_unary_angle_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4763160Z   test_zero_to_zero_correspondence_unary_angle_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4763321Z   test_zero_to_zero_correspondence_unary_angle_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4763467Z   test_zero_to_zero_correspondence_unary_angle_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4763620Z   test_zero_to_zero_correspondence_unary_angle_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4763781Z   test_zero_to_zero_correspondence_unary_angle_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4763939Z   test_zero_to_zero_correspondence_unary_angle_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4764096Z   test_zero_to_zero_correspondence_unary_asin_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4764256Z   test_zero_to_zero_correspondence_unary_asin_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4764419Z   test_zero_to_zero_correspondence_unary_asin_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4764580Z   test_zero_to_zero_correspondence_unary_asin_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4764741Z   test_zero_to_zero_correspondence_unary_asin_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4764930Z   test_zero_to_zero_correspondence_unary_asin_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4765089Z   test_zero_to_zero_correspondence_unary_asin_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4765244Z   test_zero_to_zero_correspondence_unary_asin_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4765393Z   test_zero_to_zero_correspondence_unary_asin_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4765550Z   test_zero_to_zero_correspondence_unary_asin_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4765703Z   test_zero_to_zero_correspondence_unary_asin_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4765862Z   test_zero_to_zero_correspondence_unary_asinh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4766017Z   test_zero_to_zero_correspondence_unary_asinh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4766185Z   test_zero_to_zero_correspondence_unary_asinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4766370Z   test_zero_to_zero_correspondence_unary_asinh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4766536Z   test_zero_to_zero_correspondence_unary_asinh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4766692Z   test_zero_to_zero_correspondence_unary_asinh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4766852Z   test_zero_to_zero_correspondence_unary_asinh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4767003Z   test_zero_to_zero_correspondence_unary_asinh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4767158Z   test_zero_to_zero_correspondence_unary_asinh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4767322Z   test_zero_to_zero_correspondence_unary_asinh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4767480Z   test_zero_to_zero_correspondence_unary_asinh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4767637Z   test_zero_to_zero_correspondence_unary_atan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4767785Z   test_zero_to_zero_correspondence_unary_atan_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4767953Z   test_zero_to_zero_correspondence_unary_atan_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4768115Z   test_zero_to_zero_correspondence_unary_atan_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4768279Z   test_zero_to_zero_correspondence_unary_atan_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4768432Z   test_zero_to_zero_correspondence_unary_atan_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4768589Z   test_zero_to_zero_correspondence_unary_atan_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4768748Z   test_zero_to_zero_correspondence_unary_atan_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4768898Z   test_zero_to_zero_correspondence_unary_atan_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4769054Z   test_zero_to_zero_correspondence_unary_atan_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4769198Z   test_zero_to_zero_correspondence_unary_atan_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4769356Z   test_zero_to_zero_correspondence_unary_atanh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4769512Z   test_zero_to_zero_correspondence_unary_atanh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4769676Z   test_zero_to_zero_correspondence_unary_atanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4769880Z   test_zero_to_zero_correspondence_unary_atanh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4770042Z   test_zero_to_zero_correspondence_unary_atanh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4770203Z   test_zero_to_zero_correspondence_unary_atanh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4770357Z   test_zero_to_zero_correspondence_unary_atanh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4770506Z   test_zero_to_zero_correspondence_unary_atanh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4770650Z   test_zero_to_zero_correspondence_unary_atanh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4770807Z   test_zero_to_zero_correspondence_unary_atanh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4770956Z   test_zero_to_zero_correspondence_unary_atanh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4771120Z   test_zero_to_zero_correspondence_unary_ceil_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4771304Z   test_zero_to_zero_correspondence_unary_ceil_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4771463Z   test_zero_to_zero_correspondence_unary_ceil_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4771619Z   test_zero_to_zero_correspondence_unary_ceil_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4771771Z   test_zero_to_zero_correspondence_unary_ceil_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4771925Z   test_zero_to_zero_correspondence_unary_ceil_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4772073Z   test_zero_to_zero_correspondence_unary_ceil_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4772224Z   test_zero_to_zero_correspondence_unary_ceil_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4772402Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4772572Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4772753Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4772929Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4773099Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4773274Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4773442Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4773608Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4773777Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4773946Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4774115Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4774284Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4774448Z   test_zero_to_zero_correspondence_unary_conj_physical_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4774615Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4774818Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4774980Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4775140Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4775306Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4775467Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4775631Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4775786Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4775941Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4776105Z   test_zero_to_zero_correspondence_unary_deg2rad_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4776294Z   test_zero_to_zero_correspondence_unary_erf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4776454Z   test_zero_to_zero_correspondence_unary_erf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4776605Z   test_zero_to_zero_correspondence_unary_erf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4776759Z   test_zero_to_zero_correspondence_unary_erf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4776912Z   test_zero_to_zero_correspondence_unary_erf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4777065Z   test_zero_to_zero_correspondence_unary_erf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4777283Z   test_zero_to_zero_correspondence_unary_erf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4777447Z   test_zero_to_zero_correspondence_unary_erf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4777607Z   test_zero_to_zero_correspondence_unary_erf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4777774Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4777934Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4778088Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.016s)
2023-01-11T21:22:05.4778243Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4778407Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4778561Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4778714Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4778880Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4779038Z   test_zero_to_zero_correspondence_unary_erfinv_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4779200Z   test_zero_to_zero_correspondence_unary_expm1_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4779351Z   test_zero_to_zero_correspondence_unary_expm1_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4779510Z   test_zero_to_zero_correspondence_unary_expm1_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4779674Z   test_zero_to_zero_correspondence_unary_expm1_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4779829Z   test_zero_to_zero_correspondence_unary_expm1_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4780019Z   test_zero_to_zero_correspondence_unary_expm1_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4780171Z   test_zero_to_zero_correspondence_unary_expm1_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4780329Z   test_zero_to_zero_correspondence_unary_expm1_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4780478Z   test_zero_to_zero_correspondence_unary_expm1_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4780639Z   test_zero_to_zero_correspondence_unary_floor_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4780789Z   test_zero_to_zero_correspondence_unary_floor_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4780943Z   test_zero_to_zero_correspondence_unary_floor_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4781100Z   test_zero_to_zero_correspondence_unary_floor_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4781257Z   test_zero_to_zero_correspondence_unary_floor_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4781450Z   test_zero_to_zero_correspondence_unary_floor_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4781609Z   test_zero_to_zero_correspondence_unary_floor_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4781763Z   test_zero_to_zero_correspondence_unary_floor_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4781924Z   test_zero_to_zero_correspondence_unary_frac_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4782083Z   test_zero_to_zero_correspondence_unary_frac_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4782234Z   test_zero_to_zero_correspondence_unary_frac_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4782392Z   test_zero_to_zero_correspondence_unary_frac_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4782553Z   test_zero_to_zero_correspondence_unary_isinf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4782713Z   test_zero_to_zero_correspondence_unary_isinf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4782878Z   test_zero_to_zero_correspondence_unary_isinf_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4783041Z   test_zero_to_zero_correspondence_unary_isinf_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4783205Z   test_zero_to_zero_correspondence_unary_isinf_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4783363Z   test_zero_to_zero_correspondence_unary_isinf_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4783524Z   test_zero_to_zero_correspondence_unary_isinf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4783679Z   test_zero_to_zero_correspondence_unary_isinf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4783837Z   test_zero_to_zero_correspondence_unary_isinf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4783991Z   test_zero_to_zero_correspondence_unary_isinf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4784139Z   test_zero_to_zero_correspondence_unary_isinf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4784294Z   test_zero_to_zero_correspondence_unary_isinf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4784450Z   test_zero_to_zero_correspondence_unary_isinf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4784610Z   test_zero_to_zero_correspondence_unary_isnan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4784769Z   test_zero_to_zero_correspondence_unary_isnan_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4784967Z   test_zero_to_zero_correspondence_unary_isnan_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4785126Z   test_zero_to_zero_correspondence_unary_isnan_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4785289Z   test_zero_to_zero_correspondence_unary_isnan_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4785443Z   test_zero_to_zero_correspondence_unary_isnan_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4785603Z   test_zero_to_zero_correspondence_unary_isnan_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4785758Z   test_zero_to_zero_correspondence_unary_isnan_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4785911Z   test_zero_to_zero_correspondence_unary_isnan_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4786058Z   test_zero_to_zero_correspondence_unary_isnan_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4786219Z   test_zero_to_zero_correspondence_unary_isnan_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4786407Z   test_zero_to_zero_correspondence_unary_isnan_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4786568Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4786730Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4786896Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4787059Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4787224Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4787387Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4787547Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4787711Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4787869Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4788024Z   test_zero_to_zero_correspondence_unary_isneginf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4788193Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4788349Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4788515Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4788676Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4788844Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4789004Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4789163Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4789327Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4789479Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4789639Z   test_zero_to_zero_correspondence_unary_isposinf_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4789797Z   test_zero_to_zero_correspondence_unary_log1p_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4789988Z   test_zero_to_zero_correspondence_unary_log1p_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4790154Z   test_zero_to_zero_correspondence_unary_log1p_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4790320Z   test_zero_to_zero_correspondence_unary_log1p_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4790481Z   test_zero_to_zero_correspondence_unary_log1p_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4790644Z   test_zero_to_zero_correspondence_unary_log1p_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4790805Z   test_zero_to_zero_correspondence_unary_log1p_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4790954Z   test_zero_to_zero_correspondence_unary_log1p_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4791116Z   test_zero_to_zero_correspondence_unary_log1p_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4791276Z   test_zero_to_zero_correspondence_unary_log1p_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4791458Z   test_zero_to_zero_correspondence_unary_log1p_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4791619Z   test_zero_to_zero_correspondence_unary_neg_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4791779Z   test_zero_to_zero_correspondence_unary_neg_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4791942Z   test_zero_to_zero_correspondence_unary_neg_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4792105Z   test_zero_to_zero_correspondence_unary_neg_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4792267Z   test_zero_to_zero_correspondence_unary_neg_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4792416Z   test_zero_to_zero_correspondence_unary_neg_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4792568Z   test_zero_to_zero_correspondence_unary_neg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4792724Z   test_zero_to_zero_correspondence_unary_neg_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4792880Z   test_zero_to_zero_correspondence_unary_neg_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4793029Z   test_zero_to_zero_correspondence_unary_neg_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4793186Z   test_zero_to_zero_correspondence_unary_neg_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4793344Z   test_zero_to_zero_correspondence_unary_neg_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.017s)
2023-01-11T21:22:05.4793525Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4793705Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4793869Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4794044Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4794222Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4794399Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4794576Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4794754Z   test_zero_to_zero_correspondence_unary_nn_functional_relu_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4794955Z   test_zero_to_zero_correspondence_unary_positive_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4795134Z   test_zero_to_zero_correspondence_unary_positive_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4795308Z   test_zero_to_zero_correspondence_unary_positive_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4795468Z   test_zero_to_zero_correspondence_unary_positive_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4795637Z   test_zero_to_zero_correspondence_unary_positive_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4795803Z   test_zero_to_zero_correspondence_unary_positive_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4795973Z   test_zero_to_zero_correspondence_unary_positive_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4796139Z   test_zero_to_zero_correspondence_unary_positive_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4796309Z   test_zero_to_zero_correspondence_unary_positive_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4796502Z   test_zero_to_zero_correspondence_unary_positive_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4796673Z   test_zero_to_zero_correspondence_unary_positive_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4796835Z   test_zero_to_zero_correspondence_unary_positive_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4796994Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4797161Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4797331Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4797501Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4797671Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4797839Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4798003Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4798164Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4798327Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4798478Z   test_zero_to_zero_correspondence_unary_rad2deg_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4798644Z   test_zero_to_zero_correspondence_unary_round_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4798809Z   test_zero_to_zero_correspondence_unary_round_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4798967Z   test_zero_to_zero_correspondence_unary_round_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4799132Z   test_zero_to_zero_correspondence_unary_round_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4799292Z   test_zero_to_zero_correspondence_unary_round_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4799450Z   test_zero_to_zero_correspondence_unary_round_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4799610Z   test_zero_to_zero_correspondence_unary_round_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4799770Z   test_zero_to_zero_correspondence_unary_round_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4799921Z   test_zero_to_zero_correspondence_unary_sgn_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4800109Z   test_zero_to_zero_correspondence_unary_sgn_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4800279Z   test_zero_to_zero_correspondence_unary_sgn_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4800446Z   test_zero_to_zero_correspondence_unary_sgn_cpu_complex32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4800828Z   test_zero_to_zero_correspondence_unary_sgn_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4800997Z   test_zero_to_zero_correspondence_unary_sgn_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4801155Z   test_zero_to_zero_correspondence_unary_sgn_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4801313Z   test_zero_to_zero_correspondence_unary_sgn_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4801477Z   test_zero_to_zero_correspondence_unary_sgn_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4801628Z   test_zero_to_zero_correspondence_unary_sgn_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4801836Z   test_zero_to_zero_correspondence_unary_sgn_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4801993Z   test_zero_to_zero_correspondence_unary_sgn_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4802154Z   test_zero_to_zero_correspondence_unary_sgn_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4802319Z   test_zero_to_zero_correspondence_unary_sign_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4802480Z   test_zero_to_zero_correspondence_unary_sign_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4802645Z   test_zero_to_zero_correspondence_unary_sign_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4802805Z   test_zero_to_zero_correspondence_unary_sign_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4802964Z   test_zero_to_zero_correspondence_unary_sign_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4803116Z   test_zero_to_zero_correspondence_unary_sign_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4803282Z   test_zero_to_zero_correspondence_unary_sign_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4803443Z   test_zero_to_zero_correspondence_unary_sign_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4803605Z   test_zero_to_zero_correspondence_unary_sign_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4803761Z   test_zero_to_zero_correspondence_unary_sign_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4803931Z   test_zero_to_zero_correspondence_unary_signbit_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4804096Z   test_zero_to_zero_correspondence_unary_signbit_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4804267Z   test_zero_to_zero_correspondence_unary_signbit_cpu_float16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4804425Z   test_zero_to_zero_correspondence_unary_signbit_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4804594Z   test_zero_to_zero_correspondence_unary_signbit_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4804759Z   test_zero_to_zero_correspondence_unary_signbit_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4804918Z   test_zero_to_zero_correspondence_unary_signbit_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4805081Z   test_zero_to_zero_correspondence_unary_signbit_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4805242Z   test_zero_to_zero_correspondence_unary_signbit_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4805405Z   test_zero_to_zero_correspondence_unary_signbit_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4805602Z   test_zero_to_zero_correspondence_unary_sin_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4805763Z   test_zero_to_zero_correspondence_unary_sin_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4805918Z   test_zero_to_zero_correspondence_unary_sin_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4806081Z   test_zero_to_zero_correspondence_unary_sin_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4806242Z   test_zero_to_zero_correspondence_unary_sin_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4806399Z   test_zero_to_zero_correspondence_unary_sin_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4806560Z   test_zero_to_zero_correspondence_unary_sin_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4806717Z   test_zero_to_zero_correspondence_unary_sin_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4806870Z   test_zero_to_zero_correspondence_unary_sin_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4807057Z   test_zero_to_zero_correspondence_unary_sin_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4807216Z   test_zero_to_zero_correspondence_unary_sin_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4807368Z   test_zero_to_zero_correspondence_unary_sinh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4807525Z   test_zero_to_zero_correspondence_unary_sinh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4807692Z   test_zero_to_zero_correspondence_unary_sinh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4807858Z   test_zero_to_zero_correspondence_unary_sinh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4808021Z   test_zero_to_zero_correspondence_unary_sinh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4808180Z   test_zero_to_zero_correspondence_unary_sinh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4808342Z   test_zero_to_zero_correspondence_unary_sinh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4808499Z   test_zero_to_zero_correspondence_unary_sinh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4808652Z   test_zero_to_zero_correspondence_unary_sinh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4808797Z   test_zero_to_zero_correspondence_unary_sinh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4808955Z   test_zero_to_zero_correspondence_unary_sinh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4809118Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4809274Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4809445Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.015s)
2023-01-11T21:22:05.4809611Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4809772Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4809928Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4810086Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4810228Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4810378Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4810581Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4810740Z   test_zero_to_zero_correspondence_unary_sqrt_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4810902Z   test_zero_to_zero_correspondence_unary_tan_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4811061Z   test_zero_to_zero_correspondence_unary_tan_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4811227Z   test_zero_to_zero_correspondence_unary_tan_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4811391Z   test_zero_to_zero_correspondence_unary_tan_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4811553Z   test_zero_to_zero_correspondence_unary_tan_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4811700Z   test_zero_to_zero_correspondence_unary_tan_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4811862Z   test_zero_to_zero_correspondence_unary_tan_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4812044Z   test_zero_to_zero_correspondence_unary_tan_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4812206Z   test_zero_to_zero_correspondence_unary_tan_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4812363Z   test_zero_to_zero_correspondence_unary_tan_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4812521Z   test_zero_to_zero_correspondence_unary_tan_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4812684Z   test_zero_to_zero_correspondence_unary_tanh_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4812841Z   test_zero_to_zero_correspondence_unary_tanh_cpu_bool (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4813005Z   test_zero_to_zero_correspondence_unary_tanh_cpu_complex128 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4813164Z   test_zero_to_zero_correspondence_unary_tanh_cpu_complex64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4813328Z   test_zero_to_zero_correspondence_unary_tanh_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4813484Z   test_zero_to_zero_correspondence_unary_tanh_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4813643Z   test_zero_to_zero_correspondence_unary_tanh_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4813804Z   test_zero_to_zero_correspondence_unary_tanh_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4813962Z   test_zero_to_zero_correspondence_unary_tanh_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4814119Z   test_zero_to_zero_correspondence_unary_tanh_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4814274Z   test_zero_to_zero_correspondence_unary_tanh_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4814440Z   test_zero_to_zero_correspondence_unary_trunc_cpu_bfloat16 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4814595Z   test_zero_to_zero_correspondence_unary_trunc_cpu_float32 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4814758Z   test_zero_to_zero_correspondence_unary_trunc_cpu_float64 (__main__.TestSparseCSRCPU) ... ok (0.004s)
2023-01-11T21:22:05.4814921Z   test_zero_to_zero_correspondence_unary_trunc_cpu_int16 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4815080Z   test_zero_to_zero_correspondence_unary_trunc_cpu_int32 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4815236Z   test_zero_to_zero_correspondence_unary_trunc_cpu_int64 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4815393Z   test_zero_to_zero_correspondence_unary_trunc_cpu_int8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4815553Z   test_zero_to_zero_correspondence_unary_trunc_cpu_uint8 (__main__.TestSparseCSRCPU) ... ok (0.003s)
2023-01-11T21:22:05.4815716Z   test_make_crow_indices (__main__.TestSparseCSRSampler) ... ok (1.819s)
2023-01-11T21:22:05.4815864Z   test_clone_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.536s)
2023-01-11T21:22:05.4816014Z   test_clone_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.499s)
2023-01-11T21:22:05.4816172Z   test_clone_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.538s)
2023-01-11T21:22:05.4816329Z   test_clone_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.567s)
2023-01-11T21:22:05.4816482Z   test_clone_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.515s)
2023-01-11T21:22:05.4816630Z   test_clone_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.529s)
2023-01-11T21:22:05.4816776Z   test_clone_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.503s)
2023-01-11T21:22:05.4816925Z   test_clone_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.527s)
2023-01-11T21:22:05.4817073Z   test_clone_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.485s)
2023-01-11T21:22:05.4817308Z   test_clone_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.501s)
2023-01-11T21:22:05.4817458Z   test_clone_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.496s)
2023-01-11T21:22:05.4817604Z   test_clone_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.501s)
2023-01-11T21:22:05.4817759Z   test_clone_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.453s)
2023-01-11T21:22:05.4817907Z   test_clone_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.446s)
2023-01-11T21:22:05.4818065Z   test_clone_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.468s)
2023-01-11T21:22:05.4818222Z   test_clone_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.480s)
2023-01-11T21:22:05.4818380Z   test_clone_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.459s)
2023-01-11T21:22:05.4818518Z   test_clone_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.471s)
2023-01-11T21:22:05.4818672Z   test_clone_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.446s)
2023-01-11T21:22:05.4818818Z   test_clone_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.480s)
2023-01-11T21:22:05.4818963Z   test_clone_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.432s)
2023-01-11T21:22:05.4819106Z   test_clone_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.430s)
2023-01-11T21:22:05.4819254Z   test_clone_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.455s)
2023-01-11T21:22:05.4819398Z   test_clone_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.433s)
2023-01-11T21:22:05.4819552Z   test_clone_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.4819690Z   test_clone_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.299s)
2023-01-11T21:22:05.4819845Z   test_clone_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.320s)
2023-01-11T21:22:05.4820001Z   test_clone_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.349s)
2023-01-11T21:22:05.4820150Z   test_clone_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.314s)
2023-01-11T21:22:05.4820298Z   test_clone_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.4820443Z   test_clone_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.4820593Z   test_clone_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.292s)
2023-01-11T21:22:05.4820738Z   test_clone_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.4820869Z   test_clone_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.327s)
2023-01-11T21:22:05.4821046Z   test_clone_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.303s)
2023-01-11T21:22:05.4821190Z   test_clone_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.295s)
2023-01-11T21:22:05.4821343Z   test_clone_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.277s)
2023-01-11T21:22:05.4821492Z   test_clone_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.235s)
2023-01-11T21:22:05.4821649Z   test_clone_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.261s)
2023-01-11T21:22:05.4821802Z   test_clone_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.280s)
2023-01-11T21:22:05.4821951Z   test_clone_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.4822098Z   test_clone_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.245s)
2023-01-11T21:22:05.4822230Z   test_clone_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.247s)
2023-01-11T21:22:05.4822383Z   test_clone_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.252s)
2023-01-11T21:22:05.4822534Z   test_clone_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.231s)
2023-01-11T21:22:05.4822706Z   test_clone_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.232s)
2023-01-11T21:22:05.4822857Z   test_clone_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.233s)
2023-01-11T21:22:05.4823004Z   test_clone_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.256s)
2023-01-11T21:22:05.4823172Z   test_consistency_SparseBSC_abs_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4823340Z   test_consistency_SparseBSC_abs_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4823495Z   test_consistency_SparseBSC_abs_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4823661Z   test_consistency_SparseBSC_abs_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4823828Z   test_consistency_SparseBSC_abs_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4823992Z   test_consistency_SparseBSC_abs_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4824155Z   test_consistency_SparseBSC_abs_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4824315Z   test_consistency_SparseBSC_abs_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4824475Z   test_consistency_SparseBSC_abs_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4824634Z   test_consistency_SparseBSC_abs_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4824793Z   test_consistency_SparseBSC_abs_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4824940Z   test_consistency_SparseBSC_abs_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4825110Z   test_consistency_SparseBSC_angle_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4825276Z   test_consistency_SparseBSC_angle_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4825451Z   test_consistency_SparseBSC_angle_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4825622Z   test_consistency_SparseBSC_angle_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4825787Z   test_consistency_SparseBSC_angle_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4825951Z   test_consistency_SparseBSC_angle_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4826116Z   test_consistency_SparseBSC_angle_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4826282Z   test_consistency_SparseBSC_angle_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4826462Z   test_consistency_SparseBSC_angle_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4826624Z   test_consistency_SparseBSC_angle_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4826788Z   test_consistency_SparseBSC_angle_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4826949Z   test_consistency_SparseBSC_angle_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4827114Z   test_consistency_SparseBSC_asin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4827275Z   test_consistency_SparseBSC_asin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4827442Z   test_consistency_SparseBSC_asin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4827614Z   test_consistency_SparseBSC_asin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4827772Z   test_consistency_SparseBSC_asin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4827936Z   test_consistency_SparseBSC_asin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4828125Z   test_consistency_SparseBSC_asin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4828287Z   test_consistency_SparseBSC_asin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.027s)
2023-01-11T21:22:05.4828446Z   test_consistency_SparseBSC_asin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4828605Z   test_consistency_SparseBSC_asin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4828766Z   test_consistency_SparseBSC_asin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4828938Z   test_consistency_SparseBSC_asinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4829100Z   test_consistency_SparseBSC_asinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4829263Z   test_consistency_SparseBSC_asinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4829434Z   test_consistency_SparseBSC_asinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4829597Z   test_consistency_SparseBSC_asinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4829762Z   test_consistency_SparseBSC_asinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4829926Z   test_consistency_SparseBSC_asinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4830088Z   test_consistency_SparseBSC_asinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4830252Z   test_consistency_SparseBSC_asinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4830414Z   test_consistency_SparseBSC_asinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4830576Z   test_consistency_SparseBSC_asinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4830729Z   test_consistency_SparseBSC_atan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4830890Z   test_consistency_SparseBSC_atan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4831059Z   test_consistency_SparseBSC_atan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4831227Z   test_consistency_SparseBSC_atan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4831393Z   test_consistency_SparseBSC_atan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4831561Z   test_consistency_SparseBSC_atan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4831722Z   test_consistency_SparseBSC_atan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4831910Z   test_consistency_SparseBSC_atan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4832061Z   test_consistency_SparseBSC_atan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4832223Z   test_consistency_SparseBSC_atan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4832381Z   test_consistency_SparseBSC_atan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4832548Z   test_consistency_SparseBSC_atanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4832709Z   test_consistency_SparseBSC_atanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4832881Z   test_consistency_SparseBSC_atanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4833049Z   test_consistency_SparseBSC_atanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4833216Z   test_consistency_SparseBSC_atanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4833407Z   test_consistency_SparseBSC_atanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4833561Z   test_consistency_SparseBSC_atanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4833721Z   test_consistency_SparseBSC_atanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4833883Z   test_consistency_SparseBSC_atanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4834044Z   test_consistency_SparseBSC_atanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4834205Z   test_consistency_SparseBSC_atanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4834369Z   test_consistency_SparseBSC_ceil_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4834535Z   test_consistency_SparseBSC_ceil_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4834703Z   test_consistency_SparseBSC_ceil_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4834864Z   test_consistency_SparseBSC_ceil_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.044s)
2023-01-11T21:22:05.4835013Z   test_consistency_SparseBSC_ceil_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4835172Z   test_consistency_SparseBSC_ceil_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4835331Z   test_consistency_SparseBSC_ceil_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4835490Z   test_consistency_SparseBSC_ceil_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4835666Z   test_consistency_SparseBSC_conj_physical_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4835842Z   test_consistency_SparseBSC_conj_physical_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4836026Z   test_consistency_SparseBSC_conj_physical_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4836207Z   test_consistency_SparseBSC_conj_physical_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4836386Z   test_consistency_SparseBSC_conj_physical_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4836551Z   test_consistency_SparseBSC_conj_physical_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4836728Z   test_consistency_SparseBSC_conj_physical_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4836900Z   test_consistency_SparseBSC_conj_physical_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4837076Z   test_consistency_SparseBSC_conj_physical_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4837280Z   test_consistency_SparseBSC_conj_physical_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4837455Z   test_consistency_SparseBSC_conj_physical_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4837629Z   test_consistency_SparseBSC_conj_physical_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4837806Z   test_consistency_SparseBSC_conj_physical_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4837984Z   test_consistency_SparseBSC_deg2rad_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4838139Z   test_consistency_SparseBSC_deg2rad_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4838309Z   test_consistency_SparseBSC_deg2rad_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4838473Z   test_consistency_SparseBSC_deg2rad_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4838645Z   test_consistency_SparseBSC_deg2rad_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4838841Z   test_consistency_SparseBSC_deg2rad_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4839010Z   test_consistency_SparseBSC_deg2rad_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4839178Z   test_consistency_SparseBSC_deg2rad_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4839346Z   test_consistency_SparseBSC_deg2rad_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4839498Z   test_consistency_SparseBSC_deg2rad_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4839668Z   test_consistency_SparseBSC_erf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4839832Z   test_consistency_SparseBSC_erf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4839999Z   test_consistency_SparseBSC_erf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4840165Z   test_consistency_SparseBSC_erf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4840328Z   test_consistency_SparseBSC_erf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4840488Z   test_consistency_SparseBSC_erf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4840794Z   test_consistency_SparseBSC_erf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4840962Z   test_consistency_SparseBSC_erf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4841108Z   test_consistency_SparseBSC_erf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4841281Z   test_consistency_SparseBSC_erfinv_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4841450Z   test_consistency_SparseBSC_erfinv_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4841623Z   test_consistency_SparseBSC_erfinv_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4841790Z   test_consistency_SparseBSC_erfinv_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4841960Z   test_consistency_SparseBSC_erfinv_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.026s)
2023-01-11T21:22:05.4842125Z   test_consistency_SparseBSC_erfinv_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4842291Z   test_consistency_SparseBSC_erfinv_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4842455Z   test_consistency_SparseBSC_erfinv_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4842608Z   test_consistency_SparseBSC_erfinv_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4842831Z   test_consistency_SparseBSC_expm1_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4842999Z   test_consistency_SparseBSC_expm1_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4843165Z   test_consistency_SparseBSC_expm1_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4843332Z   test_consistency_SparseBSC_expm1_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4843495Z   test_consistency_SparseBSC_expm1_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4843658Z   test_consistency_SparseBSC_expm1_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4843823Z   test_consistency_SparseBSC_expm1_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4843973Z   test_consistency_SparseBSC_expm1_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4844138Z   test_consistency_SparseBSC_expm1_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4844306Z   test_consistency_SparseBSC_floor_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4844508Z   test_consistency_SparseBSC_floor_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4844676Z   test_consistency_SparseBSC_floor_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4844836Z   test_consistency_SparseBSC_floor_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4844998Z   test_consistency_SparseBSC_floor_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4845159Z   test_consistency_SparseBSC_floor_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4845322Z   test_consistency_SparseBSC_floor_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4845477Z   test_consistency_SparseBSC_floor_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4845641Z   test_consistency_SparseBSC_frac_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4845809Z   test_consistency_SparseBSC_frac_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4845977Z   test_consistency_SparseBSC_frac_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4846143Z   test_consistency_SparseBSC_frac_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4846309Z   test_consistency_SparseBSC_isinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4846475Z   test_consistency_SparseBSC_isinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4846647Z   test_consistency_SparseBSC_isinf_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4846816Z   test_consistency_SparseBSC_isinf_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4846973Z   test_consistency_SparseBSC_isinf_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4847143Z   test_consistency_SparseBSC_isinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4847307Z   test_consistency_SparseBSC_isinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4847472Z   test_consistency_SparseBSC_isinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4847636Z   test_consistency_SparseBSC_isinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4847798Z   test_consistency_SparseBSC_isinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4847957Z   test_consistency_SparseBSC_isinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4848121Z   test_consistency_SparseBSC_isinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4848332Z   test_consistency_SparseBSC_isinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4848493Z   test_consistency_SparseBSC_isnan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4848656Z   test_consistency_SparseBSC_isnan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4848827Z   test_consistency_SparseBSC_isnan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4848995Z   test_consistency_SparseBSC_isnan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.026s)
2023-01-11T21:22:05.4849160Z   test_consistency_SparseBSC_isnan_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4849324Z   test_consistency_SparseBSC_isnan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4849489Z   test_consistency_SparseBSC_isnan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4849652Z   test_consistency_SparseBSC_isnan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4849830Z   test_consistency_SparseBSC_isnan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4849994Z   test_consistency_SparseBSC_isnan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4850155Z   test_consistency_SparseBSC_isnan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4850316Z   test_consistency_SparseBSC_isnan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4850490Z   test_consistency_SparseBSC_isneginf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4850658Z   test_consistency_SparseBSC_isneginf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4850830Z   test_consistency_SparseBSC_isneginf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4850999Z   test_consistency_SparseBSC_isneginf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4851167Z   test_consistency_SparseBSC_isneginf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4851323Z   test_consistency_SparseBSC_isneginf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4851487Z   test_consistency_SparseBSC_isneginf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4851648Z   test_consistency_SparseBSC_isneginf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4851811Z   test_consistency_SparseBSC_isneginf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4851976Z   test_consistency_SparseBSC_isneginf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4852145Z   test_consistency_SparseBSC_isposinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4852311Z   test_consistency_SparseBSC_isposinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4852481Z   test_consistency_SparseBSC_isposinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4852646Z   test_consistency_SparseBSC_isposinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4852798Z   test_consistency_SparseBSC_isposinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4852962Z   test_consistency_SparseBSC_isposinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4853125Z   test_consistency_SparseBSC_isposinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4853285Z   test_consistency_SparseBSC_isposinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4853451Z   test_consistency_SparseBSC_isposinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4853652Z   test_consistency_SparseBSC_isposinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4853823Z   test_consistency_SparseBSC_log1p_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4853984Z   test_consistency_SparseBSC_log1p_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4854152Z   test_consistency_SparseBSC_log1p_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4854310Z   test_consistency_SparseBSC_log1p_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4854477Z   test_consistency_SparseBSC_log1p_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4854641Z   test_consistency_SparseBSC_log1p_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4854803Z   test_consistency_SparseBSC_log1p_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4854968Z   test_consistency_SparseBSC_log1p_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4855156Z   test_consistency_SparseBSC_log1p_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4855317Z   test_consistency_SparseBSC_log1p_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4855481Z   test_consistency_SparseBSC_log1p_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4855732Z   test_consistency_SparseBSC_masked_amax_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4855967Z   test_consistency_SparseBSC_masked_amax_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4856211Z   test_consistency_SparseBSC_masked_amax_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.024s)
2023-01-11T21:22:05.4856458Z   test_consistency_SparseBSC_masked_amax_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4856702Z   test_consistency_SparseBSC_masked_amax_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4856942Z   test_consistency_SparseBSC_masked_amax_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4857232Z   test_consistency_SparseBSC_masked_amax_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4857478Z   test_consistency_SparseBSC_masked_amax_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4857726Z   test_consistency_SparseBSC_masked_amax_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4857972Z   test_consistency_SparseBSC_masked_amin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4858215Z   test_consistency_SparseBSC_masked_amin_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4858457Z   test_consistency_SparseBSC_masked_amin_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4858704Z   test_consistency_SparseBSC_masked_amin_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4858969Z   test_consistency_SparseBSC_masked_amin_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4859208Z   test_consistency_SparseBSC_masked_amin_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4859443Z   test_consistency_SparseBSC_masked_amin_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4859685Z   test_consistency_SparseBSC_masked_amin_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4859924Z   test_consistency_SparseBSC_masked_amin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4860167Z   test_consistency_SparseBSC_masked_mean_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4860435Z   test_consistency_SparseBSC_masked_mean_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4860685Z   test_consistency_SparseBSC_masked_mean_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4860934Z   test_consistency_SparseBSC_masked_mean_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4861177Z   test_consistency_SparseBSC_masked_mean_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4861420Z   test_consistency_SparseBSC_masked_mean_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4861668Z   test_consistency_SparseBSC_masked_mean_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4861910Z   test_consistency_SparseBSC_masked_mean_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4862136Z   test_consistency_SparseBSC_masked_mean_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4862371Z   test_consistency_SparseBSC_masked_mean_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4862615Z   test_consistency_SparseBSC_masked_mean_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4862858Z   test_consistency_SparseBSC_masked_mean_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4863102Z   test_consistency_SparseBSC_masked_prod_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4863340Z   test_consistency_SparseBSC_masked_prod_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4863586Z   test_consistency_SparseBSC_masked_prod_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4863835Z   test_consistency_SparseBSC_masked_prod_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4864114Z   test_consistency_SparseBSC_masked_prod_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4864357Z   test_consistency_SparseBSC_masked_prod_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4864597Z   test_consistency_SparseBSC_masked_prod_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4864837Z   test_consistency_SparseBSC_masked_prod_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4865076Z   test_consistency_SparseBSC_masked_prod_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4865335Z   test_consistency_SparseBSC_masked_prod_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4865615Z   test_consistency_SparseBSC_masked_prod_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4865929Z   test_consistency_SparseBSC_masked_sum_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4866167Z   test_consistency_SparseBSC_masked_sum_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4866413Z   test_consistency_SparseBSC_masked_sum_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4866660Z   test_consistency_SparseBSC_masked_sum_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4866901Z   test_consistency_SparseBSC_masked_sum_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4867140Z   test_consistency_SparseBSC_masked_sum_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4867379Z   test_consistency_SparseBSC_masked_sum_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4867617Z   test_consistency_SparseBSC_masked_sum_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4867851Z   test_consistency_SparseBSC_masked_sum_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.023s)
2023-01-11T21:22:05.4868087Z   test_consistency_SparseBSC_masked_sum_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4868313Z   test_consistency_SparseBSC_masked_sum_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4868544Z   test_consistency_SparseBSC_masked_sum_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4868711Z   test_consistency_SparseBSC_neg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4868878Z   test_consistency_SparseBSC_neg_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4869078Z   test_consistency_SparseBSC_neg_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4869249Z   test_consistency_SparseBSC_neg_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4869415Z   test_consistency_SparseBSC_neg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4869577Z   test_consistency_SparseBSC_neg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4869739Z   test_consistency_SparseBSC_neg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4869890Z   test_consistency_SparseBSC_neg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4870050Z   test_consistency_SparseBSC_neg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4870207Z   test_consistency_SparseBSC_neg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4870370Z   test_consistency_SparseBSC_neg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4870555Z   test_consistency_SparseBSC_neg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4870740Z   test_consistency_SparseBSC_nn_functional_relu_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4870925Z   test_consistency_SparseBSC_nn_functional_relu_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4871104Z   test_consistency_SparseBSC_nn_functional_relu_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4871282Z   test_consistency_SparseBSC_nn_functional_relu_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4871448Z   test_consistency_SparseBSC_nn_functional_relu_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4871626Z   test_consistency_SparseBSC_nn_functional_relu_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4871806Z   test_consistency_SparseBSC_nn_functional_relu_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4871979Z   test_consistency_SparseBSC_nn_functional_relu_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4872153Z   test_consistency_SparseBSC_positive_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4872331Z   test_consistency_SparseBSC_positive_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4872505Z   test_consistency_SparseBSC_positive_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4872677Z   test_consistency_SparseBSC_positive_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4872849Z   test_consistency_SparseBSC_positive_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4873007Z   test_consistency_SparseBSC_positive_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4873181Z   test_consistency_SparseBSC_positive_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4873349Z   test_consistency_SparseBSC_positive_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4873512Z   test_consistency_SparseBSC_positive_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4873675Z   test_consistency_SparseBSC_positive_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4873839Z   test_consistency_SparseBSC_positive_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4874007Z   test_consistency_SparseBSC_positive_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4874179Z   test_consistency_SparseBSC_rad2deg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4874374Z   test_consistency_SparseBSC_rad2deg_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4874565Z   test_consistency_SparseBSC_rad2deg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4874746Z   test_consistency_SparseBSC_rad2deg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4874916Z   test_consistency_SparseBSC_rad2deg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4875081Z   test_consistency_SparseBSC_rad2deg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4875246Z   test_consistency_SparseBSC_rad2deg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.024s)
2023-01-11T21:22:05.4875412Z   test_consistency_SparseBSC_rad2deg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4875581Z   test_consistency_SparseBSC_rad2deg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4875746Z   test_consistency_SparseBSC_rad2deg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4876022Z   test_consistency_SparseBSC_randn_like_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4876259Z   test_consistency_SparseBSC_randn_like_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4876504Z   test_consistency_SparseBSC_randn_like_cpu_complex32 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4876748Z   test_consistency_SparseBSC_randn_like_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4876990Z   test_consistency_SparseBSC_randn_like_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4877235Z   test_consistency_SparseBSC_randn_like_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4877466Z   test_consistency_SparseBSC_randn_like_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4877639Z   test_consistency_SparseBSC_round_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4877805Z   test_consistency_SparseBSC_round_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4877970Z   test_consistency_SparseBSC_round_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4878135Z   test_consistency_SparseBSC_round_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4878303Z   test_consistency_SparseBSC_round_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4878456Z   test_consistency_SparseBSC_round_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4878619Z   test_consistency_SparseBSC_round_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4878783Z   test_consistency_SparseBSC_round_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4878948Z   test_consistency_SparseBSC_sgn_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4879111Z   test_consistency_SparseBSC_sgn_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4879277Z   test_consistency_SparseBSC_sgn_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4879442Z   test_consistency_SparseBSC_sgn_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4879638Z   test_consistency_SparseBSC_sgn_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4879789Z   test_consistency_SparseBSC_sgn_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4879954Z   test_consistency_SparseBSC_sgn_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4880116Z   test_consistency_SparseBSC_sgn_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4880278Z   test_consistency_SparseBSC_sgn_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4880437Z   test_consistency_SparseBSC_sgn_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4880696Z   test_consistency_SparseBSC_sgn_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4880874Z   test_consistency_SparseBSC_sgn_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4881035Z   test_consistency_SparseBSC_sgn_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4881204Z   test_consistency_SparseBSC_sign_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4881405Z   test_consistency_SparseBSC_sign_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4881572Z   test_consistency_SparseBSC_sign_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4881736Z   test_consistency_SparseBSC_sign_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4881899Z   test_consistency_SparseBSC_sign_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4882064Z   test_consistency_SparseBSC_sign_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4882226Z   test_consistency_SparseBSC_sign_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4882388Z   test_consistency_SparseBSC_sign_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4882553Z   test_consistency_SparseBSC_sign_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4882718Z   test_consistency_SparseBSC_sign_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4882877Z   test_consistency_SparseBSC_signbit_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4883043Z   test_consistency_SparseBSC_signbit_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.024s)
2023-01-11T21:22:05.4883213Z   test_consistency_SparseBSC_signbit_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4883378Z   test_consistency_SparseBSC_signbit_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4883542Z   test_consistency_SparseBSC_signbit_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4883707Z   test_consistency_SparseBSC_signbit_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4883875Z   test_consistency_SparseBSC_signbit_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4884042Z   test_consistency_SparseBSC_signbit_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4884196Z   test_consistency_SparseBSC_signbit_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4884362Z   test_consistency_SparseBSC_signbit_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4884527Z   test_consistency_SparseBSC_sin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4884689Z   test_consistency_SparseBSC_sin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4884855Z   test_consistency_SparseBSC_sin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4885022Z   test_consistency_SparseBSC_sin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4885242Z   test_consistency_SparseBSC_sin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4885408Z   test_consistency_SparseBSC_sin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4885569Z   test_consistency_SparseBSC_sin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4885718Z   test_consistency_SparseBSC_sin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4885877Z   test_consistency_SparseBSC_sin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4886037Z   test_consistency_SparseBSC_sin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4886198Z   test_consistency_SparseBSC_sin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4886363Z   test_consistency_SparseBSC_sinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4886525Z   test_consistency_SparseBSC_sinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4886697Z   test_consistency_SparseBSC_sinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4886894Z   test_consistency_SparseBSC_sinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4887059Z   test_consistency_SparseBSC_sinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4887212Z   test_consistency_SparseBSC_sinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4887378Z   test_consistency_SparseBSC_sinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4887539Z   test_consistency_SparseBSC_sinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4887701Z   test_consistency_SparseBSC_sinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4887862Z   test_consistency_SparseBSC_sinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4888050Z   test_consistency_SparseBSC_sinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4888225Z   test_consistency_SparseBSC_sqrt_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4888384Z   test_consistency_SparseBSC_sqrt_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4888552Z   test_consistency_SparseBSC_sqrt_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4888709Z   test_consistency_SparseBSC_sqrt_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4888872Z   test_consistency_SparseBSC_sqrt_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4889036Z   test_consistency_SparseBSC_sqrt_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4889199Z   test_consistency_SparseBSC_sqrt_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4889361Z   test_consistency_SparseBSC_sqrt_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4889527Z   test_consistency_SparseBSC_sqrt_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4889686Z   test_consistency_SparseBSC_sqrt_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.026s)
2023-01-11T21:22:05.4889847Z   test_consistency_SparseBSC_sqrt_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4889999Z   test_consistency_SparseBSC_tan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4890160Z   test_consistency_SparseBSC_tan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4890331Z   test_consistency_SparseBSC_tan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4890499Z   test_consistency_SparseBSC_tan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4890701Z   test_consistency_SparseBSC_tan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4890869Z   test_consistency_SparseBSC_tan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4891031Z   test_consistency_SparseBSC_tan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4891194Z   test_consistency_SparseBSC_tan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4891351Z   test_consistency_SparseBSC_tan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4891501Z   test_consistency_SparseBSC_tan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4891664Z   test_consistency_SparseBSC_tan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4891831Z   test_consistency_SparseBSC_tanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4891991Z   test_consistency_SparseBSC_tanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4892191Z   test_consistency_SparseBSC_tanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4892364Z   test_consistency_SparseBSC_tanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4892530Z   test_consistency_SparseBSC_tanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4892697Z   test_consistency_SparseBSC_tanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4892862Z   test_consistency_SparseBSC_tanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4893013Z   test_consistency_SparseBSC_tanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4893175Z   test_consistency_SparseBSC_tanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4893339Z   test_consistency_SparseBSC_tanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4893502Z   test_consistency_SparseBSC_tanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4893749Z   test_consistency_SparseBSC_to_sparse_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4893989Z   test_consistency_SparseBSC_to_sparse_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4894238Z   test_consistency_SparseBSC_to_sparse_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4894483Z   test_consistency_SparseBSC_to_sparse_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4894725Z   test_consistency_SparseBSC_to_sparse_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4894970Z   test_consistency_SparseBSC_to_sparse_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4895192Z   test_consistency_SparseBSC_to_sparse_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4895432Z   test_consistency_SparseBSC_to_sparse_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4895664Z   test_consistency_SparseBSC_to_sparse_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4895896Z   test_consistency_SparseBSC_to_sparse_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4896165Z   test_consistency_SparseBSC_to_sparse_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4896401Z   test_consistency_SparseBSC_to_sparse_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsc layout (0.003s)
2023-01-11T21:22:05.4896572Z   test_consistency_SparseBSC_trunc_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4896740Z   test_consistency_SparseBSC_trunc_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4896908Z   test_consistency_SparseBSC_trunc_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4897075Z   test_consistency_SparseBSC_trunc_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4897295Z   test_consistency_SparseBSC_trunc_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4897498Z   test_consistency_SparseBSC_trunc_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4897664Z   test_consistency_SparseBSC_trunc_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.062s)
2023-01-11T21:22:05.4897827Z   test_consistency_SparseBSC_trunc_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4897993Z   test_consistency_SparseBSR_abs_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4898165Z   test_consistency_SparseBSR_abs_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4898333Z   test_consistency_SparseBSR_abs_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4898503Z   test_consistency_SparseBSR_abs_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4898669Z   test_consistency_SparseBSR_abs_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4898823Z   test_consistency_SparseBSR_abs_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4898988Z   test_consistency_SparseBSR_abs_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4899148Z   test_consistency_SparseBSR_abs_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4899310Z   test_consistency_SparseBSR_abs_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4899472Z   test_consistency_SparseBSR_abs_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4899631Z   test_consistency_SparseBSR_abs_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4899790Z   test_consistency_SparseBSR_abs_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4899964Z   test_consistency_SparseBSR_angle_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4900129Z   test_consistency_SparseBSR_angle_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4900291Z   test_consistency_SparseBSR_angle_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4900464Z   test_consistency_SparseBSR_angle_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4900635Z   test_consistency_SparseBSR_angle_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4900815Z   test_consistency_SparseBSR_angle_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4900998Z   test_consistency_SparseBSR_angle_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4901165Z   test_consistency_SparseBSR_angle_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4901367Z   test_consistency_SparseBSR_angle_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4901528Z   test_consistency_SparseBSR_angle_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4901693Z   test_consistency_SparseBSR_angle_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4901843Z   test_consistency_SparseBSR_angle_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4902008Z   test_consistency_SparseBSR_asin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4902168Z   test_consistency_SparseBSR_asin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4902339Z   test_consistency_SparseBSR_asin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4902508Z   test_consistency_SparseBSR_asin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4902674Z   test_consistency_SparseBSR_asin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4902841Z   test_consistency_SparseBSR_asin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4903030Z   test_consistency_SparseBSR_asin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4903181Z   test_consistency_SparseBSR_asin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4903342Z   test_consistency_SparseBSR_asin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4903503Z   test_consistency_SparseBSR_asin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4903663Z   test_consistency_SparseBSR_asin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4903837Z   test_consistency_SparseBSR_asinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4903998Z   test_consistency_SparseBSR_asinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4904173Z   test_consistency_SparseBSR_asinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4904345Z   test_consistency_SparseBSR_asinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4904510Z   test_consistency_SparseBSR_asinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.4904664Z   test_consistency_SparseBSR_asinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4904825Z   test_consistency_SparseBSR_asinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4904988Z   test_consistency_SparseBSR_asinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4905147Z   test_consistency_SparseBSR_asinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4905308Z   test_consistency_SparseBSR_asinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4905468Z   test_consistency_SparseBSR_asinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4905637Z   test_consistency_SparseBSR_atan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4905800Z   test_consistency_SparseBSR_atan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4905967Z   test_consistency_SparseBSR_atan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4906124Z   test_consistency_SparseBSR_atan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4906288Z   test_consistency_SparseBSR_atan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4906456Z   test_consistency_SparseBSR_atan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4906614Z   test_consistency_SparseBSR_atan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4906805Z   test_consistency_SparseBSR_atan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4906969Z   test_consistency_SparseBSR_atan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4907129Z   test_consistency_SparseBSR_atan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4907290Z   test_consistency_SparseBSR_atan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4907448Z   test_consistency_SparseBSR_atanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4907610Z   test_consistency_SparseBSR_atanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4907779Z   test_consistency_SparseBSR_atanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4907947Z   test_consistency_SparseBSR_atanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4908118Z   test_consistency_SparseBSR_atanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4908309Z   test_consistency_SparseBSR_atanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4908475Z   test_consistency_SparseBSR_atanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4908636Z   test_consistency_SparseBSR_atanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4908794Z   test_consistency_SparseBSR_atanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4908944Z   test_consistency_SparseBSR_atanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4909105Z   test_consistency_SparseBSR_atanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4909268Z   test_consistency_SparseBSR_ceil_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4909433Z   test_consistency_SparseBSR_ceil_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4909599Z   test_consistency_SparseBSR_ceil_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4909763Z   test_consistency_SparseBSR_ceil_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4909924Z   test_consistency_SparseBSR_ceil_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4910083Z   test_consistency_SparseBSR_ceil_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4910242Z   test_consistency_SparseBSR_ceil_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4910388Z   test_consistency_SparseBSR_ceil_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4910567Z   test_consistency_SparseBSR_conj_physical_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4910744Z   test_consistency_SparseBSR_conj_physical_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4910927Z   test_consistency_SparseBSR_conj_physical_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4911111Z   test_consistency_SparseBSR_conj_physical_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4911291Z   test_consistency_SparseBSR_conj_physical_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.4911468Z   test_consistency_SparseBSR_conj_physical_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4911642Z   test_consistency_SparseBSR_conj_physical_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4911817Z   test_consistency_SparseBSR_conj_physical_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4911978Z   test_consistency_SparseBSR_conj_physical_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4912180Z   test_consistency_SparseBSR_conj_physical_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4912353Z   test_consistency_SparseBSR_conj_physical_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4912528Z   test_consistency_SparseBSR_conj_physical_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4912699Z   test_consistency_SparseBSR_conj_physical_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4912874Z   test_consistency_SparseBSR_deg2rad_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4913042Z   test_consistency_SparseBSR_deg2rad_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4913210Z   test_consistency_SparseBSR_deg2rad_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4913376Z   test_consistency_SparseBSR_deg2rad_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4913531Z   test_consistency_SparseBSR_deg2rad_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4913722Z   test_consistency_SparseBSR_deg2rad_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4913888Z   test_consistency_SparseBSR_deg2rad_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4914050Z   test_consistency_SparseBSR_deg2rad_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4914218Z   test_consistency_SparseBSR_deg2rad_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4914384Z   test_consistency_SparseBSR_deg2rad_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4914551Z   test_consistency_SparseBSR_erf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4914714Z   test_consistency_SparseBSR_erf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4914870Z   test_consistency_SparseBSR_erf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4915033Z   test_consistency_SparseBSR_erf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4915195Z   test_consistency_SparseBSR_erf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4915352Z   test_consistency_SparseBSR_erf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4915511Z   test_consistency_SparseBSR_erf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4915674Z   test_consistency_SparseBSR_erf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4915836Z   test_consistency_SparseBSR_erf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4916005Z   test_consistency_SparseBSR_erfinv_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4916170Z   test_consistency_SparseBSR_erfinv_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4916331Z   test_consistency_SparseBSR_erfinv_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4916494Z   test_consistency_SparseBSR_erfinv_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4916658Z   test_consistency_SparseBSR_erfinv_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4916826Z   test_consistency_SparseBSR_erfinv_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4916991Z   test_consistency_SparseBSR_erfinv_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4917153Z   test_consistency_SparseBSR_erfinv_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4917318Z   test_consistency_SparseBSR_erfinv_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4917515Z   test_consistency_SparseBSR_expm1_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4917678Z   test_consistency_SparseBSR_expm1_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4917834Z   test_consistency_SparseBSR_expm1_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4918000Z   test_consistency_SparseBSR_expm1_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4918161Z   test_consistency_SparseBSR_expm1_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.4918329Z   test_consistency_SparseBSR_expm1_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4918490Z   test_consistency_SparseBSR_expm1_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4918650Z   test_consistency_SparseBSR_expm1_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4918812Z   test_consistency_SparseBSR_expm1_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4918978Z   test_consistency_SparseBSR_floor_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4919158Z   test_consistency_SparseBSR_floor_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4919325Z   test_consistency_SparseBSR_floor_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4919483Z   test_consistency_SparseBSR_floor_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4919642Z   test_consistency_SparseBSR_floor_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4919804Z   test_consistency_SparseBSR_floor_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4919963Z   test_consistency_SparseBSR_floor_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4920120Z   test_consistency_SparseBSR_floor_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4920287Z   test_consistency_SparseBSR_frac_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4920455Z   test_consistency_SparseBSR_frac_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4920739Z   test_consistency_SparseBSR_frac_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4920928Z   test_consistency_SparseBSR_frac_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4921099Z   test_consistency_SparseBSR_isinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4921261Z   test_consistency_SparseBSR_isinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4921434Z   test_consistency_SparseBSR_isinf_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4921603Z   test_consistency_SparseBSR_isinf_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4921770Z   test_consistency_SparseBSR_isinf_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4921942Z   test_consistency_SparseBSR_isinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4922109Z   test_consistency_SparseBSR_isinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4922263Z   test_consistency_SparseBSR_isinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4922426Z   test_consistency_SparseBSR_isinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4922588Z   test_consistency_SparseBSR_isinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4922751Z   test_consistency_SparseBSR_isinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4922917Z   test_consistency_SparseBSR_isinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4923149Z   test_consistency_SparseBSR_isinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4923321Z   test_consistency_SparseBSR_isnan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4923487Z   test_consistency_SparseBSR_isnan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4923659Z   test_consistency_SparseBSR_isnan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4923815Z   test_consistency_SparseBSR_isnan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4923979Z   test_consistency_SparseBSR_isnan_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4924144Z   test_consistency_SparseBSR_isnan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4924311Z   test_consistency_SparseBSR_isnan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4924477Z   test_consistency_SparseBSR_isnan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4924674Z   test_consistency_SparseBSR_isnan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4924837Z   test_consistency_SparseBSR_isnan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4925001Z   test_consistency_SparseBSR_isnan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4925151Z   test_consistency_SparseBSR_isnan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4925327Z   test_consistency_SparseBSR_isneginf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.4925494Z   test_consistency_SparseBSR_isneginf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4925671Z   test_consistency_SparseBSR_isneginf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4925840Z   test_consistency_SparseBSR_isneginf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4926005Z   test_consistency_SparseBSR_isneginf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4926173Z   test_consistency_SparseBSR_isneginf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4926342Z   test_consistency_SparseBSR_isneginf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4926506Z   test_consistency_SparseBSR_isneginf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4926659Z   test_consistency_SparseBSR_isneginf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4926825Z   test_consistency_SparseBSR_isneginf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4926997Z   test_consistency_SparseBSR_isposinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4927164Z   test_consistency_SparseBSR_isposinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4927334Z   test_consistency_SparseBSR_isposinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4927498Z   test_consistency_SparseBSR_isposinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4927660Z   test_consistency_SparseBSR_isposinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4927827Z   test_consistency_SparseBSR_isposinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4927994Z   test_consistency_SparseBSR_isposinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4928145Z   test_consistency_SparseBSR_isposinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4928309Z   test_consistency_SparseBSR_isposinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4928507Z   test_consistency_SparseBSR_isposinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4928675Z   test_consistency_SparseBSR_log1p_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4928842Z   test_consistency_SparseBSR_log1p_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4929012Z   test_consistency_SparseBSR_log1p_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4929183Z   test_consistency_SparseBSR_log1p_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4929349Z   test_consistency_SparseBSR_log1p_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4929514Z   test_consistency_SparseBSR_log1p_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4929665Z   test_consistency_SparseBSR_log1p_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4929830Z   test_consistency_SparseBSR_log1p_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4930017Z   test_consistency_SparseBSR_log1p_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4930183Z   test_consistency_SparseBSR_log1p_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4930344Z   test_consistency_SparseBSR_log1p_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4930595Z   test_consistency_SparseBSR_masked_amax_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4930843Z   test_consistency_SparseBSR_masked_amax_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4931090Z   test_consistency_SparseBSR_masked_amax_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4931331Z   test_consistency_SparseBSR_masked_amax_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4931573Z   test_consistency_SparseBSR_masked_amax_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4931805Z   test_consistency_SparseBSR_masked_amax_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4932040Z   test_consistency_SparseBSR_masked_amax_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4932285Z   test_consistency_SparseBSR_masked_amax_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4932531Z   test_consistency_SparseBSR_masked_amax_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4932774Z   test_consistency_SparseBSR_masked_amin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4933017Z   test_consistency_SparseBSR_masked_amin_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4933259Z   test_consistency_SparseBSR_masked_amin_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4933499Z   test_consistency_SparseBSR_masked_amin_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.005s)
2023-01-11T21:22:05.4933801Z   test_consistency_SparseBSR_masked_amin_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4934041Z   test_consistency_SparseBSR_masked_amin_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4934274Z   test_consistency_SparseBSR_masked_amin_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4934519Z   test_consistency_SparseBSR_masked_amin_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4934762Z   test_consistency_SparseBSR_masked_amin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4934996Z   test_consistency_SparseBSR_masked_mean_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4935268Z   test_consistency_SparseBSR_masked_mean_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4935520Z   test_consistency_SparseBSR_masked_mean_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4935813Z   test_consistency_SparseBSR_masked_mean_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4936058Z   test_consistency_SparseBSR_masked_mean_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4936304Z   test_consistency_SparseBSR_masked_mean_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4936547Z   test_consistency_SparseBSR_masked_mean_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4936789Z   test_consistency_SparseBSR_masked_mean_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4937028Z   test_consistency_SparseBSR_masked_mean_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4937325Z   test_consistency_SparseBSR_masked_mean_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4937576Z   test_consistency_SparseBSR_masked_mean_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4937824Z   test_consistency_SparseBSR_masked_mean_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4938059Z   test_consistency_SparseBSR_masked_prod_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4938302Z   test_consistency_SparseBSR_masked_prod_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4938555Z   test_consistency_SparseBSR_masked_prod_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4938806Z   test_consistency_SparseBSR_masked_prod_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4939092Z   test_consistency_SparseBSR_masked_prod_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4939334Z   test_consistency_SparseBSR_masked_prod_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4939578Z   test_consistency_SparseBSR_masked_prod_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4939818Z   test_consistency_SparseBSR_masked_prod_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4940057Z   test_consistency_SparseBSR_masked_prod_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4940329Z   test_consistency_SparseBSR_masked_prod_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4940569Z   test_consistency_SparseBSR_masked_prod_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4940814Z   test_consistency_SparseBSR_masked_sum_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4941055Z   test_consistency_SparseBSR_masked_sum_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4941289Z   test_consistency_SparseBSR_masked_sum_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4941540Z   test_consistency_SparseBSR_masked_sum_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4941781Z   test_consistency_SparseBSR_masked_sum_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4942021Z   test_consistency_SparseBSR_masked_sum_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4942264Z   test_consistency_SparseBSR_masked_sum_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4942503Z   test_consistency_SparseBSR_masked_sum_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4942751Z   test_consistency_SparseBSR_masked_sum_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4942993Z   test_consistency_SparseBSR_masked_sum_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4943231Z   test_consistency_SparseBSR_masked_sum_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4943467Z   test_consistency_SparseBSR_masked_sum_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4943638Z   test_consistency_SparseBSR_neg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.4943810Z   test_consistency_SparseBSR_neg_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4943997Z   test_consistency_SparseBSR_neg_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4944169Z   test_consistency_SparseBSR_neg_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4944337Z   test_consistency_SparseBSR_neg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4944503Z   test_consistency_SparseBSR_neg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.4944672Z   test_consistency_SparseBSR_neg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4944841Z   test_consistency_SparseBSR_neg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4945005Z   test_consistency_SparseBSR_neg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4945165Z   test_consistency_SparseBSR_neg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4945317Z   test_consistency_SparseBSR_neg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4945502Z   test_consistency_SparseBSR_neg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4945693Z   test_consistency_SparseBSR_nn_functional_relu_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4945875Z   test_consistency_SparseBSR_nn_functional_relu_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4946059Z   test_consistency_SparseBSR_nn_functional_relu_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4946242Z   test_consistency_SparseBSR_nn_functional_relu_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4946422Z   test_consistency_SparseBSR_nn_functional_relu_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4946603Z   test_consistency_SparseBSR_nn_functional_relu_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4946787Z   test_consistency_SparseBSR_nn_functional_relu_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4946955Z   test_consistency_SparseBSR_nn_functional_relu_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4947134Z   test_consistency_SparseBSR_positive_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4947310Z   test_consistency_SparseBSR_positive_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4947488Z   test_consistency_SparseBSR_positive_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4947663Z   test_consistency_SparseBSR_positive_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4947836Z   test_consistency_SparseBSR_positive_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4948010Z   test_consistency_SparseBSR_positive_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4948186Z   test_consistency_SparseBSR_positive_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4948356Z   test_consistency_SparseBSR_positive_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4948510Z   test_consistency_SparseBSR_positive_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4948672Z   test_consistency_SparseBSR_positive_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4948838Z   test_consistency_SparseBSR_positive_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4949009Z   test_consistency_SparseBSR_positive_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4949181Z   test_consistency_SparseBSR_rad2deg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4949380Z   test_consistency_SparseBSR_rad2deg_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4949556Z   test_consistency_SparseBSR_rad2deg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4949724Z   test_consistency_SparseBSR_rad2deg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4949897Z   test_consistency_SparseBSR_rad2deg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4950052Z   test_consistency_SparseBSR_rad2deg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4950222Z   test_consistency_SparseBSR_rad2deg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4950389Z   test_consistency_SparseBSR_rad2deg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4950557Z   test_consistency_SparseBSR_rad2deg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4950725Z   test_consistency_SparseBSR_rad2deg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4951001Z   test_consistency_SparseBSR_randn_like_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4951253Z   test_consistency_SparseBSR_randn_like_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4951498Z   test_consistency_SparseBSR_randn_like_cpu_complex32 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4951745Z   test_consistency_SparseBSR_randn_like_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4951990Z   test_consistency_SparseBSR_randn_like_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4952239Z   test_consistency_SparseBSR_randn_like_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4952463Z   test_consistency_SparseBSR_randn_like_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_bsr layout (0.006s)
2023-01-11T21:22:05.4952632Z   test_consistency_SparseBSR_round_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4952798Z   test_consistency_SparseBSR_round_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4952968Z   test_consistency_SparseBSR_round_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4953135Z   test_consistency_SparseBSR_round_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4953305Z   test_consistency_SparseBSR_round_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4953468Z   test_consistency_SparseBSR_round_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4953634Z   test_consistency_SparseBSR_round_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4953794Z   test_consistency_SparseBSR_round_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4953951Z   test_consistency_SparseBSR_sgn_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4954113Z   test_consistency_SparseBSR_sgn_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4954284Z   test_consistency_SparseBSR_sgn_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4954450Z   test_consistency_SparseBSR_sgn_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4954616Z   test_consistency_SparseBSR_sgn_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4954805Z   test_consistency_SparseBSR_sgn_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4954973Z   test_consistency_SparseBSR_sgn_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4955134Z   test_consistency_SparseBSR_sgn_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4955285Z   test_consistency_SparseBSR_sgn_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4955444Z   test_consistency_SparseBSR_sgn_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4955605Z   test_consistency_SparseBSR_sgn_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4955763Z   test_consistency_SparseBSR_sgn_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4955919Z   test_consistency_SparseBSR_sgn_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4956086Z   test_consistency_SparseBSR_sign_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4956279Z   test_consistency_SparseBSR_sign_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4956446Z   test_consistency_SparseBSR_sign_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4956612Z   test_consistency_SparseBSR_sign_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4956765Z   test_consistency_SparseBSR_sign_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4956926Z   test_consistency_SparseBSR_sign_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4957089Z   test_consistency_SparseBSR_sign_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4957250Z   test_consistency_SparseBSR_sign_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4957412Z   test_consistency_SparseBSR_sign_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4957575Z   test_consistency_SparseBSR_sign_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4957748Z   test_consistency_SparseBSR_signbit_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4957915Z   test_consistency_SparseBSR_signbit_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4958085Z   test_consistency_SparseBSR_signbit_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4958240Z   test_consistency_SparseBSR_signbit_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4958403Z   test_consistency_SparseBSR_signbit_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4958567Z   test_consistency_SparseBSR_signbit_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4958734Z   test_consistency_SparseBSR_signbit_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4958900Z   test_consistency_SparseBSR_signbit_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4959065Z   test_consistency_SparseBSR_signbit_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4959228Z   test_consistency_SparseBSR_signbit_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4959393Z   test_consistency_SparseBSR_sin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.4959542Z   test_consistency_SparseBSR_sin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4959710Z   test_consistency_SparseBSR_sin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4959874Z   test_consistency_SparseBSR_sin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4960079Z   test_consistency_SparseBSR_sin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4960243Z   test_consistency_SparseBSR_sin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4960402Z   test_consistency_SparseBSR_sin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4960560Z   test_consistency_SparseBSR_sin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4960870Z   test_consistency_SparseBSR_sin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4961036Z   test_consistency_SparseBSR_sin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4961182Z   test_consistency_SparseBSR_sin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4961349Z   test_consistency_SparseBSR_sinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4961509Z   test_consistency_SparseBSR_sinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4961678Z   test_consistency_SparseBSR_sinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4961901Z   test_consistency_SparseBSR_sinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4962069Z   test_consistency_SparseBSR_sinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4962236Z   test_consistency_SparseBSR_sinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4962403Z   test_consistency_SparseBSR_sinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4962566Z   test_consistency_SparseBSR_sinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4962715Z   test_consistency_SparseBSR_sinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4962875Z   test_consistency_SparseBSR_sinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4963037Z   test_consistency_SparseBSR_sinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4963207Z   test_consistency_SparseBSR_sqrt_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4963369Z   test_consistency_SparseBSR_sqrt_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4963540Z   test_consistency_SparseBSR_sqrt_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4963709Z   test_consistency_SparseBSR_sqrt_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4963874Z   test_consistency_SparseBSR_sqrt_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4964039Z   test_consistency_SparseBSR_sqrt_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4964187Z   test_consistency_SparseBSR_sqrt_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4964353Z   test_consistency_SparseBSR_sqrt_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4964520Z   test_consistency_SparseBSR_sqrt_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4964682Z   test_consistency_SparseBSR_sqrt_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4964841Z   test_consistency_SparseBSR_sqrt_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4965007Z   test_consistency_SparseBSR_tan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4965171Z   test_consistency_SparseBSR_tan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4965341Z   test_consistency_SparseBSR_tan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4965497Z   test_consistency_SparseBSR_tan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4965697Z   test_consistency_SparseBSR_tan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4965861Z   test_consistency_SparseBSR_tan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4966023Z   test_consistency_SparseBSR_tan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4966182Z   test_consistency_SparseBSR_tan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.4966337Z   test_consistency_SparseBSR_tan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4966497Z   test_consistency_SparseBSR_tan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4966656Z   test_consistency_SparseBSR_tan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4966824Z   test_consistency_SparseBSR_tanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4966974Z   test_consistency_SparseBSR_tanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4967170Z   test_consistency_SparseBSR_tanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4967338Z   test_consistency_SparseBSR_tanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4967506Z   test_consistency_SparseBSR_tanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4967671Z   test_consistency_SparseBSR_tanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4967834Z   test_consistency_SparseBSR_tanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4967995Z   test_consistency_SparseBSR_tanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4968156Z   test_consistency_SparseBSR_tanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4968317Z   test_consistency_SparseBSR_tanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4968467Z   test_consistency_SparseBSR_tanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4968715Z   test_consistency_SparseBSR_to_sparse_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4968954Z   test_consistency_SparseBSR_to_sparse_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4969197Z   test_consistency_SparseBSR_to_sparse_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4969442Z   test_consistency_SparseBSR_to_sparse_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4969683Z   test_consistency_SparseBSR_to_sparse_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4969923Z   test_consistency_SparseBSR_to_sparse_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4970161Z   test_consistency_SparseBSR_to_sparse_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4970395Z   test_consistency_SparseBSR_to_sparse_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4970624Z   test_consistency_SparseBSR_to_sparse_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4970850Z   test_consistency_SparseBSR_to_sparse_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4971107Z   test_consistency_SparseBSR_to_sparse_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4971341Z   test_consistency_SparseBSR_to_sparse_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: to_sparse does not support input with torch.sparse_bsr layout (0.003s)
2023-01-11T21:22:05.4971511Z   test_consistency_SparseBSR_trunc_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4971677Z   test_consistency_SparseBSR_trunc_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4971847Z   test_consistency_SparseBSR_trunc_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4972012Z   test_consistency_SparseBSR_trunc_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4972181Z   test_consistency_SparseBSR_trunc_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4972369Z   test_consistency_SparseBSR_trunc_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4972532Z   test_consistency_SparseBSR_trunc_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4972682Z   test_consistency_SparseBSR_trunc_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4972848Z   test_consistency_SparseCSC_abs_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4973017Z   test_consistency_SparseCSC_abs_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4973181Z   test_consistency_SparseCSC_abs_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4973347Z   test_consistency_SparseCSC_abs_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4973512Z   test_consistency_SparseCSC_abs_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4973675Z   test_consistency_SparseCSC_abs_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4973840Z   test_consistency_SparseCSC_abs_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4974001Z   test_consistency_SparseCSC_abs_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.4974151Z   test_consistency_SparseCSC_abs_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4974311Z   test_consistency_SparseCSC_abs_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4974472Z   test_consistency_SparseCSC_abs_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4974631Z   test_consistency_SparseCSC_abs_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4974802Z   test_consistency_SparseCSC_angle_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4974968Z   test_consistency_SparseCSC_angle_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4975144Z   test_consistency_SparseCSC_angle_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4975313Z   test_consistency_SparseCSC_angle_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4975478Z   test_consistency_SparseCSC_angle_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4975631Z   test_consistency_SparseCSC_angle_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4975797Z   test_consistency_SparseCSC_angle_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4975959Z   test_consistency_SparseCSC_angle_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4976122Z   test_consistency_SparseCSC_angle_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4976314Z   test_consistency_SparseCSC_angle_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4976479Z   test_consistency_SparseCSC_angle_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4976639Z   test_consistency_SparseCSC_angle_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4976806Z   test_consistency_SparseCSC_asin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4976967Z   test_consistency_SparseCSC_asin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4977127Z   test_consistency_SparseCSC_asin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4977383Z   test_consistency_SparseCSC_asin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4977553Z   test_consistency_SparseCSC_asin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4977723Z   test_consistency_SparseCSC_asin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4977921Z   test_consistency_SparseCSC_asin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4978084Z   test_consistency_SparseCSC_asin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4978246Z   test_consistency_SparseCSC_asin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4978407Z   test_consistency_SparseCSC_asin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4978554Z   test_consistency_SparseCSC_asin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4978724Z   test_consistency_SparseCSC_asinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4978886Z   test_consistency_SparseCSC_asinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4979060Z   test_consistency_SparseCSC_asinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4979231Z   test_consistency_SparseCSC_asinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4979396Z   test_consistency_SparseCSC_asinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4979562Z   test_consistency_SparseCSC_asinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4979725Z   test_consistency_SparseCSC_asinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4979889Z   test_consistency_SparseCSC_asinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4980040Z   test_consistency_SparseCSC_asinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4980202Z   test_consistency_SparseCSC_asinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4980364Z   test_consistency_SparseCSC_asinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4980530Z   test_consistency_SparseCSC_atan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4980691Z   test_consistency_SparseCSC_atan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.4980860Z   test_consistency_SparseCSC_atan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4981029Z   test_consistency_SparseCSC_atan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4981197Z   test_consistency_SparseCSC_atan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4981364Z   test_consistency_SparseCSC_atan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4981515Z   test_consistency_SparseCSC_atan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4981707Z   test_consistency_SparseCSC_atan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4981867Z   test_consistency_SparseCSC_atan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4982032Z   test_consistency_SparseCSC_atan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4982192Z   test_consistency_SparseCSC_atan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4982358Z   test_consistency_SparseCSC_atanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4982522Z   test_consistency_SparseCSC_atanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4982694Z   test_consistency_SparseCSC_atanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4982853Z   test_consistency_SparseCSC_atanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4983018Z   test_consistency_SparseCSC_atanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4983183Z   test_consistency_SparseCSC_atanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4983375Z   test_consistency_SparseCSC_atanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4983539Z   test_consistency_SparseCSC_atanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4983701Z   test_consistency_SparseCSC_atanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4983861Z   test_consistency_SparseCSC_atanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4984022Z   test_consistency_SparseCSC_atanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4984186Z   test_consistency_SparseCSC_ceil_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4984339Z   test_consistency_SparseCSC_ceil_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4984507Z   test_consistency_SparseCSC_ceil_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4984669Z   test_consistency_SparseCSC_ceil_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4984830Z   test_consistency_SparseCSC_ceil_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4984991Z   test_consistency_SparseCSC_ceil_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4985149Z   test_consistency_SparseCSC_ceil_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4985310Z   test_consistency_SparseCSC_ceil_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4985492Z   test_consistency_SparseCSC_conj_physical_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4985667Z   test_consistency_SparseCSC_conj_physical_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4985839Z   test_consistency_SparseCSC_conj_physical_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4986021Z   test_consistency_SparseCSC_conj_physical_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4986199Z   test_consistency_SparseCSC_conj_physical_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4986374Z   test_consistency_SparseCSC_conj_physical_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4986551Z   test_consistency_SparseCSC_conj_physical_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4986723Z   test_consistency_SparseCSC_conj_physical_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4986897Z   test_consistency_SparseCSC_conj_physical_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4987099Z   test_consistency_SparseCSC_conj_physical_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4987275Z   test_consistency_SparseCSC_conj_physical_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4987438Z   test_consistency_SparseCSC_conj_physical_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.4987611Z   test_consistency_SparseCSC_conj_physical_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4987785Z   test_consistency_SparseCSC_deg2rad_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4987951Z   test_consistency_SparseCSC_deg2rad_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4988121Z   test_consistency_SparseCSC_deg2rad_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4988286Z   test_consistency_SparseCSC_deg2rad_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4988456Z   test_consistency_SparseCSC_deg2rad_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4988655Z   test_consistency_SparseCSC_deg2rad_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4988823Z   test_consistency_SparseCSC_deg2rad_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4988976Z   test_consistency_SparseCSC_deg2rad_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4989144Z   test_consistency_SparseCSC_deg2rad_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4989308Z   test_consistency_SparseCSC_deg2rad_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4989473Z   test_consistency_SparseCSC_erf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4989636Z   test_consistency_SparseCSC_erf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4989802Z   test_consistency_SparseCSC_erf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4989966Z   test_consistency_SparseCSC_erf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4990125Z   test_consistency_SparseCSC_erf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4990274Z   test_consistency_SparseCSC_erf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4990434Z   test_consistency_SparseCSC_erf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4990595Z   test_consistency_SparseCSC_erf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4990753Z   test_consistency_SparseCSC_erf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4990921Z   test_consistency_SparseCSC_erfinv_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4991085Z   test_consistency_SparseCSC_erfinv_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4991253Z   test_consistency_SparseCSC_erfinv_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4991419Z   test_consistency_SparseCSC_erfinv_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4991583Z   test_consistency_SparseCSC_erfinv_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4991734Z   test_consistency_SparseCSC_erfinv_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4991897Z   test_consistency_SparseCSC_erfinv_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4992061Z   test_consistency_SparseCSC_erfinv_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4992229Z   test_consistency_SparseCSC_erfinv_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4992397Z   test_consistency_SparseCSC_expm1_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4992589Z   test_consistency_SparseCSC_expm1_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4992759Z   test_consistency_SparseCSC_expm1_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4992926Z   test_consistency_SparseCSC_expm1_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4993091Z   test_consistency_SparseCSC_expm1_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4993241Z   test_consistency_SparseCSC_expm1_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4993404Z   test_consistency_SparseCSC_expm1_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4993567Z   test_consistency_SparseCSC_expm1_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4993730Z   test_consistency_SparseCSC_expm1_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.4993901Z   test_consistency_SparseCSC_floor_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.4994096Z   test_consistency_SparseCSC_floor_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4994264Z   test_consistency_SparseCSC_floor_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4994424Z   test_consistency_SparseCSC_floor_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4994574Z   test_consistency_SparseCSC_floor_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4994736Z   test_consistency_SparseCSC_floor_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4994898Z   test_consistency_SparseCSC_floor_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4995060Z   test_consistency_SparseCSC_floor_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4995228Z   test_consistency_SparseCSC_frac_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4995400Z   test_consistency_SparseCSC_frac_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4995570Z   test_consistency_SparseCSC_frac_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4995735Z   test_consistency_SparseCSC_frac_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4995905Z   test_consistency_SparseCSC_isinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4996056Z   test_consistency_SparseCSC_isinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4996233Z   test_consistency_SparseCSC_isinf_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4996403Z   test_consistency_SparseCSC_isinf_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4996572Z   test_consistency_SparseCSC_isinf_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4996741Z   test_consistency_SparseCSC_isinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4996906Z   test_consistency_SparseCSC_isinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4997072Z   test_consistency_SparseCSC_isinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4997236Z   test_consistency_SparseCSC_isinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4997398Z   test_consistency_SparseCSC_isinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4997548Z   test_consistency_SparseCSC_isinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4997713Z   test_consistency_SparseCSC_isinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4997913Z   test_consistency_SparseCSC_isinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4998085Z   test_consistency_SparseCSC_isnan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4998250Z   test_consistency_SparseCSC_isnan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4998425Z   test_consistency_SparseCSC_isnan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4998596Z   test_consistency_SparseCSC_isnan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4998764Z   test_consistency_SparseCSC_isnan_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4998930Z   test_consistency_SparseCSC_isnan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4999084Z   test_consistency_SparseCSC_isnan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4999247Z   test_consistency_SparseCSC_isnan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4999435Z   test_consistency_SparseCSC_isnan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4999601Z   test_consistency_SparseCSC_isnan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4999765Z   test_consistency_SparseCSC_isnan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.4999925Z   test_consistency_SparseCSC_isnan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5000102Z   test_consistency_SparseCSC_isneginf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5000269Z   test_consistency_SparseCSC_isneginf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5000430Z   test_consistency_SparseCSC_isneginf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5000714Z   test_consistency_SparseCSC_isneginf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5000992Z   test_consistency_SparseCSC_isneginf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.007s)
2023-01-11T21:22:05.5001165Z   test_consistency_SparseCSC_isneginf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5001332Z   test_consistency_SparseCSC_isneginf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5001493Z   test_consistency_SparseCSC_isneginf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5001662Z   test_consistency_SparseCSC_isneginf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5001830Z   test_consistency_SparseCSC_isneginf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5002003Z   test_consistency_SparseCSC_isposinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5002205Z   test_consistency_SparseCSC_isposinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5002379Z   test_consistency_SparseCSC_isposinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5002550Z   test_consistency_SparseCSC_isposinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5002715Z   test_consistency_SparseCSC_isposinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5002880Z   test_consistency_SparseCSC_isposinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5003043Z   test_consistency_SparseCSC_isposinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5003203Z   test_consistency_SparseCSC_isposinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5003366Z   test_consistency_SparseCSC_isposinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5003595Z   test_consistency_SparseCSC_isposinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5003755Z   test_consistency_SparseCSC_log1p_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5003920Z   test_consistency_SparseCSC_log1p_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5004091Z   test_consistency_SparseCSC_log1p_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5004262Z   test_consistency_SparseCSC_log1p_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5004430Z   test_consistency_SparseCSC_log1p_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5004595Z   test_consistency_SparseCSC_log1p_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5004761Z   test_consistency_SparseCSC_log1p_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5004928Z   test_consistency_SparseCSC_log1p_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5005127Z   test_consistency_SparseCSC_log1p_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5005280Z   test_consistency_SparseCSC_log1p_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5005443Z   test_consistency_SparseCSC_log1p_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5005693Z   test_consistency_SparseCSC_masked_amax_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5005942Z   test_consistency_SparseCSC_masked_amax_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5006187Z   test_consistency_SparseCSC_masked_amax_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5006437Z   test_consistency_SparseCSC_masked_amax_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5006681Z   test_consistency_SparseCSC_masked_amax_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5006924Z   test_consistency_SparseCSC_masked_amax_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5007156Z   test_consistency_SparseCSC_masked_amax_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5007396Z   test_consistency_SparseCSC_masked_amax_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5007642Z   test_consistency_SparseCSC_masked_amax_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5007877Z   test_consistency_SparseCSC_masked_amin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5008117Z   test_consistency_SparseCSC_masked_amin_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5008358Z   test_consistency_SparseCSC_masked_amin_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5008600Z   test_consistency_SparseCSC_masked_amin_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5008876Z   test_consistency_SparseCSC_masked_amin_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5009116Z   test_consistency_SparseCSC_masked_amin_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5009344Z   test_consistency_SparseCSC_masked_amin_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5009588Z   test_consistency_SparseCSC_masked_amin_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.006s)
2023-01-11T21:22:05.5009826Z   test_consistency_SparseCSC_masked_amin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5010070Z   test_consistency_SparseCSC_masked_mean_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5010338Z   test_consistency_SparseCSC_masked_mean_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5010587Z   test_consistency_SparseCSC_masked_mean_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5010832Z   test_consistency_SparseCSC_masked_mean_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5011063Z   test_consistency_SparseCSC_masked_mean_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5011309Z   test_consistency_SparseCSC_masked_mean_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5011553Z   test_consistency_SparseCSC_masked_mean_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5011794Z   test_consistency_SparseCSC_masked_mean_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5012031Z   test_consistency_SparseCSC_masked_mean_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5012263Z   test_consistency_SparseCSC_masked_mean_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5012505Z   test_consistency_SparseCSC_masked_mean_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5012748Z   test_consistency_SparseCSC_masked_mean_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5012996Z   test_consistency_SparseCSC_masked_prod_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5013237Z   test_consistency_SparseCSC_masked_prod_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5013487Z   test_consistency_SparseCSC_masked_prod_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5013733Z   test_consistency_SparseCSC_masked_prod_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5014006Z   test_consistency_SparseCSC_masked_prod_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5014238Z   test_consistency_SparseCSC_masked_prod_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5014480Z   test_consistency_SparseCSC_masked_prod_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5014717Z   test_consistency_SparseCSC_masked_prod_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5014958Z   test_consistency_SparseCSC_masked_prod_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5015266Z   test_consistency_SparseCSC_masked_prod_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5015509Z   test_consistency_SparseCSC_masked_prod_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.prod does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5015751Z   test_consistency_SparseCSC_masked_sum_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5015988Z   test_consistency_SparseCSC_masked_sum_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5016235Z   test_consistency_SparseCSC_masked_sum_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5016487Z   test_consistency_SparseCSC_masked_sum_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5016729Z   test_consistency_SparseCSC_masked_sum_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5016971Z   test_consistency_SparseCSC_masked_sum_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5017286Z   test_consistency_SparseCSC_masked_sum_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5017527Z   test_consistency_SparseCSC_masked_sum_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5017772Z   test_consistency_SparseCSC_masked_sum_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5018015Z   test_consistency_SparseCSC_masked_sum_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5018253Z   test_consistency_SparseCSC_masked_sum_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5018489Z   test_consistency_SparseCSC_masked_sum_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.sum does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5018659Z   test_consistency_SparseCSC_neg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5018830Z   test_consistency_SparseCSC_neg_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5019035Z   test_consistency_SparseCSC_neg_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5019207Z   test_consistency_SparseCSC_neg_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5019372Z   test_consistency_SparseCSC_neg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5019526Z   test_consistency_SparseCSC_neg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5019692Z   test_consistency_SparseCSC_neg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5019853Z   test_consistency_SparseCSC_neg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5020013Z   test_consistency_SparseCSC_neg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5020169Z   test_consistency_SparseCSC_neg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5020331Z   test_consistency_SparseCSC_neg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5020519Z   test_consistency_SparseCSC_neg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5020707Z   test_consistency_SparseCSC_nn_functional_relu_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5020878Z   test_consistency_SparseCSC_nn_functional_relu_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5021061Z   test_consistency_SparseCSC_nn_functional_relu_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5021239Z   test_consistency_SparseCSC_nn_functional_relu_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5021417Z   test_consistency_SparseCSC_nn_functional_relu_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5021595Z   test_consistency_SparseCSC_nn_functional_relu_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5021775Z   test_consistency_SparseCSC_nn_functional_relu_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5021952Z   test_consistency_SparseCSC_nn_functional_relu_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5022125Z   test_consistency_SparseCSC_positive_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5022302Z   test_consistency_SparseCSC_positive_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5022466Z   test_consistency_SparseCSC_positive_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5022639Z   test_consistency_SparseCSC_positive_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5022811Z   test_consistency_SparseCSC_positive_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5022981Z   test_consistency_SparseCSC_positive_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5023152Z   test_consistency_SparseCSC_positive_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5023324Z   test_consistency_SparseCSC_positive_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5023487Z   test_consistency_SparseCSC_positive_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5023648Z   test_consistency_SparseCSC_positive_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5023814Z   test_consistency_SparseCSC_positive_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5023969Z   test_consistency_SparseCSC_positive_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5024139Z   test_consistency_SparseCSC_rad2deg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5024337Z   test_consistency_SparseCSC_rad2deg_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5024508Z   test_consistency_SparseCSC_rad2deg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5024675Z   test_consistency_SparseCSC_rad2deg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5024845Z   test_consistency_SparseCSC_rad2deg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5025010Z   test_consistency_SparseCSC_rad2deg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5025177Z   test_consistency_SparseCSC_rad2deg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5025343Z   test_consistency_SparseCSC_rad2deg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5025498Z   test_consistency_SparseCSC_rad2deg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5025662Z   test_consistency_SparseCSC_rad2deg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5025941Z   test_consistency_SparseCSC_randn_like_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5026189Z   test_consistency_SparseCSC_randn_like_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5026434Z   test_consistency_SparseCSC_randn_like_cpu_complex32 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5026678Z   test_consistency_SparseCSC_randn_like_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5026921Z   test_consistency_SparseCSC_randn_like_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5027158Z   test_consistency_SparseCSC_randn_like_cpu_float32 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5027397Z   test_consistency_SparseCSC_randn_like_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: randn_like does not support input with torch.sparse_csc layout (0.003s)
2023-01-11T21:22:05.5027568Z   test_consistency_SparseCSC_round_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5027735Z   test_consistency_SparseCSC_round_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5027890Z   test_consistency_SparseCSC_round_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5028053Z   test_consistency_SparseCSC_round_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5028218Z   test_consistency_SparseCSC_round_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5028387Z   test_consistency_SparseCSC_round_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5028553Z   test_consistency_SparseCSC_round_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5028713Z   test_consistency_SparseCSC_round_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5028881Z   test_consistency_SparseCSC_sgn_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5029044Z   test_consistency_SparseCSC_sgn_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5029214Z   test_consistency_SparseCSC_sgn_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5029369Z   test_consistency_SparseCSC_sgn_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5029535Z   test_consistency_SparseCSC_sgn_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5029725Z   test_consistency_SparseCSC_sgn_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5029892Z   test_consistency_SparseCSC_sgn_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5030054Z   test_consistency_SparseCSC_sgn_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5030217Z   test_consistency_SparseCSC_sgn_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5030377Z   test_consistency_SparseCSC_sgn_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5030539Z   test_consistency_SparseCSC_sgn_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5030690Z   test_consistency_SparseCSC_sgn_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5030847Z   test_consistency_SparseCSC_sgn_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5031015Z   test_consistency_SparseCSC_sign_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5031197Z   test_consistency_SparseCSC_sign_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5031363Z   test_consistency_SparseCSC_sign_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5031529Z   test_consistency_SparseCSC_sign_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5031696Z   test_consistency_SparseCSC_sign_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5031861Z   test_consistency_SparseCSC_sign_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5032025Z   test_consistency_SparseCSC_sign_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5032174Z   test_consistency_SparseCSC_sign_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5032337Z   test_consistency_SparseCSC_sign_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5032498Z   test_consistency_SparseCSC_sign_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5032670Z   test_consistency_SparseCSC_signbit_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5032836Z   test_consistency_SparseCSC_signbit_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5033005Z   test_consistency_SparseCSC_signbit_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5033170Z   test_consistency_SparseCSC_signbit_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5033331Z   test_consistency_SparseCSC_signbit_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5033496Z   test_consistency_SparseCSC_signbit_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5033652Z   test_consistency_SparseCSC_signbit_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5033819Z   test_consistency_SparseCSC_signbit_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5033984Z   test_consistency_SparseCSC_signbit_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5034149Z   test_consistency_SparseCSC_signbit_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5034313Z   test_consistency_SparseCSC_sin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5034473Z   test_consistency_SparseCSC_sin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.5034642Z   test_consistency_SparseCSC_sin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5034806Z   test_consistency_SparseCSC_sin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5035001Z   test_consistency_SparseCSC_sin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5035166Z   test_consistency_SparseCSC_sin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5035328Z   test_consistency_SparseCSC_sin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5035493Z   test_consistency_SparseCSC_sin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5035648Z   test_consistency_SparseCSC_sin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5035810Z   test_consistency_SparseCSC_sin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5035965Z   test_consistency_SparseCSC_sin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5036129Z   test_consistency_SparseCSC_sinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5036287Z   test_consistency_SparseCSC_sinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5036447Z   test_consistency_SparseCSC_sinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5036648Z   test_consistency_SparseCSC_sinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5036815Z   test_consistency_SparseCSC_sinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5036980Z   test_consistency_SparseCSC_sinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5037142Z   test_consistency_SparseCSC_sinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5037302Z   test_consistency_SparseCSC_sinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5037464Z   test_consistency_SparseCSC_sinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5037622Z   test_consistency_SparseCSC_sinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5037783Z   test_consistency_SparseCSC_sinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5037939Z   test_consistency_SparseCSC_sqrt_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5038099Z   test_consistency_SparseCSC_sqrt_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5038268Z   test_consistency_SparseCSC_sqrt_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5038435Z   test_consistency_SparseCSC_sqrt_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5038600Z   test_consistency_SparseCSC_sqrt_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5038770Z   test_consistency_SparseCSC_sqrt_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5038932Z   test_consistency_SparseCSC_sqrt_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5039093Z   test_consistency_SparseCSC_sqrt_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5039247Z   test_consistency_SparseCSC_sqrt_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5039407Z   test_consistency_SparseCSC_sqrt_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5039565Z   test_consistency_SparseCSC_sqrt_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5039731Z   test_consistency_SparseCSC_tan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5039890Z   test_consistency_SparseCSC_tan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5040056Z   test_consistency_SparseCSC_tan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5040217Z   test_consistency_SparseCSC_tan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5040410Z   test_consistency_SparseCSC_tan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5040574Z   test_consistency_SparseCSC_tan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5040861Z   test_consistency_SparseCSC_tan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5041023Z   test_consistency_SparseCSC_tan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5041183Z   test_consistency_SparseCSC_tan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5041343Z   test_consistency_SparseCSC_tan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5041504Z   test_consistency_SparseCSC_tan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5041670Z   test_consistency_SparseCSC_tanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5041835Z   test_consistency_SparseCSC_tanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5042005Z   test_consistency_SparseCSC_tanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5042220Z   test_consistency_SparseCSC_tanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5042375Z   test_consistency_SparseCSC_tanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5042541Z   test_consistency_SparseCSC_tanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5042703Z   test_consistency_SparseCSC_tanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5042865Z   test_consistency_SparseCSC_tanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5043026Z   test_consistency_SparseCSC_tanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5043187Z   test_consistency_SparseCSC_tanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5043351Z   test_consistency_SparseCSC_tanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5043531Z   test_consistency_SparseCSC_to_sparse_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5043702Z   test_consistency_SparseCSC_to_sparse_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5043868Z   test_consistency_SparseCSC_to_sparse_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.007s)
2023-01-11T21:22:05.5044045Z   test_consistency_SparseCSC_to_sparse_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.007s)
2023-01-11T21:22:05.5044219Z   test_consistency_SparseCSC_to_sparse_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5044388Z   test_consistency_SparseCSC_to_sparse_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5044563Z   test_consistency_SparseCSC_to_sparse_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5044731Z   test_consistency_SparseCSC_to_sparse_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5044899Z   test_consistency_SparseCSC_to_sparse_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5045063Z   test_consistency_SparseCSC_to_sparse_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5045218Z   test_consistency_SparseCSC_to_sparse_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5045386Z   test_consistency_SparseCSC_to_sparse_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5045552Z   test_consistency_SparseCSC_trunc_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5045719Z   test_consistency_SparseCSC_trunc_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5045922Z   test_consistency_SparseCSC_trunc_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5046089Z   test_consistency_SparseCSC_trunc_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5046256Z   test_consistency_SparseCSC_trunc_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5046423Z   test_consistency_SparseCSC_trunc_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5046590Z   test_consistency_SparseCSC_trunc_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5046742Z   test_consistency_SparseCSC_trunc_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5046909Z   test_consistency_SparseCSR_abs_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5047080Z   test_consistency_SparseCSR_abs_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5047250Z   test_consistency_SparseCSR_abs_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5047421Z   test_consistency_SparseCSR_abs_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5047615Z   test_consistency_SparseCSR_abs_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5047782Z   test_consistency_SparseCSR_abs_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5047946Z   test_consistency_SparseCSR_abs_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5048108Z   test_consistency_SparseCSR_abs_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5048257Z   test_consistency_SparseCSR_abs_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5048416Z   test_consistency_SparseCSR_abs_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5048579Z   test_consistency_SparseCSR_abs_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5048743Z   test_consistency_SparseCSR_abs_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5048916Z   test_consistency_SparseCSR_angle_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5049079Z   test_consistency_SparseCSR_angle_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5049250Z   test_consistency_SparseCSR_angle_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5049421Z   test_consistency_SparseCSR_angle_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5049588Z   test_consistency_SparseCSR_angle_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5049742Z   test_consistency_SparseCSR_angle_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5049908Z   test_consistency_SparseCSR_angle_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5050075Z   test_consistency_SparseCSR_angle_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5050243Z   test_consistency_SparseCSR_angle_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5050408Z   test_consistency_SparseCSR_angle_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5050572Z   test_consistency_SparseCSR_angle_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5050736Z   test_consistency_SparseCSR_angle_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5050901Z   test_consistency_SparseCSR_asin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5051051Z   test_consistency_SparseCSR_asin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5051223Z   test_consistency_SparseCSR_asin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5051428Z   test_consistency_SparseCSR_asin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5051598Z   test_consistency_SparseCSR_asin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5051765Z   test_consistency_SparseCSR_asin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5051928Z   test_consistency_SparseCSR_asin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5052091Z   test_consistency_SparseCSR_asin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5052254Z   test_consistency_SparseCSR_asin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5052418Z   test_consistency_SparseCSR_asin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5052567Z   test_consistency_SparseCSR_asin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5052739Z   test_consistency_SparseCSR_asinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5052903Z   test_consistency_SparseCSR_asinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5053109Z   test_consistency_SparseCSR_asinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5053282Z   test_consistency_SparseCSR_asinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5053451Z   test_consistency_SparseCSR_asinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5053619Z   test_consistency_SparseCSR_asinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5053782Z   test_consistency_SparseCSR_asinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5053943Z   test_consistency_SparseCSR_asinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5054096Z   test_consistency_SparseCSR_asinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5054262Z   test_consistency_SparseCSR_asinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5054428Z   test_consistency_SparseCSR_asinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5054597Z   test_consistency_SparseCSR_atan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5054758Z   test_consistency_SparseCSR_atan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5054928Z   test_consistency_SparseCSR_atan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5055098Z   test_consistency_SparseCSR_atan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5055268Z   test_consistency_SparseCSR_atan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5055425Z   test_consistency_SparseCSR_atan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5055589Z   test_consistency_SparseCSR_atan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5055752Z   test_consistency_SparseCSR_atan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5055914Z   test_consistency_SparseCSR_atan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5056073Z   test_consistency_SparseCSR_atan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5056231Z   test_consistency_SparseCSR_atan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5056401Z   test_consistency_SparseCSR_atanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5056564Z   test_consistency_SparseCSR_atanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5056737Z   test_consistency_SparseCSR_atanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5056923Z   test_consistency_SparseCSR_atanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5057092Z   test_consistency_SparseCSR_atanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5057327Z   test_consistency_SparseCSR_atanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5057497Z   test_consistency_SparseCSR_atanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5057662Z   test_consistency_SparseCSR_atanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5057826Z   test_consistency_SparseCSR_atanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5057988Z   test_consistency_SparseCSR_atanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5058150Z   test_consistency_SparseCSR_atanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5058320Z   test_consistency_SparseCSR_ceil_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5058507Z   test_consistency_SparseCSR_ceil_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5058675Z   test_consistency_SparseCSR_ceil_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5058839Z   test_consistency_SparseCSR_ceil_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5059000Z   test_consistency_SparseCSR_ceil_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5059161Z   test_consistency_SparseCSR_ceil_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5059325Z   test_consistency_SparseCSR_ceil_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5059486Z   test_consistency_SparseCSR_ceil_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5059669Z   test_consistency_SparseCSR_conj_physical_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5059848Z   test_consistency_SparseCSR_conj_physical_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5060020Z   test_consistency_SparseCSR_conj_physical_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5060202Z   test_consistency_SparseCSR_conj_physical_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5060383Z   test_consistency_SparseCSR_conj_physical_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5060566Z   test_consistency_SparseCSR_conj_physical_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5060745Z   test_consistency_SparseCSR_conj_physical_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5060922Z   test_consistency_SparseCSR_conj_physical_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5061098Z   test_consistency_SparseCSR_conj_physical_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5061275Z   test_consistency_SparseCSR_conj_physical_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.5061441Z   test_consistency_SparseCSR_conj_physical_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5061605Z   test_consistency_SparseCSR_conj_physical_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5061779Z   test_consistency_SparseCSR_conj_physical_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5061955Z   test_consistency_SparseCSR_deg2rad_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5062122Z   test_consistency_SparseCSR_deg2rad_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5062319Z   test_consistency_SparseCSR_deg2rad_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5062484Z   test_consistency_SparseCSR_deg2rad_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5062648Z   test_consistency_SparseCSR_deg2rad_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5062813Z   test_consistency_SparseCSR_deg2rad_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5062966Z   test_consistency_SparseCSR_deg2rad_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5063132Z   test_consistency_SparseCSR_deg2rad_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5063295Z   test_consistency_SparseCSR_deg2rad_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5063459Z   test_consistency_SparseCSR_deg2rad_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5063626Z   test_consistency_SparseCSR_erf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5063789Z   test_consistency_SparseCSR_erf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5063979Z   test_consistency_SparseCSR_erf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5064147Z   test_consistency_SparseCSR_erf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5064307Z   test_consistency_SparseCSR_erf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5064454Z   test_consistency_SparseCSR_erf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5064610Z   test_consistency_SparseCSR_erf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5064769Z   test_consistency_SparseCSR_erf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5064929Z   test_consistency_SparseCSR_erf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5065100Z   test_consistency_SparseCSR_erfinv_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5065264Z   test_consistency_SparseCSR_erfinv_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5065433Z   test_consistency_SparseCSR_erfinv_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5065603Z   test_consistency_SparseCSR_erfinv_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5065768Z   test_consistency_SparseCSR_erfinv_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5065921Z   test_consistency_SparseCSR_erfinv_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5066085Z   test_consistency_SparseCSR_erfinv_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5066248Z   test_consistency_SparseCSR_erfinv_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5066412Z   test_consistency_SparseCSR_erfinv_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5066583Z   test_consistency_SparseCSR_expm1_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5066748Z   test_consistency_SparseCSR_expm1_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5066914Z   test_consistency_SparseCSR_expm1_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5067079Z   test_consistency_SparseCSR_expm1_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5067230Z   test_consistency_SparseCSR_expm1_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5067389Z   test_consistency_SparseCSR_expm1_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5067551Z   test_consistency_SparseCSR_expm1_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5067740Z   test_consistency_SparseCSR_expm1_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5067902Z   test_consistency_SparseCSR_expm1_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5068073Z   test_consistency_SparseCSR_floor_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5068238Z   test_consistency_SparseCSR_floor_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5068403Z   test_consistency_SparseCSR_floor_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5068562Z   test_consistency_SparseCSR_floor_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5068710Z   test_consistency_SparseCSR_floor_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5068872Z   test_consistency_SparseCSR_floor_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5069034Z   test_consistency_SparseCSR_floor_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5069224Z   test_consistency_SparseCSR_floor_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5069390Z   test_consistency_SparseCSR_frac_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5069553Z   test_consistency_SparseCSR_frac_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5069717Z   test_consistency_SparseCSR_frac_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5069883Z   test_consistency_SparseCSR_frac_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5070050Z   test_consistency_SparseCSR_isinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5070200Z   test_consistency_SparseCSR_isinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5070376Z   test_consistency_SparseCSR_isinf_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5070546Z   test_consistency_SparseCSR_isinf_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5070710Z   test_consistency_SparseCSR_isinf_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5070874Z   test_consistency_SparseCSR_isinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5071038Z   test_consistency_SparseCSR_isinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5071201Z   test_consistency_SparseCSR_isinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5071362Z   test_consistency_SparseCSR_isinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5071523Z   test_consistency_SparseCSR_isinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5071674Z   test_consistency_SparseCSR_isinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5071841Z   test_consistency_SparseCSR_isinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5072004Z   test_consistency_SparseCSR_isinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5072174Z   test_consistency_SparseCSR_isnan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5072336Z   test_consistency_SparseCSR_isnan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5072505Z   test_consistency_SparseCSR_isnan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5072672Z   test_consistency_SparseCSR_isnan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5072836Z   test_consistency_SparseCSR_isnan_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5073033Z   test_consistency_SparseCSR_isnan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5073197Z   test_consistency_SparseCSR_isnan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5073360Z   test_consistency_SparseCSR_isnan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5073523Z   test_consistency_SparseCSR_isnan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5073686Z   test_consistency_SparseCSR_isnan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5073849Z   test_consistency_SparseCSR_isnan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5074010Z   test_consistency_SparseCSR_isnan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5074183Z   test_consistency_SparseCSR_isneginf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5074350Z   test_consistency_SparseCSR_isneginf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5074511Z   test_consistency_SparseCSR_isneginf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5074708Z   test_consistency_SparseCSR_isneginf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5074877Z   test_consistency_SparseCSR_isneginf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.007s)
2023-01-11T21:22:05.5075043Z   test_consistency_SparseCSR_isneginf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5075206Z   test_consistency_SparseCSR_isneginf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5075364Z   test_consistency_SparseCSR_isneginf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5075533Z   test_consistency_SparseCSR_isneginf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5075701Z   test_consistency_SparseCSR_isneginf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5075870Z   test_consistency_SparseCSR_isposinf_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5076026Z   test_consistency_SparseCSR_isposinf_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5076196Z   test_consistency_SparseCSR_isposinf_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5076364Z   test_consistency_SparseCSR_isposinf_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5076528Z   test_consistency_SparseCSR_isposinf_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5076693Z   test_consistency_SparseCSR_isposinf_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5076854Z   test_consistency_SparseCSR_isposinf_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5077015Z   test_consistency_SparseCSR_isposinf_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5077180Z   test_consistency_SparseCSR_isposinf_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5077353Z   test_consistency_SparseCSR_isposinf_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5077506Z   test_consistency_SparseCSR_log1p_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5077671Z   test_consistency_SparseCSR_log1p_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5077843Z   test_consistency_SparseCSR_log1p_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5078011Z   test_consistency_SparseCSR_log1p_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5078174Z   test_consistency_SparseCSR_log1p_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5078371Z   test_consistency_SparseCSR_log1p_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5078535Z   test_consistency_SparseCSR_log1p_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5078701Z   test_consistency_SparseCSR_log1p_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5078853Z   test_consistency_SparseCSR_log1p_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5079013Z   test_consistency_SparseCSR_log1p_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5079174Z   test_consistency_SparseCSR_log1p_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5079940Z   test_consistency_SparseCSR_masked_amax_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... /opt/conda/lib/python3.7/site-packages/torch/masked/_ops.py:768: UserWarning: scatter_reduce() is in beta and the API may change at any time. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1739.)
2023-01-11T21:22:05.5080062Z   0, scatter_indices, values, reduce, include_self=False
2023-01-11T21:22:05.5080130Z ok (0.060s)
2023-01-11T21:22:05.5080340Z   test_consistency_SparseCSR_masked_amax_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.043s)
2023-01-11T21:22:05.5080514Z   test_consistency_SparseCSR_masked_amax_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.044s)
2023-01-11T21:22:05.5080854Z   test_consistency_SparseCSR_masked_amax_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.043s)
2023-01-11T21:22:05.5081124Z   test_consistency_SparseCSR_masked_amax_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5081351Z   test_consistency_SparseCSR_masked_amax_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5081599Z   test_consistency_SparseCSR_masked_amax_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5081845Z   test_consistency_SparseCSR_masked_amax_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5082089Z   test_consistency_SparseCSR_masked_amax_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amax does not support input with torch.sparse_csr layout (0.006s)
2023-01-11T21:22:05.5082267Z   test_consistency_SparseCSR_masked_amin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.045s)
2023-01-11T21:22:05.5082444Z   test_consistency_SparseCSR_masked_amin_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.047s)
2023-01-11T21:22:05.5082620Z   test_consistency_SparseCSR_masked_amin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.047s)
2023-01-11T21:22:05.5082792Z   test_consistency_SparseCSR_masked_amin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.046s)
2023-01-11T21:22:05.5083034Z   test_consistency_SparseCSR_masked_amin_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5083279Z   test_consistency_SparseCSR_masked_amin_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5083517Z   test_consistency_SparseCSR_masked_amin_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5083745Z   test_consistency_SparseCSR_masked_amin_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5083987Z   test_consistency_SparseCSR_masked_amin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.amin does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5084220Z   test_consistency_SparseCSR_masked_mean_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.048s)
2023-01-11T21:22:05.5084465Z   test_consistency_SparseCSR_masked_mean_cpu_bool (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5084714Z   test_consistency_SparseCSR_masked_mean_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5084963Z   test_consistency_SparseCSR_masked_mean_cpu_complex64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5085141Z   test_consistency_SparseCSR_masked_mean_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.048s)
2023-01-11T21:22:05.5085315Z   test_consistency_SparseCSR_masked_mean_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.049s)
2023-01-11T21:22:05.5085489Z   test_consistency_SparseCSR_masked_mean_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.048s)
2023-01-11T21:22:05.5085769Z   test_consistency_SparseCSR_masked_mean_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5086010Z   test_consistency_SparseCSR_masked_mean_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5086241Z   test_consistency_SparseCSR_masked_mean_cpu_int64 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5086479Z   test_consistency_SparseCSR_masked_mean_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5086722Z   test_consistency_SparseCSR_masked_mean_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: masked.mean does not support input with torch.sparse_csr layout (0.003s)
2023-01-11T21:22:05.5086906Z   test_consistency_SparseCSR_masked_prod_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.044s)
2023-01-11T21:22:05.5087078Z   test_consistency_SparseCSR_masked_prod_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.019s)
2023-01-11T21:22:05.5087257Z   test_consistency_SparseCSR_masked_prod_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.017s)
2023-01-11T21:22:05.5087440Z   test_consistency_SparseCSR_masked_prod_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.017s)
2023-01-11T21:22:05.5087614Z   test_consistency_SparseCSR_masked_prod_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.045s)
2023-01-11T21:22:05.5087787Z   test_consistency_SparseCSR_masked_prod_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.069s)
2023-01-11T21:22:05.5087959Z   test_consistency_SparseCSR_masked_prod_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5088120Z   test_consistency_SparseCSR_masked_prod_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5088289Z   test_consistency_SparseCSR_masked_prod_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5088462Z   test_consistency_SparseCSR_masked_prod_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.017s)
2023-01-11T21:22:05.5088632Z   test_consistency_SparseCSR_masked_prod_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5088804Z   test_consistency_SparseCSR_masked_sum_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.047s)
2023-01-11T21:22:05.5088973Z   test_consistency_SparseCSR_masked_sum_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.017s)
2023-01-11T21:22:05.5089151Z   test_consistency_SparseCSR_masked_sum_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.017s)
2023-01-11T21:22:05.5089326Z   test_consistency_SparseCSR_masked_sum_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5089529Z   test_consistency_SparseCSR_masked_sum_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.043s)
2023-01-11T21:22:05.5089693Z   test_consistency_SparseCSR_masked_sum_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.053s)
2023-01-11T21:22:05.5089861Z   test_consistency_SparseCSR_masked_sum_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.044s)
2023-01-11T21:22:05.5090031Z   test_consistency_SparseCSR_masked_sum_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.017s)
2023-01-11T21:22:05.5090196Z   test_consistency_SparseCSR_masked_sum_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5090359Z   test_consistency_SparseCSR_masked_sum_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5090527Z   test_consistency_SparseCSR_masked_sum_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.017s)
2023-01-11T21:22:05.5090697Z   test_consistency_SparseCSR_masked_sum_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.016s)
2023-01-11T21:22:05.5090865Z   test_consistency_SparseCSR_neg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5091063Z   test_consistency_SparseCSR_neg_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5091219Z   test_consistency_SparseCSR_neg_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5091387Z   test_consistency_SparseCSR_neg_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5091551Z   test_consistency_SparseCSR_neg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5091716Z   test_consistency_SparseCSR_neg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5091878Z   test_consistency_SparseCSR_neg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5092042Z   test_consistency_SparseCSR_neg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5092206Z   test_consistency_SparseCSR_neg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5092370Z   test_consistency_SparseCSR_neg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5092519Z   test_consistency_SparseCSR_neg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5092677Z   test_consistency_SparseCSR_neg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5092861Z   test_consistency_SparseCSR_nn_functional_relu_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5093045Z   test_consistency_SparseCSR_nn_functional_relu_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5093227Z   test_consistency_SparseCSR_nn_functional_relu_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5093410Z   test_consistency_SparseCSR_nn_functional_relu_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5093591Z   test_consistency_SparseCSR_nn_functional_relu_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5093772Z   test_consistency_SparseCSR_nn_functional_relu_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5093955Z   test_consistency_SparseCSR_nn_functional_relu_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5094122Z   test_consistency_SparseCSR_nn_functional_relu_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5094295Z   test_consistency_SparseCSR_positive_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5094469Z   test_consistency_SparseCSR_positive_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5094644Z   test_consistency_SparseCSR_positive_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5094847Z   test_consistency_SparseCSR_positive_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5095021Z   test_consistency_SparseCSR_positive_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5095191Z   test_consistency_SparseCSR_positive_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5095355Z   test_consistency_SparseCSR_positive_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5095523Z   test_consistency_SparseCSR_positive_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5095680Z   test_consistency_SparseCSR_positive_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5095842Z   test_consistency_SparseCSR_positive_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5096011Z   test_consistency_SparseCSR_positive_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5096184Z   test_consistency_SparseCSR_positive_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5096384Z   test_consistency_SparseCSR_rad2deg_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5096555Z   test_consistency_SparseCSR_rad2deg_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5096730Z   test_consistency_SparseCSR_rad2deg_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5096896Z   test_consistency_SparseCSR_rad2deg_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5097061Z   test_consistency_SparseCSR_rad2deg_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5097280Z   test_consistency_SparseCSR_rad2deg_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5097449Z   test_consistency_SparseCSR_rad2deg_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5097617Z   test_consistency_SparseCSR_rad2deg_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5097787Z   test_consistency_SparseCSR_rad2deg_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5097956Z   test_consistency_SparseCSR_rad2deg_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5098134Z   test_consistency_SparseCSR_randn_like_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.014s)
2023-01-11T21:22:05.5098313Z   test_consistency_SparseCSR_randn_like_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5098494Z   test_consistency_SparseCSR_randn_like_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5098672Z   test_consistency_SparseCSR_randn_like_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5098834Z   test_consistency_SparseCSR_randn_like_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5099019Z   test_consistency_SparseCSR_randn_like_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5099190Z   test_consistency_SparseCSR_randn_like_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5099363Z   test_consistency_SparseCSR_round_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5099531Z   test_consistency_SparseCSR_round_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5099699Z   test_consistency_SparseCSR_round_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5099867Z   test_consistency_SparseCSR_round_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5100034Z   test_consistency_SparseCSR_round_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5100202Z   test_consistency_SparseCSR_round_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5100423Z   test_consistency_SparseCSR_round_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5100590Z   test_consistency_SparseCSR_round_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5100760Z   test_consistency_SparseCSR_sgn_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5100924Z   test_consistency_SparseCSR_sgn_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5101097Z   test_consistency_SparseCSR_sgn_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5101266Z   test_consistency_SparseCSR_sgn_cpu_complex32 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5101432Z   test_consistency_SparseCSR_sgn_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5101597Z   test_consistency_SparseCSR_sgn_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5101753Z   test_consistency_SparseCSR_sgn_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5101946Z   test_consistency_SparseCSR_sgn_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5102111Z   test_consistency_SparseCSR_sgn_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5102273Z   test_consistency_SparseCSR_sgn_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5102432Z   test_consistency_SparseCSR_sgn_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5102593Z   test_consistency_SparseCSR_sgn_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5102754Z   test_consistency_SparseCSR_sgn_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5102922Z   test_consistency_SparseCSR_sign_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5103085Z   test_consistency_SparseCSR_sign_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5103238Z   test_consistency_SparseCSR_sign_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5103408Z   test_consistency_SparseCSR_sign_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5103575Z   test_consistency_SparseCSR_sign_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5103740Z   test_consistency_SparseCSR_sign_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5103904Z   test_consistency_SparseCSR_sign_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5104067Z   test_consistency_SparseCSR_sign_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5104231Z   test_consistency_SparseCSR_sign_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5104391Z   test_consistency_SparseCSR_sign_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5104566Z   test_consistency_SparseCSR_signbit_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5104723Z   test_consistency_SparseCSR_signbit_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5104891Z   test_consistency_SparseCSR_signbit_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5105061Z   test_consistency_SparseCSR_signbit_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5105225Z   test_consistency_SparseCSR_signbit_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5105391Z   test_consistency_SparseCSR_signbit_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5105555Z   test_consistency_SparseCSR_signbit_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5105724Z   test_consistency_SparseCSR_signbit_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5105917Z   test_consistency_SparseCSR_signbit_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5106071Z   test_consistency_SparseCSR_signbit_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5106235Z   test_consistency_SparseCSR_sin_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5106395Z   test_consistency_SparseCSR_sin_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.012s)
2023-01-11T21:22:05.5106561Z   test_consistency_SparseCSR_sin_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5106730Z   test_consistency_SparseCSR_sin_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5106893Z   test_consistency_SparseCSR_sin_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5107055Z   test_consistency_SparseCSR_sin_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5107217Z   test_consistency_SparseCSR_sin_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5107406Z   test_consistency_SparseCSR_sin_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5107556Z   test_consistency_SparseCSR_sin_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5107717Z   test_consistency_SparseCSR_sin_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5107875Z   test_consistency_SparseCSR_sin_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5108041Z   test_consistency_SparseCSR_sinh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5108199Z   test_consistency_SparseCSR_sinh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5108366Z   test_consistency_SparseCSR_sinh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5108535Z   test_consistency_SparseCSR_sinh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5108700Z   test_consistency_SparseCSR_sinh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5108864Z   test_consistency_SparseCSR_sinh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5109016Z   test_consistency_SparseCSR_sinh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5109179Z   test_consistency_SparseCSR_sinh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5109338Z   test_consistency_SparseCSR_sinh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5109498Z   test_consistency_SparseCSR_sinh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5109656Z   test_consistency_SparseCSR_sinh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5109822Z   test_consistency_SparseCSR_sqrt_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5109985Z   test_consistency_SparseCSR_sqrt_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5110154Z   test_consistency_SparseCSR_sqrt_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5110322Z   test_consistency_SparseCSR_sqrt_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5110472Z   test_consistency_SparseCSR_sqrt_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5110636Z   test_consistency_SparseCSR_sqrt_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5110795Z   test_consistency_SparseCSR_sqrt_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5110955Z   test_consistency_SparseCSR_sqrt_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5111160Z   test_consistency_SparseCSR_sqrt_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5111319Z   test_consistency_SparseCSR_sqrt_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5111482Z   test_consistency_SparseCSR_sqrt_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5111647Z   test_consistency_SparseCSR_tan_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5111796Z   test_consistency_SparseCSR_tan_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5111963Z   test_consistency_SparseCSR_tan_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5112127Z   test_consistency_SparseCSR_tan_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5112291Z   test_consistency_SparseCSR_tan_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5112457Z   test_consistency_SparseCSR_tan_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5112615Z   test_consistency_SparseCSR_tan_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5112804Z   test_consistency_SparseCSR_tan_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.009s)
2023-01-11T21:22:05.5112961Z   test_consistency_SparseCSR_tan_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5113122Z   test_consistency_SparseCSR_tan_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5113270Z   test_consistency_SparseCSR_tan_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5113437Z   test_consistency_SparseCSR_tanh_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5113594Z   test_consistency_SparseCSR_tanh_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5113764Z   test_consistency_SparseCSR_tanh_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5113934Z   test_consistency_SparseCSR_tanh_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.005s)
2023-01-11T21:22:05.5114102Z   test_consistency_SparseCSR_tanh_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5114268Z   test_consistency_SparseCSR_tanh_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5114430Z   test_consistency_SparseCSR_tanh_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5114592Z   test_consistency_SparseCSR_tanh_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5114741Z   test_consistency_SparseCSR_tanh_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5114900Z   test_consistency_SparseCSR_tanh_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5115058Z   test_consistency_SparseCSR_tanh_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5115236Z   test_consistency_SparseCSR_to_sparse_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.007s)
2023-01-11T21:22:05.5115407Z   test_consistency_SparseCSR_to_sparse_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5115584Z   test_consistency_SparseCSR_to_sparse_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5115758Z   test_consistency_SparseCSR_to_sparse_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.007s)
2023-01-11T21:22:05.5115929Z   test_consistency_SparseCSR_to_sparse_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5116086Z   test_consistency_SparseCSR_to_sparse_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5116249Z   test_consistency_SparseCSR_to_sparse_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5116416Z   test_consistency_SparseCSR_to_sparse_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5116614Z   test_consistency_SparseCSR_to_sparse_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5116780Z   test_consistency_SparseCSR_to_sparse_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5116946Z   test_consistency_SparseCSR_to_sparse_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5117112Z   test_consistency_SparseCSR_to_sparse_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.006s)
2023-01-11T21:22:05.5117276Z   test_consistency_SparseCSR_trunc_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5117440Z   test_consistency_SparseCSR_trunc_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5117594Z   test_consistency_SparseCSR_trunc_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5117758Z   test_consistency_SparseCSR_trunc_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5117921Z   test_consistency_SparseCSR_trunc_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5118111Z   test_consistency_SparseCSR_trunc_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5118274Z   test_consistency_SparseCSR_trunc_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5118435Z   test_consistency_SparseCSR_trunc_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.004s)
2023-01-11T21:22:05.5118589Z   test_copy_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.069s)
2023-01-11T21:22:05.5118739Z   test_copy_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.070s)
2023-01-11T21:22:05.5118895Z   test_copy_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.074s)
2023-01-11T21:22:05.5119038Z   test_copy_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.070s)
2023-01-11T21:22:05.5119192Z   test_copy_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.070s)
2023-01-11T21:22:05.5119343Z   test_copy_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.069s)
2023-01-11T21:22:05.5119490Z   test_copy_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.067s)
2023-01-11T21:22:05.5119645Z   test_copy_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.067s)
2023-01-11T21:22:05.5119796Z   test_copy_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.069s)
2023-01-11T21:22:05.5119944Z   test_copy_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.067s)
2023-01-11T21:22:05.5120092Z   test_copy_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.067s)
2023-01-11T21:22:05.5120231Z   test_copy_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.066s)
2023-01-11T21:22:05.5120384Z   test_copy_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.056s)
2023-01-11T21:22:05.5120534Z   test_copy_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.054s)
2023-01-11T21:22:05.5120812Z   test_copy_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.057s)
2023-01-11T21:22:05.5120971Z   test_copy_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.059s)
2023-01-11T21:22:05.5121125Z   test_copy_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.055s)
2023-01-11T21:22:05.5121278Z   test_copy_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.057s)
2023-01-11T21:22:05.5121428Z   test_copy_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.057s)
2023-01-11T21:22:05.5121566Z   test_copy_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.055s)
2023-01-11T21:22:05.5121715Z   test_copy_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.055s)
2023-01-11T21:22:05.5121861Z   test_copy_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.055s)
2023-01-11T21:22:05.5122057Z   test_copy_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.055s)
2023-01-11T21:22:05.5122207Z   test_copy_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.054s)
2023-01-11T21:22:05.5122367Z   test_copy_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5122523Z   test_copy_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.038s)
2023-01-11T21:22:05.5122681Z   test_copy_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.039s)
2023-01-11T21:22:05.5122837Z   test_copy_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5122979Z   test_copy_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5123129Z   test_copy_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5123275Z   test_copy_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.039s)
2023-01-11T21:22:05.5123428Z   test_copy_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.038s)
2023-01-11T21:22:05.5123575Z   test_copy_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.038s)
2023-01-11T21:22:05.5123755Z   test_copy_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.042s)
2023-01-11T21:22:05.5123904Z   test_copy_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.038s)
2023-01-11T21:22:05.5124053Z   test_copy_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.038s)
2023-01-11T21:22:05.5124195Z   test_copy_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.038s)
2023-01-11T21:22:05.5124343Z   test_copy_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.037s)
2023-01-11T21:22:05.5124497Z   test_copy_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.039s)
2023-01-11T21:22:05.5124650Z   test_copy_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.039s)
2023-01-11T21:22:05.5124804Z   test_copy_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5124951Z   test_copy_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5125099Z   test_copy_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5125250Z   test_copy_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.041s)
2023-01-11T21:22:05.5125385Z   test_copy_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.041s)
2023-01-11T21:22:05.5125525Z   test_copy_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.040s)
2023-01-11T21:22:05.5125670Z   test_copy_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.042s)
2023-01-11T21:22:05.5125819Z   test_copy_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.041s)
2023-01-11T21:22:05.5125981Z   test_copy_errors_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.128s)
2023-01-11T21:22:05.5126140Z   test_copy_errors_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.127s)
2023-01-11T21:22:05.5126304Z   test_copy_errors_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5126466Z   test_copy_errors_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.125s)
2023-01-11T21:22:05.5126613Z   test_copy_errors_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.125s)
2023-01-11T21:22:05.5126770Z   test_copy_errors_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.133s)
2023-01-11T21:22:05.5126927Z   test_copy_errors_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.123s)
2023-01-11T21:22:05.5127082Z   test_copy_errors_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.124s)
2023-01-11T21:22:05.5127235Z   test_copy_errors_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.123s)
2023-01-11T21:22:05.5127391Z   test_copy_errors_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.123s)
2023-01-11T21:22:05.5127575Z   test_copy_errors_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.121s)
2023-01-11T21:22:05.5127731Z   test_copy_errors_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.121s)
2023-01-11T21:22:05.5127892Z   test_copy_errors_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.124s)
2023-01-11T21:22:05.5128039Z   test_copy_errors_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.122s)
2023-01-11T21:22:05.5128201Z   test_copy_errors_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.124s)
2023-01-11T21:22:05.5128364Z   test_copy_errors_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.124s)
2023-01-11T21:22:05.5128523Z   test_copy_errors_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.122s)
2023-01-11T21:22:05.5128680Z   test_copy_errors_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.123s)
2023-01-11T21:22:05.5128835Z   test_copy_errors_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.123s)
2023-01-11T21:22:05.5128989Z   test_copy_errors_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.123s)
2023-01-11T21:22:05.5129171Z   test_copy_errors_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.122s)
2023-01-11T21:22:05.5129323Z   test_copy_errors_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.121s)
2023-01-11T21:22:05.5129466Z   test_copy_errors_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.156s)
2023-01-11T21:22:05.5129621Z   test_copy_errors_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.122s)
2023-01-11T21:22:05.5129781Z   test_copy_errors_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.088s)
2023-01-11T21:22:05.5129934Z   test_copy_errors_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.088s)
2023-01-11T21:22:05.5130096Z   test_copy_errors_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.088s)
2023-01-11T21:22:05.5130260Z   test_copy_errors_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5130418Z   test_copy_errors_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5130573Z   test_copy_errors_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5130714Z   test_copy_errors_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5130909Z   test_copy_errors_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5131065Z   test_copy_errors_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5131221Z   test_copy_errors_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5131377Z   test_copy_errors_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5131531Z   test_copy_errors_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.104s)
2023-01-11T21:22:05.5131690Z   test_copy_errors_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.088s)
2023-01-11T21:22:05.5131847Z   test_copy_errors_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.088s)
2023-01-11T21:22:05.5131999Z   test_copy_errors_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5132159Z   test_copy_errors_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5132315Z   test_copy_errors_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.086s)
2023-01-11T21:22:05.5132471Z   test_copy_errors_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.086s)
2023-01-11T21:22:05.5132627Z   test_copy_errors_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.086s)
2023-01-11T21:22:05.5132781Z   test_copy_errors_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.086s)
2023-01-11T21:22:05.5132965Z   test_copy_errors_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.096s)
2023-01-11T21:22:05.5133123Z   test_copy_errors_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.086s)
2023-01-11T21:22:05.5133278Z   test_copy_errors_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.086s)
2023-01-11T21:22:05.5133420Z   test_copy_errors_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.087s)
2023-01-11T21:22:05.5133563Z   test_dim_SparseBSC_cpu (__main__.TestSparseCompressedCPU) ... ok (0.102s)
2023-01-11T21:22:05.5133705Z   test_dim_SparseBSR_cpu (__main__.TestSparseCompressedCPU) ... ok (0.101s)
2023-01-11T21:22:05.5133843Z   test_dim_SparseCSC_cpu (__main__.TestSparseCompressedCPU) ... ok (0.088s)
2023-01-11T21:22:05.5133980Z   test_dim_SparseCSR_cpu (__main__.TestSparseCompressedCPU) ... ok (0.088s)
2023-01-11T21:22:05.5134134Z   test_empty_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5134287Z   test_empty_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5134471Z   test_empty_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5134618Z   test_empty_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.036s)
2023-01-11T21:22:05.5134770Z   test_empty_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5134924Z   test_empty_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5135073Z   test_empty_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5135223Z   test_empty_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.031s)
2023-01-11T21:22:05.5135371Z   test_empty_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.031s)
2023-01-11T21:22:05.5135515Z   test_empty_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5135667Z   test_empty_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.037s)
2023-01-11T21:22:05.5135808Z   test_empty_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5135961Z   test_empty_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5136108Z   test_empty_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5136265Z   test_empty_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.032s)
2023-01-11T21:22:05.5136421Z   test_empty_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5136572Z   test_empty_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5136721Z   test_empty_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5136866Z   test_empty_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.037s)
2023-01-11T21:22:05.5137018Z   test_empty_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.060s)
2023-01-11T21:22:05.5137153Z   test_empty_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5137361Z   test_empty_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5137511Z   test_empty_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5137665Z   test_empty_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.033s)
2023-01-11T21:22:05.5137829Z   test_empty_errors_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.010s)
2023-01-11T21:22:05.5137986Z   test_empty_errors_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5138153Z   test_empty_errors_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5138319Z   test_empty_errors_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5138506Z   test_empty_errors_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5138670Z   test_empty_errors_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5138827Z   test_empty_errors_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5138988Z   test_empty_errors_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5139147Z   test_empty_errors_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5139308Z   test_empty_errors_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5139466Z   test_empty_errors_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5139622Z   test_empty_errors_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5139787Z   test_empty_errors_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5139957Z   test_empty_errors_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5140124Z   test_empty_errors_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5140286Z   test_empty_errors_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5140444Z   test_empty_errors_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5140603Z   test_empty_errors_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5140761Z   test_empty_errors_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5140919Z   test_empty_errors_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5141077Z   test_empty_errors_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5141218Z   test_empty_errors_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5141376Z   test_empty_errors_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5141531Z   test_empty_errors_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.008s)
2023-01-11T21:22:05.5141703Z   test_empty_like_SparseBSC_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5141869Z   test_empty_like_SparseBSC_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.116s)
2023-01-11T21:22:05.5142043Z   test_empty_like_SparseBSC_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.117s)
2023-01-11T21:22:05.5142216Z   test_empty_like_SparseBSC_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.118s)
2023-01-11T21:22:05.5142388Z   test_empty_like_SparseBSC_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.118s)
2023-01-11T21:22:05.5142555Z   test_empty_like_SparseBSC_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.134s)
2023-01-11T21:22:05.5142709Z   test_empty_like_SparseBSC_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.121s)
2023-01-11T21:22:05.5142878Z   test_empty_like_SparseBSC_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.137s)
2023-01-11T21:22:05.5143045Z   test_empty_like_SparseBSC_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.116s)
2023-01-11T21:22:05.5143215Z   test_empty_like_SparseBSC_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.115s)
2023-01-11T21:22:05.5143384Z   test_empty_like_SparseBSC_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.116s)
2023-01-11T21:22:05.5143553Z   test_empty_like_SparseBSC_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.117s)
2023-01-11T21:22:05.5143727Z   test_empty_like_SparseBSC_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.333s)
2023-01-11T21:22:05.5143925Z   test_empty_like_SparseBSC_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.370s)
2023-01-11T21:22:05.5144105Z   test_empty_like_SparseBSC_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5144268Z   test_empty_like_SparseBSC_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5144441Z   test_empty_like_SparseBSC_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5144610Z   test_empty_like_SparseBSC_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.339s)
2023-01-11T21:22:05.5144777Z   test_empty_like_SparseBSC_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5144946Z   test_empty_like_SparseBSC_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.331s)
2023-01-11T21:22:05.5145112Z   test_empty_like_SparseBSC_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.331s)
2023-01-11T21:22:05.5145274Z   test_empty_like_SparseBSC_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.345s)
2023-01-11T21:22:05.5145474Z   test_empty_like_SparseBSC_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.327s)
2023-01-11T21:22:05.5145643Z   test_empty_like_SparseBSC_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5145803Z   test_empty_like_SparseBSC_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5145970Z   test_empty_like_SparseBSC_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.334s)
2023-01-11T21:22:05.5146148Z   test_empty_like_SparseBSC_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5146325Z   test_empty_like_SparseBSC_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5146500Z   test_empty_like_SparseBSC_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5146670Z   test_empty_like_SparseBSC_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.366s)
2023-01-11T21:22:05.5146840Z   test_empty_like_SparseBSC_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5147006Z   test_empty_like_SparseBSC_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.327s)
2023-01-11T21:22:05.5147169Z   test_empty_like_SparseBSC_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.342s)
2023-01-11T21:22:05.5147317Z   test_empty_like_SparseBSC_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.347s)
2023-01-11T21:22:05.5147484Z   test_empty_like_SparseBSC_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.357s)
2023-01-11T21:22:05.5147652Z   test_empty_like_SparseBSC_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.338s)
2023-01-11T21:22:05.5147827Z   test_empty_like_SparseBSC_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.334s)
2023-01-11T21:22:05.5147994Z   test_empty_like_SparseBSC_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.357s)
2023-01-11T21:22:05.5148171Z   test_empty_like_SparseBSC_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5148346Z   test_empty_like_SparseBSC_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.334s)
2023-01-11T21:22:05.5148517Z   test_empty_like_SparseBSC_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.334s)
2023-01-11T21:22:05.5148673Z   test_empty_like_SparseBSC_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.351s)
2023-01-11T21:22:05.5148840Z   test_empty_like_SparseBSC_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5149005Z   test_empty_like_SparseBSC_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5149224Z   test_empty_like_SparseBSC_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.360s)
2023-01-11T21:22:05.5149387Z   test_empty_like_SparseBSC_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.377s)
2023-01-11T21:22:05.5149556Z   test_empty_like_SparseBSC_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5149721Z   test_empty_like_SparseBSC_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.356s)
2023-01-11T21:22:05.5149896Z   test_empty_like_SparseBSR_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5150064Z   test_empty_like_SparseBSR_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.346s)
2023-01-11T21:22:05.5150228Z   test_empty_like_SparseBSR_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5150404Z   test_empty_like_SparseBSR_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5150578Z   test_empty_like_SparseBSR_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.327s)
2023-01-11T21:22:05.5150774Z   test_empty_like_SparseBSR_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.370s)
2023-01-11T21:22:05.5150948Z   test_empty_like_SparseBSR_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5151116Z   test_empty_like_SparseBSR_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5151281Z   test_empty_like_SparseBSR_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5151442Z   test_empty_like_SparseBSR_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.339s)
2023-01-11T21:22:05.5151608Z   test_empty_like_SparseBSR_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5151761Z   test_empty_like_SparseBSR_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5151934Z   test_empty_like_SparseBSR_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.117s)
2023-01-11T21:22:05.5152100Z   test_empty_like_SparseBSR_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.117s)
2023-01-11T21:22:05.5152275Z   test_empty_like_SparseBSR_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.151s)
2023-01-11T21:22:05.5152449Z   test_empty_like_SparseBSR_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.114s)
2023-01-11T21:22:05.5152618Z   test_empty_like_SparseBSR_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.116s)
2023-01-11T21:22:05.5152785Z   test_empty_like_SparseBSR_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.121s)
2023-01-11T21:22:05.5152949Z   test_empty_like_SparseBSR_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.122s)
2023-01-11T21:22:05.5153117Z   test_empty_like_SparseBSR_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.120s)
2023-01-11T21:22:05.5153270Z   test_empty_like_SparseBSR_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.118s)
2023-01-11T21:22:05.5153436Z   test_empty_like_SparseBSR_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.128s)
2023-01-11T21:22:05.5153601Z   test_empty_like_SparseBSR_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.113s)
2023-01-11T21:22:05.5153765Z   test_empty_like_SparseBSR_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.113s)
2023-01-11T21:22:05.5153935Z   test_empty_like_SparseBSR_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5154099Z   test_empty_like_SparseBSR_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5154272Z   test_empty_like_SparseBSR_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.343s)
2023-01-11T21:22:05.5154448Z   test_empty_like_SparseBSR_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5154648Z   test_empty_like_SparseBSR_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5154805Z   test_empty_like_SparseBSR_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5154966Z   test_empty_like_SparseBSR_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.342s)
2023-01-11T21:22:05.5155132Z   test_empty_like_SparseBSR_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5155293Z   test_empty_like_SparseBSR_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5155454Z   test_empty_like_SparseBSR_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5155617Z   test_empty_like_SparseBSR_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.344s)
2023-01-11T21:22:05.5155781Z   test_empty_like_SparseBSR_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5155954Z   test_empty_like_SparseBSR_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.341s)
2023-01-11T21:22:05.5156148Z   test_empty_like_SparseBSR_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.341s)
2023-01-11T21:22:05.5156311Z   test_empty_like_SparseBSR_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.366s)
2023-01-11T21:22:05.5156485Z   test_empty_like_SparseBSR_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5156653Z   test_empty_like_SparseBSR_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5156824Z   test_empty_like_SparseBSR_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5156991Z   test_empty_like_SparseBSR_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.344s)
2023-01-11T21:22:05.5157156Z   test_empty_like_SparseBSR_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5157320Z   test_empty_like_SparseBSR_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.321s)
2023-01-11T21:22:05.5157487Z   test_empty_like_SparseBSR_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5157640Z   test_empty_like_SparseBSR_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.334s)
2023-01-11T21:22:05.5157803Z   test_empty_like_SparseBSR_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.321s)
2023-01-11T21:22:05.5157971Z   test_empty_like_SparseCSC_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.307s)
2023-01-11T21:22:05.5158134Z   test_empty_like_SparseCSC_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5158308Z   test_empty_like_SparseCSC_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5158479Z   test_empty_like_SparseCSC_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5158651Z   test_empty_like_SparseCSC_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5158818Z   test_empty_like_SparseCSC_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.309s)
2023-01-11T21:22:05.5158980Z   test_empty_like_SparseCSC_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.309s)
2023-01-11T21:22:05.5159135Z   test_empty_like_SparseCSC_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.340s)
2023-01-11T21:22:05.5159298Z   test_empty_like_SparseCSC_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5159461Z   test_empty_like_SparseCSC_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.313s)
2023-01-11T21:22:05.5159625Z   test_empty_like_SparseCSC_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.313s)
2023-01-11T21:22:05.5159788Z   test_empty_like_SparseCSC_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5159986Z   test_empty_like_SparseCSC_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5160153Z   test_empty_like_SparseCSC_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.314s)
2023-01-11T21:22:05.5160326Z   test_empty_like_SparseCSC_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5160499Z   test_empty_like_SparseCSC_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5160814Z   test_empty_like_SparseCSC_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5161008Z   test_empty_like_SparseCSC_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5161183Z   test_empty_like_SparseCSC_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5161354Z   test_empty_like_SparseCSC_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5161518Z   test_empty_like_SparseCSC_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.309s)
2023-01-11T21:22:05.5161732Z   test_empty_like_SparseCSC_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.313s)
2023-01-11T21:22:05.5161902Z   test_empty_like_SparseCSC_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.309s)
2023-01-11T21:22:05.5162067Z   test_empty_like_SparseCSC_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.346s)
2023-01-11T21:22:05.5162240Z   test_empty_like_SparseCSC_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.105s)
2023-01-11T21:22:05.5162394Z   test_empty_like_SparseCSC_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.105s)
2023-01-11T21:22:05.5162568Z   test_empty_like_SparseCSC_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.105s)
2023-01-11T21:22:05.5162745Z   test_empty_like_SparseCSC_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.105s)
2023-01-11T21:22:05.5162914Z   test_empty_like_SparseCSC_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.106s)
2023-01-11T21:22:05.5163084Z   test_empty_like_SparseCSC_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.111s)
2023-01-11T21:22:05.5163248Z   test_empty_like_SparseCSC_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.111s)
2023-01-11T21:22:05.5163414Z   test_empty_like_SparseCSC_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.120s)
2023-01-11T21:22:05.5163579Z   test_empty_like_SparseCSC_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.103s)
2023-01-11T21:22:05.5163750Z   test_empty_like_SparseCSC_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.103s)
2023-01-11T21:22:05.5163903Z   test_empty_like_SparseCSC_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.105s)
2023-01-11T21:22:05.5164072Z   test_empty_like_SparseCSC_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.105s)
2023-01-11T21:22:05.5164244Z   test_empty_like_SparseCSC_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5164410Z   test_empty_like_SparseCSC_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5164585Z   test_empty_like_SparseCSC_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.321s)
2023-01-11T21:22:05.5164758Z   test_empty_like_SparseCSC_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.306s)
2023-01-11T21:22:05.5164929Z   test_empty_like_SparseCSC_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5165094Z   test_empty_like_SparseCSC_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5165258Z   test_empty_like_SparseCSC_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5165450Z   test_empty_like_SparseCSC_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5165617Z   test_empty_like_SparseCSC_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5165776Z   test_empty_like_SparseCSC_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.320s)
2023-01-11T21:22:05.5165940Z   test_empty_like_SparseCSC_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5166107Z   test_empty_like_SparseCSC_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5166276Z   test_empty_like_SparseCSR_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5166440Z   test_empty_like_SparseCSR_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5166613Z   test_empty_like_SparseCSR_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.369s)
2023-01-11T21:22:05.5166775Z   test_empty_like_SparseCSR_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.319s)
2023-01-11T21:22:05.5166976Z   test_empty_like_SparseCSR_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5167145Z   test_empty_like_SparseCSR_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5167310Z   test_empty_like_SparseCSR_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5167476Z   test_empty_like_SparseCSR_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5167639Z   test_empty_like_SparseCSR_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5167799Z   test_empty_like_SparseCSR_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5167965Z   test_empty_like_SparseCSR_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5168131Z   test_empty_like_SparseCSR_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.321s)
2023-01-11T21:22:05.5168291Z   test_empty_like_SparseCSR_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5168456Z   test_empty_like_SparseCSR_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5168629Z   test_empty_like_SparseCSR_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5168801Z   test_empty_like_SparseCSR_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.323s)
2023-01-11T21:22:05.5168971Z   test_empty_like_SparseCSR_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5169136Z   test_empty_like_SparseCSR_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.314s)
2023-01-11T21:22:05.5169299Z   test_empty_like_SparseCSR_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.313s)
2023-01-11T21:22:05.5169466Z   test_empty_like_SparseCSR_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.342s)
2023-01-11T21:22:05.5169635Z   test_empty_like_SparseCSR_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5169785Z   test_empty_like_SparseCSR_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5169951Z   test_empty_like_SparseCSR_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5170117Z   test_empty_like_SparseCSR_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5170285Z   test_empty_like_SparseCSR_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5170449Z   test_empty_like_SparseCSR_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.314s)
2023-01-11T21:22:05.5170623Z   test_empty_like_SparseCSR_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.314s)
2023-01-11T21:22:05.5170824Z   test_empty_like_SparseCSR_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5170995Z   test_empty_like_SparseCSR_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.308s)
2023-01-11T21:22:05.5171161Z   test_empty_like_SparseCSR_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5171317Z   test_empty_like_SparseCSR_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.309s)
2023-01-11T21:22:05.5171481Z   test_empty_like_SparseCSR_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5171642Z   test_empty_like_SparseCSR_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.327s)
2023-01-11T21:22:05.5171801Z   test_empty_like_SparseCSR_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5171964Z   test_empty_like_SparseCSR_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5172129Z   test_empty_like_SparseCSR_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5172329Z   test_empty_like_SparseCSR_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.138s)
2023-01-11T21:22:05.5172494Z   test_empty_like_SparseCSR_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.101s)
2023-01-11T21:22:05.5172667Z   test_empty_like_SparseCSR_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.102s)
2023-01-11T21:22:05.5172827Z   test_empty_like_SparseCSR_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.103s)
2023-01-11T21:22:05.5172994Z   test_empty_like_SparseCSR_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.107s)
2023-01-11T21:22:05.5173160Z   test_empty_like_SparseCSR_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.106s)
2023-01-11T21:22:05.5173321Z   test_empty_like_SparseCSR_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.104s)
2023-01-11T21:22:05.5173488Z   test_empty_like_SparseCSR_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.107s)
2023-01-11T21:22:05.5173652Z   test_empty_like_SparseCSR_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.116s)
2023-01-11T21:22:05.5173817Z   test_empty_like_SparseCSR_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.101s)
2023-01-11T21:22:05.5173982Z   test_empty_like_SparseCSR_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.101s)
2023-01-11T21:22:05.5174143Z   test_empty_like_SparseCSR_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.102s)
2023-01-11T21:22:05.5174324Z   test_invalid_input_SparseBSC_target_sparse_compressed_tensor_cpu (__main__.TestSparseCompressedCPU) ... ok (0.194s)
2023-01-11T21:22:05.5174524Z   test_invalid_input_SparseBSC_target_sparse_compressed_tensor_no_size_cpu (__main__.TestSparseCompressedCPU) ... ok (0.145s)
2023-01-11T21:22:05.5174736Z   test_invalid_input_SparseBSC_target_validate_sparse_compressed_tensor_args_cpu (__main__.TestSparseCompressedCPU) ... ok (0.182s)
2023-01-11T21:22:05.5174928Z   test_invalid_input_SparseBSR_target_sparse_compressed_tensor_cpu (__main__.TestSparseCompressedCPU) ... ok (0.193s)
2023-01-11T21:22:05.5175126Z   test_invalid_input_SparseBSR_target_sparse_compressed_tensor_no_size_cpu (__main__.TestSparseCompressedCPU) ... ok (0.148s)
2023-01-11T21:22:05.5175334Z   test_invalid_input_SparseBSR_target_validate_sparse_compressed_tensor_args_cpu (__main__.TestSparseCompressedCPU) ... ok (0.184s)
2023-01-11T21:22:05.5175522Z   test_invalid_input_SparseCSC_target_sparse_compressed_tensor_cpu (__main__.TestSparseCompressedCPU) ... ok (0.186s)
2023-01-11T21:22:05.5175719Z   test_invalid_input_SparseCSC_target_sparse_compressed_tensor_no_size_cpu (__main__.TestSparseCompressedCPU) ... ok (0.158s)
2023-01-11T21:22:05.5175925Z   test_invalid_input_SparseCSC_target_validate_sparse_compressed_tensor_args_cpu (__main__.TestSparseCompressedCPU) ... ok (0.177s)
2023-01-11T21:22:05.5176146Z   test_invalid_input_SparseCSR_target_sparse_compressed_tensor_cpu (__main__.TestSparseCompressedCPU) ... ok (0.182s)
2023-01-11T21:22:05.5176336Z   test_invalid_input_SparseCSR_target_sparse_compressed_tensor_no_size_cpu (__main__.TestSparseCompressedCPU) ... ok (0.144s)
2023-01-11T21:22:05.5176545Z   test_invalid_input_SparseCSR_target_validate_sparse_compressed_tensor_args_cpu (__main__.TestSparseCompressedCPU) ... ok (0.176s)
2023-01-11T21:22:05.5176694Z   test_layout_SparseBSC_cpu (__main__.TestSparseCompressedCPU) ... ok (0.001s)
2023-01-11T21:22:05.5176840Z   test_layout_SparseBSR_cpu (__main__.TestSparseCompressedCPU) ... ok (0.001s)
2023-01-11T21:22:05.5176988Z   test_layout_SparseCSC_cpu (__main__.TestSparseCompressedCPU) ... ok (0.001s)
2023-01-11T21:22:05.5177129Z   test_layout_SparseCSR_cpu (__main__.TestSparseCompressedCPU) ... ok (0.001s)
2023-01-11T21:22:05.5177361Z   test_pickle_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.554s)
2023-01-11T21:22:05.5177525Z   test_pickle_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.477s)
2023-01-11T21:22:05.5177698Z   test_pickle_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.367s)
2023-01-11T21:22:05.5177857Z   test_pickle_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.305s)
2023-01-11T21:22:05.5178005Z   test_print_SparseBSC_cpu (__main__.TestSparseCompressedCPU) ... ok (0.525s)
2023-01-11T21:22:05.5178155Z   test_print_SparseBSR_cpu (__main__.TestSparseCompressedCPU) ... ok (0.501s)
2023-01-11T21:22:05.5178298Z   test_print_SparseCSC_cpu (__main__.TestSparseCompressedCPU) ... ok (0.445s)
2023-01-11T21:22:05.5178442Z   test_print_SparseCSR_cpu (__main__.TestSparseCompressedCPU) ... ok (0.442s)
2023-01-11T21:22:05.5178615Z   test_select_copy_SparseBSC_int32_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.958s)
2023-01-11T21:22:05.5178779Z   test_select_copy_SparseBSC_int32_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.870s)
2023-01-11T21:22:05.5178954Z   test_select_copy_SparseBSC_int32_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (2.009s)
2023-01-11T21:22:05.5179114Z   test_select_copy_SparseBSC_int32_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (2.005s)
2023-01-11T21:22:05.5179279Z   test_select_copy_SparseBSC_int32_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.981s)
2023-01-11T21:22:05.5179445Z   test_select_copy_SparseBSC_int32_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.980s)
2023-01-11T21:22:05.5179612Z   test_select_copy_SparseBSC_int32_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.903s)
2023-01-11T21:22:05.5179775Z   test_select_copy_SparseBSC_int32_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.877s)
2023-01-11T21:22:05.5179939Z   test_select_copy_SparseBSC_int32_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.863s)
2023-01-11T21:22:05.5180100Z   test_select_copy_SparseBSC_int32_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.828s)
2023-01-11T21:22:05.5180264Z   test_select_copy_SparseBSC_int32_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.868s)
2023-01-11T21:22:05.5180415Z   test_select_copy_SparseBSC_int32_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.829s)
2023-01-11T21:22:05.5180581Z   test_select_copy_SparseBSC_int64_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.884s)
2023-01-11T21:22:05.5180742Z   test_select_copy_SparseBSC_int64_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.846s)
2023-01-11T21:22:05.5180912Z   test_select_copy_SparseBSC_int64_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (1.909s)
2023-01-11T21:22:05.5181079Z   test_select_copy_SparseBSC_int64_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (1.950s)
2023-01-11T21:22:05.5181241Z   test_select_copy_SparseBSC_int64_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.880s)
2023-01-11T21:22:05.5181404Z   test_select_copy_SparseBSC_int64_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.861s)
2023-01-11T21:22:05.5181599Z   test_select_copy_SparseBSC_int64_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.863s)
2023-01-11T21:22:05.5181762Z   test_select_copy_SparseBSC_int64_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.803s)
2023-01-11T21:22:05.5181909Z   test_select_copy_SparseBSC_int64_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.798s)
2023-01-11T21:22:05.5182070Z   test_select_copy_SparseBSC_int64_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.833s)
2023-01-11T21:22:05.5182229Z   test_select_copy_SparseBSC_int64_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.804s)
2023-01-11T21:22:05.5182388Z   test_select_copy_SparseBSC_int64_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.784s)
2023-01-11T21:22:05.5182556Z   test_select_copy_SparseBSR_int32_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.868s)
2023-01-11T21:22:05.5182717Z   test_select_copy_SparseBSR_int32_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.764s)
2023-01-11T21:22:05.5182888Z   test_select_copy_SparseBSR_int32_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (1.969s)
2023-01-11T21:22:05.5183117Z   test_select_copy_SparseBSR_int32_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (1.938s)
2023-01-11T21:22:05.5183280Z   test_select_copy_SparseBSR_int32_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.862s)
2023-01-11T21:22:05.5183432Z   test_select_copy_SparseBSR_int32_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.960s)
2023-01-11T21:22:05.5183597Z   test_select_copy_SparseBSR_int32_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.904s)
2023-01-11T21:22:05.5183758Z   test_select_copy_SparseBSR_int32_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.845s)
2023-01-11T21:22:05.5183918Z   test_select_copy_SparseBSR_int32_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.829s)
2023-01-11T21:22:05.5184081Z   test_select_copy_SparseBSR_int32_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.800s)
2023-01-11T21:22:05.5184245Z   test_select_copy_SparseBSR_int32_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.829s)
2023-01-11T21:22:05.5184407Z   test_select_copy_SparseBSR_int32_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.823s)
2023-01-11T21:22:05.5184577Z   test_select_copy_SparseBSR_int64_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.884s)
2023-01-11T21:22:05.5184730Z   test_select_copy_SparseBSR_int64_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.780s)
2023-01-11T21:22:05.5184900Z   test_select_copy_SparseBSR_int64_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (1.939s)
2023-01-11T21:22:05.5185067Z   test_select_copy_SparseBSR_int64_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (1.958s)
2023-01-11T21:22:05.5185231Z   test_select_copy_SparseBSR_int64_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.853s)
2023-01-11T21:22:05.5185395Z   test_select_copy_SparseBSR_int64_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.857s)
2023-01-11T21:22:05.5185560Z   test_select_copy_SparseBSR_int64_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.819s)
2023-01-11T21:22:05.5185719Z   test_select_copy_SparseBSR_int64_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.724s)
2023-01-11T21:22:05.5185881Z   test_select_copy_SparseBSR_int64_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.685s)
2023-01-11T21:22:05.5186040Z   test_select_copy_SparseBSR_int64_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.669s)
2023-01-11T21:22:05.5186187Z   test_select_copy_SparseBSR_int64_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.684s)
2023-01-11T21:22:05.5186344Z   test_select_copy_SparseBSR_int64_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.698s)
2023-01-11T21:22:05.5186511Z   test_select_copy_SparseCSC_int32_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.540s)
2023-01-11T21:22:05.5186670Z   test_select_copy_SparseCSC_int32_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.418s)
2023-01-11T21:22:05.5186840Z   test_select_copy_SparseCSC_int32_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (1.529s)
2023-01-11T21:22:05.5187052Z   test_select_copy_SparseCSC_int32_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (1.565s)
2023-01-11T21:22:05.5187218Z   test_select_copy_SparseCSC_int32_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.534s)
2023-01-11T21:22:05.5187381Z   test_select_copy_SparseCSC_int32_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.520s)
2023-01-11T21:22:05.5187542Z   test_select_copy_SparseCSC_int32_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.477s)
2023-01-11T21:22:05.5187690Z   test_select_copy_SparseCSC_int32_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.449s)
2023-01-11T21:22:05.5187847Z   test_select_copy_SparseCSC_int32_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.382s)
2023-01-11T21:22:05.5188007Z   test_select_copy_SparseCSC_int32_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.392s)
2023-01-11T21:22:05.5188167Z   test_select_copy_SparseCSC_int32_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.387s)
2023-01-11T21:22:05.5188328Z   test_select_copy_SparseCSC_int32_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.396s)
2023-01-11T21:22:05.5188526Z   test_select_copy_SparseCSC_int64_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.488s)
2023-01-11T21:22:05.5188688Z   test_select_copy_SparseCSC_int64_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.384s)
2023-01-11T21:22:05.5188859Z   test_select_copy_SparseCSC_int64_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (1.509s)
2023-01-11T21:22:05.5189027Z   test_select_copy_SparseCSC_int64_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (1.515s)
2023-01-11T21:22:05.5189177Z   test_select_copy_SparseCSC_int64_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.485s)
2023-01-11T21:22:05.5189341Z   test_select_copy_SparseCSC_int64_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.577s)
2023-01-11T21:22:05.5189504Z   test_select_copy_SparseCSC_int64_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.505s)
2023-01-11T21:22:05.5189663Z   test_select_copy_SparseCSC_int64_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.386s)
2023-01-11T21:22:05.5189825Z   test_select_copy_SparseCSC_int64_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.417s)
2023-01-11T21:22:05.5189985Z   test_select_copy_SparseCSC_int64_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.382s)
2023-01-11T21:22:05.5190145Z   test_select_copy_SparseCSC_int64_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.393s)
2023-01-11T21:22:05.5190301Z   test_select_copy_SparseCSC_int64_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.405s)
2023-01-11T21:22:05.5190452Z   test_select_copy_SparseCSR_int32_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.419s)
2023-01-11T21:22:05.5190611Z   test_select_copy_SparseCSR_int32_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.302s)
2023-01-11T21:22:05.5190779Z   test_select_copy_SparseCSR_int32_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (1.450s)
2023-01-11T21:22:05.5190949Z   test_select_copy_SparseCSR_int32_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (1.464s)
2023-01-11T21:22:05.5191113Z   test_select_copy_SparseCSR_int32_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.472s)
2023-01-11T21:22:05.5191276Z   test_select_copy_SparseCSR_int32_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.405s)
2023-01-11T21:22:05.5191436Z   test_select_copy_SparseCSR_int32_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.382s)
2023-01-11T21:22:05.5191600Z   test_select_copy_SparseCSR_int32_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.290s)
2023-01-11T21:22:05.5191760Z   test_select_copy_SparseCSR_int32_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.331s)
2023-01-11T21:22:05.5191908Z   test_select_copy_SparseCSR_int32_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.321s)
2023-01-11T21:22:05.5192069Z   test_select_copy_SparseCSR_int32_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.303s)
2023-01-11T21:22:05.5192267Z   test_select_copy_SparseCSR_int32_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.321s)
2023-01-11T21:22:05.5192436Z   test_select_copy_SparseCSR_int64_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (1.399s)
2023-01-11T21:22:05.5198886Z   test_select_copy_SparseCSR_int64_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (1.279s)
2023-01-11T21:22:05.5199115Z   test_select_copy_SparseCSR_int64_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (1.442s)
2023-01-11T21:22:05.5199294Z   test_select_copy_SparseCSR_int64_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (1.492s)
2023-01-11T21:22:05.5199465Z   test_select_copy_SparseCSR_int64_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (1.412s)
2023-01-11T21:22:05.5199636Z   test_select_copy_SparseCSR_int64_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (1.385s)
2023-01-11T21:22:05.5199790Z   test_select_copy_SparseCSR_int64_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (1.387s)
2023-01-11T21:22:05.5199960Z   test_select_copy_SparseCSR_int64_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (1.308s)
2023-01-11T21:22:05.5200196Z   test_select_copy_SparseCSR_int64_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (1.309s)
2023-01-11T21:22:05.5200360Z   test_select_copy_SparseCSR_int64_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (1.267s)
2023-01-11T21:22:05.5200526Z   test_select_copy_SparseCSR_int64_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (1.312s)
2023-01-11T21:22:05.5200863Z   test_select_copy_SparseCSR_int64_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (1.287s)
2023-01-11T21:22:05.5201078Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.161s)
2023-01-11T21:22:05.5201275Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.142s)
2023-01-11T21:22:05.5201474Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.184s)
2023-01-11T21:22:05.5201664Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.171s)
2023-01-11T21:22:05.5201860Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5202056Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5202252Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.174s)
2023-01-11T21:22:05.5202440Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5202632Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.146s)
2023-01-11T21:22:05.5202820Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.142s)
2023-01-11T21:22:05.5203013Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5203203Z   test_sparse_compressed_constructor_____from_list_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.159s)
2023-01-11T21:22:05.5203399Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5203578Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5203773Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.167s)
2023-01-11T21:22:05.5203970Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.197s)
2023-01-11T21:22:05.5204243Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.159s)
2023-01-11T21:22:05.5204436Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5204627Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.158s)
2023-01-11T21:22:05.5204815Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.151s)
2023-01-11T21:22:05.5205003Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.172s)
2023-01-11T21:22:05.5205185Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.147s)
2023-01-11T21:22:05.5205360Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.159s)
2023-01-11T21:22:05.5205552Z   test_sparse_compressed_constructor_____from_list_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.145s)
2023-01-11T21:22:05.5205781Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.150s)
2023-01-11T21:22:05.5205973Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.145s)
2023-01-11T21:22:05.5206172Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.154s)
2023-01-11T21:22:05.5206366Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.157s)
2023-01-11T21:22:05.5206558Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.146s)
2023-01-11T21:22:05.5206749Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.146s)
2023-01-11T21:22:05.5206942Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.171s)
2023-01-11T21:22:05.5207130Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.130s)
2023-01-11T21:22:05.5207304Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.130s)
2023-01-11T21:22:05.5207486Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.124s)
2023-01-11T21:22:05.5207672Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.130s)
2023-01-11T21:22:05.5207861Z   test_sparse_compressed_constructor_____from_list_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.146s)
2023-01-11T21:22:05.5208055Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.144s)
2023-01-11T21:22:05.5208244Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.130s)
2023-01-11T21:22:05.5208439Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.149s)
2023-01-11T21:22:05.5208633Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.152s)
2023-01-11T21:22:05.5208824Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5209013Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.145s)
2023-01-11T21:22:05.5209190Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5209412Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.128s)
2023-01-11T21:22:05.5209597Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.139s)
2023-01-11T21:22:05.5209778Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.138s)
2023-01-11T21:22:05.5209967Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.125s)
2023-01-11T21:22:05.5210156Z   test_sparse_compressed_constructor_____from_list_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.125s)
2023-01-11T21:22:05.5210351Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5210543Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.303s)
2023-01-11T21:22:05.5210744Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.344s)
2023-01-11T21:22:05.5210970Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.362s)
2023-01-11T21:22:05.5211153Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5211348Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.341s)
2023-01-11T21:22:05.5211539Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.322s)
2023-01-11T21:22:05.5211729Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.320s)
2023-01-11T21:22:05.5211921Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.286s)
2023-01-11T21:22:05.5212113Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.277s)
2023-01-11T21:22:05.5212306Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.299s)
2023-01-11T21:22:05.5212495Z   test_sparse_compressed_constructor_____from_tensor_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.285s)
2023-01-11T21:22:05.5212688Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.342s)
2023-01-11T21:22:05.5212881Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.286s)
2023-01-11T21:22:05.5213068Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.339s)
2023-01-11T21:22:05.5213266Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.363s)
2023-01-11T21:22:05.5213461Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5213653Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.343s)
2023-01-11T21:22:05.5213843Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5214034Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.301s)
2023-01-11T21:22:05.5214221Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.287s)
2023-01-11T21:22:05.5214411Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.303s)
2023-01-11T21:22:05.5214637Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.285s)
2023-01-11T21:22:05.5214828Z   test_sparse_compressed_constructor_____from_tensor_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.287s)
2023-01-11T21:22:05.5215011Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5215200Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.261s)
2023-01-11T21:22:05.5215396Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5215594Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5215788Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.298s)
2023-01-11T21:22:05.5216006Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5216197Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.290s)
2023-01-11T21:22:05.5216385Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.275s)
2023-01-11T21:22:05.5216574Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.266s)
2023-01-11T21:22:05.5216764Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.254s)
2023-01-11T21:22:05.5216943Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.275s)
2023-01-11T21:22:05.5217134Z   test_sparse_compressed_constructor_____from_tensor_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.277s)
2023-01-11T21:22:05.5217401Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.319s)
2023-01-11T21:22:05.5217594Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.283s)
2023-01-11T21:22:05.5217793Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.309s)
2023-01-11T21:22:05.5217989Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.327s)
2023-01-11T21:22:05.5218181Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.297s)
2023-01-11T21:22:05.5218373Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5218571Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.290s)
2023-01-11T21:22:05.5218761Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.263s)
2023-01-11T21:22:05.5218940Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.276s)
2023-01-11T21:22:05.5219128Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.252s)
2023-01-11T21:22:05.5219318Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.287s)
2023-01-11T21:22:05.5219508Z   test_sparse_compressed_constructor_____from_tensor_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.276s)
2023-01-11T21:22:05.5219746Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.159s)
2023-01-11T21:22:05.5219947Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.140s)
2023-01-11T21:22:05.5220152Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.164s)
2023-01-11T21:22:05.5220354Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.179s)
2023-01-11T21:22:05.5220554Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.159s)
2023-01-11T21:22:05.5220753Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.158s)
2023-01-11T21:22:05.5220941Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.158s)
2023-01-11T21:22:05.5221172Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.155s)
2023-01-11T21:22:05.5221370Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.140s)
2023-01-11T21:22:05.5221565Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.139s)
2023-01-11T21:22:05.5221759Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.141s)
2023-01-11T21:22:05.5221954Z   test_sparse_compressed_constructor___factory_from_list_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5222154Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.171s)
2023-01-11T21:22:05.5222353Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.138s)
2023-01-11T21:22:05.5222555Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.164s)
2023-01-11T21:22:05.5222759Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.170s)
2023-01-11T21:22:05.5222946Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.174s)
2023-01-11T21:22:05.5223145Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.160s)
2023-01-11T21:22:05.5223343Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.157s)
2023-01-11T21:22:05.5223538Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.140s)
2023-01-11T21:22:05.5223738Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.139s)
2023-01-11T21:22:05.5223933Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.165s)
2023-01-11T21:22:05.5224127Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.140s)
2023-01-11T21:22:05.5224321Z   test_sparse_compressed_constructor___factory_from_list_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.139s)
2023-01-11T21:22:05.5224521Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5224716Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.128s)
2023-01-11T21:22:05.5224934Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5225134Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.149s)
2023-01-11T21:22:05.5225332Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5225530Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.143s)
2023-01-11T21:22:05.5225729Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.140s)
2023-01-11T21:22:05.5225929Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.140s)
2023-01-11T21:22:05.5226123Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5226347Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.122s)
2023-01-11T21:22:05.5226544Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.127s)
2023-01-11T21:22:05.5226740Z   test_sparse_compressed_constructor___factory_from_list_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.128s)
2023-01-11T21:22:05.5226929Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.158s)
2023-01-11T21:22:05.5227124Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.128s)
2023-01-11T21:22:05.5227326Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.148s)
2023-01-11T21:22:05.5227531Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.150s)
2023-01-11T21:22:05.5227731Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.146s)
2023-01-11T21:22:05.5227928Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.157s)
2023-01-11T21:22:05.5228124Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.141s)
2023-01-11T21:22:05.5228318Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5228513Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.127s)
2023-01-11T21:22:05.5228706Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.124s)
2023-01-11T21:22:05.5228892Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.142s)
2023-01-11T21:22:05.5229087Z   test_sparse_compressed_constructor___factory_from_list_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5229293Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5229492Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.300s)
2023-01-11T21:22:05.5229698Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.339s)
2023-01-11T21:22:05.5229902Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.356s)
2023-01-11T21:22:05.5230147Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.326s)
2023-01-11T21:22:05.5230348Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5230543Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.357s)
2023-01-11T21:22:05.5230743Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.290s)
2023-01-11T21:22:05.5230928Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5231124Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.278s)
2023-01-11T21:22:05.5231326Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.305s)
2023-01-11T21:22:05.5231550Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.287s)
2023-01-11T21:22:05.5231755Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.325s)
2023-01-11T21:22:05.5231954Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.321s)
2023-01-11T21:22:05.5232159Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.361s)
2023-01-11T21:22:05.5232363Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.367s)
2023-01-11T21:22:05.5232560Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.332s)
2023-01-11T21:22:05.5232765Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.343s)
2023-01-11T21:22:05.5232949Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5233146Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.329s)
2023-01-11T21:22:05.5233343Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.298s)
2023-01-11T21:22:05.5233539Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.288s)
2023-01-11T21:22:05.5233734Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.327s)
2023-01-11T21:22:05.5233931Z   test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5234136Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.323s)
2023-01-11T21:22:05.5234333Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.275s)
2023-01-11T21:22:05.5234537Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.313s)
2023-01-11T21:22:05.5234744Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5234943Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.297s)
2023-01-11T21:22:05.5235160Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.316s)
2023-01-11T21:22:05.5235358Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.295s)
2023-01-11T21:22:05.5235551Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.259s)
2023-01-11T21:22:05.5235746Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.273s)
2023-01-11T21:22:05.5235942Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.257s)
2023-01-11T21:22:05.5236140Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.266s)
2023-01-11T21:22:05.5236336Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.279s)
2023-01-11T21:22:05.5236542Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.297s)
2023-01-11T21:22:05.5236767Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.278s)
2023-01-11T21:22:05.5236972Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5237163Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.344s)
2023-01-11T21:22:05.5237361Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.300s)
2023-01-11T21:22:05.5237558Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.296s)
2023-01-11T21:22:05.5237754Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.304s)
2023-01-11T21:22:05.5237953Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.260s)
2023-01-11T21:22:05.5238149Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.263s)
2023-01-11T21:22:05.5238348Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.268s)
2023-01-11T21:22:05.5238545Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.263s)
2023-01-11T21:22:05.5238742Z   test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.277s)
2023-01-11T21:22:05.5239005Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5239220Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.145s)
2023-01-11T21:22:05.5239483Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5239709Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.170s)
2023-01-11T21:22:05.5239971Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5240194Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5240485Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5240967Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5241226Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5241444Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.155s)
2023-01-11T21:22:05.5241704Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5242014Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5242275Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.005s)
2023-01-11T21:22:05.5242494Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.147s)
2023-01-11T21:22:05.5242746Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.005s)
2023-01-11T21:22:05.5242969Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.173s)
2023-01-11T21:22:05.5243232Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.005s)
2023-01-11T21:22:05.5243452Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.161s)
2023-01-11T21:22:05.5243710Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5243967Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5244222Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.018s)
2023-01-11T21:22:05.5244444Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.139s)
2023-01-11T21:22:05.5244700Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5244957Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5245213Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5245475Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.131s)
2023-01-11T21:22:05.5245920Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5246225Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.154s)
2023-01-11T21:22:05.5246581Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5246967Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.146s)
2023-01-11T21:22:05.5247446Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5248012Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.019s)
2023-01-11T21:22:05.5248294Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5248512Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5248906Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5249378Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5249826Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5250046Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.128s)
2023-01-11T21:22:05.5250308Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5250531Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.151s)
2023-01-11T21:22:05.5250776Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5251001Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.145s)
2023-01-11T21:22:05.5251261Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5251517Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.018s)
2023-01-11T21:22:05.5251774Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5251992Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5252292Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5252546Z   test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5252772Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5252992Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.307s)
2023-01-11T21:22:05.5253217Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.339s)
2023-01-11T21:22:05.5253445Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.343s)
2023-01-11T21:22:05.5253689Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.311s)
2023-01-11T21:22:05.5253900Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.317s)
2023-01-11T21:22:05.5254124Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.339s)
2023-01-11T21:22:05.5254343Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.292s)
2023-01-11T21:22:05.5254557Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.299s)
2023-01-11T21:22:05.5254773Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.272s)
2023-01-11T21:22:05.5254989Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.278s)
2023-01-11T21:22:05.5255209Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.292s)
2023-01-11T21:22:05.5255435Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.310s)
2023-01-11T21:22:05.5255648Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.276s)
2023-01-11T21:22:05.5255873Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.337s)
2023-01-11T21:22:05.5256099Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5256309Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.324s)
2023-01-11T21:22:05.5256529Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5256741Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.338s)
2023-01-11T21:22:05.5256962Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.276s)
2023-01-11T21:22:05.5257291Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.279s)
2023-01-11T21:22:05.5257507Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.279s)
2023-01-11T21:22:05.5257723Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.278s)
2023-01-11T21:22:05.5257939Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.300s)
2023-01-11T21:22:05.5258158Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.283s)
2023-01-11T21:22:05.5258379Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.249s)
2023-01-11T21:22:05.5258604Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.309s)
2023-01-11T21:22:05.5258847Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.297s)
2023-01-11T21:22:05.5259064Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.283s)
2023-01-11T21:22:05.5259284Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.297s)
2023-01-11T21:22:05.5259505Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.277s)
2023-01-11T21:22:05.5259724Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.268s)
2023-01-11T21:22:05.5259945Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.249s)
2023-01-11T21:22:05.5260156Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.243s)
2023-01-11T21:22:05.5260372Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.268s)
2023-01-11T21:22:05.5260590Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5260809Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.288s)
2023-01-11T21:22:05.5261025Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.284s)
2023-01-11T21:22:05.5261250Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.294s)
2023-01-11T21:22:05.5261460Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.300s)
2023-01-11T21:22:05.5261682Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.305s)
2023-01-11T21:22:05.5261898Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.287s)
2023-01-11T21:22:05.5262115Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.296s)
2023-01-11T21:22:05.5262363Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.252s)
2023-01-11T21:22:05.5262579Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.249s)
2023-01-11T21:22:05.5262790Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.261s)
2023-01-11T21:22:05.5263005Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.249s)
2023-01-11T21:22:05.5263220Z   test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5263488Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5263746Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.158s)
2023-01-11T21:22:05.5264005Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5264232Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.169s)
2023-01-11T21:22:05.5264502Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5264728Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.160s)
2023-01-11T21:22:05.5265000Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5265265Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5265530Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5265753Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.141s)
2023-01-11T21:22:05.5266014Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5266286Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5266554Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5266779Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.161s)
2023-01-11T21:22:05.5267048Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5267334Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.169s)
2023-01-11T21:22:05.5267591Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5267820Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.160s)
2023-01-11T21:22:05.5268084Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5268345Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5268609Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5268862Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.137s)
2023-01-11T21:22:05.5269126Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5269389Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.018s)
2023-01-11T21:22:05.5269655Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5269883Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.127s)
2023-01-11T21:22:05.5270150Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5270378Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.153s)
2023-01-11T21:22:05.5270646Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5270861Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.144s)
2023-01-11T21:22:05.5271130Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5271390Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5271650Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5271874Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.124s)
2023-01-11T21:22:05.5272135Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.020s)
2023-01-11T21:22:05.5272471Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5272738Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5272962Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.131s)
2023-01-11T21:22:05.5273237Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5273466Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.154s)
2023-01-11T21:22:05.5273763Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5273991Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.146s)
2023-01-11T21:22:05.5274258Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5274511Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5274773Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.004s)
2023-01-11T21:22:05.5275002Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.126s)
2023-01-11T21:22:05.5275267Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.035s)
2023-01-11T21:22:05.5275536Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... skip: dtype not supported with list values (0.003s)
2023-01-11T21:22:05.5275766Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5275994Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.282s)
2023-01-11T21:22:05.5276231Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.364s)
2023-01-11T21:22:05.5276467Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.343s)
2023-01-11T21:22:05.5276699Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.334s)
2023-01-11T21:22:05.5276928Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.312s)
2023-01-11T21:22:05.5277152Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.321s)
2023-01-11T21:22:05.5277397Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.274s)
2023-01-11T21:22:05.5277623Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.279s)
2023-01-11T21:22:05.5277847Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.286s)
2023-01-11T21:22:05.5278071Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.279s)
2023-01-11T21:22:05.5278293Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.276s)
2023-01-11T21:22:05.5278520Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.330s)
2023-01-11T21:22:05.5278772Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.278s)
2023-01-11T21:22:05.5279005Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.340s)
2023-01-11T21:22:05.5279240Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.328s)
2023-01-11T21:22:05.5279467Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.341s)
2023-01-11T21:22:05.5279693Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5279921Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.317s)
2023-01-11T21:22:05.5280136Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.288s)
2023-01-11T21:22:05.5280362Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.274s)
2023-01-11T21:22:05.5280582Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.286s)
2023-01-11T21:22:05.5280952Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.277s)
2023-01-11T21:22:05.5281174Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.278s)
2023-01-11T21:22:05.5281405Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.300s)
2023-01-11T21:22:05.5281625Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5281855Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.293s)
2023-01-11T21:22:05.5282088Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.315s)
2023-01-11T21:22:05.5282316Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.286s)
2023-01-11T21:22:05.5282603Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.301s)
2023-01-11T21:22:05.5282819Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.280s)
2023-01-11T21:22:05.5283043Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5283267Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.271s)
2023-01-11T21:22:05.5283489Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.245s)
2023-01-11T21:22:05.5283712Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5283968Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.286s)
2023-01-11T21:22:05.5284197Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.287s)
2023-01-11T21:22:05.5284417Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.253s)
2023-01-11T21:22:05.5284646Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.308s)
2023-01-11T21:22:05.5284878Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.297s)
2023-01-11T21:22:05.5285107Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.299s)
2023-01-11T21:22:05.5285335Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.284s)
2023-01-11T21:22:05.5285548Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.278s)
2023-01-11T21:22:05.5285771Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.268s)
2023-01-11T21:22:05.5285992Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.250s)
2023-01-11T21:22:05.5286216Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.245s)
2023-01-11T21:22:05.5286442Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.264s)
2023-01-11T21:22:05.5286664Z   test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5286824Z   test_to_dtype_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.465s)
2023-01-11T21:22:05.5286980Z   test_to_dtype_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.444s)
2023-01-11T21:22:05.5287141Z   test_to_dtype_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.462s)
2023-01-11T21:22:05.5287299Z   test_to_dtype_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.464s)
2023-01-11T21:22:05.5287443Z   test_to_dtype_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.501s)
2023-01-11T21:22:05.5287634Z   test_to_dtype_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.476s)
2023-01-11T21:22:05.5287790Z   test_to_dtype_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.447s)
2023-01-11T21:22:05.5287942Z   test_to_dtype_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.457s)
2023-01-11T21:22:05.5288097Z   test_to_dtype_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.446s)
2023-01-11T21:22:05.5288249Z   test_to_dtype_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.460s)
2023-01-11T21:22:05.5288399Z   test_to_dtype_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.440s)
2023-01-11T21:22:05.5288552Z   test_to_dtype_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.457s)
2023-01-11T21:22:05.5288710Z   test_to_dtype_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.459s)
2023-01-11T21:22:05.5288851Z   test_to_dtype_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.441s)
2023-01-11T21:22:05.5289015Z   test_to_dtype_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.456s)
2023-01-11T21:22:05.5289203Z   test_to_dtype_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.471s)
2023-01-11T21:22:05.5289360Z   test_to_dtype_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.487s)
2023-01-11T21:22:05.5289511Z   test_to_dtype_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.449s)
2023-01-11T21:22:05.5289667Z   test_to_dtype_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.478s)
2023-01-11T21:22:05.5289819Z   test_to_dtype_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.458s)
2023-01-11T21:22:05.5289969Z   test_to_dtype_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.443s)
2023-01-11T21:22:05.5290108Z   test_to_dtype_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.464s)
2023-01-11T21:22:05.5290263Z   test_to_dtype_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.444s)
2023-01-11T21:22:05.5290412Z   test_to_dtype_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.462s)
2023-01-11T21:22:05.5290570Z   test_to_dtype_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5290723Z   test_to_dtype_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.247s)
2023-01-11T21:22:05.5290885Z   test_to_dtype_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.273s)
2023-01-11T21:22:05.5291044Z   test_to_dtype_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.252s)
2023-01-11T21:22:05.5291197Z   test_to_dtype_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.249s)
2023-01-11T21:22:05.5291340Z   test_to_dtype_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.297s)
2023-01-11T21:22:05.5291494Z   test_to_dtype_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.246s)
2023-01-11T21:22:05.5291646Z   test_to_dtype_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.244s)
2023-01-11T21:22:05.5291793Z   test_to_dtype_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.245s)
2023-01-11T21:22:05.5291944Z   test_to_dtype_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.275s)
2023-01-11T21:22:05.5292094Z   test_to_dtype_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.251s)
2023-01-11T21:22:05.5292245Z   test_to_dtype_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.249s)
2023-01-11T21:22:05.5292400Z   test_to_dtype_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.243s)
2023-01-11T21:22:05.5292550Z   test_to_dtype_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.217s)
2023-01-11T21:22:05.5292698Z   test_to_dtype_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.225s)
2023-01-11T21:22:05.5292855Z   test_to_dtype_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.225s)
2023-01-11T21:22:05.5293038Z   test_to_dtype_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.246s)
2023-01-11T21:22:05.5293194Z   test_to_dtype_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.224s)
2023-01-11T21:22:05.5293347Z   test_to_dtype_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.224s)
2023-01-11T21:22:05.5293497Z   test_to_dtype_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.226s)
2023-01-11T21:22:05.5293652Z   test_to_dtype_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.247s)
2023-01-11T21:22:05.5293802Z   test_to_dtype_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.222s)
2023-01-11T21:22:05.5293942Z   test_to_dtype_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.220s)
2023-01-11T21:22:05.5294090Z   test_to_dtype_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.246s)
2023-01-11T21:22:05.5294249Z   test_validate_SparseBSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.211s)
2023-01-11T21:22:05.5294404Z   test_validate_SparseBSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.225s)
2023-01-11T21:22:05.5294597Z   test_validate_SparseBSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.210s)
2023-01-11T21:22:05.5294758Z   test_validate_SparseBSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.206s)
2023-01-11T21:22:05.5294912Z   test_validate_SparseBSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.199s)
2023-01-11T21:22:05.5295067Z   test_validate_SparseBSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.198s)
2023-01-11T21:22:05.5295223Z   test_validate_SparseBSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.217s)
2023-01-11T21:22:05.5295362Z   test_validate_SparseBSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.185s)
2023-01-11T21:22:05.5295514Z   test_validate_SparseBSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.186s)
2023-01-11T21:22:05.5295666Z   test_validate_SparseBSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.183s)
2023-01-11T21:22:05.5295818Z   test_validate_SparseBSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.185s)
2023-01-11T21:22:05.5295973Z   test_validate_SparseBSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.217s)
2023-01-11T21:22:05.5296130Z   test_validate_SparseBSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.197s)
2023-01-11T21:22:05.5296283Z   test_validate_SparseBSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.186s)
2023-01-11T21:22:05.5296446Z   test_validate_SparseBSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.206s)
2023-01-11T21:22:05.5296594Z   test_validate_SparseBSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.205s)
2023-01-11T21:22:05.5296750Z   test_validate_SparseBSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.226s)
2023-01-11T21:22:05.5296904Z   test_validate_SparseBSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.205s)
2023-01-11T21:22:05.5297060Z   test_validate_SparseBSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.201s)
2023-01-11T21:22:05.5297294Z   test_validate_SparseBSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.188s)
2023-01-11T21:22:05.5297450Z   test_validate_SparseBSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.188s)
2023-01-11T21:22:05.5297603Z   test_validate_SparseBSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.185s)
2023-01-11T21:22:05.5297754Z   test_validate_SparseBSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.206s)
2023-01-11T21:22:05.5297896Z   test_validate_SparseBSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.189s)
2023-01-11T21:22:05.5298053Z   test_validate_SparseCSC_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.182s)
2023-01-11T21:22:05.5298210Z   test_validate_SparseCSC_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.164s)
2023-01-11T21:22:05.5298371Z   test_validate_SparseCSC_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.176s)
2023-01-11T21:22:05.5298564Z   test_validate_SparseCSC_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.195s)
2023-01-11T21:22:05.5298721Z   test_validate_SparseCSC_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.172s)
2023-01-11T21:22:05.5298873Z   test_validate_SparseCSC_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.172s)
2023-01-11T21:22:05.5299026Z   test_validate_SparseCSC_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.171s)
2023-01-11T21:22:05.5299177Z   test_validate_SparseCSC_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.170s)
2023-01-11T21:22:05.5299317Z   test_validate_SparseCSC_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.167s)
2023-01-11T21:22:05.5299468Z   test_validate_SparseCSC_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5299619Z   test_validate_SparseCSC_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.186s)
2023-01-11T21:22:05.5299774Z   test_validate_SparseCSC_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.165s)
2023-01-11T21:22:05.5299934Z   test_validate_SparseCSR_cpu_bfloat16 (__main__.TestSparseCompressedCPU) ... ok (0.174s)
2023-01-11T21:22:05.5300117Z   test_validate_SparseCSR_cpu_bool (__main__.TestSparseCompressedCPU) ... ok (0.165s)
2023-01-11T21:22:05.5300278Z   test_validate_SparseCSR_cpu_complex128 (__main__.TestSparseCompressedCPU) ... ok (0.179s)
2023-01-11T21:22:05.5300440Z   test_validate_SparseCSR_cpu_complex64 (__main__.TestSparseCompressedCPU) ... ok (0.181s)
2023-01-11T21:22:05.5300582Z   test_validate_SparseCSR_cpu_float16 (__main__.TestSparseCompressedCPU) ... ok (0.209s)
2023-01-11T21:22:05.5300737Z   test_validate_SparseCSR_cpu_float32 (__main__.TestSparseCompressedCPU) ... ok (0.177s)
2023-01-11T21:22:05.5300892Z   test_validate_SparseCSR_cpu_float64 (__main__.TestSparseCompressedCPU) ... ok (0.171s)
2023-01-11T21:22:05.5301042Z   test_validate_SparseCSR_cpu_int16 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5301192Z   test_validate_SparseCSR_cpu_int32 (__main__.TestSparseCompressedCPU) ... ok (0.162s)
2023-01-11T21:22:05.5301346Z   test_validate_SparseCSR_cpu_int64 (__main__.TestSparseCompressedCPU) ... ok (0.159s)
2023-01-11T21:22:05.5301500Z   test_validate_SparseCSR_cpu_int8 (__main__.TestSparseCompressedCPU) ... ok (0.178s)
2023-01-11T21:22:05.5301654Z   test_validate_SparseCSR_cpu_uint8 (__main__.TestSparseCompressedCPU) ... ok (0.161s)
2023-01-11T21:22:05.5301665Z 
2023-01-11T21:22:05.5301965Z ----------------------------------------------------------------------
2023-01-11T21:22:05.5302032Z Ran 4351 tests in 1007.546s
2023-01-11T21:22:05.5302037Z 
2023-01-11T21:22:05.5302108Z OK (skipped=475)
2023-01-11T21:22:05.5302113Z 
2023-01-11T21:22:05.5302199Z Generating XML reports...
2023-01-11T21:22:05.5302507Z Generated XML report: test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRCPU-20230111210516.xml
2023-01-11T21:22:05.5302811Z Generated XML report: test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRSampler-20230111210516.xml
2023-01-11T21:22:05.5303125Z Generated XML report: test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCompressedCPU-20230111210516.xml
2023-01-11T21:22:05.5303130Z 
2023-01-11T21:22:05.5303540Z ##[endgroup]
2023-01-11T21:22:05.5303822Z FINISHED PRINTING LOG FILE of test_sparse_csr (/var/lib/jenkins/workspace/test/test-reports/test_sparse_csr_1aons3sa)
2023-01-11T21:22:05.5303827Z 
2023-01-11T21:22:05.5303968Z Running test_torch ... [2023-01-11 21:22:05.427010]
2023-01-11T21:22:05.5304292Z Executing ['/opt/conda/bin/python', '-bb', 'test_torch.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:22:05.427466]
2023-01-11T21:23:04.6542772Z 
2023-01-11T21:23:04.6543211Z Expand the folded group to see the log file of test_torch
2023-01-11T21:23:04.6544913Z ##[group]PRINTING LOG FILE of test_torch (/var/lib/jenkins/workspace/test/test-reports/test_torch_74lr60y8)
2023-01-11T21:23:04.6547944Z Test results will be stored in test-reports/python-unittest/test_torch
2023-01-11T21:23:04.6548486Z 
2023-01-11T21:23:04.6548631Z Running tests...
2023-01-11T21:23:04.6549124Z ----------------------------------------------------------------------
2023-01-11T21:23:04.6549610Z   test_basic_vitals (__main__.TestBasicVitalSigns) ... ok (0.002s)
2023-01-11T21:23:04.6550075Z   test_basic_vitals_read_write (__main__.TestBasicVitalSigns) ... ok (0.001s)
2023-01-11T21:23:04.6550562Z   test_dataloader_vitals (__main__.TestBasicVitalSigns) ... ok (0.002s)
2023-01-11T21:23:04.6550979Z   test_RNGState (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6551379Z   test_RNGStateAliasing (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6551768Z   test_RNG_after_pickle (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6552155Z   test_Size (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6552501Z   test_Size_iter (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6552868Z   test_Size_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6553289Z   test_add_meta_scalar (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6553722Z   test_allow_tensor_metadata_change (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6554305Z   test_apply (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6554692Z   test_as_subclass (__main__.TestTorch) ... ok (0.008s)
2023-01-11T21:23:04.6555046Z   test_assert_async (__main__.TestTorch) ... ok (0.028s)
2023-01-11T21:23:04.6555350Z   test_backward_hooks_traverse (__main__.TestTorch) ... ok (0.062s)
2023-01-11T21:23:04.6555628Z   test_batch_norm_cpu_inference (__main__.TestTorch) ... ok (0.017s)
2023-01-11T21:23:04.6556739Z   test_bmm_multithreaded (__main__.TestTorch) ... test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6557729Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6558743Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6559585Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6560562Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [1, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6561612Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6562474Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 23, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6563355Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6564342Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6565351Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6566773Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6567621Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6568644Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [1, 23, 12], which does not match the required output shape [0, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6569530Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6570473Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6571317Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6572228Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6573061Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6573990Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [10, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6574611Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6575298Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 23, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6575944Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6576636Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 23, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6577425Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6578093Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 0, 12]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6578722Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6579429Z test_torch.py:8590: UserWarning: An output with one or more elements was resized since it had shape [10, 23, 12], which does not match the required output shape [0, 0, 0]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.6580174Z   torch.bmm(b1, b2, out=res2)
2023-01-11T21:23:04.6580386Z ok (4.492s)
2023-01-11T21:23:04.6580592Z   test_boxMullerState (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6580926Z   test_c10_layer_norm (__main__.TestTorch) ... skip: Pytorch is compiled without Caffe2 (0.001s)
2023-01-11T21:23:04.6581261Z   test_cat_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6581493Z   test_chunk_neg_dim (__main__.TestTorch) ... ok (0.031s)
2023-01-11T21:23:04.6581746Z   test_conj_neg_tolist (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6581995Z   test_contains (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6582243Z   test_copy_broadcast (__main__.TestTorch) ... ok (0.017s)
2023-01-11T21:23:04.6582479Z   test_copy_dtypes (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6582721Z   test_copy_float16 (__main__.TestTorch) ... ok (0.273s)
2023-01-11T21:23:04.6582969Z   test_copy_many_to_one (__main__.TestTorch) ... ok (0.016s)
2023-01-11T21:23:04.6583629Z   test_copy_transpose (__main__.TestTorch) ... test_torch.py:7754: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/EmptyTensor.cpp:32.)
2023-01-11T21:23:04.6584093Z   x = torch.arange(100 * 100).reshape(100, 100).to(dtype=torch.complex32).t()
2023-01-11T21:23:04.6584318Z ok (0.010s)
2023-01-11T21:23:04.6584528Z   test_cuda_not_built (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6584763Z   test_cummax_neg_dim (__main__.TestTorch) ... ok (0.007s)
2023-01-11T21:23:04.6585014Z   test_cummin_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6585259Z   test_cumprod_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6585500Z   test_cumsum_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6585734Z   test_cxx_flags (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6586516Z   test_dead_weak_ref (__main__.TestTorch) ... [W python_variable.cpp:319] Warning: Deallocating Tensor that still has live PyObject references.  This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it.  Subsequent accesses to this tensor via the PyObject will now fail. (function decref)
2023-01-11T21:23:04.6587000Z ok (0.007s)
2023-01-11T21:23:04.6587201Z   test_deepcopy_gradient (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6587464Z   test_deepcopy_parameter (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6587729Z   test_deterministic_flag (__main__.TestTorch) ... ok (0.007s)
2023-01-11T21:23:04.6588024Z   test_device (__main__.TestTorch) ... ok (0.035s)
2023-01-11T21:23:04.6588240Z   test_dir (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6588465Z   test_doc (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6588685Z   test_doc_template (__main__.TestTorch)
2023-01-11T21:23:04.6588949Z Test that all public API doc strings use the same standard template for ... ok (0.026s)
2023-01-11T21:23:04.6589234Z   test_dot_data_use (__main__.TestTorch) ... ok (0.027s)
2023-01-11T21:23:04.6589484Z   test_dtype_is_signed (__main__.TestTorch) ... ok (0.007s)
2023-01-11T21:23:04.6590032Z   test_element_size (__main__.TestTorch) ... test_torch.py:5939: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6590559Z   byte = torch.ByteStorage().element_size()
2023-01-11T21:23:04.6591104Z test_torch.py:5940: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6591586Z   char = torch.CharStorage().element_size()
2023-01-11T21:23:04.6592090Z test_torch.py:5941: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6592554Z   short = torch.ShortStorage().element_size()
2023-01-11T21:23:04.6593065Z test_torch.py:5942: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6593541Z   int = torch.IntStorage().element_size()
2023-01-11T21:23:04.6594040Z test_torch.py:5943: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6594511Z   long = torch.LongStorage().element_size()
2023-01-11T21:23:04.6594994Z test_torch.py:5944: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6595479Z   float = torch.FloatStorage().element_size()
2023-01-11T21:23:04.6595984Z test_torch.py:5945: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6596459Z   double = torch.DoubleStorage().element_size()
2023-01-11T21:23:04.6596944Z test_torch.py:5946: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6597416Z   bool = torch.BoolStorage().element_size()
2023-01-11T21:23:04.6597912Z test_torch.py:5947: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6598434Z   bfloat16 = torch.BFloat16Storage().element_size()
2023-01-11T21:23:04.6598940Z test_torch.py:5948: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6599431Z   complexfloat = torch.ComplexFloatStorage().element_size()
2023-01-11T21:23:04.6599948Z test_torch.py:5949: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6600451Z   complexdouble = torch.ComplexDoubleStorage().element_size()
2023-01-11T21:23:04.6600821Z ok (0.004s)
2023-01-11T21:23:04.6601016Z   test_empty_meta (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:23:04.6601328Z   test_empty_storage_view (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6601576Z   test_equal (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6601820Z   test_error_msg_type_translation (__main__.TestTorch) ... ok (0.024s)
2023-01-11T21:23:04.6602084Z   test_fill_diagonal (__main__.TestTorch) ... ok (0.007s)
2023-01-11T21:23:04.6602345Z   test_fix_weakref_no_leak (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6602603Z   test_format_scalar_meta (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6603144Z   test_from_buffer (__main__.TestTorch) ... test_torch.py:6322: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6603698Z   self.assertEqual(torch.ByteStorage.from_buffer(a).tolist(), [1, 2, 3, 4])
2023-01-11T21:23:04.6604528Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6605023Z   return list(self)
2023-01-11T21:23:04.6605751Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:649: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6606278Z   return iter(map(lambda i: self[i], range(self.size())))
2023-01-11T21:23:04.6606794Z test_torch.py:6323: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6607341Z   shorts = torch.ShortStorage.from_buffer(a, 'big')
2023-01-11T21:23:04.6607860Z test_torch.py:6324: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6608328Z   self.assertEqual(shorts.size(), 2)
2023-01-11T21:23:04.6608816Z test_torch.py:6325: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6609346Z   self.assertEqual(shorts.tolist(), [258, 772])
2023-01-11T21:23:04.6609843Z test_torch.py:6326: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6610373Z   ints = torch.IntStorage.from_buffer(a, 'little')
2023-01-11T21:23:04.6610870Z test_torch.py:6327: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6611337Z   self.assertEqual(ints.size(), 1)
2023-01-11T21:23:04.6611865Z test_torch.py:6328: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6612331Z   self.assertEqual(ints[0], 67305985)
2023-01-11T21:23:04.6612822Z test_torch.py:6330: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6613350Z   floats = torch.FloatStorage.from_buffer(f, 'big')
2023-01-11T21:23:04.6613857Z test_torch.py:6331: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6614324Z   self.assertEqual(floats.size(), 1)
2023-01-11T21:23:04.6614822Z test_torch.py:6332: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6615276Z   self.assertEqual(floats[0], 2.25)
2023-01-11T21:23:04.6615765Z test_torch.py:6335: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6616294Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:23:04.6616802Z test_torch.py:6336: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6617324Z   self.assertEqual(bools.size(), 8)
2023-01-11T21:23:04.6617811Z test_torch.py:6337: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6618335Z   self.assertEqual(bools.tolist(), [False, True, True, True, True, True, True, True])
2023-01-11T21:23:04.6618875Z test_torch.py:6338: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6619455Z   self.assertEqual(bools.type(), 'torch.BoolStorage')
2023-01-11T21:23:04.6620240Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6620796Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:23:04.6621544Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6622147Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:23:04.6622973Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:978: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6623548Z   return (cls_device == instance.device.type) and (cls.dtype == instance.dtype)
2023-01-11T21:23:04.6624337Z /opt/conda/lib/python3.7/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6624861Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:23:04.6625364Z test_torch.py:6342: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6625893Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:23:04.6626389Z test_torch.py:6343: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6626848Z   self.assertEqual(bools.size(), 19)
2023-01-11T21:23:04.6627342Z test_torch.py:6346: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6627882Z   bools = torch.BoolStorage.from_buffer(f, 'big')
2023-01-11T21:23:04.6628388Z test_torch.py:6347: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6628840Z   self.assertEqual(bools.size(), 4)
2023-01-11T21:23:04.6629330Z test_torch.py:6348: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6629857Z   self.assertEqual(bools.tolist(), [False, True, True, True])
2023-01-11T21:23:04.6630373Z test_torch.py:6349: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6630856Z   bytes = torch.ByteStorage.from_buffer(a)
2023-01-11T21:23:04.6631343Z test_torch.py:6350: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6631809Z   self.assertEqual(bytes.nbytes(), 4)
2023-01-11T21:23:04.6632299Z test_torch.py:6351: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6632811Z   self.assertEqual(bytes.tolist(), [1, 2, 3, 4])
2023-01-11T21:23:04.6632998Z ok (0.011s)
2023-01-11T21:23:04.6633504Z   test_from_file (__main__.TestTorch) ... test_torch.py:6663: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6634034Z   s1 = torch.FloatStorage.from_file(filename, True, size)
2023-01-11T21:23:04.6634820Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:899: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6635352Z   storage = cls(wrap_storage=untyped_storage)
2023-01-11T21:23:04.6635844Z test_torch.py:6665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6636359Z   self.assertEqual(s1.data_ptr(), torch.FloatTensor(s1).data_ptr())
2023-01-11T21:23:04.6636892Z test_torch.py:6668: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6637394Z   s2 = torch.FloatStorage.from_file(filename, True, size)
2023-01-11T21:23:04.6637605Z ok (0.015s)
2023-01-11T21:23:04.6637821Z   test_gather_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6638075Z   test_generator_cpu (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6638325Z   test_has_internal_overlap (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6638890Z   test_has_storage (__main__.TestTorch) ... test_torch.py:7615: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6639419Z   self.assertIsNotNone(torch.tensor([]).storage())
2023-01-11T21:23:04.6639937Z test_torch.py:7616: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6640468Z   self.assertIsNotNone(torch.empty(0).storage())
2023-01-11T21:23:04.6641075Z test_torch.py:7617: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6641577Z   self.assertIsNotNone(torch.tensor([]).clone().storage())
2023-01-11T21:23:04.6642100Z test_torch.py:7618: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6642607Z   self.assertIsNotNone(torch.tensor([0, 0, 0]).nonzero().storage())
2023-01-11T21:23:04.6643189Z test_torch.py:7619: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6643668Z   self.assertIsNotNone(torch.tensor([]).new().storage())
2023-01-11T21:23:04.6643882Z ok (0.001s)
2023-01-11T21:23:04.6644090Z   test_index_add (__main__.TestTorch) ... ok (0.070s)
2023-01-11T21:23:04.6644336Z   test_index_add_all_dtypes (__main__.TestTorch) ... ok (0.018s)
2023-01-11T21:23:04.6645196Z   test_index_add_correctness (__main__.TestTorch) ... skip: Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/91184 for platform(s) linux, rocm, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests. (0.003s)
2023-01-11T21:23:04.6645756Z   test_index_add_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6646019Z   test_index_copy_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6646269Z   test_index_fill_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6646533Z   test_index_select_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6646805Z   test_invalid_generator_raises (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6647064Z   test_is_nonzero (__main__.TestTorch) ... ok (0.010s)
2023-01-11T21:23:04.6647520Z   test_is_same_size (__main__.TestTorch) ... test_torch.py:5821: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:179.)
2023-01-11T21:23:04.6648010Z   nt1 = torch.nested.nested_tensor([torch.ones(2, 4), torch.ones(3, 4), torch.ones(5, 4)])
2023-01-11T21:23:04.6648250Z ok (0.011s)
2023-01-11T21:23:04.6648431Z   test_iter (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6648677Z   test_kthvalue_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6648939Z   test_logcumsumexp_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6649194Z   test_manual_seed (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6649416Z   test_map (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6649644Z   test_map2 (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6649876Z   test_max_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6650100Z   test_mean_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6650345Z   test_median_neg_dim (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:23:04.6650590Z   test_memory_format (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:23:04.6650883Z   test_memory_format_contiguous_returns_same_tensor_if_already_satisfies (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6651236Z   test_memory_format_empty (__main__.TestTorch) ... ok (0.019s)
2023-01-11T21:23:04.6651488Z   test_min_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6651731Z   test_mode_neg_dim (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:23:04.6652048Z   test_multinomial_invalid_probs (__main__.TestTorch) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:23:04.6652373Z   test_nanmedian_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6652628Z   test_narrow_neg_dim (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:23:04.6652851Z   test_ndim (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6653385Z   test_new (__main__.TestTorch) ... test_torch.py:7016: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6653915Z   self.assertEqual(x.new(y.storage()).data_ptr(), y.data_ptr())
2023-01-11T21:23:04.6654468Z test_torch.py:7022: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6654980Z   self.assertRaises(RuntimeError, lambda: x.new(z.storage()))
2023-01-11T21:23:04.6655187Z ok (0.009s)
2023-01-11T21:23:04.6655411Z   test_newaxis_numpy_comparison (__main__.TestTorch) ... ok (0.011s)
2023-01-11T21:23:04.6655668Z   test_newindex (__main__.TestTorch) ... ok (0.026s)
2023-01-11T21:23:04.6655908Z   test_no_cuda_monkeypatch (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6656160Z   test_norm_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6656405Z   test_normal_shape (__main__.TestTorch) ... ok (0.057s)
2023-01-11T21:23:04.6656630Z   test_numel (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6656868Z   test_parallel_info (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6657119Z   test_parsing_double (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6657431Z   test_parsing_int64 (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6657670Z   test_parsing_intlist (__main__.TestTorch) ... ok (0.027s)
2023-01-11T21:23:04.6657913Z   test_permute (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6658149Z   test_pickle (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6658381Z   test_pickle_dtype (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6658633Z   test_pickle_function (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6658892Z   test_pickle_parameter (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6659156Z   test_pickle_parameter_no_requires_grad (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6659427Z   test_pickle_size (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6659672Z   test_pin_memory (__main__.TestTorch) ... ok (0.013s)
2023-01-11T21:23:04.6659901Z   test_print (__main__.TestTorch) ... ok (0.152s)
2023-01-11T21:23:04.6660130Z   test_prod_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6660380Z   test_pyobj_preserved (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6660619Z   test_qengine (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6660848Z   test_renorm_neg_dim (__main__.TestTorch) ... ok (0.008s)
2023-01-11T21:23:04.6661104Z   test_resurrected_weak_ref (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6661353Z   test_reversed (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6661584Z   test_scatter_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6661830Z   test_select_neg_dim (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6662119Z   test_set_flush_denormal (__main__.TestTorch) ... skip: flush_denormal not supported (0.002s)
2023-01-11T21:23:04.6662464Z   test_setting_real_imag_to_a_number (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6662714Z   test_show_config (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6662960Z   test_size_neg_dim (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6663509Z   test_sizeof (__main__.TestTorch) ... test_torch.py:6980: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6664025Z   sizeof_empty = torch.randn(0).storage().__sizeof__()
2023-01-11T21:23:04.6664802Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6665354Z   return super(TypedStorage, self).__sizeof__() + self.nbytes()
2023-01-11T21:23:04.6665909Z test_torch.py:6981: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6666395Z   sizeof_10 = torch.randn(10).storage().__sizeof__()
2023-01-11T21:23:04.6666889Z test_torch.py:6982: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6667371Z   sizeof_100 = torch.randn(100).storage().__sizeof__()
2023-01-11T21:23:04.6667875Z test_torch.py:6986: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6668370Z   sizeof_empty = torch.randn(0).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:23:04.6668890Z test_torch.py:6987: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6669375Z   sizeof_10 = torch.randn(10).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:23:04.6669891Z test_torch.py:6988: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6670391Z   sizeof_100 = torch.randn(100).to(torch.uint8).storage().__sizeof__()
2023-01-11T21:23:04.6670607Z ok (0.004s)
2023-01-11T21:23:04.6670795Z   test_slice (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6671104Z   test_slow_test (__main__.TestTorch) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:23:04.6671417Z   test_sobolengine_bounds (__main__.TestTorch) ... ok (0.008s)
2023-01-11T21:23:04.6671690Z   test_sobolengine_bounds_scrambled (__main__.TestTorch) ... ok (0.018s)
2023-01-11T21:23:04.6671953Z   test_sobolengine_continuing (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6672237Z   test_sobolengine_continuing_scrambled (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6672523Z   test_sobolengine_distribution (__main__.TestTorch) ... ok (0.007s)
2023-01-11T21:23:04.6672839Z   test_sobolengine_distribution_scrambled (__main__.TestTorch) ... ok (0.012s)
2023-01-11T21:23:04.6673118Z   test_sobolengine_draw (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6673380Z   test_sobolengine_draw_base2 (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6673645Z   test_sobolengine_draw_base2_scrambled (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6673932Z   test_sobolengine_draw_scrambled (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6674210Z   test_sobolengine_fast_forward (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6674494Z   test_sobolengine_fast_forward_scrambled (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6674766Z   test_sobolengine_first_point (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6675035Z   test_sobolengine_high_dim (__main__.TestTorch) ... ok (0.008s)
2023-01-11T21:23:04.6675295Z   test_sobolengine_raise (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6675549Z   test_sobolengine_reset (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6675827Z   test_sobolengine_reset_scrambled (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6676089Z   test_sort_neg_dim (__main__.TestTorch) ... ok (0.008s)
2023-01-11T21:23:04.6676358Z   test_split_neg_dim (__main__.TestTorch) ... ok (0.008s)
2023-01-11T21:23:04.6676607Z   test_squeeze_neg_dim (__main__.TestTorch) ... ok (0.009s)
2023-01-11T21:23:04.6676849Z   test_std_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6677403Z   test_storage_casts (__main__.TestTorch) ... test_torch.py:6470: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6677976Z   storage = torch.IntStorage([-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6678480Z test_torch.py:6471: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6678958Z   self.assertEqual(storage.size(), 6)
2023-01-11T21:23:04.6679455Z test_torch.py:6472: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6679994Z   self.assertEqual(storage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6680848Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6681347Z   return list(self)
2023-01-11T21:23:04.6682087Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:649: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6682629Z   return iter(map(lambda i: self[i], range(self.size())))
2023-01-11T21:23:04.6683149Z test_torch.py:6473: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6683684Z   self.assertEqual(storage.type(), 'torch.IntStorage')
2023-01-11T21:23:04.6684527Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6685092Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:23:04.6685840Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6686436Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:23:04.6686939Z test_torch.py:6476: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6687456Z   floatStorage = storage.float()
2023-01-11T21:23:04.6688338Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:808: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6688930Z   storage = torch.tensor([], dtype=self.dtype, device=self.device).set_(self).to(dtype)._typed_storage()
2023-01-11T21:23:04.6689744Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:809: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6690243Z   if storage.data_ptr() == self.data_ptr():
2023-01-11T21:23:04.6690746Z test_torch.py:6477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6691235Z   self.assertEqual(floatStorage.size(), 6)
2023-01-11T21:23:04.6691742Z test_torch.py:6478: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6692305Z   self.assertEqual(floatStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6692814Z test_torch.py:6479: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6693378Z   self.assertEqual(floatStorage.type(), 'torch.FloatStorage')
2023-01-11T21:23:04.6693915Z test_torch.py:6480: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6694476Z   self.assertEqual(floatStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6694989Z test_torch.py:6483: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6695498Z   halfStorage = storage.half()
2023-01-11T21:23:04.6695984Z test_torch.py:6484: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6696469Z   self.assertEqual(halfStorage.size(), 6)
2023-01-11T21:23:04.6696975Z test_torch.py:6485: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6697628Z   self.assertEqual(halfStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6698197Z test_torch.py:6486: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6698914Z   self.assertEqual(halfStorage.type(), 'torch.HalfStorage')
2023-01-11T21:23:04.6699468Z test_torch.py:6487: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6700034Z   self.assertEqual(halfStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6700538Z test_torch.py:6490: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6701020Z   bfloat16Storage = storage.bfloat16()
2023-01-11T21:23:04.6701518Z test_torch.py:6491: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6702003Z   self.assertEqual(bfloat16Storage.size(), 6)
2023-01-11T21:23:04.6702498Z test_torch.py:6492: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6703060Z   self.assertEqual(bfloat16Storage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6703583Z test_torch.py:6493: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6704161Z   self.assertEqual(bfloat16Storage.type(), 'torch.BFloat16Storage')
2023-01-11T21:23:04.6704701Z test_torch.py:6494: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6705252Z   self.assertEqual(bfloat16Storage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6705780Z test_torch.py:6497: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6706302Z   longStorage = storage.long()
2023-01-11T21:23:04.6706785Z test_torch.py:6498: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6707262Z   self.assertEqual(longStorage.size(), 6)
2023-01-11T21:23:04.6707753Z test_torch.py:6499: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6708301Z   self.assertEqual(longStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6708850Z test_torch.py:6500: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6709404Z   self.assertEqual(longStorage.type(), 'torch.LongStorage')
2023-01-11T21:23:04.6709910Z test_torch.py:6501: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6710462Z   self.assertEqual(longStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6710981Z test_torch.py:6504: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6711441Z   shortStorage = storage.short()
2023-01-11T21:23:04.6711926Z test_torch.py:6505: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6712386Z   self.assertEqual(shortStorage.size(), 6)
2023-01-11T21:23:04.6712890Z test_torch.py:6506: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6713432Z   self.assertEqual(shortStorage.tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6713949Z test_torch.py:6507: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6714509Z   self.assertEqual(shortStorage.type(), 'torch.ShortStorage')
2023-01-11T21:23:04.6715020Z test_torch.py:6508: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6715571Z   self.assertEqual(shortStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6716126Z test_torch.py:6511: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6716595Z   doubleStorage = storage.double()
2023-01-11T21:23:04.6717070Z test_torch.py:6512: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6717546Z   self.assertEqual(doubleStorage.size(), 6)
2023-01-11T21:23:04.6718049Z test_torch.py:6513: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6718617Z   self.assertEqual(doubleStorage.tolist(), [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
2023-01-11T21:23:04.6719171Z test_torch.py:6514: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6719785Z   self.assertEqual(doubleStorage.type(), 'torch.DoubleStorage')
2023-01-11T21:23:04.6720773Z test_torch.py:6515: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6721666Z   self.assertEqual(doubleStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6722233Z test_torch.py:6518: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6722703Z   charStorage = storage.char()
2023-01-11T21:23:04.6723180Z test_torch.py:6519: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6723659Z   self.assertEqual(charStorage.size(), 6)
2023-01-11T21:23:04.6724158Z test_torch.py:6520: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6724734Z   self.assertEqual(charStorage.tolist(), [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
2023-01-11T21:23:04.6725244Z test_torch.py:6521: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6725798Z   self.assertEqual(charStorage.type(), 'torch.CharStorage')
2023-01-11T21:23:04.6726326Z test_torch.py:6522: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6726974Z   self.assertEqual(charStorage.int().tolist(), [-1, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6727497Z test_torch.py:6525: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6727951Z   byteStorage = storage.byte()
2023-01-11T21:23:04.6728436Z test_torch.py:6526: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6728916Z   self.assertEqual(byteStorage.size(), 6)
2023-01-11T21:23:04.6729417Z test_torch.py:6527: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6729956Z   self.assertEqual(byteStorage.tolist(), [255, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6730453Z test_torch.py:6528: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6731005Z   self.assertEqual(byteStorage.type(), 'torch.ByteStorage')
2023-01-11T21:23:04.6731531Z test_torch.py:6529: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6732030Z   self.assertEqual(byteStorage.int().tolist(), [255, 0, 1, 2, 3, 4])
2023-01-11T21:23:04.6732542Z test_torch.py:6532: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6733011Z   boolStorage = storage.bool()
2023-01-11T21:23:04.6733505Z test_torch.py:6533: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6733981Z   self.assertEqual(boolStorage.size(), 6)
2023-01-11T21:23:04.6734471Z test_torch.py:6534: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6734987Z   self.assertEqual(boolStorage.tolist(), [True, False, True, True, True, True])
2023-01-11T21:23:04.6735519Z test_torch.py:6535: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6736066Z   self.assertEqual(boolStorage.type(), 'torch.BoolStorage')
2023-01-11T21:23:04.6736586Z test_torch.py:6536: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6737111Z   self.assertEqual(boolStorage.int().tolist(), [1, 0, 1, 1, 1, 1])
2023-01-11T21:23:04.6737692Z test_torch.py:6539: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6738287Z   complexfloat_storage = torch.ComplexFloatStorage([-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:23:04.6738845Z test_torch.py:6540: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6739336Z   self.assertEqual(complexfloat_storage.size(), 6)
2023-01-11T21:23:04.6739889Z test_torch.py:6541: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6740466Z   self.assertEqual(complexfloat_storage.tolist(), [-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:23:04.6741000Z test_torch.py:6542: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6741599Z   self.assertEqual(complexfloat_storage.type(), 'torch.ComplexFloatStorage')
2023-01-11T21:23:04.6742153Z test_torch.py:6545: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6742670Z   complexdouble_storage = complexfloat_storage.complex_double()
2023-01-11T21:23:04.6743182Z test_torch.py:6546: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6743676Z   self.assertEqual(complexdouble_storage.size(), 6)
2023-01-11T21:23:04.6744186Z test_torch.py:6547: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6744779Z   self.assertEqual(complexdouble_storage.tolist(), [-1, 0, 1 + 2j, 2.5j, 3.5, 4 - 2j])
2023-01-11T21:23:04.6745305Z test_torch.py:6548: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6745901Z   self.assertEqual(complexdouble_storage.type(), 'torch.ComplexDoubleStorage')
2023-01-11T21:23:04.6746152Z ok (0.025s)
2023-01-11T21:23:04.6746655Z   test_storage_error (__main__.TestTorch) ... test_torch.py:6364: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6747197Z   torch.storage._LegacyStorage()
2023-01-11T21:23:04.6747931Z /opt/conda/lib/python3.7/site-packages/torch/_utils.py:768: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6748445Z   return self.fget.__get__(instance, owner)()
2023-01-11T21:23:04.6748945Z test_torch.py:6378: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6749436Z   storage_class(device='cpu')
2023-01-11T21:23:04.6749906Z test_torch.py:6381: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6750406Z   storage_class(dtype=torch.float)
2023-01-11T21:23:04.6750893Z test_torch.py:6387: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6751334Z   storage_class(0, 0)
2023-01-11T21:23:04.6751810Z test_torch.py:6390: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6752281Z   storage_class('string')
2023-01-11T21:23:04.6752758Z test_torch.py:6393: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6753220Z   storage_class(torch.tensor([]))
2023-01-11T21:23:04.6753703Z test_torch.py:6395: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6754129Z   s = storage_class()
2023-01-11T21:23:04.6754597Z test_torch.py:6398: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6755061Z   storage_class(0, wrap_storage=s.untyped())
2023-01-11T21:23:04.6755555Z test_torch.py:6401: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6756015Z   storage_class(wrap_storage=s)
2023-01-11T21:23:04.6756481Z test_torch.py:6420: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6756988Z   torch.TypedStorage(0, wrap_storage=s.untyped(), dtype=dtype)
2023-01-11T21:23:04.6757545Z test_torch.py:6423: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6758020Z   torch.TypedStorage(wrap_storage=s.untyped())
2023-01-11T21:23:04.6758506Z test_torch.py:6426: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6758996Z   torch.TypedStorage(wrap_storage=s.untyped(), dtype=0)
2023-01-11T21:23:04.6759510Z test_torch.py:6429: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6760032Z   torch.TypedStorage(wrap_storage=s.untyped(), dtype=dtype, device=device)
2023-01-11T21:23:04.6760728Z test_torch.py:6432: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6761222Z   torch.TypedStorage(wrap_storage=s, dtype=dtype)
2023-01-11T21:23:04.6761730Z test_torch.py:6435: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6762272Z   torch.TypedStorage(dtype=dtype, device='xla')
2023-01-11T21:23:04.6762788Z test_torch.py:6443: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6763291Z   torch.TypedStorage(torch.tensor([]), dtype=dtype, device=device)
2023-01-11T21:23:04.6763805Z test_torch.py:6446: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6764290Z   torch.TypedStorage(0, 0, dtype=dtype, device=device)
2023-01-11T21:23:04.6764799Z test_torch.py:6449: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6765307Z   s_other = torch.TypedStorage([1, 2, 3, 4], device=device, dtype=dtype)
2023-01-11T21:23:04.6765814Z test_torch.py:6452: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6766253Z   s.fill_(s_other)
2023-01-11T21:23:04.6766421Z ok (0.079s)
2023-01-11T21:23:04.6766949Z   test_storage_error_no_attribute (__main__.TestTorch) ... test_torch.py:6461: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6767529Z   storage_class.from_buffer()
2023-01-11T21:23:04.6767702Z ok (0.001s)
2023-01-11T21:23:04.6767922Z   test_structseq_repr (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6768186Z   test_subclass_preserved (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6768434Z   test_subclass_tensors (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6768684Z   test_sum_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6768935Z   test_t_not_2d_error (__main__.TestTorch) ... ok (0.022s)
2023-01-11T21:23:04.6769175Z   test_tensor_base_init (__main__.TestTorch) ... ok (0.003s)
2023-01-11T21:23:04.6769431Z   test_tensor_base_new (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6769688Z   test_tensor_ctor_scalar (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6769943Z   test_tensor_cycle_via_dict (__main__.TestTorch) ... ok (0.108s)
2023-01-11T21:23:04.6770215Z   test_tensor_cycle_via_slots (__main__.TestTorch) ... ok (0.050s)
2023-01-11T21:23:04.6770485Z   test_tensor_dict_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6770816Z   test_tensor_finalizer_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6771379Z   test_tensor_set (__main__.TestTorch) ... test_torch.py:5839: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6771925Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:23:04.6772452Z test_torch.py:5841: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6772920Z   t1.set_(t2.storage(), 0, size)
2023-01-11T21:23:04.6773409Z test_torch.py:5843: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6773858Z   t1.set_(t2.storage(), 0, tuple(size))
2023-01-11T21:23:04.6774355Z test_torch.py:5847: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6774827Z   t1.set_(t2.storage(), 0, size, stride)
2023-01-11T21:23:04.6775314Z test_torch.py:5849: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6775788Z   t1.set_(t2.storage(), 0, size=size, stride=stride)
2023-01-11T21:23:04.6776292Z test_torch.py:5857: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6776794Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:23:04.6777369Z test_torch.py:5859: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6777867Z   t1.set_(source=t2.storage())
2023-01-11T21:23:04.6778333Z test_torch.py:5860: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6778826Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:23:04.6779334Z test_torch.py:5862: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6779836Z   t1.set_(source=t2.storage(), storage_offset=0, size=size, stride=stride)
2023-01-11T21:23:04.6780388Z test_torch.py:5869: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6780867Z   self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
2023-01-11T21:23:04.6781077Z ok (0.005s)
2023-01-11T21:23:04.6781585Z   test_tensor_set_errors (__main__.TestTorch) ... test_torch.py:5876: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6782140Z   self.assertRaises(RuntimeError, lambda: f_cpu.set_(d_cpu.storage()))
2023-01-11T21:23:04.6782663Z test_torch.py:5878: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6783175Z   lambda: f_cpu.set_(d_cpu.storage(), 0, d_cpu.size(), d_cpu.stride()))
2023-01-11T21:23:04.6783396Z ok (0.008s)
2023-01-11T21:23:04.6783616Z   test_tensor_slot_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6783867Z   test_tensor_weakref_dealloc (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6784143Z   test_tensoriterator_output_setup (__main__.TestTorch) ... ok (0.624s)
2023-01-11T21:23:04.6784400Z   test_to (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6784623Z   test_to_with_tensor (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6784869Z   test_topk_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6785118Z   test_torch_from_file (__main__.TestTorch) ... ok (0.016s)
2023-01-11T21:23:04.6785359Z   test_transpose_neg_dim (__main__.TestTorch) ... ok (0.032s)
2023-01-11T21:23:04.6785602Z   test_type (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6785836Z   test_type_alias (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6786107Z   test_type_conversion_via_dtype_name (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:23:04.6786698Z   test_typed_storage_deprecation_warning (__main__.TestTorch) ... test_torch.py:6624: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6787225Z   s0 = torch.FloatStorage(10)
2023-01-11T21:23:04.6787412Z ok (0.003s)
2023-01-11T21:23:04.6787934Z   test_typed_storage_internal_no_warning (__main__.TestTorch) ... test_torch.py:6554: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6788474Z   s0 = torch.FloatStorage(10)
2023-01-11T21:23:04.6788963Z test_torch.py:6555: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6789421Z   s0_untyped = s0.untyped()
2023-01-11T21:23:04.6789602Z ok (0.004s)
2023-01-11T21:23:04.6789797Z   test_unbind_neg_dim (__main__.TestTorch) ... ok (0.006s)
2023-01-11T21:23:04.6790041Z   test_unflatten (__main__.TestTorch) ... ok (0.043s)
2023-01-11T21:23:04.6790285Z   test_unfold_neg_dim (__main__.TestTorch) ... ok (0.004s)
2023-01-11T21:23:04.6790521Z   test_unsqueeze_neg_dim (__main__.TestTorch) ... ok (0.008s)
2023-01-11T21:23:04.6790787Z   test_upsample_nearest1d_meta (__main__.TestTorch) ... ok (0.020s)
2023-01-11T21:23:04.6791060Z   test_upsample_nearest2d_meta (__main__.TestTorch) ... ok (0.042s)
2023-01-11T21:23:04.6791347Z   test_var_neg_dim (__main__.TestTorch) ... ok (0.005s)
2023-01-11T21:23:04.6791578Z   test_warn_types (__main__.TestTorch) ... ok (0.002s)
2023-01-11T21:23:04.6791823Z   test_wildcard_import (__main__.TestTorch) ... ok (0.001s)
2023-01-11T21:23:04.6792116Z   test_addcdiv_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6792424Z   test_addcdiv_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6792737Z   test_addcdiv_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6793044Z   test_addcdiv_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6793337Z   test_addcdiv_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.6793639Z   test_addcdiv_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6793945Z   test_addcdiv_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6794246Z   test_addcdiv_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6794537Z   test_addcdiv_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6794848Z   test_addcmul_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.6795159Z   test_addcmul_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6795460Z   test_addcmul_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6795766Z   test_addcmul_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6796062Z   test_addcmul_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6796363Z   test_addcmul_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6796649Z   test_addcmul_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6796946Z   test_addcmul_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6797246Z   test_addcmul_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6797576Z   test_assertRaisesRegex_ignore_msg_non_native_device_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.021s)
2023-01-11T21:23:04.6798005Z   test_bernoulli_edge_cases_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:23:04.6798464Z   test_bernoulli_edge_cases_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:23:04.6798848Z   test_bernoulli_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.044s)
2023-01-11T21:23:04.6799159Z   test_bernoulli_p_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6799512Z   test_bernoulli_p_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6799824Z   test_bernoulli_p_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6800133Z   test_bernoulli_self_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6800438Z   test_bernoulli_self_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6800897Z   test_bernoulli_self_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6801219Z   test_bernoulli_self_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6801521Z   test_bernoulli_self_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6801832Z   test_bernoulli_self_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6802143Z   test_bernoulli_self_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6802458Z   test_bernoulli_self_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6802758Z   test_bfloat16_float_copy_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.497s)
2023-01-11T21:23:04.6803139Z   test_bool_tensor_value_change_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6803460Z   test_broadcast_fn_add_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.058s)
2023-01-11T21:23:04.6803761Z   test_broadcast_fn_addcdiv_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.065s)
2023-01-11T21:23:04.6804075Z   test_broadcast_fn_addcmul_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.064s)
2023-01-11T21:23:04.6804392Z   test_broadcast_fn_atan2_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.056s)
2023-01-11T21:23:04.6804708Z   test_broadcast_fn_copy_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.059s)
2023-01-11T21:23:04.6805004Z   test_broadcast_fn_dist_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:23:04.6805315Z   test_broadcast_fn_div_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.055s)
2023-01-11T21:23:04.6805628Z   test_broadcast_fn_eq_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.054s)
2023-01-11T21:23:04.6805929Z   test_broadcast_fn_fmod_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.056s)
2023-01-11T21:23:04.6806243Z   test_broadcast_fn_ge_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.055s)
2023-01-11T21:23:04.6806548Z   test_broadcast_fn_gt_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.056s)
2023-01-11T21:23:04.6806848Z   test_broadcast_fn_le_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.064s)
2023-01-11T21:23:04.6807136Z   test_broadcast_fn_lerp_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.058s)
2023-01-11T21:23:04.6807443Z   test_broadcast_fn_lt_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.056s)
2023-01-11T21:23:04.6807745Z   test_broadcast_fn_map2_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.051s)
2023-01-11T21:23:04.6808044Z   test_broadcast_fn_map_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.050s)
2023-01-11T21:23:04.6808366Z   test_broadcast_fn_masked_fill_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.059s)
2023-01-11T21:23:04.6808697Z   test_broadcast_fn_masked_scatter_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.062s)
2023-01-11T21:23:04.6809034Z   test_broadcast_fn_masked_select_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6809341Z   test_broadcast_fn_max_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:23:04.6809648Z   test_broadcast_fn_min_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:23:04.6809953Z   test_broadcast_fn_mul_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.055s)
2023-01-11T21:23:04.6810245Z   test_broadcast_fn_ne_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.054s)
2023-01-11T21:23:04.6810549Z   test_broadcast_fn_pow_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.055s)
2023-01-11T21:23:04.6810864Z   test_broadcast_fn_remainder_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.056s)
2023-01-11T21:23:04.6811178Z   test_broadcast_fn_sub_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.058s)
2023-01-11T21:23:04.6811832Z   test_bytes_to_scalar_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:161: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6812417Z   self.assertEqual(scalar.storage().untyped().tolist(), bytes_list)
2023-01-11T21:23:04.6812643Z ok (0.004s)
2023-01-11T21:23:04.6812895Z   test_bytes_to_scalar_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6813211Z   test_bytes_to_scalar_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6813541Z   test_bytes_to_scalar_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.6813861Z   test_bytes_to_scalar_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:23:04.6814166Z   test_bytes_to_scalar_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6814513Z   test_bytes_to_scalar_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.6814828Z   test_bytes_to_scalar_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:23:04.6815141Z   test_bytes_to_scalar_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6815441Z   test_bytes_to_scalar_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6815760Z   test_cauchy_kstest_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.196s)
2023-01-11T21:23:04.6816081Z   test_cauchy_kstest_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6816382Z   test_cauchy_kstest_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6816696Z   test_cauchy_kstest_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6817083Z   test_cauchy_no_inf_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:23:04.6817592Z   test_cauchy_no_inf_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:23:04.6817977Z   test_cdist_cuda_backward_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:23:04.6818313Z   test_cdist_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6818631Z   test_cdist_euclidean_large_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.102s)
2023-01-11T21:23:04.6818956Z   test_cdist_grad_p_lt_1_no_nan_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:23:04.6819327Z   test_cdist_large_batch_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.002s)
2023-01-11T21:23:04.6819697Z   test_cdist_large_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.836s)
2023-01-11T21:23:04.6820016Z   test_cdist_non_contiguous_batch_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6820337Z   test_cdist_non_contiguous_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:23:04.6820657Z   test_cdist_norm_batch_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (1.592s)
2023-01-11T21:23:04.6820962Z   test_cdist_norm_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.408s)
2023-01-11T21:23:04.6821274Z   test_cdist_same_inputs_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6821591Z   test_clone_all_dtypes_and_devices_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.6821926Z   test_clone_not_memory_dense_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6822249Z   test_clone_zero_stride_dim_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6822580Z   test_complex_half_experimental_warning_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6822947Z   test_constants_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6823298Z   test_conv_transposed_backward_agnostic_to_memory_format_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.091s)
2023-01-11T21:23:04.6823680Z   test_conv_transposed_large_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.6824183Z   test_copy__cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:5264: UserWarning: Casting complex values to real discards the imaginary part (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Copy.cpp:276.)
2023-01-11T21:23:04.6824581Z   t.copy_(src)
2023-01-11T21:23:04.6824751Z ok (0.010s)
2023-01-11T21:23:04.6824972Z   test_copy__cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6825277Z   test_copy__cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:23:04.6825592Z   test_copy__cpu_complex32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6825903Z   test_copy__cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:23:04.6826225Z   test_copy__cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:23:04.6826529Z   test_copy__cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.6826831Z   test_copy__cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.6827109Z   test_copy__cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6827403Z   test_copy__cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6827692Z   test_copy__cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6827987Z   test_copy__cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6828269Z   test_copy__cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6828586Z   test_copy_all_dtypes_and_devices_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.6828905Z   test_copy_math_view_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.6829210Z   test_copy_mem_overlap_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.048s)
2023-01-11T21:23:04.6829547Z   test_copy_transpose_math_view_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.071s)
2023-01-11T21:23:04.6829889Z   test_copy_transpose_math_view_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.018s)
2023-01-11T21:23:04.6830223Z   test_copy_transpose_math_view_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6830691Z   test_corrcoef_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:1944: UserWarning: cov(): degrees of freedom is <= 0 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Correlation.cpp:117.)
2023-01-11T21:23:04.6831082Z   res = torch.corrcoef(x)
2023-01-11T21:23:04.6831500Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:393: RuntimeWarning: Mean of empty slice.
2023-01-11T21:23:04.6831775Z   avg = a.mean(axis)
2023-01-11T21:23:04.6832158Z /opt/conda/lib/python3.7/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide
2023-01-11T21:23:04.6832544Z   ret, rcount, out=ret, casting='unsafe', subok=False)
2023-01-11T21:23:04.6832970Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice
2023-01-11T21:23:04.6833239Z   c = cov(x, y, rowvar)
2023-01-11T21:23:04.6833639Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: divide by zero encountered in true_divide
2023-01-11T21:23:04.6833943Z   c *= np.true_divide(1, fact)
2023-01-11T21:23:04.6834341Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: invalid value encountered in multiply
2023-01-11T21:23:04.6834662Z   c *= np.true_divide(1, fact)
2023-01-11T21:23:04.6834839Z ok (0.016s)
2023-01-11T21:23:04.6835083Z   test_corrcoef_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.012s)
2023-01-11T21:23:04.6835386Z   test_corrcoef_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6835850Z   test_cov_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:1951: UserWarning: cov(): degrees of freedom is <= 0 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Correlation.cpp:117.)
2023-01-11T21:23:04.6836300Z   res = torch.cov(t, correction=correction, fweights=fweights, aweights=aweights)
2023-01-11T21:23:04.6836606Z test_torch.py:1955: RuntimeWarning: Degrees of freedom <= 0 for slice
2023-01-11T21:23:04.6836884Z   ref = np.cov(t, ddof=correction, fweights=fweights, aweights=aweights)
2023-01-11T21:23:04.6837334Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:425: RuntimeWarning: invalid value encountered in multiply
2023-01-11T21:23:04.6837673Z   avg = np.multiply(a, wgt, dtype=result_dtype).sum(axis)/scl
2023-01-11T21:23:04.6837871Z ok (0.156s)
2023-01-11T21:23:04.6838103Z   test_cov_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.136s)
2023-01-11T21:23:04.6838433Z   test_cov_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.122s)
2023-01-11T21:23:04.6838743Z   test_cpp_warnings_have_python_context_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.444s)
2023-01-11T21:23:04.6839119Z   test_cublas_config_nondeterministic_alert_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:23:04.6839468Z   test_cummax_cummin_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.6839779Z   test_cummax_discontiguous_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6840087Z   test_cummin_discontiguous_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6840394Z   test_cumprod_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:23:04.6840824Z   test_cumsum_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.020s)
2023-01-11T21:23:04.6841439Z   test_deepcopy_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:385: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6841972Z   q = [a, [a.storage(), b.storage()], b, c]
2023-01-11T21:23:04.6842488Z /opt/conda/lib/python3.7/copy.py:161: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6842959Z   y = copier(memo)
2023-01-11T21:23:04.6843737Z /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6844271Z   device=typed_storage.device,
2023-01-11T21:23:04.6844751Z test_torch.py:396: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6845221Z   self.assertEqual(w[1][0][i], q[1][0][i] + 1)
2023-01-11T21:23:04.6845710Z test_torch.py:400: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.6846297Z   self.assertEqual(w[1][1][i], q[1][1][i] - 1)
2023-01-11T21:23:04.6846478Z ok (0.016s)
2023-01-11T21:23:04.6846725Z   test_deepcopy_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6847052Z   test_deepcopy_scalar_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6847388Z   test_deepcopy_scalar_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6847728Z   test_device_guard_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: fewer than 2 devices detected (0.004s)
2023-01-11T21:23:04.6848067Z   test_diff_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.116s)
2023-01-11T21:23:04.6848373Z   test_diff_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.169s)
2023-01-11T21:23:04.6848668Z   test_diff_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.176s)
2023-01-11T21:23:04.6848977Z   test_diff_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.159s)
2023-01-11T21:23:04.6849285Z   test_diff_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.159s)
2023-01-11T21:23:04.6849638Z   test_diff_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.149s)
2023-01-11T21:23:04.6849938Z   test_diff_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.137s)
2023-01-11T21:23:04.6850285Z   test_diff_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.113s)
2023-01-11T21:23:04.6850605Z   test_diff_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.105s)
2023-01-11T21:23:04.6850888Z   test_diff_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.112s)
2023-01-11T21:23:04.6851179Z   test_diff_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.115s)
2023-01-11T21:23:04.6851484Z   test_diff_noncontig_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.104s)
2023-01-11T21:23:04.6851800Z   test_diff_noncontig_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.168s)
2023-01-11T21:23:04.6852134Z   test_diff_noncontig_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.166s)
2023-01-11T21:23:04.6852458Z   test_diff_noncontig_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.170s)
2023-01-11T21:23:04.6852781Z   test_diff_noncontig_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.144s)
2023-01-11T21:23:04.6853088Z   test_diff_noncontig_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.134s)
2023-01-11T21:23:04.6853410Z   test_diff_noncontig_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.097s)
2023-01-11T21:23:04.6853728Z   test_diff_noncontig_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.107s)
2023-01-11T21:23:04.6854028Z   test_diff_noncontig_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.086s)
2023-01-11T21:23:04.6854340Z   test_diff_noncontig_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.095s)
2023-01-11T21:23:04.6854649Z   test_diff_noncontig_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.096s)
2023-01-11T21:23:04.6854964Z   test_dim_function_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.054s)
2023-01-11T21:23:04.6855273Z   test_discontiguous_out_cumsum_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6855579Z   test_dist_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6855877Z   test_errors_index_copy_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.060s)
2023-01-11T21:23:04.6856182Z   test_expected_failure_xla_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6856503Z   test_exponential_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.017s)
2023-01-11T21:23:04.6856822Z   test_exponential_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6857137Z   test_exponential_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6857514Z   test_exponential_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6857836Z   test_exponential_kstest_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.6858225Z   test_exponential_kstest_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6858548Z   test_exponential_kstest_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6858878Z   test_exponential_kstest_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6859233Z   test_exponential_no_zero_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.6859606Z   test_exponential_no_zero_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.6859957Z   test_gather_backward_deterministic_path_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (3.458s)
2023-01-11T21:23:04.6860301Z   test_gather_backward_one_dim_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (3.434s)
2023-01-11T21:23:04.6860621Z   test_geometric_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6860938Z   test_geometric_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6861234Z   test_geometric_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6861573Z   test_geometric_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6861881Z   test_geometric_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6862172Z   test_geometric_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6862614Z   test_geometric_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6863115Z   test_geometric_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6863560Z   test_geometric_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6864046Z   test_geometric_kstest_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.6864534Z   test_geometric_kstest_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6865037Z   test_geometric_kstest_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.6865501Z   test_geometric_kstest_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6866024Z   test_geometric_kstest_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6866528Z   test_geometric_kstest_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6866979Z   test_geometric_kstest_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6867450Z   test_geometric_kstest_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6867954Z   test_geometric_kstest_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6868451Z   test_gradient_all_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.576s)
2023-01-11T21:23:04.6868947Z   test_gradient_all_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.417s)
2023-01-11T21:23:04.6869882Z   test_gradient_all_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1061: RuntimeWarning: divide by zero encountered in true_divide
2023-01-11T21:23:04.6870475Z   a = -(dx2)/(dx1 * (dx1 + dx2))
2023-01-11T21:23:04.6871074Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1062: RuntimeWarning: divide by zero encountered in true_divide
2023-01-11T21:23:04.6871514Z   b = (dx2 - dx1) / (dx1 * dx2)
2023-01-11T21:23:04.6872139Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1063: RuntimeWarning: divide by zero encountered in true_divide
2023-01-11T21:23:04.6872582Z   c = dx1 / (dx2 * (dx1 + dx2))
2023-01-11T21:23:04.6873184Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1069: RuntimeWarning: invalid value encountered in add
2023-01-11T21:23:04.6873690Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6898062Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1117: RuntimeWarning: divide by zero encountered in double_scalars
2023-01-11T21:23:04.6898654Z   a = (dx2) / (dx1 * (dx1 + dx2))
2023-01-11T21:23:04.6899308Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1118: RuntimeWarning: divide by zero encountered in double_scalars
2023-01-11T21:23:04.6899795Z   b = - (dx2 + dx1) / (dx1 * dx2)
2023-01-11T21:23:04.6900383Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1121: RuntimeWarning: invalid value encountered in double_scalars
2023-01-11T21:23:04.6900930Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6901643Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1100: RuntimeWarning: divide by zero encountered in double_scalars
2023-01-11T21:23:04.6902223Z   a = -(2. * dx1 + dx2)/(dx1 * (dx1 + dx2))
2023-01-11T21:23:04.6902804Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1101: RuntimeWarning: divide by zero encountered in double_scalars
2023-01-11T21:23:04.6903213Z   b = (dx1 + dx2) / (dx1 * dx2)
2023-01-11T21:23:04.6903917Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1104: RuntimeWarning: invalid value encountered in add
2023-01-11T21:23:04.6904401Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6904998Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1121: RuntimeWarning: invalid value encountered in add
2023-01-11T21:23:04.6905469Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6906093Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1078: RuntimeWarning: divide by zero encountered in true_divide
2023-01-11T21:23:04.6906633Z   out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0
2023-01-11T21:23:04.6907231Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1078: RuntimeWarning: invalid value encountered in true_divide
2023-01-11T21:23:04.6907769Z   out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0
2023-01-11T21:23:04.6908368Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1069: RuntimeWarning: invalid value encountered in multiply
2023-01-11T21:23:04.6908827Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6909446Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1102: RuntimeWarning: divide by zero encountered in double_scalars
2023-01-11T21:23:04.6909917Z   c = - dx1 / (dx2 * (dx1 + dx2))
2023-01-11T21:23:04.6910457Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1104: RuntimeWarning: invalid value encountered in multiply
2023-01-11T21:23:04.6910942Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6911645Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1121: RuntimeWarning: invalid value encountered in multiply
2023-01-11T21:23:04.6912215Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6912957Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1119: RuntimeWarning: divide by zero encountered in double_scalars
2023-01-11T21:23:04.6913364Z   c = (2. * dx2 + dx1) / (dx2 * (dx1 + dx2))
2023-01-11T21:23:04.6913952Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1085: RuntimeWarning: divide by zero encountered in true_divide
2023-01-11T21:23:04.6914510Z   out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n
2023-01-11T21:23:04.6915099Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1085: RuntimeWarning: invalid value encountered in true_divide
2023-01-11T21:23:04.6915668Z   out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n
2023-01-11T21:23:04.6916095Z ok (0.504s)
2023-01-11T21:23:04.6916451Z   test_gradient_extreme_cases_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.315s)
2023-01-11T21:23:04.6916911Z   test_gradient_extreme_cases_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.234s)
2023-01-11T21:23:04.6917368Z   test_gradient_extreme_cases_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.218s)
2023-01-11T21:23:04.6918234Z   test_gradient_type_promotion_cpu (__main__.TestTorchDeviceTypeCPU) ... /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1057: ComplexWarning: Casting complex values to real discards the imaginary part
2023-01-11T21:23:04.6918946Z   out[tuple(slice1)] = (f[tuple(slice4)] - f[tuple(slice2)]) / (2. * ax_dx)
2023-01-11T21:23:04.6919608Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1078: ComplexWarning: Casting complex values to real discards the imaginary part
2023-01-11T21:23:04.6920179Z   out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0
2023-01-11T21:23:04.6920991Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1085: ComplexWarning: Casting complex values to real discards the imaginary part
2023-01-11T21:23:04.6921668Z   out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n
2023-01-11T21:23:04.6923346Z /opt/conda/lib/python3.7/site-packages/torch/testing/_comparison.py:679: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:210.)
2023-01-11T21:23:04.6924307Z   return torch.as_tensor(tensor_like)
2023-01-11T21:23:04.6924929Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1104: ComplexWarning: Casting complex values to real discards the imaginary part
2023-01-11T21:23:04.6925458Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6926117Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1121: ComplexWarning: Casting complex values to real discards the imaginary part
2023-01-11T21:23:04.6926618Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6927271Z /opt/conda/lib/python3.7/site-packages/numpy/lib/function_base.py:1069: ComplexWarning: Casting complex values to real discards the imaginary part
2023-01-11T21:23:04.6927766Z   out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
2023-01-11T21:23:04.6928047Z ok (0.164s)
2023-01-11T21:23:04.6928364Z   test_hook_remove_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6928793Z   test_index_add_deterministic_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (1.541s)
2023-01-11T21:23:04.6929225Z   test_index_add_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.061s)
2023-01-11T21:23:04.6929675Z   test_index_copy_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.026s)
2023-01-11T21:23:04.6930094Z   test_index_copy_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.6930525Z   test_index_copy_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.026s)
2023-01-11T21:23:04.6930948Z   test_index_copy_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.026s)
2023-01-11T21:23:04.6931382Z   test_index_copy_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.6931806Z   test_index_copy_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.6932229Z   test_index_copy_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.023s)
2023-01-11T21:23:04.6932691Z   test_index_copy_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.6933155Z   test_index_copy_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.6933760Z   test_index_copy_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.018s)
2023-01-11T21:23:04.6934239Z   test_index_copy_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.6934663Z   test_index_copy_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.6935091Z   test_index_copy_deterministic_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (8.830s)
2023-01-11T21:23:04.6935535Z   test_index_copy_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.6935973Z   test_index_copy_scalars_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6936410Z   test_index_copy_scalars_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6936854Z   test_index_copy_scalars_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6937367Z   test_index_copy_scalars_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6937835Z   test_index_copy_scalars_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6938357Z   test_index_copy_scalars_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6938791Z   test_index_copy_scalars_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6939231Z   test_index_copy_scalars_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6939669Z   test_index_copy_scalars_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6940106Z   test_index_copy_scalars_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6940517Z   test_index_copy_scalars_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6940951Z   test_index_copy_scalars_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6941396Z   test_index_fill_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6941798Z   test_index_fill_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6942233Z   test_index_fill_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6942654Z   test_index_fill_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6943124Z   test_index_fill_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6943588Z   test_index_fill_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6944067Z   test_index_fill_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6944559Z   test_index_fill_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6944998Z   test_index_fill_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6945429Z   test_index_fill_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6945847Z   test_index_fill_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6946268Z   test_index_fill_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6946677Z   test_index_fill_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.017s)
2023-01-11T21:23:04.6947113Z   test_index_put_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.087s)
2023-01-11T21:23:04.6947581Z   test_index_put_non_accumulate_deterministic_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.6948337Z   test_index_reduce_reduce_amax_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:2997: UserWarning: index_reduce() is in beta and the API may change at any time. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1128.)
2023-01-11T21:23:04.6949052Z   dest.index_reduce_(dim, idx, src, reduce, include_self=include_self)
2023-01-11T21:23:04.6949374Z ok (0.168s)
2023-01-11T21:23:04.6949823Z   test_index_reduce_reduce_amax_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.155s)
2023-01-11T21:23:04.6950262Z   test_index_reduce_reduce_amax_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.157s)
2023-01-11T21:23:04.6950717Z   test_index_reduce_reduce_amax_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.155s)
2023-01-11T21:23:04.6951172Z   test_index_reduce_reduce_amax_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.129s)
2023-01-11T21:23:04.6951621Z   test_index_reduce_reduce_amax_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.129s)
2023-01-11T21:23:04.6952050Z   test_index_reduce_reduce_amax_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.131s)
2023-01-11T21:23:04.6952499Z   test_index_reduce_reduce_amax_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.129s)
2023-01-11T21:23:04.6952945Z   test_index_reduce_reduce_amax_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.130s)
2023-01-11T21:23:04.6953384Z   test_index_reduce_reduce_amin_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.156s)
2023-01-11T21:23:04.6953891Z   test_index_reduce_reduce_amin_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.158s)
2023-01-11T21:23:04.6954493Z   test_index_reduce_reduce_amin_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.160s)
2023-01-11T21:23:04.6955043Z   test_index_reduce_reduce_amin_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.161s)
2023-01-11T21:23:04.6955551Z   test_index_reduce_reduce_amin_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.130s)
2023-01-11T21:23:04.6955997Z   test_index_reduce_reduce_amin_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.128s)
2023-01-11T21:23:04.6956439Z   test_index_reduce_reduce_amin_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.124s)
2023-01-11T21:23:04.6956907Z   test_index_reduce_reduce_amin_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.128s)
2023-01-11T21:23:04.6957370Z   test_index_reduce_reduce_amin_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.128s)
2023-01-11T21:23:04.6957856Z   test_index_reduce_reduce_mean_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.189s)
2023-01-11T21:23:04.6958321Z   test_index_reduce_reduce_mean_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.184s)
2023-01-11T21:23:04.6958775Z   test_index_reduce_reduce_mean_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.185s)
2023-01-11T21:23:04.6959250Z   test_index_reduce_reduce_mean_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.183s)
2023-01-11T21:23:04.6959694Z   test_index_reduce_reduce_mean_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.157s)
2023-01-11T21:23:04.6960140Z   test_index_reduce_reduce_mean_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.155s)
2023-01-11T21:23:04.6960570Z   test_index_reduce_reduce_mean_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.170s)
2023-01-11T21:23:04.6961180Z   test_index_reduce_reduce_mean_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.158s)
2023-01-11T21:23:04.6961639Z   test_index_reduce_reduce_mean_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.156s)
2023-01-11T21:23:04.6962087Z   test_index_reduce_reduce_prod_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.155s)
2023-01-11T21:23:04.6962562Z   test_index_reduce_reduce_prod_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.160s)
2023-01-11T21:23:04.6963044Z   test_index_reduce_reduce_prod_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.155s)
2023-01-11T21:23:04.6963513Z   test_index_reduce_reduce_prod_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.158s)
2023-01-11T21:23:04.6963947Z   test_index_reduce_reduce_prod_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.127s)
2023-01-11T21:23:04.6964468Z   test_index_reduce_reduce_prod_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.125s)
2023-01-11T21:23:04.6964984Z   test_index_reduce_reduce_prod_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.121s)
2023-01-11T21:23:04.6965521Z   test_index_reduce_reduce_prod_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.126s)
2023-01-11T21:23:04.6966107Z   test_index_reduce_reduce_prod_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.126s)
2023-01-11T21:23:04.6966570Z   test_index_select_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6967010Z   test_index_select_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6967425Z   test_index_select_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6967872Z   test_index_select_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.6968318Z   test_index_select_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.6968744Z   test_index_select_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.6969175Z   test_index_select_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.6969609Z   test_index_select_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6970028Z   test_index_select_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.6970450Z   test_index_select_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.6970972Z   test_index_select_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6971397Z   test_index_select_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6971830Z   test_invalid_shapes_grid_sampler_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.078s)
2023-01-11T21:23:04.6972250Z   test_is_set_to_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.6972658Z   test_is_signed_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6973068Z   test_item_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6973461Z   test_item_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6973872Z   test_item_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6974294Z   test_item_cpu_complex32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6974712Z   test_item_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6975160Z   test_item_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6975619Z   test_item_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6976082Z   test_item_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6976558Z   test_item_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6976970Z   test_item_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6977474Z   test_item_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6977860Z   test_item_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6978265Z   test_item_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6978724Z   test_large_cumprod_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.6979218Z   test_large_cumsum_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.6979664Z   test_log_normal_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6980088Z   test_log_normal_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6980516Z   test_log_normal_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6980949Z   test_log_normal_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.6981370Z   test_logcumsumexp_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.022s)
2023-01-11T21:23:04.6981810Z   test_lognormal_kstest_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6982254Z   test_lognormal_kstest_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.6982774Z   test_lognormal_kstest_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.6983237Z   test_lognormal_kstest_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.012s)
2023-01-11T21:23:04.6983661Z   test_masked_fill_bool_tensor_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6984096Z   test_masked_fill_cpu_bfloat16_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6984526Z   test_masked_fill_cpu_bfloat16_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6984980Z   test_masked_fill_cpu_bool_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6985423Z   test_masked_fill_cpu_bool_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6985910Z   test_masked_fill_cpu_complex128_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6986424Z   test_masked_fill_cpu_complex128_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6986951Z   test_masked_fill_cpu_complex64_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6987540Z   test_masked_fill_cpu_complex64_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6987993Z   test_masked_fill_cpu_float16_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6988447Z   test_masked_fill_cpu_float16_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6988887Z   test_masked_fill_cpu_float32_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6989308Z   test_masked_fill_cpu_float32_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6989747Z   test_masked_fill_cpu_float64_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6990191Z   test_masked_fill_cpu_float64_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.6990636Z   test_masked_fill_cpu_int16_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6991060Z   test_masked_fill_cpu_int16_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6991501Z   test_masked_fill_cpu_int32_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.6991935Z   test_masked_fill_cpu_int32_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.025s)
2023-01-11T21:23:04.6992356Z   test_masked_fill_cpu_int64_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6992788Z   test_masked_fill_cpu_int64_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6993224Z   test_masked_fill_cpu_int8_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6993661Z   test_masked_fill_cpu_int8_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6994079Z   test_masked_fill_cpu_uint8_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6994516Z   test_masked_fill_cpu_uint8_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6994967Z   test_masked_fill_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.6995460Z   test_masked_scatter_bool_tensor_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.6995975Z   test_masked_scatter_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.6996523Z   test_masked_scatter_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.6996966Z   test_masked_scatter_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.6997416Z   test_masked_scatter_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.6997861Z   test_masked_scatter_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.6998303Z   test_masked_scatter_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.6998801Z   test_masked_scatter_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.6999234Z   test_masked_scatter_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.6999669Z   test_masked_scatter_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.7000100Z   test_masked_scatter_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.7000519Z   test_masked_scatter_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.7001095Z   test_masked_scatter_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:23:04.7001587Z   test_masked_scatter_large_tensor_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7002059Z   test_masked_scatter_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.7003020Z   test_masked_select_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7003776Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7004046Z ok (0.011s)
2023-01-11T21:23:04.7004825Z   test_masked_select_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7005628Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7005936Z ok (0.009s)
2023-01-11T21:23:04.7006774Z   test_masked_select_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7007521Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7007775Z ok (0.010s)
2023-01-11T21:23:04.7008504Z   test_masked_select_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7009247Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7009508Z ok (0.010s)
2023-01-11T21:23:04.7010196Z   test_masked_select_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7010910Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7011174Z ok (0.006s)
2023-01-11T21:23:04.7011882Z   test_masked_select_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7012578Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7012841Z ok (0.010s)
2023-01-11T21:23:04.7013542Z   test_masked_select_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7014339Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7014612Z ok (0.010s)
2023-01-11T21:23:04.7015432Z   test_masked_select_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7016310Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7016574Z ok (0.015s)
2023-01-11T21:23:04.7017345Z   test_masked_select_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7018072Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7018329Z ok (0.009s)
2023-01-11T21:23:04.7019093Z   test_masked_select_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7019792Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7020051Z ok (0.008s)
2023-01-11T21:23:04.7020766Z   test_masked_select_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7021478Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7021720Z ok (0.009s)
2023-01-11T21:23:04.7022426Z   test_masked_select_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:3641: UserWarning: masked_select received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1854.)
2023-01-11T21:23:04.7023113Z   torch.masked_select(src, mask, out=dst3)
2023-01-11T21:23:04.7023367Z ok (0.009s)
2023-01-11T21:23:04.7023689Z   test_masked_select_discontiguous_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.052s)
2023-01-11T21:23:04.7024125Z   test_memory_format_clone_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.050s)
2023-01-11T21:23:04.7024553Z   test_memory_format_consistency_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7025084Z   test_memory_format_cpu_and_cuda_ops_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7025639Z   test_memory_format_empty_like_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.7026222Z   test_memory_format_factory_like_functions_preserve_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (1.107s)
2023-01-11T21:23:04.7026742Z   test_memory_format_operators_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.738s)
2023-01-11T21:23:04.7027181Z   test_memory_format_preserved_after_permute_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7027640Z   test_memory_format_propagation_rules_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.7028070Z   test_memory_format_to_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.047s)
2023-01-11T21:23:04.7028485Z   test_memory_format_type_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.052s)
2023-01-11T21:23:04.7028921Z   test_memory_format_type_shortcuts_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.385s)
2023-01-11T21:23:04.7029462Z   test_module_share_memory_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7029664Z   test_multinomial_cpu_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7029871Z   test_multinomial_cpu_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7030072Z   test_multinomial_cpu_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7030254Z   test_multinomial_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.043s)
2023-01-11T21:23:04.7030449Z   test_multinomial_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:23:04.7030701Z   test_multinomial_deterministic_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:23:04.7030944Z   test_multinomial_deterministic_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7031182Z   test_multinomial_deterministic_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7031428Z   test_multinomial_device_constrain_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7031691Z   test_multinomial_empty_w_replacement_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7031911Z   test_multinomial_empty_wo_replacement_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7032177Z   test_multinomial_gpu_device_constrain_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: fewer than 2 devices detected (0.001s)
2023-01-11T21:23:04.7032473Z   test_multinomial_rng_state_advance_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... skip: test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test (0.001s)
2023-01-11T21:23:04.7032658Z   test_narrow_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7032902Z   test_nondeterministic_alert_AdaptiveAvgPool2d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7033140Z   test_nondeterministic_alert_AdaptiveAvgPool3d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7033374Z   test_nondeterministic_alert_AdaptiveMaxPool2d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7033594Z   test_nondeterministic_alert_AvgPool3d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7033810Z   test_nondeterministic_alert_CTCLoss_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7034043Z   test_nondeterministic_alert_EmbeddingBag_max_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7034287Z   test_nondeterministic_alert_FractionalMaxPool2d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7034520Z   test_nondeterministic_alert_FractionalMaxPool3d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7034743Z   test_nondeterministic_alert_MaxPool3d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7035032Z   test_nondeterministic_alert_MaxUnpool1d_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... skip: float16 not implemented on CPU (0.001s)
2023-01-11T21:23:04.7035273Z   test_nondeterministic_alert_MaxUnpool1d_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.7035509Z   test_nondeterministic_alert_MaxUnpool1d_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.012s)
2023-01-11T21:23:04.7035788Z   test_nondeterministic_alert_MaxUnpool2d_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... skip: float16 not implemented on CPU (0.001s)
2023-01-11T21:23:04.7036022Z   test_nondeterministic_alert_MaxUnpool2d_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.012s)
2023-01-11T21:23:04.7036256Z   test_nondeterministic_alert_MaxUnpool2d_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.012s)
2023-01-11T21:23:04.7036546Z   test_nondeterministic_alert_MaxUnpool3d_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... skip: float16 not implemented on CPU (0.001s)
2023-01-11T21:23:04.7036880Z   test_nondeterministic_alert_MaxUnpool3d_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.7037133Z   test_nondeterministic_alert_MaxUnpool3d_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.7037382Z   test_nondeterministic_alert_NLLLoss_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7037663Z   test_nondeterministic_alert_ReflectionPad1d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.7037945Z   test_nondeterministic_alert_ReflectionPad2d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7038241Z   test_nondeterministic_alert_ReflectionPad3d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.7038498Z   test_nondeterministic_alert_ReplicationPad1d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7038733Z   test_nondeterministic_alert_ReplicationPad2d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7038968Z   test_nondeterministic_alert_ReplicationPad3d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7039232Z   test_nondeterministic_alert_bincount_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7039464Z   test_nondeterministic_alert_cumsum_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7039698Z   test_nondeterministic_alert_cumsum_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7039925Z   test_nondeterministic_alert_cumsum_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7040153Z   test_nondeterministic_alert_cumsum_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7040373Z   test_nondeterministic_alert_cumsum_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7041100Z   test_nondeterministic_alert_cumsum_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7041326Z   test_nondeterministic_alert_cumsum_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7041549Z   test_nondeterministic_alert_cumsum_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7041775Z   test_nondeterministic_alert_cumsum_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7041977Z   test_nondeterministic_alert_cumsum_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7042206Z   test_nondeterministic_alert_grid_sample_2d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.7042431Z   test_nondeterministic_alert_grid_sample_3d_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.7042643Z   test_nondeterministic_alert_histc_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7042882Z   test_nondeterministic_alert_interpolate_bicubic_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7043126Z   test_nondeterministic_alert_interpolate_bilinear_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7043363Z   test_nondeterministic_alert_interpolate_linear_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7043606Z   test_nondeterministic_alert_interpolate_trilinear_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7043822Z   test_nondeterministic_alert_kthvalue_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7044636Z   test_nondeterministic_alert_median_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:1653: UserWarning: An output with one or more elements was resized since it had shape [10], which does not match the required output shape []. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Resize.cpp:33.)
2023-01-11T21:23:04.7044868Z   torch.median(a, 0, out=(result, indices))
2023-01-11T21:23:04.7044959Z ok (0.003s)
2023-01-11T21:23:04.7045190Z   test_nondeterministic_alert_put_accumulate_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7045404Z   test_nondeterministic_alert_put_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.032s)
2023-01-11T21:23:04.7045602Z   test_normal_kstest_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.7045794Z   test_normal_kstest_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.012s)
2023-01-11T21:23:04.7045990Z   test_normal_kstest_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:23:04.7046189Z   test_nullary_op_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.115s)
2023-01-11T21:23:04.7046381Z   test_pairwise_distance_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.7046569Z   test_pdist_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7046848Z   test_pdist_norm_large_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7047554Z   test_pickle_gradscaler_cpu (__main__.TestTorchDeviceTypeCPU) ... /opt/conda/lib/python3.7/site-packages/torch/cuda/amp/grad_scaler.py:118: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
2023-01-11T21:23:04.7047787Z   warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.")
2023-01-11T21:23:04.7047876Z ok (0.004s)
2023-01-11T21:23:04.7048119Z   test_pin_memory_from_constructor_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:23:04.7048336Z   test_put_accumulate_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7048576Z   test_put_accumulate_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7048808Z   test_put_accumulate_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7049051Z   test_put_accumulate_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7049283Z   test_put_accumulate_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7049505Z   test_put_accumulate_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7049733Z   test_put_accumulate_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7049979Z   test_put_accumulate_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7050232Z   test_put_accumulate_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7050430Z   test_put_accumulate_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7050610Z   test_put_accumulate_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7050797Z   test_put_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.118s)
2023-01-11T21:23:04.7050983Z   test_put_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.128s)
2023-01-11T21:23:04.7051173Z   test_put_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.135s)
2023-01-11T21:23:04.7051353Z   test_put_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.117s)
2023-01-11T21:23:04.7051529Z   test_put_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.117s)
2023-01-11T21:23:04.7051710Z   test_put_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.146s)
2023-01-11T21:23:04.7051886Z   test_put_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.084s)
2023-01-11T21:23:04.7052051Z   test_put_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.083s)
2023-01-11T21:23:04.7052227Z   test_put_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.077s)
2023-01-11T21:23:04.7052403Z   test_put_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.085s)
2023-01-11T21:23:04.7052651Z   test_put_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.086s)
2023-01-11T21:23:04.7052831Z   test_put_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.7053022Z   test_put_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.080s)
2023-01-11T21:23:04.7053217Z   test_repeat_interleave_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7053811Z   test_scalar_check_cpu (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:670: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7053987Z   self.assertEqual((), zero_d_clone.set_(one_d.storage(), 0, (), ()).shape)
2023-01-11T21:23:04.7054490Z test_torch.py:671: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7054731Z   self.assertEqual((1,), zero_d_clone.set_(one_d.storage(), 0, (1,), (1,)).shape)
2023-01-11T21:23:04.7055235Z test_torch.py:672: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7055412Z   self.assertEqual((), one_d_clone.set_(one_d.storage(), 0, (), ()).shape)
2023-01-11T21:23:04.7055906Z test_torch.py:673: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7056090Z   self.assertEqual((1,), one_d_clone.set_(one_d.storage(), 0, (1,), (1,)).shape)
2023-01-11T21:23:04.7056177Z ok (0.177s)
2023-01-11T21:23:04.7056375Z   test_scatter_add_bool_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7056586Z   test_scatter_add_non_unique_index_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.044s)
2023-01-11T21:23:04.7056808Z   test_scatter_add_one_dim_deterministic_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (3.492s)
2023-01-11T21:23:04.7057015Z   test_scatter_add_to_large_input_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7057187Z   test_scatter_bool_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7057461Z   test_scatter_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:23:04.7057747Z   test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7058028Z   test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7058260Z   test_scatter_reduce_non_unique_index_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:23:04.7058480Z   test_scatter_reduce_non_unique_index_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7058711Z   test_scatter_reduce_non_unique_index_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:23:04.7058937Z   test_scatter_reduce_non_unique_index_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:23:04.7059161Z   test_scatter_reduce_non_unique_index_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.7059367Z   test_scatter_reduce_non_unique_index_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.7059646Z   test_scatter_reduce_non_unique_index_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:23:04.7059867Z   test_scatter_reduce_non_unique_index_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7060083Z   test_scatter_reduce_non_unique_index_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7060320Z   test_scatter_reduce_non_unique_index_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7060580Z   test_scatter_reduce_non_unique_index_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7060834Z   test_scatter_reduce_non_unique_index_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7061119Z   test_scatter_reduce_operations_to_large_input_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7061396Z   test_scatter_reduce_operations_to_large_input_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7061682Z   test_scatter_reduce_operations_to_large_input_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7062057Z   test_scatter_reduce_operations_to_large_input_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7062304Z   test_scatter_reduce_operations_to_large_input_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7062540Z   test_scatter_reduce_operations_to_large_input_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7062774Z   test_scatter_reduce_operations_to_large_input_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7063012Z   test_scatter_reduce_operations_to_large_input_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7063248Z   test_scatter_reduce_operations_to_large_input_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7063480Z   test_scatter_reduce_operations_to_large_input_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7063718Z   test_scatter_reduce_operations_to_large_input_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7063936Z   test_scatter_reduce_operations_to_large_input_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7064154Z   test_scatter_reduce_scalar_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7064360Z   test_scatter_reduce_scalar_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7064573Z   test_scatter_reduce_scalar_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7064786Z   test_scatter_reduce_scalar_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7064996Z   test_scatter_reduce_scalar_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7065204Z   test_scatter_reduce_scalar_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7065411Z   test_scatter_reduce_scalar_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7065606Z   test_scatter_reduce_scalar_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7065810Z   test_scatter_reduce_scalar_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7066015Z   test_scatter_reduce_scalar_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7066219Z   test_scatter_reduce_scalar_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7066423Z   test_scatter_reduce_scalar_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7066620Z   test_scatter_to_large_input_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7066822Z   test_scatter_zero_size_index_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7067097Z   test_serialization_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7067696Z   test_set_storage_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:257: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7067793Z   a_s = a.storage()
2023-01-11T21:23:04.7068273Z test_torch.py:260: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7068475Z   c = torch.tensor([], device=device, dtype=dtype).set_(a_s.untyped()).reshape(a.size())
2023-01-11T21:23:04.7069008Z test_torch.py:267: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7069150Z   error_storage = a.to(error_dtype).storage()
2023-01-11T21:23:04.7069238Z ok (0.025s)
2023-01-11T21:23:04.7069430Z   test_set_storage_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.7069632Z   test_set_storage_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.033s)
2023-01-11T21:23:04.7069831Z   test_set_storage_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.025s)
2023-01-11T21:23:04.7070026Z   test_set_storage_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.025s)
2023-01-11T21:23:04.7070208Z   test_set_storage_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.025s)
2023-01-11T21:23:04.7070401Z   test_set_storage_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.025s)
2023-01-11T21:23:04.7070596Z   test_set_storage_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.7070789Z   test_set_storage_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.7070978Z   test_set_storage_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.7071164Z   test_set_storage_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.7071351Z   test_set_storage_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:23:04.7071544Z   test_shift_mem_overlap_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.027s)
2023-01-11T21:23:04.7071711Z   test_skip_xla_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7071962Z   test_storage_all_devices_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7072672Z   test_storage_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:168: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7072833Z   self.assertEqual(v.storage()[0], v[0][0])
2023-01-11T21:23:04.7073442Z test_torch.py:169: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7073616Z   self.assertEqual(v.storage()[14], v[2][4])
2023-01-11T21:23:04.7074149Z test_torch.py:170: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7074308Z   v_s = v.storage()
2023-01-11T21:23:04.7074809Z test_torch.py:176: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7074899Z   v_s[el_num],
2023-01-11T21:23:04.7075387Z test_torch.py:179: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7075503Z   v_s_byte = v.storage().untyped()
2023-01-11T21:23:04.7075576Z ok (0.011s)
2023-01-11T21:23:04.7075773Z   test_storage_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.026s)
2023-01-11T21:23:04.7075970Z   test_storage_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.021s)
2023-01-11T21:23:04.7076208Z   test_storage_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.017s)
2023-01-11T21:23:04.7076401Z   test_storage_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:23:04.7076586Z   test_storage_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:23:04.7076767Z   test_storage_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.7076950Z   test_storage_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.015s)
2023-01-11T21:23:04.7077119Z   test_storage_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.7077304Z   test_storage_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:23:04.7077914Z   test_storage_meta_errors_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:331: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7078197Z   s0 = torch.TypedStorage([1, 2, 3, 4], device='meta', dtype=dtype)
2023-01-11T21:23:04.7078693Z test_torch.py:334: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7078783Z   s0.cpu()
2023-01-11T21:23:04.7079277Z test_torch.py:353: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7079373Z   s0.resize_(10)
2023-01-11T21:23:04.7079870Z test_torch.py:356: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7079968Z   s0.share_memory_()
2023-01-11T21:23:04.7080469Z test_torch.py:359: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7080557Z   s0.tolist()
2023-01-11T21:23:04.7081566Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:675: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7081780Z   return list(self)
2023-01-11T21:23:04.7082650Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:649: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7082812Z   return iter(map(lambda i: self[i], range(self.size())))
2023-01-11T21:23:04.7083309Z test_torch.py:363: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7083471Z   s0._write_file(f, True, True, s0.element_size())
2023-01-11T21:23:04.7084116Z test_torch.py:366: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7084312Z   s1 = torch.TypedStorage([1, 2, 3, 4], device=device, dtype=dtype)
2023-01-11T21:23:04.7084901Z test_torch.py:369: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7085005Z   s1.copy_(s0)
2023-01-11T21:23:04.7085104Z ok (0.056s)
2023-01-11T21:23:04.7085349Z   test_storage_meta_errors_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.054s)
2023-01-11T21:23:04.7085612Z   test_storage_meta_errors_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.054s)
2023-01-11T21:23:04.7085852Z   test_storage_meta_errors_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7086075Z   test_storage_meta_errors_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7086287Z   test_storage_meta_errors_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7086496Z   test_storage_meta_errors_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.060s)
2023-01-11T21:23:04.7086698Z   test_storage_meta_errors_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7086904Z   test_storage_meta_errors_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7087106Z   test_storage_meta_errors_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7087306Z   test_storage_meta_errors_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7087511Z   test_storage_meta_errors_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.053s)
2023-01-11T21:23:04.7088129Z   test_storage_meta_from_tensor_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:324: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7088245Z   s_check = t_check.storage()
2023-01-11T21:23:04.7088741Z test_torch.py:325: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7088901Z   s = t.storage()
2023-01-11T21:23:04.7089411Z test_torch.py:279: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7089639Z   self.assertEqual(s.device.type, 'meta')
2023-01-11T21:23:04.7090154Z test_torch.py:280: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7090305Z   self.assertEqual(s.nbytes(), s_check.nbytes())
2023-01-11T21:23:04.7090798Z test_torch.py:281: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7090941Z   self.assertEqual(s.size(), s_check.size())
2023-01-11T21:23:04.7091485Z test_torch.py:282: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7091597Z   self.assertEqual(s.data_ptr(), 0)
2023-01-11T21:23:04.7092099Z test_torch.py:285: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7092201Z   s[0]
2023-01-11T21:23:04.7092834Z test_torch.py:289: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7093039Z   self._check_storage_meta(s.untyped(), s_check.untyped())
2023-01-11T21:23:04.7093146Z ok (0.002s)
2023-01-11T21:23:04.7093414Z   test_storage_meta_from_tensor_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7093693Z   test_storage_meta_from_tensor_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7093974Z   test_storage_meta_from_tensor_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7094232Z   test_storage_meta_from_tensor_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7094482Z   test_storage_meta_from_tensor_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7094733Z   test_storage_meta_from_tensor_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7094998Z   test_storage_meta_from_tensor_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7095258Z   test_storage_meta_from_tensor_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7095506Z   test_storage_meta_from_tensor_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7095768Z   test_storage_meta_from_tensor_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7096025Z   test_storage_meta_from_tensor_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:23:04.7096745Z   test_storage_setitem_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:210: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7096936Z   s = storage_type(N)
2023-01-11T21:23:04.7097567Z test_torch.py:211: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7097664Z   s[:] = 0
2023-01-11T21:23:04.7098207Z test_torch.py:213: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7098347Z   self.assertEqual(s, storage_type(l))
2023-01-11T21:23:04.7099500Z /opt/conda/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py:1904: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7099646Z   device=typed_storage.device,
2023-01-11T21:23:04.7100242Z test_torch.py:216: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7100343Z   s[i] = i
2023-01-11T21:23:04.7100930Z test_torch.py:219: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7101081Z   self.assertEqual(s, storage_type(l))
2023-01-11T21:23:04.7101661Z test_torch.py:222: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7101779Z   s[2:7] = 1
2023-01-11T21:23:04.7102391Z test_torch.py:223: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7102519Z   self.assertEqual(s, storage_type(l))
2023-01-11T21:23:04.7102630Z ok (0.005s)
2023-01-11T21:23:04.7102879Z   test_storage_setitem_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.7103140Z   test_storage_setitem_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.7103430Z   test_storage_setitem_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.7103719Z   test_storage_setitem_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:23:04.7103981Z   test_storage_setitem_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7104240Z   test_storage_setitem_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7104474Z   test_storage_setitem_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7104738Z   test_storage_setitem_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7105011Z   test_storage_setitem_cpu_qint32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7105266Z   test_storage_setitem_cpu_qint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7105657Z   test_storage_setitem_cpu_quint4x2 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7105922Z   test_storage_setitem_cpu_quint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7106199Z   test_storage_setitem_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7106493Z   test_strides_propagation_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.162s)
2023-01-11T21:23:04.7106781Z   test_sync_warning_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:23:04.7107029Z   test_take_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.020s)
2023-01-11T21:23:04.7107270Z   test_take_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.7107518Z   test_take_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.020s)
2023-01-11T21:23:04.7107768Z   test_take_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.020s)
2023-01-11T21:23:04.7108013Z   test_take_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.018s)
2023-01-11T21:23:04.7108253Z   test_take_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.018s)
2023-01-11T21:23:04.7108567Z   test_take_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.017s)
2023-01-11T21:23:04.7108765Z   test_take_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.7108987Z   test_take_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.7109207Z   test_take_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.013s)
2023-01-11T21:23:04.7109436Z   test_take_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.7109664Z   test_take_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.014s)
2023-01-11T21:23:04.7109874Z   test_take_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:23:04.7110622Z   test_tensor_from_storage_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:240: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7110737Z   a_s = a.storage()
2023-01-11T21:23:04.7111388Z test_torch.py:241: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7111607Z   b = torch.tensor(a_s, device=device, dtype=dtype).reshape(a.size())
2023-01-11T21:23:04.7112225Z test_torch.py:243: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7112475Z   c = torch.tensor(a_s.untyped(), device=device, dtype=dtype).reshape(a.size())
2023-01-11T21:23:04.7113132Z test_torch.py:250: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7113302Z   error_storage = a.to(error_dtype).storage()
2023-01-11T21:23:04.7113953Z test_torch.py:251: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7114157Z   torch.tensor(error_storage, device=device, dtype=dtype)
2023-01-11T21:23:04.7114336Z ok (0.029s)
2023-01-11T21:23:04.7114599Z   test_tensor_from_storage_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.028s)
2023-01-11T21:23:04.7114877Z   test_tensor_from_storage_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.029s)
2023-01-11T21:23:04.7115155Z   test_tensor_from_storage_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.029s)
2023-01-11T21:23:04.7115406Z   test_tensor_from_storage_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.029s)
2023-01-11T21:23:04.7115664Z   test_tensor_from_storage_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.029s)
2023-01-11T21:23:04.7115932Z   test_tensor_from_storage_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.029s)
2023-01-11T21:23:04.7116190Z   test_tensor_from_storage_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.028s)
2023-01-11T21:23:04.7116433Z   test_tensor_from_storage_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.028s)
2023-01-11T21:23:04.7116688Z   test_tensor_from_storage_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.028s)
2023-01-11T21:23:04.7116925Z   test_tensor_from_storage_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.028s)
2023-01-11T21:23:04.7117221Z   test_tensor_from_storage_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.028s)
2023-01-11T21:23:04.7117540Z   test_tensor_set_errors_multigpu_cpu (__main__.TestTorchDeviceTypeCPU) ... skip: fewer than 2 devices detected (0.001s)
2023-01-11T21:23:04.7117767Z   test_tensor_shape_empty_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.025s)
2023-01-11T21:23:04.7118519Z   test_tensor_storage_type_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:234: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7118729Z   self.assertEqual(a.storage_type(), expected_storage_type)
2023-01-11T21:23:04.7119834Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:959: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7120112Z   if self.device.type not in ['cpu', 'cuda']:
2023-01-11T21:23:04.7121385Z /opt/conda/lib/python3.7/site-packages/torch/storage.py:962: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7121686Z   module = torch if self.device.type == 'cpu' else torch.cuda
2023-01-11T21:23:04.7121802Z ok (0.008s)
2023-01-11T21:23:04.7122078Z   test_tensor_storage_type_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7122362Z   test_tensor_storage_type_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7122648Z   test_tensor_storage_type_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7122890Z   test_tensor_storage_type_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7123164Z   test_tensor_storage_type_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7123448Z   test_tensor_storage_type_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7123726Z   test_tensor_storage_type_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7124005Z   test_tensor_storage_type_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7124280Z   test_tensor_storage_type_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7124688Z   test_tensor_storage_type_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7124975Z   test_tensor_storage_type_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7125259Z   test_ternary_op_mem_overlap_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.331s)
2023-01-11T21:23:04.7126116Z   test_typed_storage_meta_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... test_torch.py:301: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7126360Z   s_check = torch.TypedStorage(*args, dtype=dtype, device=device)
2023-01-11T21:23:04.7126980Z test_torch.py:302: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
2023-01-11T21:23:04.7127405Z   s = torch.TypedStorage(*args, dtype=dtype, device='meta')
2023-01-11T21:23:04.7127525Z ok (0.004s)
2023-01-11T21:23:04.7127815Z   test_typed_storage_meta_cpu_bool (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7128111Z   test_typed_storage_meta_cpu_complex128 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7128405Z   test_typed_storage_meta_cpu_complex64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7128694Z   test_typed_storage_meta_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7128969Z   test_typed_storage_meta_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7129253Z   test_typed_storage_meta_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7129540Z   test_typed_storage_meta_cpu_int16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7129816Z   test_typed_storage_meta_cpu_int32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7130090Z   test_typed_storage_meta_cpu_int64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7130354Z   test_typed_storage_meta_cpu_int8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7130609Z   test_typed_storage_meta_cpu_uint8 (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7130890Z   test_unfold_all_devices_and_dtypes_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7131133Z   test_unfold_scalars_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7131372Z   test_uniform_kstest_cpu_bfloat16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.7131625Z   test_uniform_kstest_cpu_float16 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.7131876Z   test_uniform_kstest_cpu_float32 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.7132124Z   test_uniform_kstest_cpu_float64 (__main__.TestTorchDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:23:04.7132376Z   test_untyped_storage_meta_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:23:04.7132623Z   test_warn_always_caught_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.001s)
2023-01-11T21:23:04.7132903Z   test_where_scalar_handcrafted_values_cpu (__main__.TestTorchDeviceTypeCPU) ... ok (0.043s)
2023-01-11T21:23:04.7133174Z   test_cuda_vitals_gpu_only_cpu (__main__.TestVitalSignsCudaCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:23:04.7133188Z 
2023-01-11T21:23:04.7133521Z ----------------------------------------------------------------------
2023-01-11T21:23:04.7133655Z Ran 832 tests in 54.353s
2023-01-11T21:23:04.7133666Z 
2023-01-11T21:23:04.7133804Z OK (skipped=39)
2023-01-11T21:23:04.7133811Z 
2023-01-11T21:23:04.7134055Z Generating XML reports...
2023-01-11T21:23:04.7134543Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestBasicVitalSigns-20230111212209.xml
2023-01-11T21:23:04.7134986Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestTorch-20230111212209.xml
2023-01-11T21:23:04.7135493Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestTorchDeviceTypeCPU-20230111212209.xml
2023-01-11T21:23:04.7135984Z Generated XML report: test-reports/python-unittest/test_torch/TEST-TestVitalSignsCudaCPU-20230111212209.xml
2023-01-11T21:23:04.7136165Z [TORCH_VITAL] Dataloader.enabled		 True
2023-01-11T21:23:04.7136369Z [TORCH_VITAL] Dataloader.basic_unit_test		 TEST_VALUE_STRING
2023-01-11T21:23:04.7136535Z [TORCH_VITAL] CUDA.used		 False
2023-01-11T21:23:04.7136543Z 
2023-01-11T21:23:04.7137064Z ##[endgroup]
2023-01-11T21:23:04.7137653Z FINISHED PRINTING LOG FILE of test_torch (/var/lib/jenkins/workspace/test/test-reports/test_torch_74lr60y8)
2023-01-11T21:23:04.7137662Z 
2023-01-11T21:23:04.7137953Z Running test_ops_gradients ... [2023-01-11 21:23:04.657922]
2023-01-11T21:23:08.7661176Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:08.7796547Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:23:08.9256148Z Ignoring disabled issues:  ['91003']
2023-01-11T21:23:08.9347291Z Ignoring disabled issues:  ['91003']
2023-01-11T21:23:08.9477717Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=0', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:08.947429]
2023-01-11T21:23:08.9569073Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '--shard-id=1', '--num-shards=2', '-k=not _linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:23:08.956518]
2023-01-11T21:41:34.2746801Z 
2023-01-11T21:41:34.2747223Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T21:41:34.2747890Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_5y0iga9b)
2023-01-11T21:41:34.2765078Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-9b1ea14156fe1d78.xml
2023-01-11T21:41:34.2765642Z ============================= test session starts ==============================
2023-01-11T21:41:34.2766229Z platform linux -- Python 3.7.15, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:41:34.2766633Z cachedir: .pytest_cache
2023-01-11T21:41:34.2767295Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:41:34.2767868Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:41:34.2768526Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:41:34.2769026Z collecting ... collected 4945 items / 12 deselected / 4933 selected
2023-01-11T21:41:34.3089691Z Running 2416 items in this shard: test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_H_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_T_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___getitem___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___radd___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rdiv___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmatmul___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmul___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rsub___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_abs_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_acos_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_acosh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_add_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addbmm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcdiv_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcdiv_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcmul_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcmul_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_decomposed_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_decomposed_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addr_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_allclose_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_aminmax_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_angle_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_any_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_any_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_arange_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_argmax_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_argwhere_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_as_strided_partial_views_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_as_strided_scatter_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_as_strided_scatter_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_asinh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atan_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atan_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atanh_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atanh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atleast_1d_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atleast_1d_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atleast_2d_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atleast_3d_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atleast_3d_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_baddbmm_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_baddbmm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bfloat16_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_block_diag_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_block_diag_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bmm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bool_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_broadcast_tensors_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_broadcast_tensors_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_broadcast_to_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bucketize_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_byte_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cartesian_prod_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cfloat_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cfloat_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_chalf_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_chalf_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cholesky_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cholesky_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cholesky_inverse_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cholesky_solve_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cholesky_solve_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_chunk_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_chunk_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clamp_min_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clone_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_combinations_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_conj_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_conj_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_conj_physical_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_constant_pad_nd_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_contiguous_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_contiguous_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cos_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cov_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cummin_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumprod_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumprod_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumsum_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumulative_trapezoid_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diag_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diag_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diag_embed_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diag_embed_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagonal_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagonal_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagonal_scatter_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagonal_scatter_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diff_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diff_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dist_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dist_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_floor_rounding_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_no_rounding_mode_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dot_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dot_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_double_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_double_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dstack_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_einsum_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eq_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eq_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_erfinv_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_exp_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expand_as_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expand_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expm1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eye_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fft2_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fftn_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fftshift_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fftshift_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfftn_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifft2_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ihfft2_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ihfft_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfft2_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfft_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfftn_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fill_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fill_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flatten_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fliplr_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fliplr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flipud_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flipud_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_float_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_float_power_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_floor_divide_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fmax_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fmin_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_full_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_full_like_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_geqrf_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_gradient_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_gradient_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_grid_sampler_2d_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_symeig_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_symeig_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_along_dim_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_along_dim_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tan_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tanh_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensor_split_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensordot_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensordot_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tile_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tile_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_topk_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trace_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trace_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapezoid_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapezoid_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapz_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapz_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triangular_solve_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triu_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triu_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_true_divide_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trunc_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_uniform_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_uniform_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unique_consecutive_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unique_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_real_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vsplit_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vstack_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_xlogy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zero__cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_like_cpu_float64
2023-01-11T21:41:34.3340349Z 
2023-01-11T21:41:34.3340642Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_H_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  0%]
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2023-01-11T21:41:34.3387171Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diff_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:41:34.3387625Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dist_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:41:34.3388062Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dist_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:41:34.3388525Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_floor_rounding_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:41:34.3389006Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_no_rounding_mode_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:41:34.3389537Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dot_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3390032Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dot_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3390490Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_double_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3390945Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_double_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3391400Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dstack_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3391844Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_einsum_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3392438Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eq_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  4%]
2023-01-11T21:41:34.3393017Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eq_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  4%]
2023-01-11T21:41:34.3393516Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_erfinv_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3393960Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_exp_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3394420Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expand_as_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3394941Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expand_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3395400Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expm1_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3395976Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eye_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  4%]
2023-01-11T21:41:34.3396437Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fft2_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3396893Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fftn_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3397357Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fftshift_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3397821Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fftshift_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3398271Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3398734Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfftn_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3399273Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifft2_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3399742Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ihfft2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3400186Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ihfft_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3400760Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfft2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:41:34.3401257Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfft_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3401791Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfftn_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3402234Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fill_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3402684Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fill_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3403135Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flatten_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3403586Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fliplr_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3404034Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fliplr_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3404526Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flipud_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3404987Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flipud_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3405445Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_float_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3405901Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_float_power_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3406486Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_floor_divide_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:41:34.3406956Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fmax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3407409Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fmin_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3407972Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_full_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:41:34.3408670Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_full_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:41:34.3409259Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_geqrf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:41:34.3409732Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_gradient_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3410312Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_gradient_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:41:34.3410727Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_grid_sampler_2d_cpu_float64 PASSED [  5%]
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2023-01-11T21:41:34.3411852Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_heaviside_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:41:34.3412443Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_histc_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:41:34.3413019Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_histogram_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:41:34.3413536Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_hsplit_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3413993Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_hstack_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3414447Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_i0_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3414999Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_igamma_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3415472Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_add_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3415998Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_add_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3416546Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_copy_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3417122Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_copy_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3417576Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_fill_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3418045Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_reduce_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3418510Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_select_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3418977Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_inner_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3419425Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_inner_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3420002Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_int_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3420592Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isclose_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3421180Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isclose_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3421744Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3422329Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3422916Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isnan_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3423503Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isnan_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3424073Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isposinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3424665Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isreal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3425251Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isreal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:41:34.3425766Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_istft_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:41:34.3426330Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_4inputs_with_extra_args_cpu_float64 SKIPPED (Only runs on cuda) [  6%]
2023-01-11T21:41:34.3426760Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_unary_cpu_complex128 SKIPPED (Only runs on cuda) [  7%]
2023-01-11T21:41:34.3427203Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_kron_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3427655Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_kron_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3428110Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ldexp_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3428598Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lerp_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3429059Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lerp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3429511Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lgamma_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3429977Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cond_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3430433Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cond_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3430909Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_det_singular_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3431394Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_det_singular_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3431901Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eigh_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3432368Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eigvalsh_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3432846Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eigvalsh_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3433334Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_householder_product_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3433820Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_inv_ex_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3434425Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_ldl_factor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:41:34.3434867Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lstsq_cpu_complex128 SKIPPED (Skipped!) [  7%]
2023-01-11T21:41:34.3435325Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lstsq_grad_oriented_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3435929Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_factor_ex_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3436410Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_solve_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3436912Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_solve_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3437383Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_power_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:41:34.3438005Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_rank_hermitian_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:41:34.3438497Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3438973Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_norm_subgradients_at_zero_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3439516Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_norm_subgradients_at_zero_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3440009Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_pinv_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3440477Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_pinv_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3441159Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_pinv_hermitian_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3441654Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_pinv_singular_cpu_float64 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [  8%]
2023-01-11T21:41:34.3442141Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_qr_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3442607Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_slogdet_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3443070Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_solve_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3443538Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_solve_triangular_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3444027Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_svdvals_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3444506Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_tensorsolve_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3444993Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_tensorsolve_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3445585Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  8%]
2023-01-11T21:41:34.3446179Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  8%]
2023-01-11T21:41:34.3446712Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log10_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3447191Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log10_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3447705Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log2_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3448224Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3448696Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log_softmax_with_dtype_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3449172Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logaddexp2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3449636Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logdet_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3450085Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logdet_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:41:34.3450681Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_and_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3451427Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_and_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3452031Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_not_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3452622Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_not_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3453212Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_or_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3453798Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_xor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3454270Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logit_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3454835Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_long_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3455412Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_long_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3456087Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lt_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3456551Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lu_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3457001Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lu_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3457448Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lu_unpack_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3457904Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mH_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3458353Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mT_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3458812Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_amin_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3459392Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_argmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:41:34.3460004Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_cumsum_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3460483Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_log_softmax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3460963Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_logaddexp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3461497Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_logsumexp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3461972Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_mean_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3462437Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_median_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3462902Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:41:34.3463392Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_prod_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3463863Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_prod_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3464332Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_scatter_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3464816Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_select_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3465287Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_select_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3465752Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_softmin_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3466220Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_std_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3466687Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_std_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3467149Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_sum_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3467597Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_var_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3468062Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_var_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3468527Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_matmul_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3468987Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_matmul_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3469526Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_max_pool2d_with_indices_backward_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3470027Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_max_reduction_no_dim_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3470497Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_maximum_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3471011Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_meshgrid_list_of_tensors_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3471509Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_meshgrid_variadic_tensors_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3471995Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_meshgrid_variadic_tensors_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3472484Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_min_reduction_with_dim_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3472955Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_movedim_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3473414Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mul_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3473958Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mv_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3474427Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mvlgamma_mvlgamma_p_3_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:41:34.3474905Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mvlgamma_mvlgamma_p_5_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3475371Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nanmean_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3475952Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_narrow_copy_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3476434Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_narrow_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3476893Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_narrow_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3477363Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_native_batch_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3477928Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ne_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3478505Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ne_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3478967Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_neg_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3479431Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_neg_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3480079Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_new_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3480795Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_new_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3481418Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_new_ones_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3482009Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_new_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3482598Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nextafter_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 11%]
2023-01-11T21:41:34.3483166Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional__scaled_dot_product_attention_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3483791Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_adaptive_max_pool1d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3484300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_alpha_dropout_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3484790Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_avg_pool3d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3485267Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_batch_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3485813Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_binary_cross_entropy_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3486345Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_binary_cross_entropy_with_logits_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3486848Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_celu_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3487332Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_conv1d_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:41:34.3487805Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_conv1d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3488300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_conv_transpose2d_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3488810Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_conv_transpose3d_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3489322Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_conv_transpose3d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3489815Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_cosine_embedding_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3490319Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_cross_entropy_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3490812Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_dropout2d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3491306Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_dropout3d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3491796Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_dropout_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3492337Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_feature_alpha_dropout_without_train_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3492948Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_feature_alpha_dropout_without_train_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3493473Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_fractional_max_pool2d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3494024Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_fractional_max_pool3d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3494513Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_gaussian_nll_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3495003Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_gelu_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3495478Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_glu_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3495916Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_grid_sample_cpu_float64 PASSED [ 12%]
2023-01-11T21:41:34.3496390Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_hardsigmoid_cpu_float64 PASSED [ 12%]
2023-01-11T21:41:34.3496973Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_hardswish_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3497469Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_hardtanh_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3497961Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_instance_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3498459Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_interpolate_bicubic_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3498953Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_interpolate_bilinear_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3499477Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_interpolate_linear_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3499989Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_interpolate_nearest_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:41:34.3500490Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_l1_loss_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3500960Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_layer_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3501450Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_linear_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:41:34.3502519Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_logsigmoid_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3503018Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_margin_ranking_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3503501Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_max_pool2d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:41:34.3504518Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_max_unpool2d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:41:34.3505503Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_max_unpool3d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3505983Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_mish_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3506578Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_normalize_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:41:34.3507614Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pad_circular_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3508100Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pad_reflect_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3508593Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pad_reflect_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3509092Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pad_replicate_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3509547Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pdist_cpu_float64 PASSED [ 13%]
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2023-01-11T21:41:34.3510485Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_poisson_nll_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3510972Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_relu6_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:41:34.3511626Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_silu_complex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 13%]
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2023-01-11T21:41:34.3512598Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_smooth_l1_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3513093Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_softmin_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3513592Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_softmin_with_dtype_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3514101Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_softmin_with_dtype_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3514586Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_softshrink_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3515177Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_softsign_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3515709Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_tanhshrink_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3516207Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_triplet_margin_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3516817Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_triplet_margin_with_distance_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3517331Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_unfold_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3517944Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 14%]
2023-01-11T21:41:34.3518543Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 14%]
2023-01-11T21:41:34.3519047Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_norm_nuc_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3519486Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_normal_number_mean_cpu_float64 SKIPPED (Gradients are incorrect!) [ 14%]
2023-01-11T21:41:34.3520049Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 14%]
2023-01-11T21:41:34.3520729Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ones_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 14%]
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2023-01-11T21:41:34.3521665Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ormqr_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3522112Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_outer_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3522569Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_pca_lowrank_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3523033Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_permute_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3523577Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_pinverse_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:41:34.3524022Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_polygamma_polygamma_n_1_cpu_float64 SKIPPED (Skipped!) [ 14%]
2023-01-11T21:41:34.3524447Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_polygamma_polygamma_n_4_cpu_float64 SKIPPED (Skipped!) [ 14%]
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2023-01-11T21:41:34.3526702Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_quantile_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3527230Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_rad2deg_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3527822Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_rand_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 15%]
2023-01-11T21:41:34.3528414Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_rand_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 15%]
2023-01-11T21:41:34.3529004Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_randint_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 15%]
2023-01-11T21:41:34.3529598Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_randint_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 15%]
2023-01-11T21:41:34.3530180Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_randn_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 15%]
2023-01-11T21:41:34.3530696Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ravel_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
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2023-01-11T21:41:34.3531693Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_real_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3532146Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_reciprocal_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3532610Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_remainder_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3533076Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_renorm_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3533536Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_renorm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3533993Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_repeat_interleave_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3534468Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_reshape_as_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3534941Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_resolve_conj_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3535412Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_resolve_neg_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3535866Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_roll_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3536395Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_roll_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:41:34.3536853Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_rot90_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3537315Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_round_decimals_0_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3537756Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_round_decimals_3_cpu_float64 SKIPPED (Skipped!) [ 16%]
2023-01-11T21:41:34.3538176Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_rsub_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3538840Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_scalar_tensor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3539325Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_scatter_add_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3539799Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_scatter_reduce_amax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3540263Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_scatter_reduce_amin_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3540742Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_scatter_reduce_mean_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3541436Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_searchsorted_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3541925Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_segment_reduce_lengths_cpu_float64 PASSED [ 16%]
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2023-01-11T21:41:34.3542816Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_select_scatter_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3543284Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sigmoid_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3543741Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sign_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 16%]
2023-01-11T21:41:34.3544332Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_blackman_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3544960Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_cosine_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3545586Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_exponential_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3546295Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_general_cosine_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3546939Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_general_hamming_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3547557Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_hann_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3548177Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_kaiser_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3548799Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signal_windows_nuttall_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3549396Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_signbit_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 16%]
2023-01-11T21:41:34.3549847Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sin_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3550305Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sinc_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3550797Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sinh_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3551259Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_slice_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3551777Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_slice_scatter_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3552245Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_softmax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3552713Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_softmax_with_dtype_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3553194Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_softmax_with_dtype_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3553689Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sort_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3554152Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sparse_sampled_addmm_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3554645Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sparse_sampled_addmm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3555249Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_airy_ai_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3555930Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_bessel_j0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3556524Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_bessel_y1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3557151Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_chebyshev_polynomial_u_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3557809Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_chebyshev_polynomial_v_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 17%]
2023-01-11T21:41:34.3558306Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_entr_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3558924Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_hermite_polynomial_he_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3559408Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_i0e_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3559874Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_i1_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 17%]
2023-01-11T21:41:34.3560502Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_legendre_polynomial_p_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 17%]
2023-01-11T21:41:34.3561314Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_modified_bessel_i1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3561936Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_modified_bessel_k0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3562566Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_modified_bessel_k1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 17%]
2023-01-11T21:41:34.3563128Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_ndtr_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3563629Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_polygamma_special_polygamma_n_0_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3564279Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_scaled_modified_bessel_k0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 18%]
2023-01-11T21:41:34.3565053Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_shifted_chebyshev_polynomial_t_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 18%]
2023-01-11T21:41:34.3565733Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_shifted_chebyshev_polynomial_u_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 18%]
2023-01-11T21:41:34.3566567Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_shifted_chebyshev_polynomial_w_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 18%]
2023-01-11T21:41:34.3567233Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_spherical_bessel_j0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 18%]
2023-01-11T21:41:34.3567722Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_special_xlog1py_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3568188Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_split_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3568661Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_split_list_args_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3569145Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_split_list_args_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3569610Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_square_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3570055Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_squeeze_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3570518Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_squeeze_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3570970Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_stack_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3571427Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_std_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3571876Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_std_mean_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3572348Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_std_mean_unbiased_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3572816Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_stft_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3573273Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_stft_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3573708Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sub_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3574196Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_sum_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3574768Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_symeig_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3575239Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_take_along_dim_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3575692Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_tanh_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:41:34.3576230Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_tensor_split_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3576704Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_tensordot_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3577171Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_tile_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3577647Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_tile_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3578097Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_to_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3578687Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_to_sparse_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 19%]
2023-01-11T21:41:34.3579274Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_trace_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3579729Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_transpose_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3580185Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_trapezoid_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3580664Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_triangular_solve_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3581130Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_tril_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3581584Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_tril_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3582027Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_triu_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3582484Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_true_divide_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3582952Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_unbind_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3583420Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_unflatten_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3583880Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_unfold_copy_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3584341Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_unfold_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3585055Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_unique_consecutive_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 19%]
2023-01-11T21:41:34.3585650Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_unique_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 19%]
2023-01-11T21:41:34.3586144Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3586608Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_mean_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3587073Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_unbiased_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3587537Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_unbiased_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:41:34.3587984Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_vdot_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:41:34.3588535Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_vdot_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:41:34.3589026Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_view_as_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:41:34.3589488Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_view_as_real_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:41:34.3589948Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_vstack_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:41:34.3590392Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_where_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:41:34.3590846Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_xlogy_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:41:34.3591429Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_zeros_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 20%]
2023-01-11T21:41:34.3592020Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_zeros_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 20%]
2023-01-11T21:41:34.3592417Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_H_cpu_complex128 PASSED [ 20%]
2023-01-11T21:41:34.3592765Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_T_cpu_complex128 PASSED [ 20%]
2023-01-11T21:41:34.3593108Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_T_cpu_float64 PASSED [ 20%]
2023-01-11T21:41:34.3593446Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___getitem___cpu_complex128 PASSED [ 20%]
2023-01-11T21:41:34.3593808Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___radd___cpu_complex128 PASSED [ 20%]
2023-01-11T21:41:34.3594162Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___radd___cpu_float64 PASSED [ 20%]
2023-01-11T21:41:34.3594516Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rdiv___cpu_complex128 PASSED [ 20%]
2023-01-11T21:41:34.3594857Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rdiv___cpu_float64 PASSED [ 20%]
2023-01-11T21:41:34.3595200Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rmod___cpu_float64 PASSED [ 20%]
2023-01-11T21:41:34.3595546Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rmul___cpu_complex128 PASSED [ 20%]
2023-01-11T21:41:34.3595888Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rmul___cpu_float64 PASSED [ 20%]
2023-01-11T21:41:34.3596239Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rpow___cpu_complex128 SKIPPED (Skipped!) [ 20%]
2023-01-11T21:41:34.3596618Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rpow___cpu_float64 SKIPPED (Skipped!) [ 20%]
2023-01-11T21:41:34.3596998Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad__native_batch_norm_legit_cpu_float64 PASSED [ 20%]
2023-01-11T21:41:34.3597547Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad__softmax_backward_data_cpu_float64 PASSED [ 20%]
2023-01-11T21:41:34.3597906Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_abs_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3598248Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_acosh_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3598590Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_add_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3598924Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addbmm_cpu_complex128 PASSED [ 21%]
2023-01-11T21:41:34.3599278Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addcmul_cpu_complex128 PASSED [ 21%]
2023-01-11T21:41:34.3599632Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addmv_cpu_complex128 PASSED [ 21%]
2023-01-11T21:41:34.3599976Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addr_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3600373Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_all_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:41:34.3600973Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_all_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:41:34.3601438Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_allclose_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:41:34.3601935Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_amax_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3602332Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_aminmax_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:41:34.3602785Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_any_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:41:34.3603239Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_argmax_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:41:34.3603701Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_argsort_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:41:34.3604119Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_as_strided_cpu_float64 SKIPPED (Numerous errors) [ 21%]
2023-01-11T21:41:34.3604509Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_as_strided_partial_views_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3604875Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_asin_cpu_complex128 PASSED [ 21%]
2023-01-11T21:41:34.3605209Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_asinh_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3605551Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atanh_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3605905Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atleast_1d_cpu_complex128 PASSED [ 21%]
2023-01-11T21:41:34.3606267Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atleast_2d_cpu_complex128 PASSED [ 21%]
2023-01-11T21:41:34.3606612Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atleast_2d_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3606974Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atleast_3d_cpu_float64 PASSED [ 21%]
2023-01-11T21:41:34.3607364Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_baddbmm_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3607711Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bernoulli_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3608041Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bfloat16_cpu_float64 XFAIL [ 22%]
2023-01-11T21:41:34.3608396Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_block_diag_cpu_complex128 PASSED [ 22%]
2023-01-11T21:41:34.3608751Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_block_diag_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3609153Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_broadcast_tensors_cpu_complex128 PASSED [ 22%]
2023-01-11T21:41:34.3609534Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_broadcast_tensors_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3609897Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_broadcast_to_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3610313Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bucketize_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 22%]
2023-01-11T21:41:34.3610762Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_byte_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 22%]
2023-01-11T21:41:34.3611171Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cartesian_prod_cpu_complex128 PASSED [ 22%]
2023-01-11T21:41:34.3611610Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cdist_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3611964Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cdouble_cpu_complex128 PASSED [ 22%]
2023-01-11T21:41:34.3612301Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cdouble_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3612682Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_ceil_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3613029Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_chalf_cpu_complex128 XFAIL [ 22%]
2023-01-11T21:41:34.3613382Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cholesky_inverse_cpu_complex128 PASSED [ 22%]
2023-01-11T21:41:34.3613750Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cholesky_solve_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3614102Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_chunk_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3614448Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_clamp_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3614779Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_clone_cpu_complex128 PASSED [ 22%]
2023-01-11T21:41:34.3615128Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_clone_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3615491Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_combinations_cpu_complex128 PASSED [ 22%]
2023-01-11T21:41:34.3615857Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_combinations_cpu_float64 PASSED [ 22%]
2023-01-11T21:41:34.3616275Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_conj_cpu_complex128 PASSED [ 23%]
2023-01-11T21:41:34.3616620Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_conj_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3616970Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_conj_physical_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3617319Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_constant_pad_nd_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3617686Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_contiguous_cpu_complex128 PASSED [ 23%]
2023-01-11T21:41:34.3618051Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_contiguous_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3618405Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_copysign_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3618742Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_corrcoef_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3619086Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cos_cpu_complex128 PASSED [ 23%]
2023-01-11T21:41:34.3619426Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cos_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3619753Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cosh_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3620252Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_count_nonzero_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 23%]
2023-01-11T21:41:34.3620658Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cov_cpu_complex128 XFAIL [ 23%]
2023-01-11T21:41:34.3621072Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cross_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3621400Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cummax_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3621829Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cumprod_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3622180Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cumsum_cpu_complex128 PASSED [ 23%]
2023-01-11T21:41:34.3622548Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cumulative_trapezoid_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3622897Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_deg2rad_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3623245Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diag_cpu_complex128 PASSED [ 23%]
2023-01-11T21:41:34.3623586Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diag_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3623923Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diag_embed_cpu_complex128 PASSED [ 23%]
2023-01-11T21:41:34.3624286Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagflat_cpu_complex128 PASSED [ 23%]
2023-01-11T21:41:34.3624676Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagflat_cpu_float64 PASSED [ 23%]
2023-01-11T21:41:34.3625041Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagonal_copy_cpu_complex128 PASSED [ 24%]
2023-01-11T21:41:34.3625391Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagonal_cpu_complex128 PASSED [ 24%]
2023-01-11T21:41:34.3625739Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diff_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3626079Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_digamma_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3626445Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_div_no_rounding_mode_cpu_complex128 PASSED [ 24%]
2023-01-11T21:41:34.3626811Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_div_trunc_rounding_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3627172Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_double_cpu_complex128 PASSED [ 24%]
2023-01-11T21:41:34.3627521Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_double_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3627857Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dsplit_cpu_complex128 PASSED [ 24%]
2023-01-11T21:41:34.3628277Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dsplit_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3628625Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dstack_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3628977Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_einsum_cpu_complex128 PASSED [ 24%]
2023-01-11T21:41:34.3629311Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_einsum_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3629718Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_empty_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 24%]
2023-01-11T21:41:34.3630178Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_eq_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 24%]
2023-01-11T21:41:34.3630634Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_equal_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 24%]
2023-01-11T21:41:34.3631018Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_erf_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3631364Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_erfc_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3631706Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_erfinv_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3632035Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_exp2_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3632376Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_expand_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3632721Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_expm1_cpu_float64 PASSED [ 24%]
2023-01-11T21:41:34.3633167Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_eye_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 24%]
2023-01-11T21:41:34.3633614Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_eye_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 24%]
2023-01-11T21:41:34.3634017Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fft2_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3634368Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fft_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3634714Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_hfft2_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3635054Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_hfft_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3635400Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_hfft_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3635819Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifft_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3636165Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifftn_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3636544Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ihfft_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3636904Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_irfft2_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3637262Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_irfft_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3637604Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_irfft_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3637950Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_irfftn_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3638300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_rfftn_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3638645Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_flip_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3638980Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_flip_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3639326Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fliplr_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3639674Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_flipud_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3640014Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_float_power_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3640437Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_floor_divide_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:41:34.3641019Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fmod_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3641362Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_frexp_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3641752Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_full_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:41:34.3642156Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_gather_cpu_complex128 PASSED [ 25%]
2023-01-11T21:41:34.3642506Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_gather_cpu_float64 PASSED [ 25%]
2023-01-11T21:41:34.3642910Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_ge_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:41:34.3643351Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_geqrf_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3643811Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_geqrf_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3644215Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_gradient_cpu_complex128 PASSED [ 26%]
2023-01-11T21:41:34.3644634Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_gradient_cpu_float64 PASSED [ 26%]
2023-01-11T21:41:34.3644982Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_grid_sampler_2d_cpu_float64 PASSED [ 26%]
2023-01-11T21:41:34.3645395Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_gt_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3645900Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_half_cpu_complex128 XFAIL [ 26%]
2023-01-11T21:41:34.3646244Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_half_cpu_float64 XFAIL [ 26%]
2023-01-11T21:41:34.3646636Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_histc_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3647097Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_histogram_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3647504Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_imag_cpu_complex128 PASSED [ 26%]
2023-01-11T21:41:34.3647847Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_add_cpu_complex128 PASSED [ 26%]
2023-01-11T21:41:34.3648249Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_copy_cpu_float64 PASSED [ 26%]
2023-01-11T21:41:34.3648607Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_fill_cpu_complex128 PASSED [ 26%]
2023-01-11T21:41:34.3648966Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_put_cpu_complex128 PASSED [ 26%]
2023-01-11T21:41:34.3649311Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_select_cpu_float64 PASSED [ 26%]
2023-01-11T21:41:34.3649657Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_inner_cpu_complex128 PASSED [ 26%]
2023-01-11T21:41:34.3650003Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_inner_cpu_float64 PASSED [ 26%]
2023-01-11T21:41:34.3650413Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isclose_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3650981Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isfinite_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3651442Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isfinite_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3651895Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isin_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3652341Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isinf_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3652787Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isnan_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:41:34.3653247Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isreal_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:41:34.3653703Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_4inputs_with_extra_args_cpu_complex128 SKIPPED (Only runs on cuda) [ 27%]
2023-01-11T21:41:34.3654143Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_binary_return_by_ref_cpu_complex128 SKIPPED (Only runs on cuda) [ 27%]
2023-01-11T21:41:34.3654561Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_binary_return_by_ref_cpu_float64 SKIPPED (Only runs on cuda) [ 27%]
2023-01-11T21:41:34.3654976Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_unary_cpu_float64 SKIPPED (Only runs on cuda) [ 27%]
2023-01-11T21:41:34.3655349Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_kthvalue_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3655698Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_ldexp_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3656150Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cond_cpu_complex128 PASSED [ 27%]
2023-01-11T21:41:34.3656614Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cross_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3656975Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_det_cpu_complex128 PASSED [ 27%]
2023-01-11T21:41:34.3657347Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_det_singular_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3657707Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eigvals_cpu_complex128 PASSED [ 27%]
2023-01-11T21:41:34.3658077Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eigvalsh_cpu_complex128 PASSED [ 27%]
2023-01-11T21:41:34.3658462Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_householder_product_cpu_complex128 PASSED [ 27%]
2023-01-11T21:41:34.3658846Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_householder_product_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3659217Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_inv_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3659617Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_inv_ex_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3660041Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_ldl_factor_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:41:34.3660516Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_ldl_factor_ex_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:41:34.3661059Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lstsq_cpu_complex128 SKIPPED (Skipped!) [ 27%]
2023-01-11T21:41:34.3661433Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_cpu_complex128 PASSED [ 27%]
2023-01-11T21:41:34.3661789Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3662135Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_factor_cpu_float64 PASSED [ 27%]
2023-01-11T21:41:34.3662512Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_factor_ex_cpu_complex128 PASSED [ 27%]
2023-01-11T21:41:34.3662885Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_solve_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3663249Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_solve_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3663604Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_norm_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3663974Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_power_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3664414Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_rank_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 28%]
2023-01-11T21:41:34.3664901Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_rank_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 28%]
2023-01-11T21:41:34.3665380Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_rank_hermitian_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 28%]
2023-01-11T21:41:34.3665821Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_multi_dot_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3666189Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_norm_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3666575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_norm_subgradients_at_zero_cpu_complex128 XFAIL [ 28%]
2023-01-11T21:41:34.3666963Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_norm_subgradients_at_zero_cpu_float64 XFAIL [ 28%]
2023-01-11T21:41:34.3667341Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_pinv_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3667752Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_pinv_hermitian_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3668186Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_pinv_singular_cpu_float64 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 28%]
2023-01-11T21:41:34.3668606Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_qr_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3668963Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_slogdet_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3669429Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_solve_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3669789Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_solve_ex_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3670151Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_solve_ex_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3670522Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_solve_triangular_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3670941Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_svdvals_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3671394Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_tensorinv_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3671773Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_tensorsolve_cpu_complex128 PASSED [ 28%]
2023-01-11T21:41:34.3672146Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_tensorsolve_cpu_float64 PASSED [ 28%]
2023-01-11T21:41:34.3672510Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_vecdot_cpu_complex128 PASSED [ 29%]
2023-01-11T21:41:34.3672918Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linspace_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3673327Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log1p_cpu_complex128 PASSED [ 29%]
2023-01-11T21:41:34.3673682Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log2_cpu_complex128 PASSED [ 29%]
2023-01-11T21:41:34.3674015Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log2_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3674365Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log_softmax_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3674730Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log_softmax_with_dtype_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3675094Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logaddexp2_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3675505Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_and_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3675976Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_not_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3676449Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_or_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3676917Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_or_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3677371Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_xor_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3677836Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_xor_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3678300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logspace_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3678709Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logsumexp_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3679213Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_long_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3679707Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_long_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3680153Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lt_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3680549Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lu_cpu_complex128 PASSED [ 29%]
2023-01-11T21:41:34.3680981Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lu_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3681326Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lu_unpack_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3681667Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mH_cpu_float64 PASSED [ 29%]
2023-01-11T21:41:34.3682072Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_argmin_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:41:34.3682477Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_cumprod_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3682896Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_cumsum_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3683273Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_log_softmax_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3683641Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_logaddexp_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3684098Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_logsumexp_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3684465Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_mean_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3684826Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_median_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3685176Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_norm_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3685539Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_normalize_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3685906Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_prod_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3686267Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_select_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3686617Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_softmax_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3686977Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_std_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3687332Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_std_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3687688Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_sum_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3688034Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_sum_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3688389Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_var_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3688749Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_matmul_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3689119Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_max_pool2d_with_indices_backward_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3689492Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_maximum_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3689919Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mean_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3690261Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_median_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3690618Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_meshgrid_list_of_tensors_cpu_complex128 PASSED [ 30%]
2023-01-11T21:41:34.3691004Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_meshgrid_list_of_tensors_cpu_float64 PASSED [ 30%]
2023-01-11T21:41:34.3691447Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_meshgrid_variadic_tensors_cpu_complex128 PASSED [ 31%]
2023-01-11T21:41:34.3691819Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mm_cpu_complex128 PASSED [ 31%]
2023-01-11T21:41:34.3692152Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mm_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3692498Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_movedim_cpu_complex128 PASSED [ 31%]
2023-01-11T21:41:34.3692847Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mul_cpu_complex128 PASSED [ 31%]
2023-01-11T21:41:34.3693201Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mvlgamma_mvlgamma_p_3_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3693646Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mvlgamma_mvlgamma_p_5_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3694007Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nan_to_num_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3694359Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nanmean_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3694732Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nanmedian_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3695093Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nanquantile_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3695445Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nansum_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3695847Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_narrow_copy_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3696386Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_narrow_copy_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3696791Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_narrow_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3697160Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_native_dropout_backward_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3697575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_ne_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3698033Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_ne_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3698501Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_new_empty_strided_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3698973Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_new_full_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3699439Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_new_full_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3699889Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_new_zeros_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3700352Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_new_zeros_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 31%]
2023-01-11T21:41:34.3700791Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional__scaled_dot_product_attention_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3701205Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_adaptive_avg_pool2d_cpu_float64 PASSED [ 31%]
2023-01-11T21:41:34.3701592Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_adaptive_avg_pool3d_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3701990Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_adaptive_max_pool2d_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3702470Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_adaptive_max_pool3d_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3702921Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_batch_norm_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3703319Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_binary_cross_entropy_with_logits_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3703715Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_celu_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3704098Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_conv1d_cpu_complex128 PASSED [ 32%]
2023-01-11T21:41:34.3704484Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_conv_transpose3d_cpu_complex128 PASSED [ 32%]
2023-01-11T21:41:34.3704888Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_cosine_embedding_loss_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3705285Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_cross_entropy_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3705671Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_ctc_loss_cpu_float64 XFAIL [ 32%]
2023-01-11T21:41:34.3706071Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_dropout2d_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3706520Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_dropout_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3706898Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_elu_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3707278Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_embedding_bag_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3707652Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_embedding_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3708065Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_feature_alpha_dropout_without_train_cpu_complex128 PASSED [ 32%]
2023-01-11T21:41:34.3708494Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_fractional_max_pool2d_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3708895Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_gaussian_nll_loss_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3709265Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_glu_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3709643Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_grid_sample_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3710029Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_group_norm_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3710411Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_hardshrink_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3710780Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_hardsigmoid_cpu_float64 PASSED [ 32%]
2023-01-11T21:41:34.3711168Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_hardswish_cpu_float64 PASSED [ 33%]
2023-01-11T21:41:34.3711552Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_hardtanh_cpu_float64 PASSED [ 33%]
2023-01-11T21:41:34.3711985Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_huber_loss_cpu_float64 PASSED [ 33%]
2023-01-11T21:41:34.3712366Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_instance_norm_cpu_float64 PASSED [ 33%]
2023-01-11T21:41:34.3712760Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_interpolate_linear_cpu_float64 PASSED [ 33%]
2023-01-11T21:41:34.3713146Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_kl_div_cpu_float64 PASSED [ 33%]
2023-01-11T21:41:34.3713508Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_nn_functional_l1_loss_cpu_complex128 PASSED [ 33%]
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2023-01-11T21:41:34.3766051Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_tanh_cpu_complex128 PASSED [ 38%]
2023-01-11T21:41:34.3766397Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_tanh_cpu_float64 PASSED [ 38%]
2023-01-11T21:41:34.3766747Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_tensor_split_cpu_float64 PASSED [ 38%]
2023-01-11T21:41:34.3767096Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_tensordot_cpu_complex128 PASSED [ 38%]
2023-01-11T21:41:34.3767446Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_tile_cpu_complex128 PASSED [ 38%]
2023-01-11T21:41:34.3767786Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_to_cpu_float64 PASSED [ 38%]
2023-01-11T21:41:34.3768196Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_to_sparse_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 39%]
2023-01-11T21:41:34.3768584Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_topk_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3768928Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_trace_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3769291Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_transpose_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3769637Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_transpose_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3769992Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_trapezoid_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3770358Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_triangular_solve_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3770730Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_triangular_solve_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3771074Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_triu_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3771418Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_triu_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3771772Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_true_divide_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3772128Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_true_divide_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3772512Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_unbind_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3772868Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_unflatten_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3773220Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_unfold_copy_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3773618Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_uniform_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 39%]
2023-01-11T21:41:34.3774015Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_var_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3774360Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_var_mean_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3774725Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_var_mean_unbiased_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3775084Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_var_mean_unbiased_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3775456Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_var_unbiased_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3775880Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_view_as_complex_cpu_float64 PASSED [ 39%]
2023-01-11T21:41:34.3776313Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_vsplit_cpu_complex128 PASSED [ 39%]
2023-01-11T21:41:34.3776717Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_zeros_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 39%]
2023-01-11T21:41:34.3777176Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_zeros_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 40%]
2023-01-11T21:41:34.3777573Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_T_cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3777926Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___getitem___cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3778300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rdiv___cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3778661Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rdiv___cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3779014Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rmod___cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3779360Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rmul___cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3779715Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rmul___cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3780086Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rpow___cpu_complex128 SKIPPED (Skipped!) [ 40%]
2023-01-11T21:41:34.3780472Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rpow___cpu_float64 SKIPPED (Skipped!) [ 40%]
2023-01-11T21:41:34.3780834Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rsub___cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3781212Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad__softmax_backward_data_cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3781582Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_abs_cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3781937Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_acos_cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3782282Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_acosh_cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3782635Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_acosh_cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3782994Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addcdiv_cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3783342Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addcdiv_cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3783694Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addmm_cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3784125Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addmm_decomposed_cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3784507Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addmm_decomposed_cpu_float64 PASSED [ 40%]
2023-01-11T21:41:34.3784866Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addmv_cpu_complex128 PASSED [ 40%]
2023-01-11T21:41:34.3785411Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_all_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 40%]
2023-01-11T21:41:34.3785985Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_all_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 40%]
2023-01-11T21:41:34.3786562Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_allclose_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:41:34.3787128Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_aminmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:41:34.3787733Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_any_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:41:34.3788296Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_any_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:41:34.3788860Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_argmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:41:34.3789285Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_as_strided_cpu_complex128 SKIPPED (Numerous errors) [ 41%]
2023-01-11T21:41:34.3789696Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_as_strided_cpu_float64 SKIPPED (Numerous errors) [ 41%]
2023-01-11T21:41:34.3790072Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_asin_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3790430Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_asinh_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3790776Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atanh_cpu_complex128 PASSED [ 41%]
2023-01-11T21:41:34.3791136Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atleast_1d_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3791503Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atleast_2d_cpu_complex128 PASSED [ 41%]
2023-01-11T21:41:34.3791870Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atleast_3d_cpu_complex128 PASSED [ 41%]
2023-01-11T21:41:34.3792223Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atleast_3d_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3792583Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_baddbmm_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3792944Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bernoulli_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3793292Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bfloat16_cpu_float64 XFAIL [ 41%]
2023-01-11T21:41:34.3793781Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_block_diag_cpu_complex128 PASSED [ 41%]
2023-01-11T21:41:34.3794145Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bmm_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3794666Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_byte_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:41:34.3795062Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ceil_cpu_float64 PASSED [ 41%]
2023-01-11T21:41:34.3795421Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cfloat_cpu_complex128 XFAIL [ 41%]
2023-01-11T21:41:34.3795778Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cfloat_cpu_float64 XFAIL [ 41%]
2023-01-11T21:41:34.3796299Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_char_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:41:34.3796506Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cholesky_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3796681Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_chunk_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3796856Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_clamp_max_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3797026Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_clamp_min_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3797193Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_clone_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3797370Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_column_stack_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3797553Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_combinations_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3797723Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_conj_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3797906Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_conj_physical_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3798107Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_constant_pad_nd_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3798291Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_constant_pad_nd_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3798472Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_contiguous_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3798642Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_copysign_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3798819Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_corrcoef_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3798991Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_corrcoef_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3799160Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cosh_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3799519Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_count_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 42%]
2023-01-11T21:41:34.3799687Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cov_cpu_complex128 XFAIL [ 42%]
2023-01-11T21:41:34.3799838Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cov_cpu_float64 XFAIL [ 42%]
2023-01-11T21:41:34.3800006Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cross_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3800176Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cumprod_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3800343Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cumsum_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3800536Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cumulative_trapezoid_cpu_complex128 PASSED [ 42%]
2023-01-11T21:41:34.3800885Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cumulative_trapezoid_cpu_float64 PASSED [ 42%]
2023-01-11T21:41:34.3801064Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diag_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3801231Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diag_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3801397Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diag_embed_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3801567Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagflat_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3801750Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagonal_copy_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3801927Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagonal_copy_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3802094Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagonal_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3802337Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagonal_scatter_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3802510Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diff_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3802680Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dist_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3802846Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dist_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3803016Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_div_floor_rounding_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3803205Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_div_no_rounding_mode_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3803389Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_div_trunc_rounding_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3803553Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dot_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3803725Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_double_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3803938Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dstack_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3804107Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dstack_cpu_float64 PASSED [ 43%]
2023-01-11T21:41:34.3804371Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_einsum_cpu_complex128 PASSED [ 43%]
2023-01-11T21:41:34.3804777Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_empty_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 43%]
2023-01-11T21:41:34.3805100Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 43%]
2023-01-11T21:41:34.3805450Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_empty_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 43%]
2023-01-11T21:41:34.3805798Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_empty_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 43%]
2023-01-11T21:41:34.3806132Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_eq_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 43%]
2023-01-11T21:41:34.3806474Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_equal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 43%]
2023-01-11T21:41:34.3806803Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_equal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 44%]
2023-01-11T21:41:34.3806974Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_erfinv_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3807142Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_exp2_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3807315Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_exp_cpu_complex128 PASSED [ 44%]
2023-01-11T21:41:34.3807481Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_exp_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3807645Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_expand_cpu_complex128 PASSED [ 44%]
2023-01-11T21:41:34.3807814Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_expm1_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3808144Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_eye_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 44%]
2023-01-11T21:41:34.3808321Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fftn_cpu_complex128 PASSED [ 44%]
2023-01-11T21:41:34.3808503Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fftshift_cpu_complex128 PASSED [ 44%]
2023-01-11T21:41:34.3808681Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fftshift_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3808891Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_hfft2_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3809064Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_hfft_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3809240Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifft2_cpu_complex128 PASSED [ 44%]
2023-01-11T21:41:34.3809398Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifft_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3809569Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifftn_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3809741Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ihfft2_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3809914Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ihfftn_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3810093Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_irfft2_cpu_complex128 PASSED [ 44%]
2023-01-11T21:41:34.3810265Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_irfft2_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3810477Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_irfft_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3810652Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_rfftn_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3810823Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fill_cpu_complex128 PASSED [ 44%]
2023-01-11T21:41:34.3810977Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fill_cpu_float64 PASSED [ 44%]
2023-01-11T21:41:34.3811149Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_flatten_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3811316Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_flip_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3811490Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_flipud_cpu_complex128 PASSED [ 45%]
2023-01-11T21:41:34.3811662Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_flipud_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3811832Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_float_cpu_complex128 XFAIL [ 45%]
2023-01-11T21:41:34.3811999Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_float_cpu_float64 XFAIL [ 45%]
2023-01-11T21:41:34.3812179Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_float_power_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3812334Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_floor_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3812497Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fmax_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3812662Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fmin_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3812826Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_frac_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3813163Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:41:34.3813514Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_full_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:41:34.3813689Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_gather_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3814017Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ge_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:41:34.3814354Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_geqrf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:41:34.3814532Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_gradient_cpu_complex128 PASSED [ 45%]
2023-01-11T21:41:34.3814687Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_half_cpu_complex128 XFAIL [ 45%]
2023-01-11T21:41:34.3815059Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_heaviside_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:41:34.3815393Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_histc_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:41:34.3815570Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_hsplit_cpu_complex128 PASSED [ 45%]
2023-01-11T21:41:34.3815744Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_hsplit_cpu_float64 PASSED [ 45%]
2023-01-11T21:41:34.3815915Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_hstack_cpu_complex128 PASSED [ 45%]
2023-01-11T21:41:34.3816308Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_igammac_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:41:34.3816490Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_add_cpu_complex128 PASSED [ 46%]
2023-01-11T21:41:34.3816665Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_add_cpu_float64 PASSED [ 46%]
2023-01-11T21:41:34.3816858Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_copy_cpu_float64 PASSED [ 46%]
2023-01-11T21:41:34.3817039Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_fill_cpu_complex128 PASSED [ 46%]
2023-01-11T21:41:34.3817213Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_put_cpu_complex128 PASSED [ 46%]
2023-01-11T21:41:34.3817539Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_int_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3817883Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isclose_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3818217Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isclose_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3818565Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isfinite_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3818906Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isfinite_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3819245Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isinf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3819573Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isnan_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3819908Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isneginf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:41:34.3820108Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_2inputs_2outputs_cpu_float64 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:41:34.3820336Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_4inputs_with_extra_args_cpu_complex128 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:41:34.3820551Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_4inputs_with_extra_args_cpu_float64 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:41:34.3820758Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_binary_cpu_complex128 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:41:34.3820968Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_binary_return_by_ref_cpu_complex128 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:41:34.3821181Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_binary_return_by_ref_cpu_float64 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:41:34.3821379Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_unary_cpu_float64 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:41:34.3821581Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_kron_cpu_float64 PASSED [ 46%]
2023-01-11T21:41:34.3821757Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_kthvalue_cpu_float64 PASSED [ 46%]
2023-01-11T21:41:34.3821930Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ldexp_cpu_complex128 PASSED [ 46%]
2023-01-11T21:41:34.3822086Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ldexp_cpu_float64 PASSED [ 46%]
2023-01-11T21:41:34.3822258Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lerp_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3822434Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cond_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3822616Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cross_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3822791Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_det_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3822982Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_det_singular_cpu_complex128 XFAIL [ 47%]
2023-01-11T21:41:34.3823196Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_det_singular_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3823373Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eigh_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3823554Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eigvals_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3823728Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eigvalsh_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3823907Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eigvalsh_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3824107Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_householder_product_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3824285Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_inv_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3824465Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_inv_ex_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3824642Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_inv_ex_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3825002Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_ldl_factor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:41:34.3825356Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_ldl_factor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:41:34.3825782Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_ldl_factor_ex_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:41:34.3826188Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_ldl_solve_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:41:34.3826390Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lstsq_grad_oriented_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3826574Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_factor_cpu_float64 PASSED [ 47%]
2023-01-11T21:41:34.3826761Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_norm_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3826949Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_power_cpu_complex128 PASSED [ 47%]
2023-01-11T21:41:34.3827302Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_rank_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:41:34.3827678Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_rank_hermitian_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:41:34.3828085Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_rank_hermitian_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 48%]
2023-01-11T21:41:34.3828276Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_multi_dot_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3828460Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_multi_dot_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3828663Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_norm_subgradients_at_zero_cpu_complex128 XFAIL [ 48%]
2023-01-11T21:41:34.3828843Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_pinv_hermitian_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3829086Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_pinv_singular_cpu_float64 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 48%]
2023-01-11T21:41:34.3829265Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_qr_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3829437Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_qr_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3829646Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_slogdet_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3829821Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_solve_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3830000Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_solve_ex_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3830188Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_solve_triangular_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3830373Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_tensorinv_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3830548Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_tensorsolve_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3830730Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_vander_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3830908Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_vecdot_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3831094Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_vector_norm_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3831275Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_vector_norm_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3831443Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log10_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3831614Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log1p_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3831782Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log1p_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3831948Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log2_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3832103Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log_cpu_float64 PASSED [ 48%]
2023-01-11T21:41:34.3832299Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log_softmax_with_dtype_cpu_complex128 PASSED [ 48%]
2023-01-11T21:41:34.3832473Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logaddexp_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3832647Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logdet_cpu_complex128 PASSED [ 49%]
2023-01-11T21:41:34.3832815Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logdet_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3833177Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_and_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3833624Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_not_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3834026Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_xor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3834201Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logit_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3834633Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3834973Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3835150Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logsumexp_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3835479Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_long_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3835806Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lt_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3835979Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lu_cpu_complex128 PASSED [ 49%]
2023-01-11T21:41:34.3836177Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lu_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3836356Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lu_solve_cpu_complex128 PASSED [ 49%]
2023-01-11T21:41:34.3836527Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lu_solve_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3836694Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mH_cpu_complex128 PASSED [ 49%]
2023-01-11T21:41:34.3836846Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mH_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3837193Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_argmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3837541Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:41:34.3837727Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_cumprod_cpu_complex128 PASSED [ 49%]
2023-01-11T21:41:34.3837910Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_cumsum_cpu_complex128 PASSED [ 49%]
2023-01-11T21:41:34.3838090Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_fill_cpu_complex128 PASSED [ 49%]
2023-01-11T21:41:34.3838273Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_log_softmax_cpu_float64 PASSED [ 49%]
2023-01-11T21:41:34.3838450Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_mean_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3838627Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_median_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3838803Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_normalize_cpu_complex128 PASSED [ 50%]
2023-01-11T21:41:34.3838984Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_normalize_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3839162Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_prod_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3839341Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_select_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3839512Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_std_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3839684Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_sum_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3839858Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_matmul_cpu_complex128 PASSED [ 50%]
2023-01-11T21:41:34.3840029Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_matmul_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3840237Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_matrix_exp_cpu_complex128 PASSED [ 50%]
2023-01-11T21:41:34.3840399Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_matrix_exp_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3840575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_max_binary_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3840868Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_max_pool2d_with_indices_backward_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3841055Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_max_reduction_no_dim_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3841225Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_maximum_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3841395Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mean_cpu_complex128 PASSED [ 50%]
2023-01-11T21:41:34.3841591Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_meshgrid_list_of_tensors_cpu_complex128 PASSED [ 50%]
2023-01-11T21:41:34.3841765Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_min_binary_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3841983Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mm_cpu_complex128 PASSED [ 50%]
2023-01-11T21:41:34.3842141Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mode_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3842314Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mul_cpu_complex128 PASSED [ 50%]
2023-01-11T21:41:34.3842481Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mul_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3842830Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_multinomial_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 50%]
2023-01-11T21:41:34.3843021Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mvlgamma_mvlgamma_p_1_cpu_float64 PASSED [ 50%]
2023-01-11T21:41:34.3843210Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mvlgamma_mvlgamma_p_5_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3843387Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nanmedian_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3843560Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nansum_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3843901Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_narrow_copy_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3844063Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_narrow_cpu_complex128 PASSED [ 51%]
2023-01-11T21:41:34.3844234Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_narrow_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3844517Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_native_dropout_backward_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3844687Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_neg_cpu_complex128 PASSED [ 51%]
2023-01-11T21:41:34.3844853Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_neg_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3845195Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3845536Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3845872Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_full_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3846211Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_ones_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3846546Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3846923Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_zeros_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3847262Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_zeros_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:41:34.3847475Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional__scaled_dot_product_attention_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3847673Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_adaptive_avg_pool3d_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3847874Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_adaptive_max_pool2d_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3848065Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_avg_pool1d_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3848253Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv1d_cpu_complex128 PASSED [ 51%]
2023-01-11T21:41:34.3848440Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv2d_cpu_float64 PASSED [ 51%]
2023-01-11T21:41:34.3848670Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv_transpose1d_cpu_complex128 PASSED [ 51%]
2023-01-11T21:41:34.3848866Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv_transpose2d_cpu_complex128 PASSED [ 51%]
2023-01-11T21:41:34.3849051Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv_transpose2d_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3849253Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv_transpose3d_cpu_complex128 PASSED [ 52%]
2023-01-11T21:41:34.3849447Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv_transpose3d_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3849647Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_cosine_embedding_loss_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3849846Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_cosine_similarity_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3850039Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_cross_entropy_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3850224Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_ctc_loss_cpu_float64 XFAIL [ 52%]
2023-01-11T21:41:34.3850415Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_dropout2d_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3850602Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_dropout3d_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3850784Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_gaussian_nll_loss_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3850968Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_glu_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3851349Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_grid_sample_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 52%]
2023-01-11T21:41:34.3851542Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_group_norm_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3851733Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_hardshrink_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3851921Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_hardswish_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3852120Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_interpolate_bicubic_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3852318Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_interpolate_nearest_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3852508Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_layer_norm_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3852726Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_linear_cpu_complex128 PASSED [ 52%]
2023-01-11T21:41:34.3852914Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_local_response_norm_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3853105Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_logsigmoid_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3853299Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_margin_ranking_loss_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3853484Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_pool1d_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3853669Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_pool2d_cpu_float64 PASSED [ 52%]
2023-01-11T21:41:34.3853937Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_pool3d_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3854146Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_unpool1d_cpu_float64 SKIPPED (Skipped!) [ 53%]
2023-01-11T21:41:34.3854372Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_unpool3d_grad_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3854763Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_multi_margin_loss_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 53%]
2023-01-11T21:41:34.3854972Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_multilabel_soft_margin_loss_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3855153Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_normalize_cpu_complex128 PASSED [ 53%]
2023-01-11T21:41:34.3855345Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_circular_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3855543Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_constant_cpu_complex128 PASSED [ 53%]
2023-01-11T21:41:34.3855735Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_constant_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3855988Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_reflect_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3856359Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pdist_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 53%]
2023-01-11T21:41:34.3856562Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pixel_unshuffle_cpu_complex128 PASSED [ 53%]
2023-01-11T21:41:34.3856754Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pixel_unshuffle_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3856937Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_relu6_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3857123Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_relu_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3857296Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_rrelu_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3857482Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_softmin_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3857682Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_softmin_with_dtype_cpu_complex128 PASSED [ 53%]
2023-01-11T21:41:34.3857877Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_softmin_with_dtype_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3858066Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_softshrink_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3858257Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_softsign_cpu_complex128 PASSED [ 53%]
2023-01-11T21:41:34.3858479Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_softsign_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3858673Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_tanhshrink_cpu_complex128 PASSED [ 53%]
2023-01-11T21:41:34.3858862Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_tanhshrink_cpu_float64 PASSED [ 53%]
2023-01-11T21:41:34.3859048Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_triplet_margin_loss_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3859269Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_triplet_margin_with_distance_loss_cpu_complex128 PASSED [ 54%]
2023-01-11T21:41:34.3859454Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_unfold_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3859653Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_upsample_bilinear_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3859997Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:41:34.3860193Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3860370Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_fro_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3860549Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_inf_cpu_complex128 PASSED [ 54%]
2023-01-11T21:41:34.3860719Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_inf_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3860885Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_nuc_cpu_complex128 PASSED [ 54%]
2023-01-11T21:41:34.3861085Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_normal_cpu_float64 SKIPPED (Gradients are incorrect!) [ 54%]
2023-01-11T21:41:34.3861253Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_outer_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3861430Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_permute_cpu_complex128 PASSED [ 54%]
2023-01-11T21:41:34.3861602Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_pinverse_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3861805Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_polygamma_polygamma_n_1_cpu_float64 SKIPPED (Skipped!) [ 54%]
2023-01-11T21:41:34.3862008Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_polygamma_polygamma_n_2_cpu_float64 SKIPPED (Skipped!) [ 54%]
2023-01-11T21:41:34.3862177Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_put_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3862347Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_quantile_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3862506Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_rad2deg_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3862854Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_randint_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:41:34.3863188Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_randn_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:41:34.3863356Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ravel_cpu_float64 PASSED [ 54%]
2023-01-11T21:41:34.3863526Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_real_cpu_complex128 PASSED [ 54%]
2023-01-11T21:41:34.3863708Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_reciprocal_cpu_complex128 PASSED [ 54%]
2023-01-11T21:41:34.3863885Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_reciprocal_cpu_float64 PASSED [ 55%]
2023-01-11T21:41:34.3864055Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_renorm_cpu_float64 PASSED [ 55%]
2023-01-11T21:41:34.3864229Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_repeat_cpu_complex128 PASSED [ 55%]
2023-01-11T21:41:34.3864436Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_repeat_interleave_cpu_float64 PASSED [ 55%]
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2023-01-11T21:41:34.3892715Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_addr_cpu_complex128 PASSED [ 60%]
2023-01-11T21:41:34.3892883Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_addr_cpu_float64 PASSED [ 60%]
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2023-01-11T21:41:34.3893564Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_allclose_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 60%]
2023-01-11T21:41:34.3893772Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_angle_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 60%]
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2023-01-11T21:41:34.3894325Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_any_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 60%]
2023-01-11T21:41:34.3894729Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_arange_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 60%]
2023-01-11T21:41:34.3895066Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_argsort_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 60%]
2023-01-11T21:41:34.3895299Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_as_strided_cpu_complex128 SKIPPED (Numerous errors) [ 61%]
2023-01-11T21:41:34.3895518Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_asin_cpu_complex128 PASSED [ 61%]
2023-01-11T21:41:34.3895692Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_asinh_cpu_complex128 PASSED [ 61%]
2023-01-11T21:41:34.3895898Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atanh_cpu_float64 PASSED [ 61%]
2023-01-11T21:41:34.3896175Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atleast_1d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3896381Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atleast_2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3896591Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atleast_3d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3896749Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_baddbmm_cpu_float64 PASSED [ 61%]
2023-01-11T21:41:34.3896957Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_bfloat16_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3897159Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_bfloat16_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3897404Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_block_diag_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3897607Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_bmm_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
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2023-01-11T21:41:34.3898143Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_bool_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:41:34.3898358Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_broadcast_tensors_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3898572Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_broadcast_tensors_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3898781Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_broadcast_to_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3899117Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_byte_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:41:34.3899439Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_byte_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:41:34.3899653Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cartesian_prod_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3899857Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cdouble_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3900057Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cdouble_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3900257Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cfloat_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:41:34.3900595Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_char_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:41:34.3900806Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cholesky_solve_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3901003Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_chunk_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3901178Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_clamp_min_cpu_float64 PASSED [ 62%]
2023-01-11T21:41:34.3901389Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_combinations_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3901575Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_complex_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3901807Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_conj_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3901986Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_conj_physical_cpu_float64 PASSED [ 62%]
2023-01-11T21:41:34.3902201Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_constant_pad_nd_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3902408Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_contiguous_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3902611Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_contiguous_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3902815Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_corrcoef_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3902991Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cosh_cpu_complex128 PASSED [ 62%]
2023-01-11T21:41:34.3903369Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_count_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 62%]
2023-01-11T21:41:34.3903714Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_count_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 62%]
2023-01-11T21:41:34.3903906Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cov_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3904103Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cov_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3904302Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cummax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3904501Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cummin_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3904681Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cumprod_cpu_complex128 PASSED [ 62%]
2023-01-11T21:41:34.3904857Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cumprod_cpu_float64 PASSED [ 62%]
2023-01-11T21:41:34.3905102Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cumsum_cpu_complex128 PASSED [ 62%]
2023-01-11T21:41:34.3905313Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cumsum_cpu_float64 PASSED [ 62%]
2023-01-11T21:41:34.3905532Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cumulative_trapezoid_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3905729Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagonal_copy_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3905937Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagonal_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:41:34.3906142Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagonal_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3906357Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagonal_scatter_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3906558Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diff_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3906753Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dist_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3906939Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_div_floor_rounding_cpu_float64 PASSED [ 63%]
2023-01-11T21:41:34.3907128Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_div_no_rounding_mode_cpu_complex128 PASSED [ 63%]
2023-01-11T21:41:34.3907366Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_div_no_rounding_mode_cpu_float64 PASSED [ 63%]
2023-01-11T21:41:34.3907610Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_div_trunc_rounding_cpu_float64 PASSED [ 63%]
2023-01-11T21:41:34.3907801Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dot_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3908000Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_double_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3908203Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dstack_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3908408Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_einsum_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3908610Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_einsum_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3908955Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 63%]
2023-01-11T21:41:34.3909332Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_eq_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 63%]
2023-01-11T21:41:34.3909659Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_eq_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 63%]
2023-01-11T21:41:34.3909997Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_equal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 63%]
2023-01-11T21:41:34.3910323Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_equal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 63%]
2023-01-11T21:41:34.3910493Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_erfc_cpu_float64 PASSED [ 63%]
2023-01-11T21:41:34.3910690Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_expand_as_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3928519Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_expand_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3928785Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_expand_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3928953Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_expm1_cpu_float64 PASSED [ 63%]
2023-01-11T21:41:34.3929160Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fft2_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:41:34.3929362Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fft_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3929566Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fft_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3929776Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fftn_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3929984Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fftn_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3930186Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fftshift_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3930396Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fftshift_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3930599Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_hfft2_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3930798Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_hfftn_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3931001Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifft2_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3931338Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifft_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3931549Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifftn_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3931760Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifftshift_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3931964Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ihfft2_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3932166Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ihfftn_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3932377Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_irfft2_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3932575Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_irfft_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3932826Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_irfftn_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3933032Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_rfft2_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3933229Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_rfft_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3933419Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_rfftn_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3933583Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fill_cpu_complex128 PASSED [ 64%]
2023-01-11T21:41:34.3933743Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fill_cpu_float64 PASSED [ 64%]
2023-01-11T21:41:34.3933943Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_flatten_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3934144Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fliplr_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:41:34.3934335Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_flipud_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3934537Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_flipud_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3934739Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_float_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3934937Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_float_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3935122Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_float_power_cpu_complex128 PASSED [ 65%]
2023-01-11T21:41:34.3935294Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_floor_cpu_float64 PASSED [ 65%]
2023-01-11T21:41:34.3935492Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fmax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3935662Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fmod_cpu_float64 PASSED [ 65%]
2023-01-11T21:41:34.3935860Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_frexp_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3936351Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_full_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:41:34.3936783Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_full_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:41:34.3937000Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_gather_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3937271Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_gather_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3937619Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_geqrf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:41:34.3937954Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_geqrf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:41:34.3938163Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_gradient_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3938372Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_grid_sampler_2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3938715Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_heaviside_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:41:34.3939082Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_histc_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:41:34.3939426Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_histogramdd_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:41:34.3939632Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_hsplit_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3939825Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_hstack_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3940025Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_hstack_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:41:34.3940193Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_i0_cpu_float64 PASSED [ 65%]
2023-01-11T21:41:34.3940532Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_igamma_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3940871Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_igammac_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3941075Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_imag_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:41:34.3941249Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_add_cpu_float64 PASSED [ 66%]
2023-01-11T21:41:34.3941431Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_copy_cpu_complex128 PASSED [ 66%]
2023-01-11T21:41:34.3941611Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_put_cpu_complex128 PASSED [ 66%]
2023-01-11T21:41:34.3941773Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_reduce_cpu_float64 PASSED [ 66%]
2023-01-11T21:41:34.3941984Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_select_cpu_float64 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:41:34.3942191Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_inner_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:41:34.3942389Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_inner_cpu_float64 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:41:34.3942728Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_int_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3943061Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_int_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3943410Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isfinite_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3943778Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isinf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3944112Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3944441Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isnan_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3944777Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isneginf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3945101Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isposinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:41:34.3945316Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_2inputs_2outputs_cpu_complex128 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:41:34.3945573Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_4inputs_with_extra_args_cpu_complex128 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:41:34.3945784Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_binary_cpu_complex128 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:41:34.3945985Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_binary_cpu_float64 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:41:34.3946202Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_binary_return_by_ref_cpu_complex128 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:41:34.3946414Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_binary_return_by_ref_cpu_float64 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:41:34.3946618Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_kron_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3946796Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ldexp_cpu_complex128 PASSED [ 67%]
2023-01-11T21:41:34.3947127Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_le_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:41:34.3947320Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cond_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3947528Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cross_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3947816Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_det_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3948023Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_det_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3948242Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_det_singular_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3948453Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eig_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3948664Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eigh_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3948871Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eigvals_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3949086Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eigvalsh_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3949293Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eigvalsh_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3949503Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_inv_ex_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3949730Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_inv_ex_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3950089Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_ldl_factor_ex_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:41:34.3950442Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_ldl_solve_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:41:34.3950788Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_ldl_solve_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:41:34.3950980Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lstsq_cpu_float64 SKIPPED (Skipped!) [ 67%]
2023-01-11T21:41:34.3951201Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lstsq_grad_oriented_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3951444Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_factor_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3951659Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_factor_ex_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3951871Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_factor_ex_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3952083Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_solve_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:41:34.3952276Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_solve_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3952484Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3952840Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_rank_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 68%]
2023-01-11T21:41:34.3953051Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_multi_dot_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3953260Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_multi_dot_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3953464Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3953689Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_norm_subgradients_at_zero_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3953908Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_pinv_hermitian_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3954125Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_pinv_hermitian_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3954376Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_pinv_singular_cpu_complex128 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 68%]
2023-01-11T21:41:34.3954576Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_qr_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3954778Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_slogdet_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3954988Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_solve_ex_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3955213Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_solve_triangular_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3955506Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_svd_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3955712Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_svd_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3955920Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_svdvals_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3956127Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_vander_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3956397Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_vander_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3956618Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_vecdot_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3956830Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_vecdot_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:41:34.3957207Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 68%]
2023-01-11T21:41:34.3957370Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log10_cpu_complex128 PASSED [ 68%]
2023-01-11T21:41:34.3957539Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log10_cpu_float64 PASSED [ 68%]
2023-01-11T21:41:34.3957713Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log1p_cpu_complex128 PASSED [ 68%]
2023-01-11T21:41:34.3957883Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log_cpu_complex128 PASSED [ 69%]
2023-01-11T21:41:34.3958049Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log_cpu_float64 PASSED [ 69%]
2023-01-11T21:41:34.3958271Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log_softmax_with_dtype_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3958491Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log_softmax_with_dtype_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3958698Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logaddexp2_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3958900Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logdet_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3959087Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logdet_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3959437Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_and_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3959786Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_not_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3960133Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_or_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3960476Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_xor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3961002Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_xor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3961352Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3961688Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_long_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3961954Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lu_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3962160Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lu_solve_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3962365Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lu_unpack_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3962552Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mH_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3962752Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mT_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3962958Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_amax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3963308Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:41:34.3963564Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_cumprod_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3963782Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_cumsum_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:41:34.3963959Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_fill_cpu_float64 PASSED [ 69%]
2023-01-11T21:41:34.3964173Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_log_softmax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3964386Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_logaddexp_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3964600Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_logsumexp_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3964794Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_mean_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3965000Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3965205Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_prod_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3965385Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_scatter_cpu_float64 PASSED [ 70%]
2023-01-11T21:41:34.3965593Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_select_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3965799Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_softmax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3966003Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_softmin_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3966207Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_sum_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3966410Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_matrix_exp_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3966635Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_max_pool2d_with_indices_backward_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3966838Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_max_reduction_no_dim_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3967055Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_max_reduction_with_dim_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3967258Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_maximum_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3967519Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_meshgrid_list_of_tensors_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3967833Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_min_reduction_no_dim_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:41:34.3968035Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_minimum_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
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2023-01-11T21:41:34.3968796Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mul_cpu_float64 PASSED [ 70%]
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2023-01-11T21:41:34.3969201Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mvlgamma_mvlgamma_p_1_cpu_float64 PASSED [ 71%]
2023-01-11T21:41:34.3969386Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mvlgamma_mvlgamma_p_3_cpu_float64 PASSED [ 71%]
2023-01-11T21:41:34.3969566Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mvlgamma_mvlgamma_p_5_cpu_float64 PASSED [ 71%]
2023-01-11T21:41:34.3969764Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nanmean_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
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2023-01-11T21:41:34.3970324Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_narrow_copy_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:41:34.3970532Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_narrow_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 71%]
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2023-01-11T21:41:34.3970940Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_native_batch_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3971140Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_native_layer_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3971476Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ne_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:41:34.3971818Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:41:34.3972159Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_full_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:41:34.3972495Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nextafter_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:41:34.3972726Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_adaptive_avg_pool1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3972950Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_adaptive_avg_pool2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3973181Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_adaptive_avg_pool3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3973458Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_adaptive_max_pool1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3973682Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_adaptive_max_pool2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3973910Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_adaptive_max_pool3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3974129Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_avg_pool1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3974333Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_batch_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3974550Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_bilinear_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3974807Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_binary_cross_entropy_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:41:34.3975050Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_binary_cross_entropy_with_logits_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3975235Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_celu_cpu_float64 PASSED [ 72%]
2023-01-11T21:41:34.3975456Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv1d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3975683Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv_transpose1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3975904Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv_transpose2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3976209Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv_transpose3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3976440Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_cosine_embedding_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3976668Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_cosine_similarity_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3976873Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_cross_entropy_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3977063Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_dropout_cpu_float64 PASSED [ 72%]
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2023-01-11T21:41:34.3977589Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_feature_alpha_dropout_without_train_cpu_complex128 PASSED [ 72%]
2023-01-11T21:41:34.3977826Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_fractional_max_pool2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3978048Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_fractional_max_pool3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3978271Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_gaussian_nll_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3978478Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_glu_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3978699Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_hardshrink_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3978991Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_hardswish_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3979208Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_hinge_embedding_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3979426Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_huber_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3979656Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_interpolate_bilinear_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3979884Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_interpolate_linear_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3980116Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_interpolate_trilinear_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3980362Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_kl_div_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:41:34.3980581Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_layer_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3980793Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_linear_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3981022Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_margin_ranking_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3981242Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_pool1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3981461Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_pool2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3981675Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_unpool1d_grad_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3981893Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_unpool2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3982114Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_unpool3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3982336Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_multi_margin_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3982565Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_multilabel_margin_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3982780Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_nll_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3983001Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_normalize_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3983220Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_normalize_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3983436Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_circular_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3983666Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pairwise_distance_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3983889Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pairwise_distance_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3984131Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pixel_shuffle_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3984358Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pixel_unshuffle_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3984576Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_poisson_nll_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3984786Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_prelu_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3984998Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_relu6_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:41:34.3985186Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_rrelu_cpu_float64 PASSED [ 73%]
2023-01-11T21:41:34.3985570Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_silu_complex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 73%]
2023-01-11T21:41:34.3985785Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_silu_cpu_float64 PASSED [ 73%]
2023-01-11T21:41:34.3986008Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_smooth_l1_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3986236Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_softmin_with_dtype_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3986459Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_softmin_with_dtype_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3986663Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_softshrink_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3986889Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_softsign_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3987114Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_tanhshrink_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3987303Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_threshold_cpu_float64 PASSED [ 74%]
2023-01-11T21:41:34.3987529Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_triplet_margin_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3987753Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_upsample_bilinear_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3987979Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_upsample_nearest_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3988323Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:41:34.3988659Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:41:34.3988857Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3989147Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_fro_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3989363Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_inf_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3989566Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_nuc_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3989803Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_normal_cpu_float64 SKIPPED (Gradients are incorrect!) [ 74%]
2023-01-11T21:41:34.3990017Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_normal_number_mean_cpu_float64 SKIPPED (Gradients are incorrect!) [ 74%]
2023-01-11T21:41:34.3990461Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ones_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:41:34.3990816Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ones_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:41:34.3991017Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_outer_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3991223Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_pca_lowrank_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3991433Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_permute_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3991709Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_permute_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:41:34.3991899Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_polar_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:41:34.3992100Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_polygamma_polygamma_n_3_cpu_float64 SKIPPED (Skipped!) [ 75%]
2023-01-11T21:41:34.3992306Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_positive_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:41:34.3992507Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_prod_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:41:34.3992705Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_prod_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:41:34.3992907Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_qr_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:41:34.3993107Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_qr_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:41:34.3993281Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_rad2deg_cpu_float64 PASSED [ 75%]
2023-01-11T21:41:34.3993628Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_rand_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
2023-01-11T21:41:34.3993968Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_rand_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
2023-01-11T21:41:34.3994292Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_randint_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
2023-01-11T21:41:34.3994640Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_randint_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
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2023-01-11T21:41:34.3995316Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_randn_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
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2023-01-11T21:41:34.3997010Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_resize_as__cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
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2023-01-11T21:41:34.3999169Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_scalar_tensor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:41:34.3999517Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_scalar_tensor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:41:34.3999691Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_scatter_add_cpu_complex128 PASSED [ 76%]
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2023-01-11T21:41:34.4001277Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_short_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:41:34.4001610Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_short_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
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2023-01-11T21:41:34.4002669Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_cosine_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:41:34.4003089Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_hamming_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:41:34.4003441Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_hann_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:41:34.4003772Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signbit_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
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2023-01-11T21:41:34.4004721Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sparse_sampled_addmm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4005078Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_bessel_j0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4005428Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_bessel_y0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4005806Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_chebyshev_polynomial_u_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4006196Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_chebyshev_polynomial_v_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 77%]
2023-01-11T21:41:34.4006410Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_entr_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4006781Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_hermite_polynomial_h_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4007152Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_hermite_polynomial_he_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4007357Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_i0e_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4007570Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_log_ndtr_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4007935Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_modified_bessel_i0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4008290Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_modified_bessel_k1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4008532Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_ndtr_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4008991Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_scaled_modified_bessel_k0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:41:34.4009391Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_shifted_chebyshev_polynomial_t_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 77%]
2023-01-11T21:41:34.4009787Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_shifted_chebyshev_polynomial_v_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 77%]
2023-01-11T21:41:34.4010226Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_shifted_chebyshev_polynomial_w_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 77%]
2023-01-11T21:41:34.4010438Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_xlog1py_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4010641Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_split_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4010855Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_split_with_sizes_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4011065Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_split_with_sizes_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:41:34.4011236Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sqrt_cpu_float64 PASSED [ 77%]
2023-01-11T21:41:34.4011396Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_square_cpu_float64 PASSED [ 78%]
2023-01-11T21:41:34.4011621Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4011840Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_mean_unbiased_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4012048Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_unbiased_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4012254Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_stft_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4012425Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sub_cpu_complex128 PASSED [ 78%]
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2023-01-11T21:41:34.4012997Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sum_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4013201Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sum_to_size_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4013389Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_svd_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
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2023-01-11T21:41:34.4013956Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_t_cpu_complex128 PASSED [ 78%]
2023-01-11T21:41:34.4014170Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_take_along_dim_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4014379Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_take_along_dim_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4014579Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_take_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4014751Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tan_cpu_complex128 PASSED [ 78%]
2023-01-11T21:41:34.4014924Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tanh_cpu_complex128 PASSED [ 78%]
2023-01-11T21:41:34.4015134Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tensor_split_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4015346Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_to_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
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2023-01-11T21:41:34.4016020Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_trapezoid_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:41:34.4016226Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_trapz_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4016423Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_trapz_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4016636Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_triangular_solve_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4016815Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_true_divide_cpu_float64 PASSED [ 79%]
2023-01-11T21:41:34.4017015Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_trunc_cpu_float64 PASSED [ 79%]
2023-01-11T21:41:34.4017203Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unbind_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4017425Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unflatten_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4017752Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unfold_copy_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4017956Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unfold_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4018323Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unique_consecutive_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 79%]
2023-01-11T21:41:34.4018661Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unique_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 79%]
2023-01-11T21:41:34.4018866Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4019115Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_mean_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4019404Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_unbiased_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4019611Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_unbiased_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4019812Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_vdot_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4020000Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_vdot_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4020210Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_view_as_complex_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4020407Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_view_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
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2023-01-11T21:41:34.4021212Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_where_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:41:34.4021446Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_xlogy_cpu_float64 PASSED [ 79%]
2023-01-11T21:41:34.4021615Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_zero__cpu_float64 PASSED [ 80%]
2023-01-11T21:41:34.4021950Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_zeros_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 80%]
2023-01-11T21:41:34.4022297Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_zeros_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 80%]
2023-01-11T21:41:34.4022640Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_zeros_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 80%]
2023-01-11T21:41:34.4022877Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_H_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4023113Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_H_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4023389Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_T_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4023623Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_T_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4023870Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___getitem___cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4024111Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___getitem___cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4024353Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___radd___cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4024593Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___radd___cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4024838Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rmatmul___cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4025064Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rmatmul___cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4025259Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rpow___cpu_complex128 SKIPPED (Skipped!) [ 80%]
2023-01-11T21:41:34.4025496Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rsub___cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4025751Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad__softmax_backward_data_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:41:34.4025927Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_acos_cpu_float64 PASSED [ 80%]
2023-01-11T21:41:34.4026100Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_add_cpu_float64 PASSED [ 80%]
2023-01-11T21:41:34.4026277Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addbmm_cpu_float64 PASSED [ 80%]
2023-01-11T21:41:34.4026458Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addcdiv_cpu_complex128 PASSED [ 80%]
2023-01-11T21:41:34.4026635Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addmm_cpu_float64 PASSED [ 80%]
2023-01-11T21:41:34.4026831Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addmm_decomposed_cpu_complex128 PASSED [ 80%]
2023-01-11T21:41:34.4027028Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addmv_cpu_complex128 PASSED [ 80%]
2023-01-11T21:41:34.4027207Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addr_cpu_complex128 PASSED [ 81%]
2023-01-11T21:41:34.4027380Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addr_cpu_float64 PASSED [ 81%]
2023-01-11T21:41:34.4027727Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_all_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4028065Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_all_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4028418Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_allclose_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4028761Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_any_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4029132Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_arange_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4029474Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_argmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4029816Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_argsort_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4030148Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_argwhere_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:41:34.4030353Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_as_strided_cpu_complex128 SKIPPED (Numerous errors) [ 81%]
2023-01-11T21:41:34.4030554Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_as_strided_cpu_float64 SKIPPED (Numerous errors) [ 81%]
2023-01-11T21:41:34.4030754Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_as_strided_partial_views_cpu_float64 XFAIL [ 81%]
2023-01-11T21:41:34.4031007Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_as_strided_scatter_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4031254Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_as_strided_scatter_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4031435Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_asin_cpu_complex128 PASSED [ 81%]
2023-01-11T21:41:34.4031672Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_asinh_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4031906Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_asinh_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4032145Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atanh_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4032386Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atleast_1d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4032614Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atleast_1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4032861Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atleast_2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4033037Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_baddbmm_cpu_float64 PASSED [ 81%]
2023-01-11T21:41:34.4033310Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bfloat16_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4033547Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bfloat16_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:41:34.4033789Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_block_diag_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4034024Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bmm_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4034256Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bmm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4034680Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bool_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 82%]
2023-01-11T21:41:34.4034970Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_broadcast_tensors_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4035220Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_broadcast_to_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4035563Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_byte_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 82%]
2023-01-11T21:41:34.4035890Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_byte_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 82%]
2023-01-11T21:41:34.4036137Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cartesian_prod_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4036526Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cat_cpu_float64 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [ 82%]
2023-01-11T21:41:34.4036764Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cdist_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4037002Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cdouble_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4037176Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ceil_cpu_float64 PASSED [ 82%]
2023-01-11T21:41:34.4037410Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_chalf_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4037752Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_char_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 82%]
2023-01-11T21:41:34.4038092Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_char_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 82%]
2023-01-11T21:41:34.4038337Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cholesky_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4038582Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cholesky_inverse_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4038803Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_chunk_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4038978Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_clamp_cpu_float64 PASSED [ 82%]
2023-01-11T21:41:34.4039161Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_clamp_max_cpu_float64 PASSED [ 82%]
2023-01-11T21:41:34.4039431Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_clone_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4039674Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_column_stack_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4039918Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_combinations_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:41:34.4040259Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_combinations_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4040524Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_complex_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4040954Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_conj_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4041243Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_conj_physical_cpu_complex128 PASSED [ 83%]
2023-01-11T21:41:34.4041433Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_conj_physical_cpu_float64 PASSED [ 83%]
2023-01-11T21:41:34.4041662Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_constant_pad_nd_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4041900Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_contiguous_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4042079Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cos_cpu_complex128 PASSED [ 83%]
2023-01-11T21:41:34.4042254Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cos_cpu_float64 PASSED [ 83%]
2023-01-11T21:41:34.4042435Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cosh_cpu_complex128 PASSED [ 83%]
2023-01-11T21:41:34.4042610Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cosh_cpu_float64 PASSED [ 83%]
2023-01-11T21:41:34.4042969Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_count_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 83%]
2023-01-11T21:41:34.4043205Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cov_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4043445Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cross_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4043682Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cummax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4043908Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cummin_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4044164Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cumulative_trapezoid_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4044342Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_deg2rad_cpu_float64 PASSED [ 83%]
2023-01-11T21:41:34.4044581Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diag_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4044822Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diag_embed_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4045060Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagflat_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4045349Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagonal_copy_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4045592Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagonal_copy_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4045836Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagonal_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:41:34.4046073Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diff_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4046306Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diff_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4046485Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_digamma_cpu_float64 PASSED [ 84%]
2023-01-11T21:41:34.4046733Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dist_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4046931Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_div_no_rounding_mode_cpu_complex128 PASSED [ 84%]
2023-01-11T21:41:34.4047122Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_div_no_rounding_mode_cpu_float64 PASSED [ 84%]
2023-01-11T21:41:34.4047312Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_div_trunc_rounding_cpu_float64 PASSED [ 84%]
2023-01-11T21:41:34.4047661Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dot_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4047902Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dot_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4048148Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_double_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4048384Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_double_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4048624Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dsplit_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4048963Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dsplit_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4049313Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_empty_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 84%]
2023-01-11T21:41:34.4049640Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_eq_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 84%]
2023-01-11T21:41:34.4049982Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_equal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 84%]
2023-01-11T21:41:34.4050323Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_equal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 84%]
2023-01-11T21:41:34.4050499Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_erfc_cpu_float64 PASSED [ 84%]
2023-01-11T21:41:34.4050675Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_erfinv_cpu_float64 PASSED [ 84%]
2023-01-11T21:41:34.4050847Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_exp_cpu_float64 PASSED [ 84%]
2023-01-11T21:41:34.4051089Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_expand_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4051304Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_expm1_cpu_float64 PASSED [ 84%]
2023-01-11T21:41:34.4051649Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_eye_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 84%]
2023-01-11T21:41:34.4051891Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fft_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:41:34.4052121Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fftshift_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4052363Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_hfft2_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4052605Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_hfft_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4052879Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_hfftn_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4053119Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifftn_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4053357Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ihfft2_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4053592Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ihfft_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4053828Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ihfftn_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4054070Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_irfft_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4054306Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_irfft_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4054541Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_irfftn_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4054774Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_rfft_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4054995Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_flatten_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4055232Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_flatten_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4055470Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_flip_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4055761Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_flipud_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4056132Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_float_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4056324Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_float_power_cpu_complex128 PASSED [ 85%]
2023-01-11T21:41:34.4056502Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_floor_cpu_float64 PASSED [ 85%]
2023-01-11T21:41:34.4056851Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_full_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:41:34.4057233Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ge_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:41:34.4057471Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_half_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:41:34.4057818Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_heaviside_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:41:34.4058146Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_histc_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:41:34.4058493Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_histogram_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4058767Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_add_cpu_float64 PASSED [ 86%]
2023-01-11T21:41:34.4059006Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_copy_cpu_float64 PASSED [ 86%]
2023-01-11T21:41:34.4059198Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_fill_cpu_complex128 PASSED [ 86%]
2023-01-11T21:41:34.4059382Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_put_cpu_complex128 PASSED [ 86%]
2023-01-11T21:41:34.4059569Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_reduce_cpu_float64 PASSED [ 86%]
2023-01-11T21:41:34.4059818Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_select_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:41:34.4060060Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_select_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:41:34.4060301Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_inner_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:41:34.4060526Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_inner_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:41:34.4060874Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_int_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4061208Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_int_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4061558Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isclose_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4061901Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isfinite_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4062253Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isinf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4062594Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4062934Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isnan_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4063270Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isnan_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:41:34.4063488Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_2inputs_2outputs_cpu_complex128 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:41:34.4063762Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_4inputs_with_extra_args_cpu_complex128 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:41:34.4064092Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_4inputs_with_extra_args_cpu_float64 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:41:34.4064290Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_binary_cpu_float64 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:41:34.4064513Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_binary_return_by_ref_cpu_complex128 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:41:34.4064720Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_unary_cpu_complex128 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:41:34.4064956Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ldexp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4065296Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_le_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:41:34.4065507Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lgamma_cpu_float64 PASSED [ 87%]
2023-01-11T21:41:34.4065756Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cond_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4065997Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cond_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4066237Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cross_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4066475Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_det_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4066791Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eig_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4067039Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eig_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4067290Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eigvals_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4067539Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eigvalsh_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4067774Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_inv_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4068018Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_inv_ex_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4068258Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_inv_ex_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4068627Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_ldl_factor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:41:34.4068986Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_ldl_solve_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:41:34.4069244Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lstsq_grad_oriented_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4069514Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4069760Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_factor_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4070003Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4070241Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_power_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4070599Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_rank_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:41:34.4070982Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_rank_hermitian_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:41:34.4071260Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_multi_dot_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4071522Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_norm_subgradients_at_zero_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:41:34.4071774Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_pinv_hermitian_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4072024Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_pinv_hermitian_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4072280Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_pinv_singular_cpu_float64 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 88%]
2023-01-11T21:41:34.4072525Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_qr_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4072776Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_slogdet_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4073020Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_solve_ex_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4073277Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_solve_triangular_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4073532Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_solve_triangular_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4073873Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_svd_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4074119Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_tensorinv_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4074360Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_vander_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4074604Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_vector_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4074960Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 88%]
2023-01-11T21:41:34.4075348Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 88%]
2023-01-11T21:41:34.4075528Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log1p_cpu_float64 PASSED [ 88%]
2023-01-11T21:41:34.4075705Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log2_cpu_float64 PASSED [ 88%]
2023-01-11T21:41:34.4075884Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log_cpu_complex128 PASSED [ 88%]
2023-01-11T21:41:34.4076142Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log_softmax_with_dtype_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4076382Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logaddexp2_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4076612Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logaddexp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4076887Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logcumsumexp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4077128Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logdet_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4077367Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logdet_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:41:34.4077828Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_not_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 88%]
2023-01-11T21:41:34.4078184Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_xor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:41:34.4078534Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:41:34.4078878Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_long_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:41:34.4079120Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lu_solve_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4079359Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lu_solve_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4079596Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lu_unpack_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4079830Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mH_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4080058Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_amax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4080300Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_amin_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4080754Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:41:34.4081002Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_cumprod_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4081243Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_cumsum_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4081497Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_fill_cpu_complex128 PASSED [ 89%]
2023-01-11T21:41:34.4081788Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_median_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4082062Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4082252Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_scatter_cpu_float64 PASSED [ 89%]
2023-01-11T21:41:34.4082500Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_select_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4082741Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_select_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4083012Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_softmax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4083256Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_softmin_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4083497Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_var_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4083734Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_var_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4083973Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_matmul_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4084217Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_matrix_exp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:41:34.4084481Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_max_pool2d_with_indices_backward_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4084724Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mean_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4084983Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_meshgrid_list_of_tensors_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4085237Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_meshgrid_variadic_tensors_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4085476Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_min_binary_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4085728Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_min_reduction_no_dim_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4086039Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_min_reduction_with_dim_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4086287Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mm_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4086520Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4086758Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_movedim_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4087065Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_movedim_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4087296Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_msort_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4087475Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mul_cpu_complex128 PASSED [ 90%]
2023-01-11T21:41:34.4087649Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mul_cpu_float64 PASSED [ 90%]
2023-01-11T21:41:34.4088008Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_multinomial_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 90%]
2023-01-11T21:41:34.4088191Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nan_to_num_cpu_float64 PASSED [ 90%]
2023-01-11T21:41:34.4088461Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nanmean_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4088695Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nanquantile_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:41:34.4089034Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ne_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 90%]
2023-01-11T21:41:34.4089209Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_neg_cpu_float64 PASSED [ 90%]
2023-01-11T21:41:34.4089561Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_empty_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 90%]
2023-01-11T21:41:34.4089907Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 90%]
2023-01-11T21:41:34.4090279Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_empty_strided_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 90%]
2023-01-11T21:41:34.4090632Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nextafter_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 90%]
2023-01-11T21:41:34.4090903Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_adaptive_avg_pool1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4091162Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_adaptive_avg_pool3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4091425Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_adaptive_max_pool1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4091694Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_adaptive_max_pool3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4091948Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_avg_pool1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4092189Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_avg_pool3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4092507Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_bilinear_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4092773Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_binary_cross_entropy_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4093094Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_binary_cross_entropy_with_logits_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4093344Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4093657Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv2d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4093920Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4094180Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv_transpose1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4094479Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv_transpose2d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4094736Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv_transpose2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4094998Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv_transpose3d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4095262Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_cosine_embedding_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4095520Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_cross_entropy_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4095770Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_ctc_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4096026Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_dropout3d_cpu_float64 PASSED [ 91%]
2023-01-11T21:41:34.4096226Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_dropout_cpu_float64 PASSED [ 91%]
2023-01-11T21:41:34.4096485Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_embedding_bag_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:41:34.4096709Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_feature_alpha_dropout_with_train_cpu_float64 PASSED [ 91%]
2023-01-11T21:41:34.4096939Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_feature_alpha_dropout_without_train_cpu_complex128 PASSED [ 91%]
2023-01-11T21:41:34.4097208Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_fractional_max_pool2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4097473Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_fractional_max_pool3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4097730Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_gaussian_nll_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4097976Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_gelu_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4098261Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_huber_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4098521Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_instance_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4098788Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_interpolate_nearest_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4099042Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_interpolate_trilinear_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4099290Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_kl_div_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4099547Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_l1_loss_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4099831Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_l1_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4100030Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_leaky_relu_cpu_float64 PASSED [ 92%]
2023-01-11T21:41:34.4100283Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_linear_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4100602Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_local_response_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4100856Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_logsigmoid_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4101123Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_margin_ranking_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4101374Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_pool2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4101630Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_unpool1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4101889Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_unpool1d_grad_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4102139Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_unpool2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4102391Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_unpool2d_grad_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4102646Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_unpool3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4102939Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_unpool3d_grad_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:41:34.4103157Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_mish_cpu_float64 PASSED [ 92%]
2023-01-11T21:41:34.4103430Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_multilabel_soft_margin_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4103738Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_circular_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4103998Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_constant_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4104255Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_reflect_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4104508Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_reflect_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4104769Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pairwise_distance_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4105056Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pixel_shuffle_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4105309Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pixel_shuffle_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4105569Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pixel_unshuffle_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4105810Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pixel_unshuffle_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4106062Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_poisson_nll_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4106317Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_prelu_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4106563Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_relu6_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4106812Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_relu_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4107005Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_rrelu_cpu_float64 PASSED [ 93%]
2023-01-11T21:41:34.4107196Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_selu_cpu_float64 PASSED [ 93%]
2023-01-11T21:41:34.4107588Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_silu_complex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 93%]
2023-01-11T21:41:34.4107782Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_silu_cpu_float64 PASSED [ 93%]
2023-01-11T21:41:34.4108106Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_smooth_l1_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4108358Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_softmin_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4108613Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_softmin_with_dtype_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4108865Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_softplus_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4109156Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_softsign_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4109413Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_tanhshrink_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4109666Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_tanhshrink_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:41:34.4109916Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_unfold_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4110177Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_upsample_nearest_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4110617Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 94%]
2023-01-11T21:41:34.4110966Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 94%]
2023-01-11T21:41:34.4111199Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4111438Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_fro_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4111656Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_normal_number_mean_cpu_float64 SKIPPED (Gradients are incorrect!) [ 94%]
2023-01-11T21:41:34.4112002Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 94%]
2023-01-11T21:41:34.4112328Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 94%]
2023-01-11T21:41:34.4112673Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 94%]
2023-01-11T21:41:34.4112912Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ormqr_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4113147Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_outer_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4113381Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_outer_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4113627Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_permute_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4113867Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pinverse_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4114060Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_0_cpu_float64 PASSED [ 94%]
2023-01-11T21:41:34.4114266Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_1_cpu_float64 SKIPPED (Skipped!) [ 94%]
2023-01-11T21:41:34.4114471Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_3_cpu_float64 SKIPPED (Skipped!) [ 94%]
2023-01-11T21:41:34.4114678Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_4_cpu_float64 SKIPPED (Skipped!) [ 94%]
2023-01-11T21:41:34.4114936Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_positive_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4115177Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_positive_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4115350Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_put_cpu_float64 PASSED [ 94%]
2023-01-11T21:41:34.4115581Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_qr_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:41:34.4115932Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rand_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 94%]
2023-01-11T21:41:34.4116280Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randint_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:41:34.4116669Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randn_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:41:34.4117055Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randn_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:41:34.4117434Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randn_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:41:34.4117673Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ravel_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4117853Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_renorm_cpu_complex128 PASSED [ 95%]
2023-01-11T21:41:34.4118017Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_renorm_cpu_float64 PASSED [ 95%]
2023-01-11T21:41:34.4118260Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_repeat_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4118513Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_repeat_interleave_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4118763Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_repeat_interleave_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4119005Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_as_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4119247Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4119595Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize__cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:41:34.4119843Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_conj_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4120080Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_roll_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4120317Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rot90_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4120548Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rot90_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4120930Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_cpu_float64 PASSED [ 95%]
2023-01-11T21:41:34.4121202Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_decimals_3_cpu_float64 SKIPPED (Skipped!) [ 95%]
2023-01-11T21:41:34.4121410Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_decimals_neg_3_cpu_float64 SKIPPED (Skipped!) [ 95%]
2023-01-11T21:41:34.4121644Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsub_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:41:34.4122012Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scalar_tensor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:41:34.4122201Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_add_cpu_complex128 PASSED [ 95%]
2023-01-11T21:41:34.4122385Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_cpu_complex128 PASSED [ 95%]
2023-01-11T21:41:34.4122569Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_cpu_float64 PASSED [ 96%]
2023-01-11T21:41:34.4122801Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_mean_cpu_float64 PASSED [ 96%]
2023-01-11T21:41:34.4123170Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_searchsorted_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4123567Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_segment_reduce_lengths_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4123825Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_segment_reduce_offsets_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4124067Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_select_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4124249Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sgn_cpu_complex128 PASSED [ 96%]
2023-01-11T21:41:34.4124424Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sgn_cpu_float64 PASSED [ 96%]
2023-01-11T21:41:34.4124764Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_short_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4124940Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sign_cpu_float64 PASSED [ 96%]
2023-01-11T21:41:34.4125308Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_cosine_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4125682Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_exponential_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4126041Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_kaiser_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4126226Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sin_cpu_complex128 PASSED [ 96%]
2023-01-11T21:41:34.4126390Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinc_cpu_complex128 PASSED [ 96%]
2023-01-11T21:41:34.4126565Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinh_cpu_complex128 PASSED [ 96%]
2023-01-11T21:41:34.4126805Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_slice_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4127042Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_slice_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4127285Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_slice_scatter_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4127558Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_softmax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4127818Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sparse_sampled_addmm_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:41:34.4128181Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_j0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4128534Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_y1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4128912Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_t_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:41:34.4129160Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_erfcx_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4129424Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i1_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4129905Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_laguerre_polynomial_l_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:41:34.4130156Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_log_ndtr_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4130531Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_i1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:41:34.4130974Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_k0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:41:34.4131272Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_polygamma_special_polygamma_n_0_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4131659Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_scaled_modified_bessel_k1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:41:34.4132063Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_t_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 97%]
2023-01-11T21:41:34.4132467Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_w_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 97%]
2023-01-11T21:41:34.4132707Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4132959Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_list_args_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4133139Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_square_cpu_float64 PASSED [ 97%]
2023-01-11T21:41:34.4133378Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stack_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4133602Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stack_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4133838Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4134114Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4134355Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4134601Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_unbiased_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4134835Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stft_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4135066Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stft_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4135247Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sub_cpu_complex128 PASSED [ 97%]
2023-01-11T21:41:34.4135514Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4135747Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:41:34.4136062Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_to_size_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4136291Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_to_size_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4136524Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4136768Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_symeig_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4137037Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_symeig_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4137358Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_along_dim_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4137602Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_along_dim_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4137840Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4138075Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4138253Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tan_cpu_complex128 PASSED [ 98%]
2023-01-11T21:41:34.4138430Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tanh_cpu_complex128 PASSED [ 98%]
2023-01-11T21:41:34.4138675Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensor_split_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4138915Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensordot_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4139144Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensordot_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4139381Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tile_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4139653Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tile_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4139889Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4140119Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_topk_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4140356Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trace_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4140588Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trace_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4140933Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapezoid_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4141208Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapezoid_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4141449Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapz_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4141679Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapz_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:41:34.4141920Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triangular_solve_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4142098Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triu_cpu_complex128 PASSED [ 99%]
2023-01-11T21:41:34.4142273Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triu_cpu_float64 PASSED [ 99%]
2023-01-11T21:41:34.4142457Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_true_divide_cpu_float64 PASSED [ 99%]
2023-01-11T21:41:34.4142633Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trunc_cpu_float64 PASSED [ 99%]
2023-01-11T21:41:34.4142877Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_copy_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4143116Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_copy_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4143351Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4143711Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_uniform_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 99%]
2023-01-11T21:41:34.4144057Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_uniform_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 99%]
2023-01-11T21:41:34.4144415Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unique_consecutive_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 99%]
2023-01-11T21:41:34.4144745Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unique_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 99%]
2023-01-11T21:41:34.4144982Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4145229Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_unbiased_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4145622Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_unbiased_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4145872Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_unbiased_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4146114Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_unbiased_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4146357Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_real_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4146587Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4146854Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vsplit_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4147089Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vstack_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:41:34.4147264Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_xlogy_cpu_float64 PASSED [ 99%]
2023-01-11T21:41:34.4147434Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zero__cpu_complex128 PASSED [ 99%]
2023-01-11T21:41:34.4147775Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 99%]
2023-01-11T21:41:34.4148221Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [100%]
2023-01-11T21:41:34.4148242Z 
2023-01-11T21:41:34.4148356Z =============================== warnings summary ===============================
2023-01-11T21:41:34.4148576Z ../../../../../opt/conda/lib/python3.7/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:41:34.4148932Z   /opt/conda/lib/python3.7/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:41:34.4149030Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:41:34.4149035Z 
2023-01-11T21:41:34.4149270Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:41:34.4149605Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-9b1ea14156fe1d78.xml -
2023-01-11T21:41:34.4149747Z = 903 passed, 1493 skipped, 12 deselected, 20 xfailed, 1 warning in 1098.49s (0:18:18) =
2023-01-11T21:41:34.4149905Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:41:34.4149925Z 
2023-01-11T21:41:34.4150299Z ##[endgroup]
2023-01-11T21:41:34.4150594Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_5y0iga9b)
2023-01-11T21:41:34.4150601Z 
2023-01-11T21:53:08.0677250Z 
2023-01-11T21:53:08.0677644Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T21:53:08.0678320Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_pwud63wt)
2023-01-11T21:53:08.0694713Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-5a1c1620e6dd6bfb.xml
2023-01-11T21:53:08.0695168Z ============================= test session starts ==============================
2023-01-11T21:53:08.0695658Z platform linux -- Python 3.7.15, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:53:08.0696353Z cachedir: .pytest_cache
2023-01-11T21:53:08.0696890Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:53:08.0697315Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:53:08.0698544Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:53:08.0699067Z collecting ... collected 4945 items / 8 deselected / 4937 selected
2023-01-11T21:53:08.0998524Z Running 2509 items in this shard: test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_H_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_T_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___getitem___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___radd___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rdiv___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmatmul___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmod___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmul___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rpow___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rpow___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rsub___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__native_batch_norm_legit_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__softmax_backward_data_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_abs_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_acos_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_acosh_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_add_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addbmm_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmv_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmv_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_all_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_all_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_allclose_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_amax_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_amin_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_angle_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_argmin_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_argsort_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_argwhere_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_as_strided_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_as_strided_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_as_strided_partial_views_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_asin_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_asin_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_asinh_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atan2_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atleast_2d_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bernoulli_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bfloat16_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bmm_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bool_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_broadcast_to_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_byte_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cartesian_prod_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cat_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cat_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cdist_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cdouble_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cdouble_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ceil_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_char_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_char_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cholesky_inverse_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clamp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clamp_max_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clone_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_column_stack_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_column_stack_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_combinations_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_complex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_conj_physical_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_constant_pad_nd_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_copysign_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_corrcoef_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_corrcoef_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cos_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cosh_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cosh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_count_nonzero_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_count_nonzero_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cov_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cross_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cross_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cummax_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumsum_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumulative_trapezoid_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_deg2rad_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagflat_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagflat_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagonal_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagonal_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_digamma_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_no_rounding_mode_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_trunc_rounding_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dsplit_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dsplit_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dstack_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_einsum_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_like_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_like_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_equal_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_equal_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_erf_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_erfc_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_exp2_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_exp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expand_as_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expand_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eye_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fft2_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fft_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fft_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fftn_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft2_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft2_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfftn_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_fro_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_inf_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_inf_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_nuc_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_nuc_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_normal_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_like_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ormqr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pca_lowrank_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_permute_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pinverse_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polar_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_2_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pow_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pow_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_prod_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_prod_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_put_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_qr_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_quantile_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rad2deg_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rand_like_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randint_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randn_like_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ravel_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_real_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_real_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reciprocal_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reciprocal_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_remainder_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_repeat_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_as_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize__cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize_as__cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize_as__cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_conj_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_neg_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_neg_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_roll_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_decimals_0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsqrt_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsqrt_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsub_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scalar_tensor_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_add_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_amax_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_amin_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_prod_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_sum_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_select_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_select_scatter_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_short_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sigmoid_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sigmoid_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_bartlett_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_blackman_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_gaussian_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_general_cosine_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_general_hamming_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_hamming_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_hann_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_nuttall_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signbit_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sin_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinc_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_softmax_with_dtype_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_softmax_with_dtype_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sort_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sparse_sampled_addmm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_airy_ai_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_j1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_y0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_u_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_v_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_w_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_entr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_hermite_polynomial_h_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_hermite_polynomial_he_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i0e_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i1e_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_legendre_polynomial_p_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_i0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_k1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtri_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_scaled_modified_bessel_k0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_u_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_v_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_spherical_bessel_j0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_xlog1py_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_zeta_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_list_args_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_square_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sub_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_lowrank_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tan_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tanh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensor_split_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triangular_solve_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_true_divide_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_complex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vsplit_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vstack_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zero__cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_like_cpu_complex128
2023-01-11T21:53:08.1254015Z 
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2023-01-11T21:53:08.1259510Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__native_batch_norm_legit_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  0%]
2023-01-11T21:53:08.1259996Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__softmax_backward_data_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  0%]
2023-01-11T21:53:08.1260450Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_abs_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  0%]
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2023-01-11T21:53:08.1268002Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  1%]
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2023-01-11T21:53:08.1270953Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_asin_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
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2023-01-11T21:53:08.1271860Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_asinh_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1272314Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atan2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1272762Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_atleast_2d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1273227Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bernoulli_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1273687Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bfloat16_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1274146Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bmm_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1274722Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_bool_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  1%]
2023-01-11T21:53:08.1275184Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_broadcast_to_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1275769Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_byte_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  1%]
2023-01-11T21:53:08.1276274Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cartesian_prod_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  1%]
2023-01-11T21:53:08.1276912Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cat_cpu_complex128 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [  1%]
2023-01-11T21:53:08.1277558Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cat_cpu_float64 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [  1%]
2023-01-11T21:53:08.1278002Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cdist_cpu_float64 PASSED [  1%]
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2023-01-11T21:53:08.1278889Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cdouble_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1279367Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ceil_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1279949Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_char_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  2%]
2023-01-11T21:53:08.1280531Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_char_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  2%]
2023-01-11T21:53:08.1281197Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cholesky_inverse_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1281656Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clamp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1282122Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clamp_max_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1282575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_clone_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1283041Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_column_stack_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1283516Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_column_stack_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1283970Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_combinations_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1284438Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_complex_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1284902Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_conj_physical_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1285375Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_constant_pad_nd_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1285831Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_copysign_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1286290Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_corrcoef_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1286749Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_corrcoef_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1287248Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cos_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1287689Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cosh_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1288139Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cosh_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1288734Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_count_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  2%]
2023-01-11T21:53:08.1289331Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_count_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  2%]
2023-01-11T21:53:08.1289789Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cov_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1290252Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cross_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1290761Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cross_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  2%]
2023-01-11T21:53:08.1291219Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cummax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1291673Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumsum_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1292134Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_cumulative_trapezoid_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1292609Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_deg2rad_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1293075Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagflat_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1293539Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagflat_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1293991Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_diagonal_copy_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
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2023-01-11T21:53:08.1294913Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_digamma_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1295374Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_no_rounding_mode_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1311176Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_div_trunc_rounding_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1311687Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dsplit_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1312157Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dsplit_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1312622Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_dstack_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1313089Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_einsum_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1313727Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  3%]
2023-01-11T21:53:08.1314432Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  3%]
2023-01-11T21:53:08.1315024Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  3%]
2023-01-11T21:53:08.1315627Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_empty_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  3%]
2023-01-11T21:53:08.1316207Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_equal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  3%]
2023-01-11T21:53:08.1316792Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_equal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  3%]
2023-01-11T21:53:08.1317256Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_erf_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1317749Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_erfc_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
2023-01-11T21:53:08.1318190Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_exp2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  3%]
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2023-01-11T21:53:08.1319098Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_expand_as_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
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2023-01-11T21:53:08.1320124Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_eye_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  4%]
2023-01-11T21:53:08.1320589Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fft2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1321252Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_fft_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
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2023-01-11T21:53:08.1322622Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft2_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1323087Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1323546Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfft_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1324005Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_hfftn_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1324453Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifft2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1324911Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifft_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1325370Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifft_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1325902Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifftn_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1326352Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifftn_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1326814Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifftshift_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1327282Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ifftshift_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1327744Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_ihfftn_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1328191Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfft2_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1328692Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfft_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1329156Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_irfftn_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1329613Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_rfft2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1330060Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_rfft_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1330517Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fft_rfftn_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  4%]
2023-01-11T21:53:08.1330978Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flatten_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1331442Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flip_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1331894Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_flip_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1332331Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_float_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1332790Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_float_power_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1333252Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_floor_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1333706Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_fmod_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1334146Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_frac_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1334595Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_frexp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1335181Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:53:08.1335856Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_full_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:53:08.1336321Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_gather_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1336820Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_gather_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1337393Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ge_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:53:08.1337974Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_geqrf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:53:08.1338427Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_half_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1338884Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_half_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1339468Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_histogramdd_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:53:08.1339973Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_hsplit_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1340426Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_hstack_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1340886Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_hypot_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1341459Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_igammac_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  5%]
2023-01-11T21:53:08.1341930Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_imag_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1342391Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_fill_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1342846Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_put_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:08.1343305Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_put_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1343771Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_index_select_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1344345Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_int_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:53:08.1344997Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isfinite_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:53:08.1345613Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isfinite_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:53:08.1346202Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isinf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:53:08.1346781Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_isneginf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:53:08.1347222Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_2inputs_2outputs_cpu_complex128 SKIPPED (Only runs on cuda) [  6%]
2023-01-11T21:53:08.1347668Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_2inputs_2outputs_cpu_float64 SKIPPED (Only runs on cuda) [  6%]
2023-01-11T21:53:08.1348113Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_4inputs_with_extra_args_cpu_complex128 SKIPPED (Only runs on cuda) [  6%]
2023-01-11T21:53:08.1348592Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_binary_cpu_complex128 SKIPPED (Only runs on cuda) [  6%]
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2023-01-11T21:53:08.1349440Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_binary_return_by_ref_cpu_complex128 SKIPPED (Only runs on cuda) [  6%]
2023-01-11T21:53:08.1349881Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_binary_return_by_ref_cpu_float64 SKIPPED (Only runs on cuda) [  6%]
2023-01-11T21:53:08.1350312Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_jiterator_unary_cpu_float64 SKIPPED (Only runs on cuda) [  6%]
2023-01-11T21:53:08.1350741Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_kthvalue_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1351198Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_ldexp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1351793Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_le_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  6%]
2023-01-11T21:53:08.1352265Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cross_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1352739Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cross_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1353189Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_det_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1353655Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_det_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1354122Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eig_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1354583Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eig_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1355037Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eigh_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  6%]
2023-01-11T21:53:08.1355505Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eigvals_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1355976Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_eigvals_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1356461Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_householder_product_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1356937Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_inv_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1357396Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_inv_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1357861Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_inv_ex_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1358458Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_ldl_factor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1359076Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_ldl_factor_ex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1359720Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_ldl_factor_ex_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1360338Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_ldl_solve_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1361124Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_ldl_solve_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1361578Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lstsq_cpu_float64 SKIPPED (Skipped!) [  7%]
2023-01-11T21:53:08.1362018Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lstsq_grad_oriented_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1362501Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1363032Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1363506Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_factor_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1363973Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_factor_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1364452Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_lu_factor_ex_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1364927Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_norm_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1365409Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1365888Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_power_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1366492Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_rank_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1367108Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_rank_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1367744Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_matrix_rank_hermitian_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  7%]
2023-01-11T21:53:08.1368248Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_multi_dot_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  7%]
2023-01-11T21:53:08.1368719Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_multi_dot_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1369195Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_norm_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1369670Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_pinv_hermitian_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1370174Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_pinv_singular_cpu_complex128 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [  8%]
2023-01-11T21:53:08.1370659Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_qr_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1371185Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_slogdet_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1371665Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_solve_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1372138Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_solve_ex_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1372616Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_solve_ex_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1373085Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_solve_triangular_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1373568Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_svd_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1374079Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_svd_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1374544Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_svdvals_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1375004Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_tensorinv_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1375547Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_tensorinv_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1376026Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_vander_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1376502Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_vander_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1376958Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_vecdot_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1377427Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_vecdot_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1377901Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_vector_norm_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1378382Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_vector_norm_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1378848Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log1p_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1379300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log1p_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1379749Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log2_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1380199Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  8%]
2023-01-11T21:53:08.1380657Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log_softmax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1381124Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_log_softmax_with_dtype_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1381600Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logaddexp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1382117Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logcumsumexp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1382717Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_or_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:53:08.1383309Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logical_xor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:53:08.1383907Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:53:08.1384500Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:53:08.1384984Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_logsumexp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1385466Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lu_solve_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1385928Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lu_solve_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1386397Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_lu_unpack_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1386856Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mH_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1387301Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_mT_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1387745Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_amax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1388339Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [  9%]
2023-01-11T21:53:08.1388827Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_cumprod_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1389303Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_cumprod_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1389765Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_cumsum_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1390235Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_fill_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1390705Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_fill_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1391172Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_mean_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1391635Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_normalize_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1392120Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_normalize_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1392596Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_scatter_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [  9%]
2023-01-11T21:53:08.1393100Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_softmax_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:53:08.1393558Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_masked_sum_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:53:08.1394025Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_matrix_exp_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:53:08.1394484Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_matrix_exp_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
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2023-01-11T21:53:08.1410694Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_adaptive_avg_pool1d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
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2023-01-11T21:53:08.1412210Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_adaptive_max_pool2d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
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2023-01-11T21:53:08.1417265Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_cosine_similarity_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
2023-01-11T21:53:08.1417749Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_ctc_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 11%]
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2023-01-11T21:53:08.1419674Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_feature_alpha_dropout_with_train_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
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2023-01-11T21:53:08.1421682Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_huber_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
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2023-01-11T21:53:08.1425132Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_max_pool1d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
2023-01-11T21:53:08.1425617Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_max_unpool1d_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 12%]
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2023-01-11T21:53:08.1427587Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_multi_margin_loss_cpu_float64 PASSED [ 12%]
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2023-01-11T21:53:08.1430978Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pad_replicate_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:53:08.1431479Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pairwise_distance_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:53:08.1432493Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_pixel_shuffle_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:53:08.1433984Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_prelu_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:53:08.1434470Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_relu_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:53:08.1435895Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_soft_margin_loss_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:53:08.1439430Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_unfold_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:53:08.1439962Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_upsample_bilinear_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
2023-01-11T21:53:08.1440467Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_nn_functional_upsample_nearest_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 13%]
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2023-01-11T21:53:08.1447807Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_polygamma_polygamma_n_0_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 14%]
2023-01-11T21:53:08.1448376Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_polygamma_polygamma_n_2_cpu_float64 SKIPPED (Skipped!) [ 14%]
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2023-01-11T21:53:08.1505018Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_unsqueeze_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 18%]
2023-01-11T21:53:08.1505477Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1505943Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_mean_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1506416Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_mean_unbiased_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1506887Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_var_mean_unbiased_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1507361Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_view_as_complex_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1507832Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_view_as_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1508317Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_view_copy_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1508769Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_view_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1509222Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_view_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1509679Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_vsplit_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1510136Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_vsplit_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1510587Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_vstack_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1511043Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_where_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1511501Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_zero__cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1511958Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_zero__cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 19%]
2023-01-11T21:53:08.1512518Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_zeros_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 19%]
2023-01-11T21:53:08.1513107Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_zeros_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 19%]
2023-01-11T21:53:08.1513519Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_H_cpu_float64 PASSED [ 19%]
2023-01-11T21:53:08.1513874Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___getitem___cpu_float64 PASSED [ 19%]
2023-01-11T21:53:08.1514222Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rmatmul___cpu_complex128 PASSED [ 19%]
2023-01-11T21:53:08.1514582Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rmatmul___cpu_float64 PASSED [ 19%]
2023-01-11T21:53:08.1514938Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rsub___cpu_complex128 PASSED [ 19%]
2023-01-11T21:53:08.1515286Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad___rsub___cpu_float64 PASSED [ 19%]
2023-01-11T21:53:08.1515619Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_abs_cpu_complex128 PASSED [ 19%]
2023-01-11T21:53:08.1515969Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_acos_cpu_complex128 PASSED [ 19%]
2023-01-11T21:53:08.1516354Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_acos_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1516689Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_acosh_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1517040Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_add_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1517385Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addbmm_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1517736Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addcdiv_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1518076Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addcdiv_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1518427Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addcmul_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1518780Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addmm_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1519130Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addmm_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1519516Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addmm_decomposed_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1519896Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addmm_decomposed_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1520257Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addmv_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1520592Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_addr_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1521202Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_allclose_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 20%]
2023-01-11T21:53:08.1521610Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_amin_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1521956Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_angle_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1522296Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_angle_cpu_float64 PASSED [ 20%]
2023-01-11T21:53:08.1522709Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_any_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 20%]
2023-01-11T21:53:08.1523174Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_arange_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 20%]
2023-01-11T21:53:08.1523635Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_argmin_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 20%]
2023-01-11T21:53:08.1524088Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_argwhere_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 20%]
2023-01-11T21:53:08.1524554Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_argwhere_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 20%]
2023-01-11T21:53:08.1524994Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_as_strided_cpu_complex128 SKIPPED (Numerous errors) [ 20%]
2023-01-11T21:53:08.1525394Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_as_strided_partial_views_cpu_complex128 PASSED [ 20%]
2023-01-11T21:53:08.1525768Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_as_strided_scatter_cpu_complex128 XFAIL [ 20%]
2023-01-11T21:53:08.1526142Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_as_strided_scatter_cpu_float64 XFAIL [ 21%]
2023-01-11T21:53:08.1526497Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_asin_cpu_float64 PASSED [ 21%]
2023-01-11T21:53:08.1526849Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_asinh_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1527187Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atan2_cpu_float64 PASSED [ 21%]
2023-01-11T21:53:08.1527531Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atan_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1527950Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atan_cpu_float64 PASSED [ 21%]
2023-01-11T21:53:08.1528286Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atanh_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1528640Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atleast_1d_cpu_float64 PASSED [ 21%]
2023-01-11T21:53:08.1529002Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_atleast_3d_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1529361Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_baddbmm_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1529703Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bfloat16_cpu_complex128 XFAIL [ 21%]
2023-01-11T21:53:08.1530048Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bmm_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1530390Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bmm_cpu_float64 PASSED [ 21%]
2023-01-11T21:53:08.1530785Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bool_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:53:08.1531310Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_bool_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:53:08.1531720Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_broadcast_to_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1532141Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_byte_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:53:08.1532537Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cartesian_prod_cpu_float64 PASSED [ 21%]
2023-01-11T21:53:08.1532888Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cat_cpu_complex128 PASSED [ 21%]
2023-01-11T21:53:08.1533232Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cat_cpu_float64 PASSED [ 21%]
2023-01-11T21:53:08.1533575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cfloat_cpu_complex128 XFAIL [ 21%]
2023-01-11T21:53:08.1533913Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cfloat_cpu_float64 XFAIL [ 21%]
2023-01-11T21:53:08.1534255Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_chalf_cpu_float64 XFAIL [ 21%]
2023-01-11T21:53:08.1534660Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_char_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:53:08.1535113Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_char_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 21%]
2023-01-11T21:53:08.1535585Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cholesky_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1535943Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cholesky_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1536305Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cholesky_inverse_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1536664Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cholesky_solve_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1537022Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_chunk_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1537380Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_clamp_max_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1537728Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_clamp_min_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1538076Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_column_stack_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1538435Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_column_stack_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1538787Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_complex_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1539149Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_conj_physical_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1539509Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_constant_pad_nd_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1539913Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_corrcoef_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1540267Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cosh_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1540670Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_count_nonzero_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 22%]
2023-01-11T21:53:08.1541072Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cov_cpu_float64 XFAIL [ 22%]
2023-01-11T21:53:08.1541414Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cross_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1541764Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cummin_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1542101Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cumprod_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1542450Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cumsum_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1542819Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_cumulative_trapezoid_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1543224Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diag_embed_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1543575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagonal_copy_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1543930Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagonal_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1544294Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagonal_scatter_cpu_complex128 PASSED [ 22%]
2023-01-11T21:53:08.1544654Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diagonal_scatter_cpu_float64 PASSED [ 22%]
2023-01-11T21:53:08.1545013Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_diff_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1545360Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dist_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1545705Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dist_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1546052Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_div_floor_rounding_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1546423Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_div_no_rounding_mode_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1546779Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dot_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1547121Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dot_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1547455Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_dstack_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1547861Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_empty_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 23%]
2023-01-11T21:53:08.1548329Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_empty_like_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 23%]
2023-01-11T21:53:08.1548805Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_empty_like_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 23%]
2023-01-11T21:53:08.1549254Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_eq_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 23%]
2023-01-11T21:53:08.1549707Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_equal_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 23%]
2023-01-11T21:53:08.1550100Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_exp_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1550445Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_exp_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1550784Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_expand_as_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1551175Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_expand_as_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1551530Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_expand_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1551868Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fft2_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1552219Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fft_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1552566Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fftn_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1552916Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fftn_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1553263Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fftshift_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1553622Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_fftshift_cpu_float64 PASSED [ 23%]
2023-01-11T21:53:08.1553975Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_hfft2_cpu_complex128 PASSED [ 23%]
2023-01-11T21:53:08.1554320Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_hfftn_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1554704Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_hfftn_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1555058Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifft2_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1555408Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifft2_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1555748Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifft_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1556099Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifftn_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1556463Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifftshift_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1556824Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ifftshift_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1557168Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ihfft2_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1557518Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_ihfftn_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1557870Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_irfft2_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1558206Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_irfftn_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1558559Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_rfft2_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1558903Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fft_rfft_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1559250Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fill_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1559581Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fill_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1559933Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_flatten_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1560278Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_flatten_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1560735Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fliplr_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1561152Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_flipud_cpu_complex128 PASSED [ 24%]
2023-01-11T21:53:08.1561497Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_float_cpu_complex128 XFAIL [ 24%]
2023-01-11T21:53:08.1561839Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_float_cpu_float64 XFAIL [ 24%]
2023-01-11T21:53:08.1562175Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_float_power_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1562525Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_floor_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1562872Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fmax_cpu_float64 PASSED [ 24%]
2023-01-11T21:53:08.1563275Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_fmin_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1563608Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_frac_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1564012Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_full_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1564478Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_full_like_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1564945Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_full_like_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1565393Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_heaviside_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1565864Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_histogramdd_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1566312Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_hsplit_cpu_complex128 PASSED [ 25%]
2023-01-11T21:53:08.1566672Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_hsplit_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1567012Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_hstack_cpu_complex128 PASSED [ 25%]
2023-01-11T21:53:08.1567364Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_hstack_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1567710Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_hypot_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1568035Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_i0_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1568441Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_igamma_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1568904Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_igammac_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1569311Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_add_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1569658Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_copy_cpu_complex128 PASSED [ 25%]
2023-01-11T21:53:08.1570015Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_fill_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1570368Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_put_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1570726Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_reduce_cpu_float64 PASSED [ 25%]
2023-01-11T21:53:08.1571079Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_index_select_cpu_complex128 PASSED [ 25%]
2023-01-11T21:53:08.1571497Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_int_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1571957Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_int_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1572415Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isclose_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1572865Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isinf_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 25%]
2023-01-11T21:53:08.1573321Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isnan_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:53:08.1573775Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isneginf_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:53:08.1574234Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isposinf_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:53:08.1574722Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_isreal_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:53:08.1575121Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_istft_cpu_complex128 XFAIL [ 26%]
2023-01-11T21:53:08.1575586Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_2inputs_2outputs_cpu_complex128 SKIPPED (Only runs on cuda) [ 26%]
2023-01-11T21:53:08.1576019Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_2inputs_2outputs_cpu_float64 SKIPPED (Only runs on cuda) [ 26%]
2023-01-11T21:53:08.1576434Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_4inputs_with_extra_args_cpu_float64 SKIPPED (Only runs on cuda) [ 26%]
2023-01-11T21:53:08.1576862Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_binary_cpu_complex128 SKIPPED (Only runs on cuda) [ 26%]
2023-01-11T21:53:08.1577274Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_binary_cpu_float64 SKIPPED (Only runs on cuda) [ 26%]
2023-01-11T21:53:08.1577713Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_jiterator_unary_cpu_complex128 SKIPPED (Only runs on cuda) [ 26%]
2023-01-11T21:53:08.1578082Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_kron_cpu_complex128 PASSED [ 26%]
2023-01-11T21:53:08.1578429Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_kron_cpu_float64 PASSED [ 26%]
2023-01-11T21:53:08.1578777Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_ldexp_cpu_complex128 PASSED [ 26%]
2023-01-11T21:53:08.1579171Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_le_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 26%]
2023-01-11T21:53:08.1579572Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lerp_cpu_complex128 PASSED [ 26%]
2023-01-11T21:53:08.1579918Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lerp_cpu_float64 PASSED [ 26%]
2023-01-11T21:53:08.1580265Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lgamma_cpu_float64 PASSED [ 26%]
2023-01-11T21:53:08.1580610Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cond_cpu_float64 PASSED [ 26%]
2023-01-11T21:53:08.1580971Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cross_cpu_complex128 PASSED [ 26%]
2023-01-11T21:53:08.1581331Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_det_cpu_float64 PASSED [ 26%]
2023-01-11T21:53:08.1581697Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_det_singular_cpu_complex128 PASSED [ 26%]
2023-01-11T21:53:08.1582057Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eig_cpu_complex128 PASSED [ 26%]
2023-01-11T21:53:08.1582417Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eig_cpu_float64 PASSED [ 26%]
2023-01-11T21:53:08.1582776Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eigh_cpu_complex128 PASSED [ 26%]
2023-01-11T21:53:08.1583126Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eigh_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1583488Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eigvals_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1583849Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_eigvalsh_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1584216Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_inv_cpu_complex128 PASSED [ 27%]
2023-01-11T21:53:08.1584567Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_inv_ex_cpu_complex128 PASSED [ 27%]
2023-01-11T21:53:08.1584997Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_ldl_factor_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:53:08.1585484Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_ldl_factor_ex_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:53:08.1586011Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_ldl_solve_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:53:08.1586481Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_ldl_solve_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:53:08.1586910Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lstsq_cpu_float64 SKIPPED (Skipped!) [ 27%]
2023-01-11T21:53:08.1587300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lstsq_grad_oriented_cpu_complex128 PASSED [ 27%]
2023-01-11T21:53:08.1587691Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lstsq_grad_oriented_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1588059Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_factor_cpu_complex128 PASSED [ 27%]
2023-01-11T21:53:08.1588436Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_lu_factor_ex_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1588817Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_norm_cpu_complex128 PASSED [ 27%]
2023-01-11T21:53:08.1589220Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_power_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1589654Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_matrix_rank_hermitian_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 27%]
2023-01-11T21:53:08.1590086Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_multi_dot_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1590448Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_norm_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1590800Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_pinv_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1591158Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_pinv_hermitian_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1591605Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_pinv_singular_cpu_complex128 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 27%]
2023-01-11T21:53:08.1592038Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_qr_cpu_complex128 PASSED [ 27%]
2023-01-11T21:53:08.1592393Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_slogdet_cpu_complex128 PASSED [ 27%]
2023-01-11T21:53:08.1592757Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_solve_cpu_float64 PASSED [ 27%]
2023-01-11T21:53:08.1593130Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_solve_triangular_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1593503Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_svd_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1593848Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_svd_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1594204Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_svdvals_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1594575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_tensorinv_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1594947Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_vander_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1595298Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_vander_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1595657Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_vecdot_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1596026Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_vector_norm_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1596386Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_vector_norm_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1596805Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linspace_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 28%]
2023-01-11T21:53:08.1597246Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log10_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1597594Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log10_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1597925Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log1p_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1598270Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1598617Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1598980Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_log_softmax_with_dtype_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1599339Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logaddexp_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1599697Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logcumsumexp_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1600054Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logdet_cpu_complex128 PASSED [ 28%]
2023-01-11T21:53:08.1600389Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logdet_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1601046Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_and_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 28%]
2023-01-11T21:53:08.1601517Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logical_not_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 28%]
2023-01-11T21:53:08.1601915Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logit_cpu_float64 PASSED [ 28%]
2023-01-11T21:53:08.1602317Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_logspace_cpu_complex128 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 28%]
2023-01-11T21:53:08.1602724Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lu_solve_cpu_complex128 PASSED [ 29%]
2023-01-11T21:53:08.1603080Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lu_solve_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1603437Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_lu_unpack_cpu_complex128 PASSED [ 29%]
2023-01-11T21:53:08.1603775Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mH_cpu_complex128 PASSED [ 29%]
2023-01-11T21:53:08.1604120Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mT_cpu_complex128 PASSED [ 29%]
2023-01-11T21:53:08.1604461Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_mT_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1604808Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_amax_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1605149Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_amin_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1605564Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_argmax_cpu_float64 SKIPPED (Skipped! Dtype is not in supported backward dtypes!) [ 29%]
2023-01-11T21:53:08.1605996Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_cumprod_cpu_complex128 PASSED [ 29%]
2023-01-11T21:53:08.1606350Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_cumsum_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1606710Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_fill_cpu_complex128 PASSED [ 29%]
2023-01-11T21:53:08.1607069Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_fill_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1607425Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_mean_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1607776Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_normalize_cpu_complex128 PASSED [ 29%]
2023-01-11T21:53:08.1608137Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_prod_cpu_float64 PASSED [ 29%]
2023-01-11T21:53:08.1608504Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_masked_scatter_cpu_complex128 PASSED [ 29%]
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2023-01-11T21:53:08.1711978Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___getitem___cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1712339Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___radd___cpu_complex128 PASSED [ 40%]
2023-01-11T21:53:08.1712698Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___radd___cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1713102Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rmatmul___cpu_complex128 PASSED [ 40%]
2023-01-11T21:53:08.1713457Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rmatmul___cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1713813Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad___rsub___cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1714190Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad__native_batch_norm_legit_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1714546Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_abs_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1714712Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_acos_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1714879Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_add_cpu_complex128 PASSED [ 40%]
2023-01-11T21:53:08.1715044Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_add_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1715220Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addbmm_cpu_complex128 PASSED [ 40%]
2023-01-11T21:53:08.1715416Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addbmm_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1715591Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addcmul_cpu_complex128 PASSED [ 40%]
2023-01-11T21:53:08.1715760Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addcmul_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1715919Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addmm_cpu_complex128 PASSED [ 40%]
2023-01-11T21:53:08.1716088Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addmv_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1716256Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addr_cpu_complex128 PASSED [ 40%]
2023-01-11T21:53:08.1716421Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_addr_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1716783Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_allclose_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 40%]
2023-01-11T21:53:08.1716953Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_amax_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1717116Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_amin_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:08.1717287Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_angle_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1717453Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_angle_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1717777Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_arange_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:53:08.1718106Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:53:08.1718445Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_argsort_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:53:08.1718789Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_argwhere_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:53:08.1719124Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_argwhere_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:53:08.1719318Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_as_strided_partial_views_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1719507Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_as_strided_partial_views_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1719697Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_as_strided_scatter_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1719935Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_as_strided_scatter_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1720105Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_asin_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1720264Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_asinh_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1720432Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atan2_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1720722Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atan_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1720974Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atan_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1721143Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atanh_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1721324Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atleast_1d_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1721500Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_atleast_2d_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1721677Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_baddbmm_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1721900Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bfloat16_cpu_complex128 XFAIL [ 41%]
2023-01-11T21:53:08.1722060Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_block_diag_cpu_float64 PASSED [ 41%]
2023-01-11T21:53:08.1722230Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bmm_cpu_complex128 PASSED [ 41%]
2023-01-11T21:53:08.1722572Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bool_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:53:08.1722901Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bool_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 41%]
2023-01-11T21:53:08.1723089Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_broadcast_tensors_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1723274Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_broadcast_tensors_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1723460Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_broadcast_to_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1723636Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_broadcast_to_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1723976Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_bucketize_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 42%]
2023-01-11T21:53:08.1724308Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_byte_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 42%]
2023-01-11T21:53:08.1724481Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cartesian_prod_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1724660Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cartesian_prod_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1725048Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cat_cpu_complex128 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [ 42%]
2023-01-11T21:53:08.1725424Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cat_cpu_float64 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [ 42%]
2023-01-11T21:53:08.1725768Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cdist_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 42%]
2023-01-11T21:53:08.1725942Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cdouble_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1726113Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cdouble_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1726283Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_chalf_cpu_complex128 XFAIL [ 42%]
2023-01-11T21:53:08.1726485Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_chalf_cpu_float64 XFAIL [ 42%]
2023-01-11T21:53:08.1726820Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_char_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 42%]
2023-01-11T21:53:08.1726979Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cholesky_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1727166Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cholesky_inverse_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1727346Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cholesky_inverse_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1727529Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cholesky_solve_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1727708Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cholesky_solve_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1727879Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_chunk_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1728050Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_clamp_cpu_float64 PASSED [ 42%]
2023-01-11T21:53:08.1728245Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_clone_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1728416Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_column_stack_cpu_complex128 PASSED [ 42%]
2023-01-11T21:53:08.1728593Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_combinations_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1728761Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_complex_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1728927Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_conj_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1729104Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_conj_physical_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1729276Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_contiguous_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1729449Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cos_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1729616Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cos_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1729785Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cosh_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1730123Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_count_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 43%]
2023-01-11T21:53:08.1730293Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cross_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1730459Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cummax_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1730625Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cummin_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1730798Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cumprod_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1730971Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_cumsum_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1731141Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_deg2rad_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1731310Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diag_embed_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1731485Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagflat_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1731643Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagonal_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1731824Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diagonal_scatter_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1731990Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_diff_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1732158Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_digamma_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1732374Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_div_no_rounding_mode_cpu_float64 PASSED [ 43%]
2023-01-11T21:53:08.1732544Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dot_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1732719Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_double_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1732891Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dsplit_cpu_complex128 PASSED [ 43%]
2023-01-11T21:53:08.1733058Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_dsplit_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1733214Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_einsum_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1733544Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_eq_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 44%]
2023-01-11T21:53:08.1733710Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_erf_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1733875Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_erfc_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1734079Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_expand_as_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1734252Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_expand_as_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1734419Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_expand_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1734754Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_eye_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 44%]
2023-01-11T21:53:08.1734928Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fft2_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1735084Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fft2_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1735256Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fft_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1735510Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fft_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1735680Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_fftn_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1735855Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_hfft2_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1736027Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_hfft_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1736204Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_hfftn_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1736376Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_hfftn_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1736546Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifft2_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1736708Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifft_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1736883Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifftn_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1737064Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifftshift_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1737242Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ifftshift_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1737412Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_ihfft_cpu_float64 PASSED [ 44%]
2023-01-11T21:53:08.1737585Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_irfft_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1737761Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_irfftn_cpu_complex128 PASSED [ 44%]
2023-01-11T21:53:08.1737932Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_irfftn_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1738124Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_rfft2_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1738297Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fft_rfft_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1738468Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_flatten_cpu_complex128 PASSED [ 45%]
2023-01-11T21:53:08.1738638Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_flip_cpu_complex128 PASSED [ 45%]
2023-01-11T21:53:08.1738809Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fliplr_cpu_complex128 PASSED [ 45%]
2023-01-11T21:53:08.1738976Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fliplr_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1739154Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_float_power_cpu_complex128 PASSED [ 45%]
2023-01-11T21:53:08.1739502Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_floor_divide_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1739671Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_fmod_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1739859Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_frexp_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1740194Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_full_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1740529Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_full_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1740703Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_gather_cpu_complex128 PASSED [ 45%]
2023-01-11T21:53:08.1741039Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_geqrf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1741211Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_gradient_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1741571Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_grid_sampler_2d_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 45%]
2023-01-11T21:53:08.1741894Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_gt_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1742060Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_half_cpu_float64 XFAIL [ 45%]
2023-01-11T21:53:08.1742396Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_histogram_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1742737Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_histogramdd_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1742896Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_hstack_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1743066Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_hypot_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1743231Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_i0_cpu_float64 PASSED [ 45%]
2023-01-11T21:53:08.1743565Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_igamma_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 45%]
2023-01-11T21:53:08.1743736Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_imag_cpu_complex128 PASSED [ 46%]
2023-01-11T21:53:08.1743916Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_copy_cpu_complex128 PASSED [ 46%]
2023-01-11T21:53:08.1744089Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_fill_cpu_float64 PASSED [ 46%]
2023-01-11T21:53:08.1744261Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_put_cpu_float64 PASSED [ 46%]
2023-01-11T21:53:08.1744439Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_reduce_cpu_float64 PASSED [ 46%]
2023-01-11T21:53:08.1744636Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_select_cpu_complex128 PASSED [ 46%]
2023-01-11T21:53:08.1744814Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_index_select_cpu_float64 PASSED [ 46%]
2023-01-11T21:53:08.1744986Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_inner_cpu_complex128 PASSED [ 46%]
2023-01-11T21:53:08.1745155Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_inner_cpu_float64 PASSED [ 46%]
2023-01-11T21:53:08.1745488Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_int_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1745815Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1746145Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1746521Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isnan_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1746858Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isposinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1747192Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isreal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1747511Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_isreal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1747684Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_istft_cpu_complex128 PASSED [ 46%]
2023-01-11T21:53:08.1747898Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_2inputs_2outputs_cpu_complex128 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:53:08.1748100Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_binary_cpu_float64 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:53:08.1748308Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_jiterator_unary_cpu_complex128 SKIPPED (Only runs on cuda) [ 46%]
2023-01-11T21:53:08.1748478Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_kron_cpu_complex128 PASSED [ 46%]
2023-01-11T21:53:08.1748802Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_le_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 46%]
2023-01-11T21:53:08.1748974Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lerp_cpu_float64 PASSED [ 46%]
2023-01-11T21:53:08.1749145Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lgamma_cpu_float64 PASSED [ 46%]
2023-01-11T21:53:08.1749313Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cond_cpu_complex128 PASSED [ 46%]
2023-01-11T21:53:08.1749493Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cross_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1749673Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_det_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1749852Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eig_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1750021Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eig_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1750203Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eigh_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1750376Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_eigvals_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1750570Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_householder_product_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1750747Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_inv_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1751126Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_ldl_factor_ex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:53:08.1751481Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_ldl_solve_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:53:08.1751674Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lstsq_cpu_complex128 SKIPPED (Skipped!) [ 47%]
2023-01-11T21:53:08.1751863Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lstsq_cpu_float64 SKIPPED (Skipped!) [ 47%]
2023-01-11T21:53:08.1752054Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lstsq_grad_oriented_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1752231Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1752402Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1752590Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_factor_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1752802Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_factor_ex_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1752988Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_factor_ex_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1753157Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_solve_cpu_complex128 PASSED [ 47%]
2023-01-11T21:53:08.1753337Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_lu_solve_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1753518Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_norm_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1753698Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_power_cpu_float64 PASSED [ 47%]
2023-01-11T21:53:08.1754049Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_matrix_rank_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 47%]
2023-01-11T21:53:08.1754230Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_norm_cpu_complex128 XFAIL [ 47%]
2023-01-11T21:53:08.1754404Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_norm_cpu_float64 XFAIL [ 47%]
2023-01-11T21:53:08.1754602Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_norm_subgradients_at_zero_cpu_float64 XFAIL [ 48%]
2023-01-11T21:53:08.1754783Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_pinv_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1754944Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_pinv_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1755131Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_pinv_hermitian_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1755384Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_pinv_singular_cpu_complex128 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 48%]
2023-01-11T21:53:08.1755564Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_slogdet_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1755745Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_solve_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1755930Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_solve_ex_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1756122Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_solve_triangular_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1756300Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_svd_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1756471Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_svd_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1756641Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_svdvals_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1756849Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_svdvals_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1757030Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_tensorinv_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1757214Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_tensorsolve_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1757390Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_vander_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1757570Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_vecdot_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1757916Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 48%]
2023-01-11T21:53:08.1758254Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linspace_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 48%]
2023-01-11T21:53:08.1758428Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log10_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1758626Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log2_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1758784Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log_cpu_complex128 PASSED [ 48%]
2023-01-11T21:53:08.1758957Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log_softmax_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1759144Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_log_softmax_with_dtype_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1759317Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logaddexp2_cpu_float64 PASSED [ 48%]
2023-01-11T21:53:08.1759495Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logcumsumexp_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1759848Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_and_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:53:08.1760188Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_not_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:53:08.1760534Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_or_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:53:08.1761164Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_or_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:53:08.1761504Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_logical_xor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:53:08.1761827Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_long_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 49%]
2023-01-11T21:53:08.1762006Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lu_unpack_cpu_complex128 PASSED [ 49%]
2023-01-11T21:53:08.1762182Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_lu_unpack_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1762352Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mT_cpu_complex128 PASSED [ 49%]
2023-01-11T21:53:08.1762513Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mT_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1762690Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_amax_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1762862Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_amin_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1763043Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_cumprod_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1763219Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_cumsum_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1763483Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_fill_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1763682Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_logaddexp_cpu_float64 SKIPPED (Skipped!) [ 49%]
2023-01-11T21:53:08.1763864Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_logsumexp_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1764041Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_mean_cpu_complex128 PASSED [ 49%]
2023-01-11T21:53:08.1764214Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_norm_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1764393Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_prod_cpu_complex128 PASSED [ 49%]
2023-01-11T21:53:08.1764575Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_scatter_cpu_complex128 PASSED [ 49%]
2023-01-11T21:53:08.1764749Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_scatter_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1764933Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_select_cpu_complex128 PASSED [ 49%]
2023-01-11T21:53:08.1765137Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_softmax_cpu_float64 PASSED [ 49%]
2023-01-11T21:53:08.1765313Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_softmin_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1765492Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_std_cpu_complex128 PASSED [ 50%]
2023-01-11T21:53:08.1765664Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_sum_cpu_complex128 PASSED [ 50%]
2023-01-11T21:53:08.1765834Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_var_cpu_complex128 PASSED [ 50%]
2023-01-11T21:53:08.1766006Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_masked_var_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1766193Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_max_reduction_with_dim_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1766362Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mean_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1766533Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_median_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1766712Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_meshgrid_list_of_tensors_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1766910Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_meshgrid_variadic_tensors_cpu_complex128 PASSED [ 50%]
2023-01-11T21:53:08.1767103Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_meshgrid_variadic_tensors_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1767288Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_min_reduction_no_dim_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1767475Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_min_reduction_with_dim_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1767649Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_minimum_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1767815Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mm_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1767989Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_movedim_cpu_complex128 PASSED [ 50%]
2023-01-11T21:53:08.1768158Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_movedim_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1768314Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_msort_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1768481Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mv_cpu_complex128 PASSED [ 50%]
2023-01-11T21:53:08.1768646Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mv_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1768831Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_mvlgamma_mvlgamma_p_3_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1769032Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nan_to_num_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1769199Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nanmean_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1769375Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nanquantile_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:08.1769726Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_narrow_copy_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 50%]
2023-01-11T21:53:08.1769908Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_native_batch_norm_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1770077Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_native_layer_norm_cpu_float64 XFAIL [ 51%]
2023-01-11T21:53:08.1770408Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ne_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:53:08.1770733Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ne_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:53:08.1771103Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_empty_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:53:08.1771458Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_empty_strided_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:53:08.1771808Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_new_empty_strided_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:53:08.1772144Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nextafter_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 51%]
2023-01-11T21:53:08.1772350Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_adaptive_avg_pool1d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1772551Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_adaptive_avg_pool2d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1772752Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_adaptive_max_pool1d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1772935Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_adaptive_max_pool3d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1773129Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_alpha_dropout_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1773316Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_avg_pool2d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1773504Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_avg_pool3d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1773691Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_batch_norm_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1773879Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_bilinear_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1774081Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_binary_cross_entropy_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1774293Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_binary_cross_entropy_with_logits_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1774474Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_celu_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1774661Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv1d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1774838Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv2d_cpu_complex128 PASSED [ 51%]
2023-01-11T21:53:08.1775032Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_conv_transpose1d_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1775248Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_dropout_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1775511Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_elu_cpu_float64 PASSED [ 51%]
2023-01-11T21:53:08.1775899Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_embedding_bag_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 52%]
2023-01-11T21:53:08.1776089Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_embedding_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1776302Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_feature_alpha_dropout_with_train_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1776522Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_feature_alpha_dropout_without_train_cpu_complex128 PASSED [ 52%]
2023-01-11T21:53:08.1776738Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_feature_alpha_dropout_without_train_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1776978Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_fractional_max_pool2d_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1777167Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_fractional_max_pool3d_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1777348Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_gelu_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1777729Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_hardsigmoid_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 52%]
2023-01-11T21:53:08.1777918Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_hardtanh_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1778117Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_hinge_embedding_loss_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1778307Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_huber_loss_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1778502Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_instance_norm_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1778697Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_interpolate_area_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1778896Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_interpolate_bilinear_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1779082Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_interpolate_linear_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1779282Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_interpolate_trilinear_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1779466Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_kl_div_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1779656Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_l1_loss_cpu_complex128 PASSED [ 52%]
2023-01-11T21:53:08.1779841Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_l1_loss_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1780027Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_leaky_relu_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1780211Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_linear_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1780404Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_unpool1d_grad_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1780610Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_unpool2d_cpu_float64 SKIPPED (Skipped!) [ 52%]
2023-01-11T21:53:08.1780803Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_unpool2d_grad_cpu_float64 PASSED [ 52%]
2023-01-11T21:53:08.1781023Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_max_unpool3d_cpu_float64 SKIPPED (Skipped!) [ 53%]
2023-01-11T21:53:08.1781211Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_mish_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1781395Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_mse_loss_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1781788Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_multilabel_margin_loss_cpu_float64 SKIPPED (Op claims it doesn't support gradgrad. This is not verified.) [ 53%]
2023-01-11T21:53:08.1781971Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_nll_loss_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1782161Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_normalize_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1782356Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_circular_cpu_complex128 PASSED [ 53%]
2023-01-11T21:53:08.1782552Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_reflect_cpu_complex128 PASSED [ 53%]
2023-01-11T21:53:08.1782774Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_replicate_cpu_complex128 PASSED [ 53%]
2023-01-11T21:53:08.1782972Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pad_replicate_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1783160Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pairwise_distance_cpu_complex128 PASSED [ 53%]
2023-01-11T21:53:08.1783356Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pairwise_distance_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1783549Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pixel_shuffle_cpu_complex128 PASSED [ 53%]
2023-01-11T21:53:08.1783739Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_pixel_shuffle_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1783931Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_poisson_nll_loss_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1784116Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_prelu_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1784298Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_selu_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1784668Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_silu_complex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 53%]
2023-01-11T21:53:08.1784849Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_silu_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1785028Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_smooth_l1_loss_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1785220Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_soft_margin_loss_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1785407Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_softplus_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1785596Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_threshold_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1785797Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_triplet_margin_loss_cpu_complex128 PASSED [ 53%]
2023-01-11T21:53:08.1786011Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_triplet_margin_with_distance_loss_cpu_float64 PASSED [ 53%]
2023-01-11T21:53:08.1786202Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_unfold_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1786395Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nn_functional_upsample_nearest_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1786731Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_nonzero_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:53:08.1786933Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1787099Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_fro_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1787267Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_norm_nuc_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1787478Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_normal_number_mean_cpu_float64 SKIPPED (Gradients are incorrect!) [ 54%]
2023-01-11T21:53:08.1787815Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ones_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:53:08.1788141Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:53:08.1788479Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ones_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:53:08.1788855Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ones_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 54%]
2023-01-11T21:53:08.1789029Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ormqr_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1789197Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ormqr_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1789369Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_outer_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1789531Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_pca_lowrank_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1789700Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_permute_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1789877Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_pinverse_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1790046Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_polar_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1790235Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_polygamma_polygamma_n_0_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1790436Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_polygamma_polygamma_n_3_cpu_float64 SKIPPED (Skipped!) [ 54%]
2023-01-11T21:53:08.1790639Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_polygamma_polygamma_n_4_cpu_float64 SKIPPED (Skipped!) [ 54%]
2023-01-11T21:53:08.1790814Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_positive_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1790986Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_positive_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1791144Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_pow_cpu_complex128 PASSED [ 54%]
2023-01-11T21:53:08.1791315Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_pow_cpu_float64 PASSED [ 54%]
2023-01-11T21:53:08.1791484Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_prod_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:08.1791653Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_prod_cpu_float64 PASSED [ 55%]
2023-01-11T21:53:08.1791821Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_put_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:08.1791987Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_qr_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:08.1792149Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_qr_cpu_float64 PASSED [ 55%]
2023-01-11T21:53:08.1792493Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_rand_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 55%]
2023-01-11T21:53:08.1792833Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_rand_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 55%]
2023-01-11T21:53:08.1793186Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_randint_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 55%]
2023-01-11T21:53:08.1793525Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_randn_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 55%]
2023-01-11T21:53:08.1793869Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_randn_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 55%]
2023-01-11T21:53:08.1794206Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_randn_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 55%]
2023-01-11T21:53:08.1794377Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_ravel_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:08.1794543Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_real_cpu_float64 PASSED [ 55%]
2023-01-11T21:53:08.1794719Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_remainder_cpu_float64 PASSED [ 55%]
2023-01-11T21:53:08.1794927Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_renorm_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:08.1795099Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_repeat_cpu_float64 PASSED [ 55%]
2023-01-11T21:53:08.1795273Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_repeat_interleave_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:08.1795447Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_reshape_as_cpu_float64 PASSED [ 55%]
2023-01-11T21:53:08.1795619Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_reshape_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:08.1795958Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_resize__cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 55%]
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2023-01-11T21:53:08.1802037Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_signal_windows_exponential_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 56%]
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2023-01-11T21:53:08.1802756Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_signal_windows_general_cosine_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 56%]
2023-01-11T21:53:08.1803113Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_signal_windows_hamming_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 56%]
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2023-01-11T21:53:08.1805730Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_bessel_j0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 57%]
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2023-01-11T21:53:08.1806460Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_chebyshev_polynomial_t_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 57%]
2023-01-11T21:53:08.1806872Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_chebyshev_polynomial_u_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 57%]
2023-01-11T21:53:08.1807257Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_chebyshev_polynomial_v_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 57%]
2023-01-11T21:53:08.1807632Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_chebyshev_polynomial_w_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 57%]
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2023-01-11T21:53:08.1809303Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_modified_bessel_i0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 57%]
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2023-01-11T21:53:08.1810033Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_modified_bessel_k1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 57%]
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2023-01-11T21:53:08.1810966Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_scaled_modified_bessel_k1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 57%]
2023-01-11T21:53:08.1811360Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_special_shifted_chebyshev_polynomial_w_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 57%]
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2023-01-11T21:53:08.1824963Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad__softmax_backward_data_cpu_float64 SKIPPED (Op has no inplace variant!) [ 60%]
2023-01-11T21:53:08.1825298Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_abs_cpu_complex128 SKIPPED (In-place abs not supported for complex tensors) [ 60%]
2023-01-11T21:53:08.1825465Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_abs_cpu_float64 PASSED [ 60%]
2023-01-11T21:53:08.1825632Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_acosh_cpu_float64 PASSED [ 60%]
2023-01-11T21:53:08.1825803Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_addcdiv_cpu_float64 PASSED [ 60%]
2023-01-11T21:53:08.1825978Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_addcmul_cpu_complex128 PASSED [ 60%]
2023-01-11T21:53:08.1826133Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_addmm_cpu_float64 PASSED [ 61%]
2023-01-11T21:53:08.1826307Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_addmv_cpu_complex128 PASSED [ 61%]
2023-01-11T21:53:08.1826669Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_all_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1827013Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_allclose_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1827212Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_amax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:53:08.1827409Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_amin_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:53:08.1827745Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_aminmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1828078Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_any_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1828415Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_argmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1828744Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1829073Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_argwhere_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1829411Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_argwhere_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 61%]
2023-01-11T21:53:08.1829607Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_as_strided_cpu_float64 SKIPPED (Numerous errors) [ 61%]
2023-01-11T21:53:08.1829799Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_as_strided_partial_views_cpu_complex128 XFAIL [ 61%]
2023-01-11T21:53:08.1829989Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_as_strided_partial_views_cpu_float64 XFAIL [ 61%]
2023-01-11T21:53:08.1830208Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_as_strided_scatter_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:53:08.1830422Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_as_strided_scatter_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:53:08.1830592Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_asin_cpu_float64 PASSED [ 61%]
2023-01-11T21:53:08.1830762Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_asinh_cpu_float64 PASSED [ 61%]
2023-01-11T21:53:08.1830930Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atan2_cpu_float64 PASSED [ 61%]
2023-01-11T21:53:08.1831089Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atan_cpu_complex128 PASSED [ 61%]
2023-01-11T21:53:08.1831286Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atan_cpu_float64 PASSED [ 61%]
2023-01-11T21:53:08.1831459Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atanh_cpu_complex128 PASSED [ 61%]
2023-01-11T21:53:08.1831666Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atleast_1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:53:08.1831875Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atleast_2d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 61%]
2023-01-11T21:53:08.1832076Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_atleast_3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1832254Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_baddbmm_cpu_complex128 PASSED [ 62%]
2023-01-11T21:53:08.1832458Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_bernoulli_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1832667Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_block_diag_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1833033Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_bool_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 62%]
2023-01-11T21:53:08.1833247Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_broadcast_to_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1833586Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_bucketize_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 62%]
2023-01-11T21:53:08.1833797Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cartesian_prod_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1834179Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cat_cpu_complex128 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [ 62%]
2023-01-11T21:53:08.1834560Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cat_cpu_float64 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [ 62%]
2023-01-11T21:53:08.1834760Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cdist_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1834927Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ceil_cpu_float64 PASSED [ 62%]
2023-01-11T21:53:08.1835130Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cfloat_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1835333Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_chalf_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1835530Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_chalf_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1835857Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_char_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 62%]
2023-01-11T21:53:08.1836067Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cholesky_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1836267Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cholesky_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1836479Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cholesky_inverse_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1836688Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cholesky_inverse_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1836897Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cholesky_solve_cpu_float64 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1837096Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_chunk_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1837296Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_clamp_cpu_float64 PASSED [ 62%]
2023-01-11T21:53:08.1837470Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_clamp_max_cpu_float64 PASSED [ 62%]
2023-01-11T21:53:08.1837670Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_clone_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 62%]
2023-01-11T21:53:08.1837856Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_clone_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1838064Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_column_stack_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1838267Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_column_stack_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1838467Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_combinations_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1838693Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_conj_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1838879Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_conj_physical_cpu_complex128 PASSED [ 63%]
2023-01-11T21:53:08.1839089Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_constant_pad_nd_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1839262Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_copysign_cpu_float64 PASSED [ 63%]
2023-01-11T21:53:08.1839471Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_corrcoef_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1839630Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cos_cpu_complex128 PASSED [ 63%]
2023-01-11T21:53:08.1839795Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cos_cpu_float64 PASSED [ 63%]
2023-01-11T21:53:08.1839963Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cosh_cpu_float64 PASSED [ 63%]
2023-01-11T21:53:08.1840166Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cross_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1840364Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cross_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1840587Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_cumulative_trapezoid_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1840959Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_deg2rad_cpu_float64 PASSED [ 63%]
2023-01-11T21:53:08.1841164Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diag_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1841362Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diag_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1841575Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diag_embed_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1841770Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diag_embed_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1841976Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagflat_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1842180Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagflat_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1842393Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagonal_copy_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1842600Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diagonal_scatter_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1842856Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_diff_cpu_float64 SKIPPED (Op has no inplace variant!) [ 63%]
2023-01-11T21:53:08.1843029Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_digamma_cpu_float64 PASSED [ 64%]
2023-01-11T21:53:08.1843232Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dist_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1843428Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dot_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1843632Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_double_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1843823Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dsplit_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1844022Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dsplit_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1844221Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_dstack_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1844604Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_empty_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 64%]
2023-01-11T21:53:08.1844952Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_empty_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 64%]
2023-01-11T21:53:08.1845296Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_empty_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 64%]
2023-01-11T21:53:08.1845463Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_erf_cpu_float64 PASSED [ 64%]
2023-01-11T21:53:08.1845633Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_erfinv_cpu_float64 PASSED [ 64%]
2023-01-11T21:53:08.1845800Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_exp2_cpu_float64 PASSED [ 64%]
2023-01-11T21:53:08.1845973Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_exp_cpu_complex128 PASSED [ 64%]
2023-01-11T21:53:08.1846127Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_exp_cpu_float64 PASSED [ 64%]
2023-01-11T21:53:08.1846332Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_expand_as_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1846670Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_eye_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 64%]
2023-01-11T21:53:08.1846998Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_eye_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 64%]
2023-01-11T21:53:08.1847199Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_fft2_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1847409Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_hfft2_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1847615Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_hfft_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1847815Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_hfft_cpu_float64 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1848020Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_hfftn_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1848226Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifft2_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1848415Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifft_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 64%]
2023-01-11T21:53:08.1848620Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifftn_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1848863Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ifftshift_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1849066Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_ihfft_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1849269Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_irfft2_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1849471Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_irfft_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1849671Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fft_irfftn_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1849873Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_flatten_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1850075Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_flip_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1850301Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_flip_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1850492Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fliplr_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1850671Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_float_power_cpu_float64 PASSED [ 65%]
2023-01-11T21:53:08.1851017Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_floor_divide_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:53:08.1851213Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_fmin_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1851381Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_frac_cpu_float64 PASSED [ 65%]
2023-01-11T21:53:08.1851719Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:53:08.1852056Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_full_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:53:08.1852382Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ge_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:53:08.1852585Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_gradient_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1852909Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_gt_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:53:08.1853097Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_half_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1853295Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_half_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1853636Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_histogram_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 65%]
2023-01-11T21:53:08.1853839Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_hsplit_cpu_float64 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:08.1854009Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_hypot_cpu_float64 PASSED [ 65%]
2023-01-11T21:53:08.1854188Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_add_cpu_complex128 PASSED [ 65%]
2023-01-11T21:53:08.1854366Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_copy_cpu_float64 PASSED [ 66%]
2023-01-11T21:53:08.1854546Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_fill_cpu_complex128 PASSED [ 66%]
2023-01-11T21:53:08.1854748Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_fill_cpu_float64 PASSED [ 66%]
2023-01-11T21:53:08.1854910Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_put_cpu_float64 PASSED [ 66%]
2023-01-11T21:53:08.1855120Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_index_select_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:53:08.1855535Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isclose_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:53:08.1855877Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isclose_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:53:08.1856216Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isfinite_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:53:08.1856542Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:53:08.1856946Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isnan_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:53:08.1857287Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isreal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:53:08.1857619Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_isreal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 66%]
2023-01-11T21:53:08.1857822Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_istft_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:53:08.1858034Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_2inputs_2outputs_cpu_float64 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:53:08.1858238Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_4inputs_with_extra_args_cpu_float64 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:53:08.1858451Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_unary_cpu_complex128 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:53:08.1858653Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_jiterator_unary_cpu_float64 SKIPPED (Only runs on cuda) [ 66%]
2023-01-11T21:53:08.1858850Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_kron_cpu_float64 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:53:08.1859053Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_kthvalue_cpu_float64 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:53:08.1859222Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ldexp_cpu_float64 PASSED [ 66%]
2023-01-11T21:53:08.1859395Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lerp_cpu_complex128 PASSED [ 66%]
2023-01-11T21:53:08.1859565Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lerp_cpu_float64 PASSED [ 66%]
2023-01-11T21:53:08.1859735Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lgamma_cpu_float64 PASSED [ 66%]
2023-01-11T21:53:08.1859948Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cond_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:53:08.1860147Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cross_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:53:08.1860360Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_det_singular_cpu_float64 SKIPPED (Op has no inplace variant!) [ 66%]
2023-01-11T21:53:08.1860569Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eig_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1860775Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eigh_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1861019Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_eigvals_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1861248Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_householder_product_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1861470Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_householder_product_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1861680Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_inv_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1861886Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_inv_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1862247Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_ldl_factor_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:53:08.1862590Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_ldl_factor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:53:08.1862978Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_ldl_factor_ex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:53:08.1863175Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lstsq_cpu_complex128 SKIPPED (Skipped!) [ 67%]
2023-01-11T21:53:08.1863398Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lstsq_grad_oriented_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1863603Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1863804Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1864016Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_lu_factor_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1864236Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_norm_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1864455Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_power_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1864668Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_power_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1865026Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_rank_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:53:08.1865387Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_rank_hermitian_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:53:08.1865762Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_matrix_rank_hermitian_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 67%]
2023-01-11T21:53:08.1865971Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_norm_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1866200Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_norm_subgradients_at_zero_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1866407Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_pinv_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1866610Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_pinv_cpu_float64 SKIPPED (Op has no inplace variant!) [ 67%]
2023-01-11T21:53:08.1866858Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_pinv_singular_cpu_float64 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 68%]
2023-01-11T21:53:08.1867099Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_qr_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1867305Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_slogdet_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1867514Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_solve_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1867717Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_solve_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1867908Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_solve_ex_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1868129Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_solve_triangular_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1868347Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_svdvals_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1868588Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_tensorinv_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1868802Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_tensorinv_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1869017Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_tensorsolve_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1869231Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_tensorsolve_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1869445Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_vector_norm_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1869661Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_vector_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1870011Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 68%]
2023-01-11T21:53:08.1870182Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log1p_cpu_float64 PASSED [ 68%]
2023-01-11T21:53:08.1870340Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log2_cpu_complex128 PASSED [ 68%]
2023-01-11T21:53:08.1870508Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log2_cpu_float64 PASSED [ 68%]
2023-01-11T21:53:08.1870711Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_log_softmax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1870916Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logaddexp_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1871129Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logcumsumexp_cpu_float64 SKIPPED (Op has no inplace variant!) [ 68%]
2023-01-11T21:53:08.1871472Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_and_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 68%]
2023-01-11T21:53:08.1871813Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_not_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 68%]
2023-01-11T21:53:08.1872155Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logical_or_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 68%]
2023-01-11T21:53:08.1872324Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logit_cpu_float64 PASSED [ 68%]
2023-01-11T21:53:08.1872657Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:53:08.1872891Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_logsumexp_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1873223Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_long_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:53:08.1873549Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lt_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:53:08.1873745Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lu_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1873951Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lu_solve_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1874158Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_lu_unpack_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1874355Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mH_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1874586Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mT_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1874793Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_amin_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1875140Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_argmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 69%]
2023-01-11T21:53:08.1875338Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_cumprod_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1875546Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_cumsum_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1875724Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_fill_cpu_complex128 PASSED [ 69%]
2023-01-11T21:53:08.1875934Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_mean_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1876137Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_median_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1876351Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_normalize_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1876564Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_normalize_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1876774Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_prod_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1876959Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_scatter_cpu_complex128 PASSED [ 69%]
2023-01-11T21:53:08.1877170Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_select_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1877361Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_std_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1877567Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_std_cpu_float64 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1877773Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_sum_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1887114Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_var_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 69%]
2023-01-11T21:53:08.1887414Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_masked_var_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1887632Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_matmul_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1887994Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_matmul_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1888209Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_matrix_exp_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1888417Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_max_binary_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1888620Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mean_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1888806Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mean_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1889005Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_median_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1889230Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_meshgrid_list_of_tensors_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1889515Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_meshgrid_variadic_tensors_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1889739Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_meshgrid_variadic_tensors_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1889948Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_min_binary_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1890164Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_min_reduction_with_dim_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1890362Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mode_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1890577Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_movedim_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1890779Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_movedim_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1890940Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mul_cpu_complex128 PASSED [ 70%]
2023-01-11T21:53:08.1891341Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_multinomial_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 70%]
2023-01-11T21:53:08.1891544Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_mv_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1891722Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nan_to_num_cpu_float64 PASSED [ 70%]
2023-01-11T21:53:08.1891927Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nanquantile_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1892130Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nansum_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1892472Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_narrow_copy_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 70%]
2023-01-11T21:53:08.1892695Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_native_dropout_backward_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:08.1893022Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ne_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 70%]
2023-01-11T21:53:08.1893196Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_neg_cpu_complex128 PASSED [ 71%]
2023-01-11T21:53:08.1893353Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_neg_cpu_float64 PASSED [ 71%]
2023-01-11T21:53:08.1893698Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_empty_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1894074Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1894428Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_empty_strided_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1894781Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_empty_strided_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1895126Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_ones_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1895556Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1895908Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_zeros_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1896281Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_new_zeros_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 71%]
2023-01-11T21:53:08.1896521Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional__scaled_dot_product_attention_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1896720Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_alpha_dropout_cpu_float64 PASSED [ 71%]
2023-01-11T21:53:08.1896929Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_avg_pool2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1897148Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_avg_pool3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1897363Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1897583Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv2d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1897792Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv2d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1898022Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv_transpose1d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1898245Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv_transpose2d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1898474Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_conv_transpose3d_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1898694Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_ctc_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1898886Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_dropout2d_cpu_float64 PASSED [ 71%]
2023-01-11T21:53:08.1899079Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_dropout3d_cpu_float64 PASSED [ 71%]
2023-01-11T21:53:08.1899290Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_embedding_bag_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1899509Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_embedding_cpu_float64 SKIPPED (Op has no inplace variant!) [ 71%]
2023-01-11T21:53:08.1899725Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_feature_alpha_dropout_with_train_cpu_float64 PASSED [ 72%]
2023-01-11T21:53:08.1899975Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_feature_alpha_dropout_without_train_cpu_float64 PASSED [ 72%]
2023-01-11T21:53:08.1900193Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_gelu_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1900413Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_grid_sample_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1900632Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_group_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1900826Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_hardsigmoid_cpu_float64 PASSED [ 72%]
2023-01-11T21:53:08.1901046Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_hardtanh_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1901267Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_instance_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1901523Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_interpolate_area_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1901741Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_interpolate_bicubic_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1901968Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_interpolate_nearest_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1902185Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_l1_loss_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1902402Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_l1_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1902596Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_leaky_relu_cpu_float64 PASSED [ 72%]
2023-01-11T21:53:08.1902815Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_linear_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1903042Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_local_response_norm_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1903263Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_logsigmoid_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1903477Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_pool3d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1903700Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_unpool1d_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1903915Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_unpool2d_grad_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1904134Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_max_unpool3d_grad_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1904321Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_mish_cpu_float64 PASSED [ 72%]
2023-01-11T21:53:08.1904535Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_mse_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1904772Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_multilabel_soft_margin_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1904995Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_circular_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 72%]
2023-01-11T21:53:08.1905256Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_constant_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1905477Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_constant_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1905700Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_reflect_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1905918Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_reflect_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1906143Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_replicate_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1906350Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pad_replicate_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1906564Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pdist_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1906805Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pixel_shuffle_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1907030Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_pixel_unshuffle_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1907240Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_relu_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1907425Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_selu_cpu_float64 PASSED [ 73%]
2023-01-11T21:53:08.1907649Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_soft_margin_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1907864Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_softmin_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1908080Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_softplus_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1908294Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_softsign_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1908509Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_tanhshrink_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1908724Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_triplet_margin_loss_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1908967Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_triplet_margin_with_distance_loss_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1909212Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_triplet_margin_with_distance_loss_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1909429Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_unfold_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1909644Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_nn_functional_unfold_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1909843Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1910048Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_fro_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1910246Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_inf_cpu_float64 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1910486Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_norm_nuc_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 73%]
2023-01-11T21:53:08.1910824Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:53:08.1911157Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ones_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:53:08.1911349Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ormqr_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1911550Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ormqr_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1911756Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_outer_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1911964Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_pinverse_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1912192Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_pinverse_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1912387Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_polygamma_polygamma_n_0_cpu_float64 PASSED [ 74%]
2023-01-11T21:53:08.1912586Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_polygamma_polygamma_n_1_cpu_float64 SKIPPED (Skipped!) [ 74%]
2023-01-11T21:53:08.1912789Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_polygamma_polygamma_n_2_cpu_float64 SKIPPED (Skipped!) [ 74%]
2023-01-11T21:53:08.1912989Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_polygamma_polygamma_n_4_cpu_float64 SKIPPED (Skipped!) [ 74%]
2023-01-11T21:53:08.1913193Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_positive_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1913355Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_pow_cpu_complex128 PASSED [ 74%]
2023-01-11T21:53:08.1913524Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_pow_cpu_float64 PASSED [ 74%]
2023-01-11T21:53:08.1913692Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_put_cpu_complex128 PASSED [ 74%]
2023-01-11T21:53:08.1913856Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_put_cpu_float64 PASSED [ 74%]
2023-01-11T21:53:08.1914056Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_quantile_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1914405Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_randn_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:53:08.1914747Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_randn_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 74%]
2023-01-11T21:53:08.1914950Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_ravel_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1915154Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_real_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1915339Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_real_cpu_float64 SKIPPED (Op has no inplace variant!) [ 74%]
2023-01-11T21:53:08.1915520Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_reciprocal_cpu_float64 PASSED [ 74%]
2023-01-11T21:53:08.1915694Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_remainder_cpu_float64 PASSED [ 74%]
2023-01-11T21:53:08.1915865Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_renorm_cpu_float64 PASSED [ 74%]
2023-01-11T21:53:08.1916083Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_repeat_interleave_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1916327Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_repeat_interleave_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1916534Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_reshape_as_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1916739Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_reshape_as_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1916945Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_reshape_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1917144Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_reshape_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1917471Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_resize__cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
2023-01-11T21:53:08.1917682Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_resolve_conj_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1917926Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_resolve_conj_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1918136Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_resolve_neg_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1918335Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_roll_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1918533Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_roll_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1918714Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_round_decimals_0_cpu_float64 PASSED [ 75%]
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2023-01-11T21:53:08.1919084Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_rsqrt_cpu_complex128 PASSED [ 75%]
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2023-01-11T21:53:08.1919447Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_scatter_add_cpu_float64 PASSED [ 75%]
2023-01-11T21:53:08.1919619Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_scatter_cpu_float64 PASSED [ 75%]
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2023-01-11T21:53:08.1919990Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_scatter_reduce_sum_cpu_float64 PASSED [ 75%]
2023-01-11T21:53:08.1920338Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_searchsorted_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 75%]
2023-01-11T21:53:08.1920563Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_segment_reduce_lengths_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1921013Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_segment_reduce_offsets_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1921229Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_select_scatter_cpu_float64 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:08.1921400Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sgn_cpu_complex128 PASSED [ 75%]
2023-01-11T21:53:08.1921553Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sgn_cpu_float64 PASSED [ 76%]
2023-01-11T21:53:08.1921720Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sign_cpu_float64 PASSED [ 76%]
2023-01-11T21:53:08.1922086Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_blackman_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1922516Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_exponential_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1922882Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_gaussian_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1923255Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_general_cosine_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1923628Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_general_hamming_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1923985Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_kaiser_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1924344Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_signal_windows_nuttall_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1924569Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sin_cpu_complex128 PASSED [ 76%]
2023-01-11T21:53:08.1924746Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sinc_cpu_complex128 PASSED [ 76%]
2023-01-11T21:53:08.1924906Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sinh_cpu_complex128 PASSED [ 76%]
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2023-01-11T21:53:08.1925313Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_slice_cpu_float64 SKIPPED (Op has no inplace variant!) [ 76%]
2023-01-11T21:53:08.1925521Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_slice_scatter_cpu_float64 SKIPPED (Op has no inplace variant!) [ 76%]
2023-01-11T21:53:08.1925722Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_softmax_cpu_float64 SKIPPED (Op has no inplace variant!) [ 76%]
2023-01-11T21:53:08.1925941Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_softmax_with_dtype_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 76%]
2023-01-11T21:53:08.1926152Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_softmax_with_dtype_cpu_float64 SKIPPED (Op has no inplace variant!) [ 76%]
2023-01-11T21:53:08.1926372Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sparse_sampled_addmm_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 76%]
2023-01-11T21:53:08.1926724Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_airy_ai_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1927076Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_bessel_j1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1927428Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_bessel_y1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1927791Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_chebyshev_polynomial_t_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 76%]
2023-01-11T21:53:08.1928177Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_chebyshev_polynomial_w_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 76%]
2023-01-11T21:53:08.1928386Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_erfcx_cpu_float64 SKIPPED (Op has no inplace variant!) [ 76%]
2023-01-11T21:53:08.1928591Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_i1_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1928797Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_i1e_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1929207Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_laguerre_polynomial_l_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:53:08.1929591Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_legendre_polynomial_p_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 77%]
2023-01-11T21:53:08.1929956Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_modified_bessel_i1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:53:08.1930320Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_modified_bessel_k0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:53:08.1930530Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_ndtri_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1930799Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_polygamma_special_polygamma_n_0_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1931177Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_scaled_modified_bessel_k1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:53:08.1931557Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_shifted_chebyshev_polynomial_u_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 77%]
2023-01-11T21:53:08.1931927Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_spherical_bessel_j0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:53:08.1932273Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_special_zeta_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 77%]
2023-01-11T21:53:08.1932478Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_split_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1932695Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_split_list_args_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1932903Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_split_list_args_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1933078Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sqrt_cpu_complex128 PASSED [ 77%]
2023-01-11T21:53:08.1933252Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_square_cpu_complex128 PASSED [ 77%]
2023-01-11T21:53:08.1933430Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_squeeze_cpu_complex128 PASSED [ 77%]
2023-01-11T21:53:08.1933600Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_squeeze_cpu_float64 PASSED [ 77%]
2023-01-11T21:53:08.1933790Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_stack_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1933993Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_stack_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1934192Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1934397Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_mean_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1934601Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_mean_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1934811Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_mean_unbiased_cpu_float64 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1935019Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_std_unbiased_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 77%]
2023-01-11T21:53:08.1935250Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_stft_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1935534Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_sum_to_size_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1935738Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_svd_lowrank_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1935930Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_symeig_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1936095Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_t_cpu_float64 PASSED [ 78%]
2023-01-11T21:53:08.1936291Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_take_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1936458Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tan_cpu_float64 PASSED [ 78%]
2023-01-11T21:53:08.1936626Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tanh_cpu_float64 PASSED [ 78%]
2023-01-11T21:53:08.1936863Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tensor_split_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1937073Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tensordot_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1937277Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tensordot_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1937475Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tile_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1937659Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tile_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1937863Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_to_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1938216Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_to_sparse_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 78%]
2023-01-11T21:53:08.1938420Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_to_sparse_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1938616Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_topk_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1938798Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_transpose_cpu_complex128 PASSED [ 78%]
2023-01-11T21:53:08.1938973Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_transpose_cpu_float64 PASSED [ 78%]
2023-01-11T21:53:08.1939179Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_trapezoid_cpu_float64 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1939395Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_triangular_solve_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 78%]
2023-01-11T21:53:08.1939570Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tril_cpu_complex128 PASSED [ 78%]
2023-01-11T21:53:08.1939724Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_tril_cpu_float64 PASSED [ 78%]
2023-01-11T21:53:08.1939895Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_triu_cpu_complex128 PASSED [ 78%]
2023-01-11T21:53:08.1940064Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_triu_cpu_float64 PASSED [ 78%]
2023-01-11T21:53:08.1940243Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_true_divide_cpu_complex128 PASSED [ 79%]
2023-01-11T21:53:08.1940448Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unbind_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1940656Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unflatten_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1940892Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unfold_copy_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1941095Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unfold_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1941440Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_uniform_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 79%]
2023-01-11T21:53:08.1941779Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_uniform_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 79%]
2023-01-11T21:53:08.1941947Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unsqueeze_cpu_complex128 PASSED [ 79%]
2023-01-11T21:53:08.1942124Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_unsqueeze_cpu_float64 PASSED [ 79%]
2023-01-11T21:53:08.1942324Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1942559Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_mean_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1942780Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_mean_unbiased_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1942993Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_var_mean_unbiased_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1943198Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_view_as_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1943396Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_view_as_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1943604Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_view_as_real_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1943809Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_view_copy_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1943996Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_view_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1944196Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_vsplit_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1944394Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_vstack_cpu_float64 SKIPPED (Op has no inplace variant!) [ 79%]
2023-01-11T21:53:08.1944566Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_zero__cpu_complex128 PASSED [ 79%]
2023-01-11T21:53:08.1944901Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_zeros_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 79%]
2023-01-11T21:53:08.1945145Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rdiv___cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 79%]
2023-01-11T21:53:08.1945385Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rdiv___cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 79%]
2023-01-11T21:53:08.1945620Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rmod___cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 79%]
2023-01-11T21:53:08.1945861Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rmul___cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1946094Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rmul___cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1946271Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rpow___cpu_float64 SKIPPED (Skipped!) [ 80%]
2023-01-11T21:53:08.1946532Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad___rsub___cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1946786Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad__native_batch_norm_legit_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1947124Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_abs_cpu_complex128 SKIPPED (In-place abs not supported for complex tensors) [ 80%]
2023-01-11T21:53:08.1947297Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_abs_cpu_float64 PASSED [ 80%]
2023-01-11T21:53:08.1947476Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_acos_cpu_complex128 PASSED [ 80%]
2023-01-11T21:53:08.1947715Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_acosh_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1947952Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_acosh_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1948160Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_add_cpu_complex128 PASSED [ 80%]
2023-01-11T21:53:08.1948345Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addbmm_cpu_complex128 PASSED [ 80%]
2023-01-11T21:53:08.1948523Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addcdiv_cpu_float64 PASSED [ 80%]
2023-01-11T21:53:08.1948692Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addcmul_cpu_complex128 PASSED [ 80%]
2023-01-11T21:53:08.1948868Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addcmul_cpu_float64 PASSED [ 80%]
2023-01-11T21:53:08.1949049Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addmm_cpu_complex128 PASSED [ 80%]
2023-01-11T21:53:08.1949243Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addmm_decomposed_cpu_float64 PASSED [ 80%]
2023-01-11T21:53:08.1949421Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_addmv_cpu_float64 PASSED [ 80%]
2023-01-11T21:53:08.1949770Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_allclose_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 80%]
2023-01-11T21:53:08.1950008Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_amax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1950241Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_amin_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1950584Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_aminmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 80%]
2023-01-11T21:53:08.1950810Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_angle_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1951050Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_angle_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 80%]
2023-01-11T21:53:08.1951387Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_any_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 80%]
2023-01-11T21:53:08.1951724Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_argmin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:53:08.1952072Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_argwhere_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:53:08.1952272Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_as_strided_partial_views_cpu_complex128 XFAIL [ 81%]
2023-01-11T21:53:08.1952477Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_asin_cpu_float64 PASSED [ 81%]
2023-01-11T21:53:08.1952652Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atan2_cpu_float64 PASSED [ 81%]
2023-01-11T21:53:08.1952834Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atan_cpu_complex128 PASSED [ 81%]
2023-01-11T21:53:08.1953008Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atan_cpu_float64 PASSED [ 81%]
2023-01-11T21:53:08.1953245Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atanh_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1953479Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atleast_2d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1953719Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atleast_3d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1953993Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_atleast_3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1954179Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_baddbmm_cpu_complex128 PASSED [ 81%]
2023-01-11T21:53:08.1954416Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bernoulli_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1954660Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_block_diag_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1955001Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bool_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:53:08.1955247Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_broadcast_tensors_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1955495Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_broadcast_to_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1955842Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_bucketize_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 81%]
2023-01-11T21:53:08.1956090Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cartesian_prod_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1956471Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cat_cpu_complex128 SKIPPED (TODO(whc) fix pre-existing bug with cat for newly added opinfo for empty+nonempty) [ 81%]
2023-01-11T21:53:08.1956716Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cdouble_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1956960Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cfloat_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1957196Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cfloat_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1957434Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_chalf_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1957674Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cholesky_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 81%]
2023-01-11T21:53:08.1957925Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cholesky_inverse_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1958240Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cholesky_solve_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1958483Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cholesky_solve_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1958716Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_chunk_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1958897Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_clamp_min_cpu_float64 PASSED [ 82%]
2023-01-11T21:53:08.1959130Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_clone_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1959362Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_column_stack_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1959596Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_conj_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1959876Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_constant_pad_nd_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1960121Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_contiguous_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1960300Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_copysign_cpu_float64 PASSED [ 82%]
2023-01-11T21:53:08.1960540Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_corrcoef_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1961043Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_corrcoef_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1961417Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_count_nonzero_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 82%]
2023-01-11T21:53:08.1961649Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cov_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1961883Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cross_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1962067Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cumprod_cpu_complex128 PASSED [ 82%]
2023-01-11T21:53:08.1962232Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cumprod_cpu_float64 PASSED [ 82%]
2023-01-11T21:53:08.1962412Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cumsum_cpu_complex128 PASSED [ 82%]
2023-01-11T21:53:08.1962588Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cumsum_cpu_float64 PASSED [ 82%]
2023-01-11T21:53:08.1962848Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_cumulative_trapezoid_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1963087Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diag_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1963332Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diag_embed_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1963574Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagflat_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1963811Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagonal_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 82%]
2023-01-11T21:53:08.1964124Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagonal_scatter_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1964372Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_diagonal_scatter_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1964606Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dist_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1964784Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_div_floor_rounding_cpu_float64 PASSED [ 83%]
2023-01-11T21:53:08.1965028Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dstack_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1965265Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_dstack_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1965551Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_einsum_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1965782Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_einsum_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1966132Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_empty_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 83%]
2023-01-11T21:53:08.1966476Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_empty_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 83%]
2023-01-11T21:53:08.1966828Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_empty_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 83%]
2023-01-11T21:53:08.1967167Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_eq_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 83%]
2023-01-11T21:53:08.1967343Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_erf_cpu_float64 PASSED [ 83%]
2023-01-11T21:53:08.1967517Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_exp2_cpu_float64 PASSED [ 83%]
2023-01-11T21:53:08.1967683Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_exp_cpu_complex128 PASSED [ 83%]
2023-01-11T21:53:08.1967926Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_expand_as_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1968166Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_expand_as_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1968405Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_expand_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1968742Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_eye_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 83%]
2023-01-11T21:53:08.1968979Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fft2_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1969216Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fft2_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1969452Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fft_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1969694Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fftn_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1969965Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fftn_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1970211Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_fftshift_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 83%]
2023-01-11T21:53:08.1970435Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_hfft2_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1970669Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_hfft_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1970905Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_hfftn_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1971147Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifft2_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1971412Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifft2_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1971654Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifft_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1971888Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifft_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1972130Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifftn_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1972375Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifftshift_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1972619Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_ifftshift_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1972857Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_irfft2_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1973093Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_irfft2_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1973335Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_irfftn_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1973559Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_rfft2_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1973796Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fft_rfftn_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1973979Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fill_cpu_complex128 PASSED [ 84%]
2023-01-11T21:53:08.1974153Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fill_cpu_float64 PASSED [ 84%]
2023-01-11T21:53:08.1974389Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_flip_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1974628Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fliplr_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1974863Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fliplr_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1975128Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_flipud_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1975365Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_float_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1975614Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_float_power_cpu_float64 PASSED [ 84%]
2023-01-11T21:53:08.1975958Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_floor_divide_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 84%]
2023-01-11T21:53:08.1976192Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fmax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 84%]
2023-01-11T21:53:08.1976423Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fmin_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1976600Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_fmod_cpu_float64 PASSED [ 85%]
2023-01-11T21:53:08.1976807Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_frac_cpu_float64 PASSED [ 85%]
2023-01-11T21:53:08.1977042Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_frexp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1977388Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1977727Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_full_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1978076Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_full_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1978319Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_gather_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1978560Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_gather_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1978894Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_geqrf_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1979234Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_geqrf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1979476Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_gradient_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1979715Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_gradient_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1979965Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_grid_sampler_2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1980303Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_gt_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1980539Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_half_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1980889Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_histogramdd_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1981125Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_hsplit_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1981393Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_hsplit_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1981637Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_hstack_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1981870Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_hstack_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:08.1982037Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_hypot_cpu_float64 PASSED [ 85%]
2023-01-11T21:53:08.1982207Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_i0_cpu_float64 PASSED [ 85%]
2023-01-11T21:53:08.1982550Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_igamma_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1982893Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_igammac_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 85%]
2023-01-11T21:53:08.1983160Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_imag_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1983348Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_add_cpu_complex128 PASSED [ 86%]
2023-01-11T21:53:08.1983537Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_copy_cpu_complex128 PASSED [ 86%]
2023-01-11T21:53:08.1983717Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_fill_cpu_float64 PASSED [ 86%]
2023-01-11T21:53:08.1983899Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_index_put_cpu_float64 PASSED [ 86%]
2023-01-11T21:53:08.1984241Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isclose_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:53:08.1984580Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isfinite_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:53:08.1984916Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isin_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:53:08.1985261Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isneginf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:53:08.1985607Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isposinf_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:53:08.1985952Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isreal_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:53:08.1986291Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_isreal_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 86%]
2023-01-11T21:53:08.1986529Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_istft_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1986746Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_2inputs_2outputs_cpu_float64 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:53:08.1986961Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_binary_cpu_complex128 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:53:08.1987182Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_binary_return_by_ref_cpu_float64 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:53:08.1987386Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_jiterator_unary_cpu_float64 SKIPPED (Only runs on cuda) [ 86%]
2023-01-11T21:53:08.1987640Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_kron_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1987879Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_kron_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1988115Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_kthvalue_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1988352Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ldexp_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1988531Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lerp_cpu_complex128 PASSED [ 86%]
2023-01-11T21:53:08.1988703Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lerp_cpu_float64 PASSED [ 86%]
2023-01-11T21:53:08.1988949Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cross_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1989223Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_det_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 86%]
2023-01-11T21:53:08.1989475Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_det_singular_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1989725Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_det_singular_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1989969Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eigh_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1990195Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eigh_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1990443Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eigvals_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1990688Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_eigvalsh_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1990947Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_householder_product_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1991203Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_householder_product_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1991442Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_inv_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1991803Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_ldl_factor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:53:08.1992170Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_ldl_factor_ex_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:53:08.1992535Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_ldl_factor_ex_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:53:08.1992889Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_ldl_solve_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:53:08.1993089Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lstsq_cpu_complex128 SKIPPED (Skipped!) [ 87%]
2023-01-11T21:53:08.1993314Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lstsq_cpu_float64 SKIPPED (Skipped!) [ 87%]
2023-01-11T21:53:08.1993556Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lstsq_grad_oriented_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1993804Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1994053Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_factor_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1994302Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_factor_ex_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1994547Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_factor_ex_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1994820Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_solve_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1995066Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_lu_solve_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1995314Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_norm_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1995561Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_power_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 87%]
2023-01-11T21:53:08.1995930Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_rank_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 87%]
2023-01-11T21:53:08.1996309Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_matrix_rank_hermitian_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 88%]
2023-01-11T21:53:08.1996556Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_multi_dot_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1996800Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_norm_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1997027Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1997294Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_norm_subgradients_at_zero_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1997542Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_pinv_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1997780Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_pinv_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1998037Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_pinv_singular_cpu_complex128 SKIPPED (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 88%]
2023-01-11T21:53:08.1998274Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_qr_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1998520Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_slogdet_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1998804Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_solve_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1999044Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_solve_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1999291Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_solve_ex_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1999531Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_svd_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.1999774Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_svdvals_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2000003Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_svdvals_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2000285Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_tensorinv_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2000537Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_tensorsolve_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2001007Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_tensorsolve_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2001258Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_vander_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2001495Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_vecdot_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2001740Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_vecdot_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2001994Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_vector_norm_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 88%]
2023-01-11T21:53:08.2002173Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log10_cpu_complex128 PASSED [ 88%]
2023-01-11T21:53:08.2002347Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log10_cpu_float64 PASSED [ 88%]
2023-01-11T21:53:08.2002527Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log1p_cpu_complex128 PASSED [ 88%]
2023-01-11T21:53:08.2002694Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log2_cpu_complex128 PASSED [ 89%]
2023-01-11T21:53:08.2002869Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log_cpu_float64 PASSED [ 89%]
2023-01-11T21:53:08.2003109Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log_softmax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2003363Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_log_softmax_with_dtype_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2003725Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_and_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2004072Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_and_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2004424Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_not_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2004834Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_or_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2005183Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_or_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2005527Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logical_xor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2005702Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logit_cpu_float64 PASSED [ 89%]
2023-01-11T21:53:08.2006041Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logspace_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2006282Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_logsumexp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2006673Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_long_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2007007Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lt_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2007244Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lu_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2007477Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lu_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2007717Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_lu_unpack_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2007956Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mH_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2008194Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mT_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2008429Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mT_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2008786Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_argmax_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 89%]
2023-01-11T21:53:08.2009031Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_cumprod_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2009260Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_cumsum_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 89%]
2023-01-11T21:53:08.2009445Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_fill_cpu_float64 PASSED [ 89%]
2023-01-11T21:53:08.2009695Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_log_softmax_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2009945Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_logaddexp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2010192Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_logsumexp_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2010436Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_mean_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2010713Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_mean_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2010969Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_normalize_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2011215Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_normalize_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2011458Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_prod_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2011700Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_prod_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2011892Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_scatter_cpu_complex128 PASSED [ 90%]
2023-01-11T21:53:08.2012156Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_std_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2012395Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_std_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2012637Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_sum_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2012876Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_masked_sum_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2013113Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_matmul_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2013355Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_matrix_exp_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2013590Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_max_binary_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2013841Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_max_reduction_no_dim_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2014091Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_max_reduction_with_dim_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2014332Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_maximum_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2014566Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mean_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2014794Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_median_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2015047Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_meshgrid_list_of_tensors_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2015308Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_meshgrid_variadic_tensors_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2015616Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_minimum_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:08.2015852Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mode_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2016128Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mv_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2016362Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mv_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2016558Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mvlgamma_mvlgamma_p_1_cpu_float64 PASSED [ 91%]
2023-01-11T21:53:08.2016751Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mvlgamma_mvlgamma_p_3_cpu_float64 PASSED [ 91%]
2023-01-11T21:53:08.2016943Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_mvlgamma_mvlgamma_p_5_cpu_float64 PASSED [ 91%]
2023-01-11T21:53:08.2017185Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nanmedian_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2017424Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nansum_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2017801Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_narrow_copy_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2018151Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_narrow_copy_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2018390Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_narrow_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2018626Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_narrow_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2018873Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_native_batch_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2019132Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_native_dropout_backward_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2019376Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_native_layer_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2019708Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ne_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2019886Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_neg_cpu_complex128 PASSED [ 91%]
2023-01-11T21:53:08.2020247Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_empty_strided_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2020597Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_full_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2020931Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_full_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2021276Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_ones_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2021620Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_ones_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2021967Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_zeros_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2022307Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_new_zeros_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 91%]
2023-01-11T21:53:08.2022618Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional__scaled_dot_product_attention_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 91%]
2023-01-11T21:53:08.2022887Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_adaptive_avg_pool2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2023152Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_adaptive_max_pool2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2023354Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_alpha_dropout_cpu_float64 PASSED [ 92%]
2023-01-11T21:53:08.2023607Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_avg_pool2d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2023890Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_batch_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2024084Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_celu_cpu_float64 PASSED [ 92%]
2023-01-11T21:53:08.2024323Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv1d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2024586Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv_transpose1d_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2024852Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_conv_transpose3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2025113Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_cosine_similarity_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2025311Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_dropout2d_cpu_float64 PASSED [ 92%]
2023-01-11T21:53:08.2025497Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_elu_cpu_float64 PASSED [ 92%]
2023-01-11T21:53:08.2025748Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_embedding_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2025972Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_feature_alpha_dropout_without_train_cpu_float64 PASSED [ 92%]
2023-01-11T21:53:08.2026219Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_glu_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2026478Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_grid_sample_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2026731Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_group_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2026981Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_hardshrink_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2027164Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_hardsigmoid_cpu_float64 XFAIL [ 92%]
2023-01-11T21:53:08.2027420Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_hardswish_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2027705Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_hardtanh_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2027971Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_hinge_embedding_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2028231Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_interpolate_area_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2028493Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_interpolate_bicubic_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2028757Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_interpolate_bilinear_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 92%]
2023-01-11T21:53:08.2029022Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_interpolate_linear_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2029301Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_layer_norm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2029555Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_linear_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2029806Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_pool1d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2030059Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_max_pool3d_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2030310Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_mse_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2030558Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_multi_margin_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2030820Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_multilabel_margin_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2031072Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_nll_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2031329Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_normalize_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2031586Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_normalize_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2031847Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_circular_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2032100Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_constant_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2032362Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_replicate_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2032618Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pad_replicate_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2032914Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pairwise_distance_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2033167Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_pdist_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2033424Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_soft_margin_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2033682Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_softmin_with_dtype_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2033919Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_softshrink_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2034177Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_softsign_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2034407Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_threshold_cpu_float64 PASSED [ 93%]
2023-01-11T21:53:08.2034673Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_triplet_margin_loss_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2034934Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_triplet_margin_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2035213Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_triplet_margin_with_distance_loss_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 93%]
2023-01-11T21:53:08.2035495Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_triplet_margin_with_distance_loss_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2035750Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_unfold_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2036014Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_nn_functional_upsample_bilinear_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2036249Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2036485Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_fro_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2036731Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_inf_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2036963Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_inf_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2037191Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_nuc_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2037420Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_norm_nuc_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2037624Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_normal_cpu_float64 SKIPPED (Gradients are incorrect!) [ 94%]
2023-01-11T21:53:08.2037987Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ones_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 94%]
2023-01-11T21:53:08.2038263Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ormqr_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2038502Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pca_lowrank_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2038738Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_permute_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2038982Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pinverse_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2039213Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polar_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2039421Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_polygamma_polygamma_n_2_cpu_float64 SKIPPED (Skipped!) [ 94%]
2023-01-11T21:53:08.2039690Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pow_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2039913Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_pow_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2040154Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_prod_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2040390Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_prod_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2040567Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_put_cpu_complex128 PASSED [ 94%]
2023-01-11T21:53:08.2040997Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_qr_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2041240Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_quantile_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 94%]
2023-01-11T21:53:08.2041420Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rad2deg_cpu_float64 PASSED [ 94%]
2023-01-11T21:53:08.2041772Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rand_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:53:08.2042112Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randint_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:53:08.2042459Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_randn_like_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:53:08.2042696Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_ravel_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2042938Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_real_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2043159Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_real_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2043348Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reciprocal_cpu_complex128 PASSED [ 95%]
2023-01-11T21:53:08.2043530Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reciprocal_cpu_float64 PASSED [ 95%]
2023-01-11T21:53:08.2043711Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_remainder_cpu_float64 PASSED [ 95%]
2023-01-11T21:53:08.2043948Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_repeat_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2044246Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_as_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2044485Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_reshape_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2044828Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize__cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:53:08.2045177Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize_as__cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:53:08.2045526Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resize_as__cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:53:08.2045765Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_conj_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2046097Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_neg_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2046341Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_resolve_neg_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2046575Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_roll_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2046765Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_round_decimals_0_cpu_float64 PASSED [ 95%]
2023-01-11T21:53:08.2046946Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsqrt_cpu_complex128 PASSED [ 95%]
2023-01-11T21:53:08.2047122Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsqrt_cpu_float64 PASSED [ 95%]
2023-01-11T21:53:08.2047361Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_rsub_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:08.2047720Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scalar_tensor_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 95%]
2023-01-11T21:53:08.2047906Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_add_cpu_float64 PASSED [ 95%]
2023-01-11T21:53:08.2048097Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_amax_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2048275Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_amin_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2048466Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_prod_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2048655Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_scatter_reduce_sum_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2048900Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_select_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:53:08.2049147Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_select_scatter_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:53:08.2049497Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_short_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2049682Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sigmoid_cpu_complex128 PASSED [ 96%]
2023-01-11T21:53:08.2049861Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sigmoid_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2050270Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_bartlett_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2050643Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_blackman_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2050995Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_gaussian_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2051371Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_general_cosine_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2051750Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_general_hamming_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2052115Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_hamming_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2052502Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_hann_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2052869Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signal_windows_nuttall_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2053211Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_signbit_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 96%]
2023-01-11T21:53:08.2053385Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sin_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2053560Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinc_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2053735Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sinh_cpu_float64 PASSED [ 96%]
2023-01-11T21:53:08.2053989Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_softmax_with_dtype_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:53:08.2054225Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_softmax_with_dtype_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:53:08.2054460Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sort_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:53:08.2054712Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sparse_sampled_addmm_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 96%]
2023-01-11T21:53:08.2055067Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_airy_ai_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2055491Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_j1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2055842Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_y0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2056220Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_u_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2056617Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_v_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 97%]
2023-01-11T21:53:08.2057006Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_w_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 97%]
2023-01-11T21:53:08.2057282Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_entr_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2057659Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_hermite_polynomial_h_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2058046Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_hermite_polynomial_he_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2058285Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i0e_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2058513Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i1e_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2058932Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_legendre_polynomial_p_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 97%]
2023-01-11T21:53:08.2059305Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_i0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2059684Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_k1_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2059926Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtr_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2060167Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtri_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2060555Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_scaled_modified_bessel_k0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2060955Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_u_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 97%]
2023-01-11T21:53:08.2061356Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_v_cpu_float64 SKIPPED (Skipping - testing takes an unreasonably long time, #79528) [ 97%]
2023-01-11T21:53:08.2061733Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_spherical_bessel_j0_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2061978Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_xlog1py_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2062333Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_zeta_cpu_float64 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 97%]
2023-01-11T21:53:08.2062573Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2062803Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_list_args_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2063050Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 97%]
2023-01-11T21:53:08.2063293Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2063502Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_complex128 PASSED [ 98%]
2023-01-11T21:53:08.2063677Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_float64 PASSED [ 98%]
2023-01-11T21:53:08.2063858Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_square_cpu_complex128 PASSED [ 98%]
2023-01-11T21:53:08.2064041Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_complex128 PASSED [ 98%]
2023-01-11T21:53:08.2064220Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_float64 PASSED [ 98%]
2023-01-11T21:53:08.2064458Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2064708Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2064948Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2065214Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_unbiased_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2065389Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sub_cpu_float64 PASSED [ 98%]
2023-01-11T21:53:08.2065627Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2065871Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_lowrank_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2066046Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_complex128 PASSED [ 98%]
2023-01-11T21:53:08.2066218Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_float64 PASSED [ 98%]
2023-01-11T21:53:08.2066389Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tan_cpu_float64 PASSED [ 98%]
2023-01-11T21:53:08.2066562Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tanh_cpu_float64 PASSED [ 98%]
2023-01-11T21:53:08.2066802Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensor_split_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2067020Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2067373Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 98%]
2023-01-11T21:53:08.2067611Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2067799Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_complex128 PASSED [ 98%]
2023-01-11T21:53:08.2067981Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_float64 PASSED [ 98%]
2023-01-11T21:53:08.2068228Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triangular_solve_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 98%]
2023-01-11T21:53:08.2068405Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_complex128 PASSED [ 98%]
2023-01-11T21:53:08.2068579Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_float64 PASSED [ 99%]
2023-01-11T21:53:08.2068769Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_true_divide_cpu_complex128 PASSED [ 99%]
2023-01-11T21:53:08.2069006Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2069279Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2069510Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2069750Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2069991Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2070181Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_complex128 PASSED [ 99%]
2023-01-11T21:53:08.2070363Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_float64 PASSED [ 99%]
2023-01-11T21:53:08.2070631Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2070875Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2071111Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2071347Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2071578Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2071824Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_complex_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2072051Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2072285Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2072520Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_copy_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2072752Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2072991Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vsplit_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2073232Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vstack_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2073471Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2073707Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 99%]
2023-01-11T21:53:08.2073881Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zero__cpu_float64 PASSED [ 99%]
2023-01-11T21:53:08.2074228Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [ 99%]
2023-01-11T21:53:08.2074616Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_like_cpu_complex128 SKIPPED (Skipped! Op doesn't support autograd for this dtype.) [100%]
2023-01-11T21:53:08.2074631Z 
2023-01-11T21:53:08.2074744Z =============================== warnings summary ===============================
2023-01-11T21:53:08.2074945Z ../../../../../opt/conda/lib/python3.7/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:53:08.2075300Z   /opt/conda/lib/python3.7/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:53:08.2075397Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:53:08.2075403Z 
2023-01-11T21:53:08.2075642Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:53:08.2075975Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-5a1c1620e6dd6bfb.xml -
2023-01-11T21:53:08.2076119Z = 968 passed, 1519 skipped, 8 deselected, 22 xfailed, 1 warning in 1792.33s (0:29:52) =
2023-01-11T21:53:08.2076291Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:53:08.2076326Z 
2023-01-11T21:53:08.2076733Z ##[endgroup]
2023-01-11T21:53:08.2077017Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_pwud63wt)
2023-01-11T21:53:08.2077023Z 
2023-01-11T21:53:08.8439944Z Executing ['/opt/conda/bin/python', '-bb', 'test_ops_gradients.py', '-v', '--use-pytest', '-vv', '-rfEX', '-x', '--reruns=2', '-k=_linalg_cholesky_', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:53:08.843566]
2023-01-11T21:53:26.8459321Z 
2023-01-11T21:53:26.8460205Z Expand the folded group to see the log file of test_ops_gradients
2023-01-11T21:53:26.8461291Z ##[group]PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_tsv3wrwm)
2023-01-11T21:53:26.8467585Z Test results will be stored in test-reports/python-pytest/test_ops_gradients/test_ops_gradients-c9b05079dbc8142b.xml
2023-01-11T21:53:26.8468201Z ============================= test session starts ==============================
2023-01-11T21:53:26.8468767Z platform linux -- Python 3.7.15, pytest-7.2.0, pluggy-1.0.0 -- /opt/conda/bin/python
2023-01-11T21:53:26.8469152Z cachedir: .pytest_cache
2023-01-11T21:53:26.8469815Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2023-01-11T21:53:26.8470372Z rootdir: /var/lib/jenkins/workspace, configfile: pytest.ini
2023-01-11T21:53:26.8471056Z plugins: hypothesis-5.35.1, flakefinder-1.1.0, rerunfailures-10.3, shard-0.1.2, xdist-3.1.0, xdoctest-1.1.0
2023-01-11T21:53:26.8471554Z collecting ... collected 4945 items / 4925 deselected / 20 selected
2023-01-11T21:53:26.8475205Z Running 20 items in this shard: test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_ex_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_ex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_ex_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_ex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_ex_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_ex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_ex_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_ex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_ex_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_ex_cpu_float64
2023-01-11T21:53:26.8478799Z 
2023-01-11T21:53:26.8479157Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [  5%]
2023-01-11T21:53:26.8479952Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 10%]
2023-01-11T21:53:26.8480871Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_ex_cpu_complex128 SKIPPED (Skipped! Operation does support gradgrad) [ 15%]
2023-01-11T21:53:26.8481609Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_linalg_cholesky_ex_cpu_float64 SKIPPED (Skipped! Operation does support gradgrad) [ 20%]
2023-01-11T21:53:26.8482246Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_cpu_complex128 PASSED [ 25%]
2023-01-11T21:53:26.8482824Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_cpu_float64 PASSED [ 30%]
2023-01-11T21:53:26.8483410Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_ex_cpu_complex128 PASSED [ 35%]
2023-01-11T21:53:26.8484002Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_grad_linalg_cholesky_ex_cpu_float64 PASSED [ 40%]
2023-01-11T21:53:26.8484586Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_cpu_complex128 PASSED [ 45%]
2023-01-11T21:53:26.8485168Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_cpu_float64 PASSED [ 50%]
2023-01-11T21:53:26.8485741Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_ex_cpu_complex128 PASSED [ 55%]
2023-01-11T21:53:26.8486295Z test_ops_gradients.py::TestBwdGradientsCPU::test_fn_gradgrad_linalg_cholesky_ex_cpu_float64 PASSED [ 60%]
2023-01-11T21:53:26.8486909Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 65%]
2023-01-11T21:53:26.8487564Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_cpu_float64 SKIPPED (Op has no inplace variant!) [ 70%]
2023-01-11T21:53:26.8488114Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_ex_cpu_complex128 SKIPPED (Op has no inplace variant!) [ 75%]
2023-01-11T21:53:26.8488581Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_grad_linalg_cholesky_ex_cpu_float64 SKIPPED (Op has no inplace variant!) [ 80%]
2023-01-11T21:53:26.8489190Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 85%]
2023-01-11T21:53:26.8489758Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 90%]
2023-01-11T21:53:26.8490325Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_ex_cpu_complex128 SKIPPED (Skipped! Operation does not support inplace autograd.) [ 95%]
2023-01-11T21:53:26.8490827Z test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_linalg_cholesky_ex_cpu_float64 SKIPPED (Skipped! Operation does not support inplace autograd.) [100%]
2023-01-11T21:53:26.8491240Z 
2023-01-11T21:53:26.8491354Z =============================== warnings summary ===============================
2023-01-11T21:53:26.8491806Z ../../../../../opt/conda/lib/python3.7/site-packages/_pytest/config/__init__.py:1171
2023-01-11T21:53:26.8492334Z   /opt/conda/lib/python3.7/site-packages/_pytest/config/__init__.py:1171: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
2023-01-11T21:53:26.8492728Z     self._mark_plugins_for_rewrite(hook)
2023-01-11T21:53:26.8492858Z 
2023-01-11T21:53:26.8493091Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
2023-01-11T21:53:26.8493662Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-c9b05079dbc8142b.xml -
2023-01-11T21:53:26.8494072Z ========== 8 passed, 12 skipped, 4925 deselected, 1 warning in 11.61s ==========
2023-01-11T21:53:26.8494399Z If in CI, skip info is located in the xml test reports, please either go to s3 or the hud to download them
2023-01-11T21:53:26.8494591Z 
2023-01-11T21:53:26.8494917Z ##[endgroup]
2023-01-11T21:53:26.8495391Z FINISHED PRINTING LOG FILE of test_ops_gradients (/var/lib/jenkins/workspace/test/test-reports/test_ops_gradients_tsv3wrwm)
2023-01-11T21:53:26.8495711Z 
2023-01-11T21:53:26.8495914Z Running distributions/test_distributions ... [2023-01-11 21:53:26.847099]
2023-01-11T21:53:26.8496479Z Executing ['/opt/conda/bin/python', '-bb', 'distributions/test_distributions.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:53:26.847542]
2023-01-11T21:55:07.3916740Z 
2023-01-11T21:55:07.3917285Z Expand the folded group to see the log file of distributions/test_distributions
2023-01-11T21:55:07.3921149Z ##[group]PRINTING LOG FILE of distributions/test_distributions (/var/lib/jenkins/workspace/test/test-reports/distributions-test_distributions_q35jv08e)
2023-01-11T21:55:07.3922224Z Test results will be stored in test-reports/python-unittest/distributions.test_distributions
2023-01-11T21:55:07.3922575Z 
2023-01-11T21:55:07.3922699Z Running tests...
2023-01-11T21:55:07.3923242Z ----------------------------------------------------------------------
2023-01-11T21:55:07.3924185Z   test_cdf (__main__.TestAgainstScipy) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.3924985Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.3925303Z ok (0.104s)
2023-01-11T21:55:07.3925573Z   test_icdf (__main__.TestAgainstScipy) ... ok (0.046s)
2023-01-11T21:55:07.3925951Z   test_mean (__main__.TestAgainstScipy) ... ok (1.404s)
2023-01-11T21:55:07.3927859Z   test_variance_stddev (__main__.TestAgainstScipy) ... /opt/conda/lib/python3.7/site-packages/torch/testing/_comparison.py:679: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:210.)
2023-01-11T21:55:07.3929082Z   return torch.as_tensor(tensor_like)
2023-01-11T21:55:07.3929396Z ok (0.082s)
2023-01-11T21:55:07.3929716Z   test_params_constraints (__main__.TestConstraints) ... ok (0.198s)
2023-01-11T21:55:07.3930159Z   test_support_constraints (__main__.TestConstraints) ... ok (0.239s)
2023-01-11T21:55:07.3930650Z   test_bernoulli_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3931160Z   test_bernoulli_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3931667Z   test_beta_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3932156Z   test_beta_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.005s)
2023-01-11T21:55:07.3932846Z   test_binomial_shape (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3933283Z   test_binomial_shape_vectorized_n (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3933789Z   test_categorical_shape (__main__.TestDistributionShapes) ... ok (0.008s)
2023-01-11T21:55:07.3934262Z   test_cauchy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3934744Z   test_cauchy_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3935169Z   test_chi2_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3935698Z   test_chi2_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3936216Z   test_continuous_bernoulli_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.006s)
2023-01-11T21:55:07.3936768Z   test_continuous_bernoulli_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.007s)
2023-01-11T21:55:07.3937255Z   test_dirichlet_shape (__main__.TestDistributionShapes) ... ok (0.005s)
2023-01-11T21:55:07.3937811Z   test_entropy_shape (__main__.TestDistributionShapes) ... ok (0.081s)
2023-01-11T21:55:07.3938289Z   test_exponential_shape_scalar_param (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3938767Z   test_exponential_shape_tensor_param (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3939294Z   test_gamma_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3939725Z   test_gamma_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3940148Z   test_geometric_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3940666Z   test_geometric_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3941124Z   test_gumbel_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3941577Z   test_halfcauchy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3942027Z   test_halfcauchy_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3942554Z   test_kumaraswamy_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.005s)
2023-01-11T21:55:07.3943037Z   test_laplace_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3943506Z   test_laplace_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3943997Z   test_mixture_same_family_shape (__main__.TestDistributionShapes) ... ok (0.006s)
2023-01-11T21:55:07.3944469Z   test_multinomial_shape (__main__.TestDistributionShapes) ... ok (0.006s)
2023-01-11T21:55:07.3944889Z   test_normal_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3945325Z   test_normal_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3945834Z   test_one_hot_categorical_shape (__main__.TestDistributionShapes) ... ok (0.013s)
2023-01-11T21:55:07.3946303Z   test_pareto_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3946757Z   test_studentT_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3947227Z   test_studentT_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3947703Z   test_uniform_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3948205Z   test_uniform_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.003s)
2023-01-11T21:55:07.3948674Z   test_vonmises_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.007s)
2023-01-11T21:55:07.3949185Z   test_vonmises_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.007s)
2023-01-11T21:55:07.3949662Z   test_weibull_scale_scalar_params (__main__.TestDistributionShapes) ... ok (0.004s)
2023-01-11T21:55:07.3950160Z   test_wishart_shape_scalar_params (__main__.TestDistributionShapes) ... ok (0.009s)
2023-01-11T21:55:07.3950593Z   test_wishart_shape_tensor_params (__main__.TestDistributionShapes) ... ok (0.014s)
2023-01-11T21:55:07.3951020Z   test_argmax_relaxed_categorical (__main__.TestDistributions) ... ok (0.104s)
2023-01-11T21:55:07.3951389Z   test_bernoulli (__main__.TestDistributions) ... ok (0.232s)
2023-01-11T21:55:07.3951752Z   test_bernoulli_3d (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:55:07.3952137Z   test_bernoulli_enumerate_support (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:55:07.3952558Z   test_beta_log_prob (__main__.TestDistributions) ... ok (0.170s)
2023-01-11T21:55:07.3952947Z   test_beta_sample (__main__.TestDistributions) ... ok (0.499s)
2023-01-11T21:55:07.3953370Z   test_beta_shape (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:55:07.3953761Z   test_beta_underflow (__main__.TestDistributions) ... ok (0.098s)
2023-01-11T21:55:07.3954204Z   test_beta_underflow_gpu (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:55:07.3954666Z   test_binomial (__main__.TestDistributions) ... ok (0.133s)
2023-01-11T21:55:07.3955220Z   test_binomial_enumerate_support (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:55:07.3955689Z   test_binomial_extreme_vals (__main__.TestDistributions) ... ok (0.010s)
2023-01-11T21:55:07.3956101Z   test_binomial_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.082s)
2023-01-11T21:55:07.3956572Z   test_binomial_log_prob_vectorized_count (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:55:07.3956993Z   test_binomial_sample (__main__.TestDistributions) ... ok (0.085s)
2023-01-11T21:55:07.3957355Z   test_binomial_stable (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:55:07.3957738Z   test_binomial_vectorized_count (__main__.TestDistributions) ... ok (0.129s)
2023-01-11T21:55:07.3958120Z   test_categorical_1d (__main__.TestDistributions) ... ok (0.019s)
2023-01-11T21:55:07.3958473Z   test_categorical_2d (__main__.TestDistributions) ... ok (0.028s)
2023-01-11T21:55:07.3958874Z   test_categorical_enumerate_support (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:55:07.3959263Z   test_cauchy (__main__.TestDistributions) ... ok (0.046s)
2023-01-11T21:55:07.3960041Z   test_cdf_icdf_inverse (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.3960510Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.3960938Z ok (0.792s)
2023-01-11T21:55:07.3961233Z   test_cdf_log_prob (__main__.TestDistributions) ... ok (0.344s)
2023-01-11T21:55:07.3961583Z   test_chi2_sample (__main__.TestDistributions) ... ok (0.126s)
2023-01-11T21:55:07.3961955Z   test_chi2_shape (__main__.TestDistributions) ... ok (0.010s)
2023-01-11T21:55:07.3962330Z   test_continuous_bernoulli (__main__.TestDistributions) ... ok (0.057s)
2023-01-11T21:55:07.3962733Z   test_continuous_bernoulli_3d (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:55:07.3963111Z   test_dirichlet_log_prob (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:55:07.3963521Z   test_dirichlet_mode (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:55:07.3963914Z   test_dirichlet_sample (__main__.TestDistributions) ... ok (0.055s)
2023-01-11T21:55:07.3964309Z   test_dirichlet_shape (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:55:07.3965039Z   test_distribution_expand (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.3965518Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.3965781Z ok (2.277s)
2023-01-11T21:55:07.3966083Z   test_distribution_subclass_expand (__main__.TestDistributions) ... ok (0.569s)
2023-01-11T21:55:07.3966484Z   test_enumerate_support_type (__main__.TestDistributions) ... ok (0.112s)
2023-01-11T21:55:07.3967000Z   test_exponential (__main__.TestDistributions) ... ok (0.112s)
2023-01-11T21:55:07.3967364Z   test_exponential_sample (__main__.TestDistributions) ... ok (0.112s)
2023-01-11T21:55:07.3967743Z   test_fishersnedecor (__main__.TestDistributions) ... ok (0.016s)
2023-01-11T21:55:07.3968129Z   test_fishersnedecor_sample (__main__.TestDistributions) ... ok (1.172s)
2023-01-11T21:55:07.3968524Z   test_gamma_gpu_sample (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:55:07.3968952Z   test_gamma_gpu_shape (__main__.TestDistributions) ... skip: CUDA not found (0.002s)
2023-01-11T21:55:07.3969357Z   test_gamma_log_prob_at_boundary (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:55:07.3969735Z   test_gamma_sample (__main__.TestDistributions) ... ok (0.394s)
2023-01-11T21:55:07.3970081Z   test_gamma_shape (__main__.TestDistributions) ... ok (0.010s)
2023-01-11T21:55:07.3970447Z   test_geometric (__main__.TestDistributions) ... ok (0.020s)
2023-01-11T21:55:07.3970844Z   test_geometric_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.017s)
2023-01-11T21:55:07.3971232Z   test_geometric_sample (__main__.TestDistributions) ... ok (0.010s)
2023-01-11T21:55:07.3971688Z   test_gumbel (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:55:07.3972043Z   test_gumbel_sample (__main__.TestDistributions) ... ok (0.777s)
2023-01-11T21:55:07.3972403Z   test_halfcauchy (__main__.TestDistributions) ... ok (0.032s)
2023-01-11T21:55:07.3972755Z   test_halfnormal (__main__.TestDistributions) ... ok (0.030s)
2023-01-11T21:55:07.3973120Z   test_halfnormal_logprob (__main__.TestDistributions) ... ok (0.008s)
2023-01-11T21:55:07.3973499Z   test_halfnormal_sample (__main__.TestDistributions) ... ok (0.131s)
2023-01-11T21:55:07.3973856Z   test_has_examples (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:55:07.3974583Z   test_independent_expand (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.3975076Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.3975342Z ok (2.438s)
2023-01-11T21:55:07.3975717Z   test_independent_shape (__main__.TestDistributions) ... ok (1.060s)
2023-01-11T21:55:07.3976122Z   test_invalid_parameter_broadcasting (__main__.TestDistributions) ... ok (0.071s)
2023-01-11T21:55:07.3976535Z   test_kumaraswamy_mean_variance (__main__.TestDistributions) ... ok (0.112s)
2023-01-11T21:55:07.3976906Z   test_kumaraswamy_shape (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:55:07.3977273Z   test_laplace (__main__.TestDistributions) ... ok (0.069s)
2023-01-11T21:55:07.3977630Z   test_laplace_sample (__main__.TestDistributions) ... ok (0.371s)
2023-01-11T21:55:07.3977992Z   test_lazy_property_grad (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:55:07.3978368Z   test_lkj_cholesky_log_prob (__main__.TestDistributions) ... ok (0.054s)
2023-01-11T21:55:07.3978747Z   test_logisticnormal (__main__.TestDistributions) ... ok (0.096s)
2023-01-11T21:55:07.3979143Z   test_logisticnormal_logprob (__main__.TestDistributions) ... ok (0.004s)
2023-01-11T21:55:07.3979520Z   test_logisticnormal_sample (__main__.TestDistributions) ... ok (0.537s)
2023-01-11T21:55:07.3979898Z   test_lognormal (__main__.TestDistributions) ... ok (0.061s)
2023-01-11T21:55:07.3980261Z   test_lognormal_logprob (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:55:07.3980619Z   test_lognormal_sample (__main__.TestDistributions) ... ok (0.355s)
2023-01-11T21:55:07.3981026Z   test_lowrank_multivariate_normal_log_prob (__main__.TestDistributions) ... ok (0.026s)
2023-01-11T21:55:07.3981459Z   test_lowrank_multivariate_normal_moments (__main__.TestDistributions) ... ok (0.094s)
2023-01-11T21:55:07.3981908Z   test_lowrank_multivariate_normal_properties (__main__.TestDistributions) ... ok (0.009s)
2023-01-11T21:55:07.3982335Z   test_lowrank_multivariate_normal_sample (__main__.TestDistributions) ... ok (0.055s)
2023-01-11T21:55:07.3982847Z   test_lowrank_multivariate_normal_shape (__main__.TestDistributions) ... ok (0.221s)
2023-01-11T21:55:07.3983272Z   test_mixture_same_family_log_prob (__main__.TestDistributions) ... ok (0.011s)
2023-01-11T21:55:07.3983667Z   test_mixture_same_family_sample (__main__.TestDistributions) ... ok (0.065s)
2023-01-11T21:55:07.3984066Z   test_mixture_same_family_shape (__main__.TestDistributions) ... ok (0.023s)
2023-01-11T21:55:07.3984781Z   test_mode (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.3985249Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.3985502Z ok (0.546s)
2023-01-11T21:55:07.3985797Z   test_multinomial_1d (__main__.TestDistributions) ... ok (0.042s)
2023-01-11T21:55:07.3986197Z   test_multinomial_1d_log_prob_and_entropy (__main__.TestDistributions) ... ok (0.011s)
2023-01-11T21:55:07.3986588Z   test_multinomial_2d (__main__.TestDistributions) ... ok (0.044s)
2023-01-11T21:55:07.3986991Z   test_multivariate_normal_log_prob (__main__.TestDistributions) ... ok (0.025s)
2023-01-11T21:55:07.3987407Z   test_multivariate_normal_moments (__main__.TestDistributions) ... ok (0.049s)
2023-01-11T21:55:07.3987869Z   test_multivariate_normal_properties (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:55:07.3988290Z   test_multivariate_normal_sample (__main__.TestDistributions) ... ok (0.144s)
2023-01-11T21:55:07.3988696Z   test_multivariate_normal_shape (__main__.TestDistributions) ... ok (0.313s)
2023-01-11T21:55:07.3989131Z   test_multivariate_normal_stable_with_precision_matrix (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:55:07.3989541Z   test_negative_binomial (__main__.TestDistributions) ... ok (0.096s)
2023-01-11T21:55:07.3989941Z   test_negative_binomial_log_prob (__main__.TestDistributions) ... ok (0.067s)
2023-01-11T21:55:07.3990382Z   test_negative_binomial_log_prob_vectorized_count (__main__.TestDistributions) ... ok (0.007s)
2023-01-11T21:55:07.3990767Z   test_normal (__main__.TestDistributions) ... ok (0.062s)
2023-01-11T21:55:07.3991123Z   test_normal_sample (__main__.TestDistributions) ... ok (0.341s)
2023-01-11T21:55:07.3991499Z   test_one_hot_categorical_1d (__main__.TestDistributions) ... ok (0.025s)
2023-01-11T21:55:07.3991891Z   test_one_hot_categorical_2d (__main__.TestDistributions) ... ok (0.025s)
2023-01-11T21:55:07.3992288Z   test_one_hot_categorical_enumerate_support (__main__.TestDistributions) ... ok (0.005s)
2023-01-11T21:55:07.3992681Z   test_pareto (__main__.TestDistributions) ... ok (0.012s)
2023-01-11T21:55:07.3993038Z   test_pareto_sample (__main__.TestDistributions) ... ok (0.346s)
2023-01-11T21:55:07.3993401Z   test_poisson_forward_ad (__main__.TestDistributions) ... ok (0.001s)
2023-01-11T21:55:07.3993811Z   test_poisson_gpu_sample (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:55:07.3994221Z   test_poisson_log_prob (__main__.TestDistributions) ... ok (0.022s)
2023-01-11T21:55:07.3994598Z   test_poisson_sample (__main__.TestDistributions) ... ok (0.011s)
2023-01-11T21:55:07.3994957Z   test_poisson_shape (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:55:07.3995341Z   test_relaxed_bernoulli (__main__.TestDistributions) ... ok (0.064s)
2023-01-11T21:55:07.3995745Z   test_relaxed_one_hot_categorical_1d (__main__.TestDistributions) ... ok (0.037s)
2023-01-11T21:55:07.3996147Z   test_relaxed_one_hot_categorical_2d (__main__.TestDistributions) ... ok (0.060s)
2023-01-11T21:55:07.3996515Z   test_repr (__main__.TestDistributions) ... ok (0.070s)
2023-01-11T21:55:07.3996884Z   test_rounded_relaxed_bernoulli (__main__.TestDistributions) ... ok (0.051s)
2023-01-11T21:55:07.3997623Z   test_rsample_requires_grad (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.3998095Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.3998361Z ok (0.073s)
2023-01-11T21:55:07.3998731Z   test_sample_detached (__main__.TestDistributions) ... ok (0.076s)
2023-01-11T21:55:07.3999087Z   test_studentT (__main__.TestDistributions) ... ok (0.016s)
2023-01-11T21:55:07.3999455Z   test_studentT_log_prob (__main__.TestDistributions) ... ok (0.157s)
2023-01-11T21:55:07.3999830Z   test_studentT_sample (__main__.TestDistributions) ... ok (1.571s)
2023-01-11T21:55:07.4000197Z   test_support_attributes (__main__.TestDistributions) ... ok (0.070s)
2023-01-11T21:55:07.4000569Z   test_uniform (__main__.TestDistributions) ... ok (0.038s)
2023-01-11T21:55:07.4001103Z   test_valid_parameter_broadcasting (__main__.TestDistributions) ... ok (0.080s)
2023-01-11T21:55:07.4001502Z   test_vonmises_logprob (__main__.TestDistributions) ... ok (0.083s)
2023-01-11T21:55:07.4001862Z   test_vonmises_sample (__main__.TestDistributions) ... ok (11.177s)
2023-01-11T21:55:07.4002582Z   test_wishart_log_prob (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4003068Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4003325Z ok (0.294s)
2023-01-11T21:55:07.4004186Z   test_wishart_moments (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:55:07.4004985Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:55:07.4005331Z ok (6.411s)
2023-01-11T21:55:07.4005619Z   test_wishart_properties (__main__.TestDistributions) ... ok (0.006s)
2023-01-11T21:55:07.4005998Z   test_wishart_sample (__main__.TestDistributions) ... ok (0.607s)
2023-01-11T21:55:07.4006691Z   test_wishart_shape (__main__.TestDistributions) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4007169Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4007715Z /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4008135Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4008689Z /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4009105Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4009637Z /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4010051Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4010602Z /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4010993Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4011551Z /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4011968Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4012220Z ok (0.639s)
2023-01-11T21:55:07.4012554Z   test_wishart_stable_with_precision_matrix (__main__.TestDistributions) ... ok (0.003s)
2023-01-11T21:55:07.4012999Z   test_zero_excluded_binomial (__main__.TestDistributions) ... skip: CUDA not found (0.001s)
2023-01-11T21:55:07.4013397Z   test_cat_event_dim (__main__.TestFunctors) ... ok (0.003s)
2023-01-11T21:55:07.4013731Z   test_cat_transform (__main__.TestFunctors) ... ok (0.008s)
2023-01-11T21:55:07.4014095Z   test_cat_transform_non_uniform (__main__.TestFunctors) ... ok (0.009s)
2023-01-11T21:55:07.4014466Z   test_stack_transform (__main__.TestFunctors) ... ok (0.008s)
2023-01-11T21:55:07.4015091Z   test_cdf (__main__.TestJit) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:253: UserWarning: Singular sample detected.
2023-01-11T21:55:07.4015634Z   warnings.warn("Singular sample detected.")
2023-01-11T21:55:07.4015996Z ok (4.193s)
2023-01-11T21:55:07.4016725Z   test_entropy (__main__.TestJit) ... /opt/conda/lib/python3.7/site-packages/torch/distributions/wishart.py:107: UserWarning: Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.
2023-01-11T21:55:07.4017461Z   warnings.warn("Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.")
2023-01-11T21:55:07.4017818Z ok (4.421s)
2023-01-11T21:55:07.4018119Z   test_enumerate_support (__main__.TestJit) ... ok (1.535s)
2023-01-11T21:55:07.4018429Z   test_log_prob (__main__.TestJit) ... ok (8.651s)
2023-01-11T21:55:07.4018745Z   test_mean (__main__.TestJit) ... ok (4.213s)
2023-01-11T21:55:07.4019044Z   test_rsample (__main__.TestJit) ... ok (2.713s)
2023-01-11T21:55:07.4019336Z   test_sample (__main__.TestJit) ... ok (3.069s)
2023-01-11T21:55:07.4019639Z   test_variance (__main__.TestJit) ... ok (5.548s)
2023-01-11T21:55:07.4019976Z   test_entropy_exponential_family (__main__.TestKL) ... ok (0.165s)
2023-01-11T21:55:07.4020336Z   test_entropy_monte_carlo (__main__.TestKL) ... ok (11.209s)
2023-01-11T21:55:07.4020648Z   test_kl_edgecases (__main__.TestKL) ... ok (0.046s)
2023-01-11T21:55:07.4021038Z   test_kl_exponential_family (__main__.TestKL) ... ok (0.088s)
2023-01-11T21:55:07.4021373Z   test_kl_infinite (__main__.TestKL) ... ok (0.071s)
2023-01-11T21:55:07.4021696Z   test_kl_lowrank_multivariate_normal (__main__.TestKL) ... ok (0.097s)
2023-01-11T21:55:07.4022086Z   test_kl_lowrank_multivariate_normal_batched (__main__.TestKL) ... ok (0.074s)
2023-01-11T21:55:07.4022444Z   test_kl_monte_carlo (__main__.TestKL) ... ok (3.866s)
2023-01-11T21:55:07.4022765Z   test_kl_multivariate_normal (__main__.TestKL) ... ok (0.162s)
2023-01-11T21:55:07.4023124Z   test_kl_multivariate_normal_batched (__main__.TestKL) ... ok (0.061s)
2023-01-11T21:55:07.4023519Z   test_kl_multivariate_normal_batched_broadcasted (__main__.TestKL) ... ok (0.062s)
2023-01-11T21:55:07.4023875Z   test_kl_shape (__main__.TestKL) ... ok (0.201s)
2023-01-11T21:55:07.4024177Z   test_kl_transformed (__main__.TestKL) ... ok (0.044s)
2023-01-11T21:55:07.4024572Z   test_lazy_logits_initialization (__main__.TestLazyLogitsInitialization) ... ok (0.015s)
2023-01-11T21:55:07.4025046Z   test_lazy_probs_initialization (__main__.TestLazyLogitsInitialization) ... ok (0.007s)
2023-01-11T21:55:07.4025475Z   test_bernoulli_gradient (__main__.TestNumericalStability) ... ok (0.052s)
2023-01-11T21:55:07.4025902Z   test_bernoulli_with_logits_overflow (__main__.TestNumericalStability) ... ok (0.008s)
2023-01-11T21:55:07.4026336Z   test_bernoulli_with_logits_underflow (__main__.TestNumericalStability) ... ok (0.008s)
2023-01-11T21:55:07.4026764Z   test_categorical_log_prob (__main__.TestNumericalStability) ... ok (0.005s)
2023-01-11T21:55:07.4027179Z   test_categorical_log_prob_with_logits (__main__.TestNumericalStability) ... ok (0.006s)
2023-01-11T21:55:07.4027631Z   test_continuous_bernoulli_gradient (__main__.TestNumericalStability) ... ok (0.113s)
2023-01-11T21:55:07.4028087Z   test_continuous_bernoulli_with_logits_overflow (__main__.TestNumericalStability) ... ok (0.015s)
2023-01-11T21:55:07.4028542Z   test_continuous_bernoulli_with_logits_underflow (__main__.TestNumericalStability) ... ok (0.015s)
2023-01-11T21:55:07.4028988Z   test_multinomial_log_prob (__main__.TestNumericalStability) ... ok (0.006s)
2023-01-11T21:55:07.4029419Z   test_multinomial_log_prob_with_logits (__main__.TestNumericalStability) ... ok (0.008s)
2023-01-11T21:55:07.4029810Z   test_beta_wrt_alpha (__main__.TestRsample) ... ok (0.090s)
2023-01-11T21:55:07.4030134Z   test_beta_wrt_beta (__main__.TestRsample) ... ok (0.089s)
2023-01-11T21:55:07.4030455Z   test_chi2 (__main__.TestRsample) ... ok (0.041s)
2023-01-11T21:55:07.4030798Z   test_dirichlet_multivariate (__main__.TestRsample) ... ok (1.435s)
2023-01-11T21:55:07.4031160Z   test_dirichlet_on_diagonal (__main__.TestRsample) ... ok (0.088s)
2023-01-11T21:55:07.4031545Z   test_dirichlet_tangent_field (__main__.TestRsample) ... ok (0.426s)
2023-01-11T21:55:07.4031957Z   test_gamma (__main__.TestRsample) ... ok (0.039s)
2023-01-11T21:55:07.4032483Z   test_invalid (__main__.TestValidation) ... skip: division-by-zero error with UBSAN (0.001s)
2023-01-11T21:55:07.4032890Z   test_invalid_log_probs_arg (__main__.TestValidation) ... ok (1.233s)
2023-01-11T21:55:07.4033243Z   test_valid (__main__.TestValidation) ... ok (0.055s)
2023-01-11T21:55:07.4033634Z   test_warning_unimplemented_constraints (__main__.TestValidation) ... ok (0.007s)
2023-01-11T21:55:07.4033881Z 
2023-01-11T21:55:07.4034134Z ----------------------------------------------------------------------
2023-01-11T21:55:07.4034453Z Ran 219 tests in 95.103s
2023-01-11T21:55:07.4034608Z 
2023-01-11T21:55:07.4034703Z OK (skipped=6)
2023-01-11T21:55:07.4034841Z 
2023-01-11T21:55:07.4034954Z Generating XML reports...
2023-01-11T21:55:07.4035546Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestAgainstScipy-20230111215331.xml
2023-01-11T21:55:07.4036318Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestConstraints-20230111215331.xml
2023-01-11T21:55:07.4037187Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributionShapes-20230111215331.xml
2023-01-11T21:55:07.4037956Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributions-20230111215331.xml
2023-01-11T21:55:07.4038696Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestFunctors-20230111215331.xml
2023-01-11T21:55:07.4039398Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestJit-20230111215331.xml
2023-01-11T21:55:07.4040099Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestKL-20230111215331.xml
2023-01-11T21:55:07.4041001Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestLazyLogitsInitialization-20230111215331.xml
2023-01-11T21:55:07.4041839Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestNumericalStability-20230111215331.xml
2023-01-11T21:55:07.4042604Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestRsample-20230111215331.xml
2023-01-11T21:55:07.4043347Z Generated XML report: test-reports/python-unittest/distributions.test_distributions/TEST-TestValidation-20230111215331.xml
2023-01-11T21:55:07.4043672Z 
2023-01-11T21:55:07.4044074Z ##[endgroup]
2023-01-11T21:55:07.4044687Z FINISHED PRINTING LOG FILE of distributions/test_distributions (/var/lib/jenkins/workspace/test/test-reports/distributions-test_distributions_q35jv08e)
2023-01-11T21:55:07.4045034Z 
2023-01-11T21:55:07.4045291Z Running nn/test_convolution ... [2023-01-11 21:55:07.392529]
2023-01-11T21:55:07.4045934Z Executing ['/opt/conda/bin/python', '-bb', 'nn/test_convolution.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:55:07.392989]
2023-01-11T21:55:59.0198470Z 
2023-01-11T21:55:59.0198946Z Expand the folded group to see the log file of nn/test_convolution
2023-01-11T21:55:59.0201828Z ##[group]PRINTING LOG FILE of nn/test_convolution (/var/lib/jenkins/workspace/test/test-reports/nn-test_convolution_1apoiion)
2023-01-11T21:55:59.0203073Z Test results will be stored in test-reports/python-unittest/nn.test_convolution
2023-01-11T21:55:59.0203588Z 
2023-01-11T21:55:59.0203857Z Running tests...
2023-01-11T21:55:59.0204407Z ----------------------------------------------------------------------
2023-01-11T21:55:59.0205617Z   test_Conv1d_module_same_padding (__main__.TestConvolutionNN) ... nn/test_convolution.py:152: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/native/Convolution.cpp:997.)
2023-01-11T21:55:59.0206607Z   expect = F.conv1d(x, module.weight, module.bias, padding='same')
2023-01-11T21:55:59.0207268Z ok (0.011s)
2023-01-11T21:55:59.0207724Z   test_Conv2d_1x1 (__main__.TestConvolutionNN) ... ok (0.021s)
2023-01-11T21:55:59.0208336Z   test_Conv2d_OneDNN (__main__.TestConvolutionNN) ... ok (0.115s)
2023-01-11T21:55:59.0208966Z   test_Conv2d_backward_twice (__main__.TestConvolutionNN) ... ok (0.013s)
2023-01-11T21:55:59.0209612Z   test_Conv2d_groups_nobias (__main__.TestConvolutionNN) ... ok (0.010s)
2023-01-11T21:55:59.0210238Z   test_Conv2d_groups_nobias_v2 (__main__.TestConvolutionNN) ... ok (0.010s)
2023-01-11T21:55:59.0210888Z   test_Conv2d_inconsistent_types (__main__.TestConvolutionNN) ... ok (0.025s)
2023-01-11T21:55:59.0211664Z   test_Conv2d_inconsistent_types_on_GPU_with_cudnn (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:55:59.0241822Z   test_Conv2d_inconsistent_types_on_GPU_without_cudnn (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:55:59.0242340Z   test_Conv2d_missing_argument (__main__.TestConvolutionNN) ... ok (0.003s)
2023-01-11T21:55:59.0242692Z   test_Conv2d_module_same_padding (__main__.TestConvolutionNN) ... ok (0.008s)
2023-01-11T21:55:59.0242989Z   test_Conv3d_groups_nobias (__main__.TestConvolutionNN) ... ok (0.012s)
2023-01-11T21:55:59.0282536Z   test_Conv3d_groups_wbias (__main__.TestConvolutionNN) ... ok (0.013s)
2023-01-11T21:55:59.0283075Z   test_Conv3d_module_same_padding (__main__.TestConvolutionNN) ... ok (0.009s)
2023-01-11T21:55:59.0283677Z   test_ConvTranspose2d_half_cublas_gemm (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:55:59.0284303Z   test_ConvTranspose2d_output_size (__main__.TestConvolutionNN) ... ok (0.006s)
2023-01-11T21:55:59.0284719Z   test_ConvTranspose2d_output_size_downsample_upsample (__main__.TestConvolutionNN) ... ok (6.999s)
2023-01-11T21:55:59.0285274Z   test_ConvTranspose3d_correct_output_size (__main__.TestConvolutionNN) ... ok (0.002s)
2023-01-11T21:55:59.0285765Z   test_conv2d_discontiguous_weight (__main__.TestConvolutionNN) ... ok (0.051s)
2023-01-11T21:55:59.0286306Z   test_conv_backcompat (__main__.TestConvolutionNN) ... ok (0.039s)
2023-01-11T21:55:59.0286813Z   test_conv_cudnn_memory_layout_dominance (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:55:59.0287126Z   test_conv_invalid_groups (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:55:59.0287450Z   test_conv_modules_raise_error_on_incorrect_input_size (__main__.TestConvolutionNN) ... ok (0.195s)
2023-01-11T21:55:59.0287764Z   test_conv_padding_mode (__main__.TestConvolutionNN) ... ok (0.001s)
2023-01-11T21:55:59.0288809Z   test_conv_shapecheck (__main__.TestConvolutionNN) ... /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1121: UserWarning: Complex modules are a new feature under active development whose design may change, and some modules might not work as expected when using complex tensors as parameters or buffers. Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml if a complex module does not work as expected.
2023-01-11T21:55:59.0289543Z   "Complex modules are a new feature under active development whose design may change, "
2023-01-11T21:55:59.0289790Z ok (0.362s)
2023-01-11T21:55:59.0289996Z   test_conv_tbc (__main__.TestConvolutionNN) ... ok (0.018s)
2023-01-11T21:55:59.0290298Z   test_cudnn_non_contiguous (__main__.TestConvolutionNN) ... skip: CUDA not available (0.001s)
2023-01-11T21:55:59.0290647Z   test_cudnn_noncontiguous_weight (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:55:59.0290953Z   test_functional_grad_conv (__main__.TestConvolutionNN) ... ok (0.010s)
2023-01-11T21:55:59.0291250Z   test_functional_grad_conv2d (__main__.TestConvolutionNN) ... ok (1.246s)
2023-01-11T21:55:59.0291537Z   test_grad_conv1d_input (__main__.TestConvolutionNN) ... ok (0.115s)
2023-01-11T21:55:59.0291808Z   test_grad_conv1d_weight (__main__.TestConvolutionNN) ... ok (0.118s)
2023-01-11T21:55:59.0292093Z   test_grad_conv2d_input (__main__.TestConvolutionNN) ... ok (0.131s)
2023-01-11T21:55:59.0292501Z   test_grad_conv2d_weight (__main__.TestConvolutionNN) ... ok (0.122s)
2023-01-11T21:55:59.0292784Z   test_grad_conv3d_input (__main__.TestConvolutionNN) ... ok (0.218s)
2023-01-11T21:55:59.0293058Z   test_grad_conv3d_weight (__main__.TestConvolutionNN) ... ok (0.182s)
2023-01-11T21:55:59.0293384Z   test_grouped_conv_cudnn_nhwc_support (__main__.TestConvolutionNN) ... skip: CUDA unavailable (0.001s)
2023-01-11T21:55:59.0294405Z   test_invalid_conv1d (__main__.TestConvolutionNN) ... /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1121: UserWarning: Complex modules are a new feature under active development whose design may change, and some modules might not work as expected when using complex tensors as parameters or buffers. Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml if a complex module does not work as expected.
2023-01-11T21:55:59.0295093Z   "Complex modules are a new feature under active development whose design may change, "
2023-01-11T21:55:59.0295326Z ok (0.163s)
2023-01-11T21:55:59.0295644Z   test_invalid_conv2d (__main__.TestConvolutionNN) ... ok (0.326s)
2023-01-11T21:55:59.0295974Z   test_invalid_conv3d (__main__.TestConvolutionNN) ... ok (0.159s)
2023-01-11T21:55:59.0296260Z   test_mismatch_shape_conv2d (__main__.TestConvolutionNN) ... ok (0.012s)
2023-01-11T21:55:59.0296527Z   test_nnpack_conv (__main__.TestConvolutionNN) ... ok (0.505s)
2023-01-11T21:55:59.0296849Z   test_thnn_conv_strided_padded_dilated (__main__.TestConvolutionNN) ... skip: CUDA not available (0.002s)
2023-01-11T21:55:59.0300280Z   test_Conv2d_backward_depthwise_cpu_complex128 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (11.739s)
2023-01-11T21:55:59.0300888Z   test_Conv2d_backward_depthwise_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.964s)
2023-01-11T21:55:59.0301590Z   test_Conv2d_depthwise_naive_groups_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.004s)
2023-01-11T21:55:59.0302355Z   test_Conv2d_depthwise_naive_groups_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0302783Z   test_Conv2d_depthwise_naive_groups_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0303189Z   test_Conv2d_deterministic_cudnn_cpu_complex128 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0303609Z   test_Conv2d_deterministic_cudnn_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0304122Z   test_Conv2d_deterministic_cudnn_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0304697Z   test_Conv2d_deterministic_cudnn_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0305349Z   test_Conv2d_deterministic_cudnn_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0306016Z   test_Conv2d_large_workspace_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0306678Z   test_Conv2d_large_workspace_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0307315Z   test_Conv2d_large_workspace_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0307881Z   test_Conv2d_naive_groups_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:55:59.0308473Z   test_Conv2d_size_1_kernel_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0309100Z   test_Conv3d_depthwise_naive_groups_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0309784Z   test_Conv3d_depthwise_naive_groups_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0310542Z   test_Conv3d_depthwise_naive_groups_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0311284Z   test_ConvTranspose2d_large_output_padding_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0312105Z   test_ConvTranspose2d_large_output_padding_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0312885Z   test_ConvTranspose2d_size_1_kernel_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0313395Z   test_ConvTranspose3d_size_1_kernel_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0313797Z   test_contig_wrong_stride_cudnn_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0314196Z   test_conv1d_same_padding_backward_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.018s)
2023-01-11T21:55:59.0314655Z   test_conv1d_same_padding_backward_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:55:59.0315022Z   test_conv1d_same_padding_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.069s)
2023-01-11T21:55:59.0315387Z   test_conv1d_same_padding_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.024s)
2023-01-11T21:55:59.0315768Z   test_conv1d_valid_padding_backward_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:55:59.0316159Z   test_conv1d_valid_padding_backward_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0316524Z   test_conv1d_valid_padding_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:55:59.0316892Z   test_conv1d_valid_padding_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:55:59.0317267Z   test_conv1d_vs_scipy_mode_same_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:55:59.0317634Z   test_conv1d_vs_scipy_mode_same_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:55:59.0318010Z   test_conv1d_vs_scipy_mode_valid_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:55:59.0318388Z   test_conv1d_vs_scipy_mode_valid_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0318749Z   test_conv2d_no_grad_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.022s)
2023-01-11T21:55:59.0319108Z   test_conv2d_same_padding_backward_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.019s)
2023-01-11T21:55:59.0319496Z   test_conv2d_same_padding_backward_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:55:59.0319871Z   test_conv2d_same_padding_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.011s)
2023-01-11T21:55:59.0320244Z   test_conv2d_same_padding_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:55:59.0320804Z   test_conv2d_valid_padding_backward_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:55:59.0321347Z   test_conv2d_valid_padding_backward_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0322047Z   test_conv2d_valid_padding_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:55:59.0322595Z   test_conv2d_valid_padding_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:55:59.0323223Z   test_conv2d_vs_scipy_mode_same_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:55:59.0323871Z   test_conv2d_vs_scipy_mode_same_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:55:59.0324251Z   test_conv2d_vs_scipy_mode_valid_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.006s)
2023-01-11T21:55:59.0324717Z   test_conv2d_vs_scipy_mode_valid_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0325105Z   test_conv3d_64bit_indexing_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0325502Z   test_conv3d_same_padding_backward_cpu_complex128 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (1.618s)
2023-01-11T21:55:59.0325893Z   test_conv3d_same_padding_backward_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.194s)
2023-01-11T21:55:59.0326257Z   test_conv3d_same_padding_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.035s)
2023-01-11T21:55:59.0326628Z   test_conv3d_same_padding_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.010s)
2023-01-11T21:55:59.0327009Z   test_conv3d_valid_padding_backward_cpu_complex128 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.612s)
2023-01-11T21:55:59.0327404Z   test_conv3d_valid_padding_backward_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.072s)
2023-01-11T21:55:59.0327820Z   test_conv3d_valid_padding_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0328197Z   test_conv3d_valid_padding_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.002s)
2023-01-11T21:55:59.0328579Z   test_conv3d_vs_scipy_mode_same_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.008s)
2023-01-11T21:55:59.0328941Z   test_conv3d_vs_scipy_mode_same_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:55:59.0329319Z   test_conv3d_vs_scipy_mode_valid_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.007s)
2023-01-11T21:55:59.0329698Z   test_conv3d_vs_scipy_mode_valid_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.005s)
2023-01-11T21:55:59.0330062Z   test_convTranspose_empty_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0330698Z   test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0331206Z   test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0331701Z   test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0332202Z   test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0332700Z   test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0333188Z   test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.029s)
2023-01-11T21:55:59.0333683Z   test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0334173Z   test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0334663Z   test_conv_backend_cuda_depthwise2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.039s)
2023-01-11T21:55:59.0335149Z   test_conv_backend_cuda_depthwise2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0335752Z   test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0336313Z   test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0336810Z   test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.033s)
2023-01-11T21:55:59.0337293Z   test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0337781Z   test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0338267Z   test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0338763Z   test_conv_backend_cuda_depthwise3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0339286Z   test_conv_backend_cuda_depthwise3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0339769Z   test_conv_backend_cuda_depthwise3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0340258Z   test_conv_backend_cuda_depthwise3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.025s)
2023-01-11T21:55:59.0340748Z   test_conv_backend_cuda_depthwise3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0341240Z   test_conv_backend_cuda_depthwise3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0341719Z   test_conv_backend_cuda_depthwise3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0342211Z   test_conv_backend_cuda_depthwise3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0342695Z   test_conv_backend_cudnn1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0343172Z   test_conv_backend_cudnn1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0343641Z   test_conv_backend_cudnn1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.027s)
2023-01-11T21:55:59.0344103Z   test_conv_backend_cudnn1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0344577Z   test_conv_backend_cudnn1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0345051Z   test_conv_backend_cudnn1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0345520Z   test_conv_backend_cudnn1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0345973Z   test_conv_backend_cudnn1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0346490Z   test_conv_backend_cudnn1d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0346997Z   test_conv_backend_cudnn1d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.027s)
2023-01-11T21:55:59.0347497Z   test_conv_backend_cudnn1d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0347980Z   test_conv_backend_cudnn1d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0348474Z   test_conv_backend_cudnn1d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0348969Z   test_conv_backend_cudnn1d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0349496Z   test_conv_backend_cudnn1d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0349986Z   test_conv_backend_cudnn1d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0363930Z   test_conv_backend_cudnn2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.024s)
2023-01-11T21:55:59.0364523Z   test_conv_backend_cudnn2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0365008Z   test_conv_backend_cudnn2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0365671Z   test_conv_backend_cudnn2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0366528Z   test_conv_backend_cudnn2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0367028Z   test_conv_backend_cudnn2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0367495Z   test_conv_backend_cudnn2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0367964Z   test_conv_backend_cudnn2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.025s)
2023-01-11T21:55:59.0368449Z   test_conv_backend_cudnn2d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0368953Z   test_conv_backend_cudnn2d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0369441Z   test_conv_backend_cudnn2d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0369940Z   test_conv_backend_cudnn2d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0370435Z   test_conv_backend_cudnn2d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0370923Z   test_conv_backend_cudnn2d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0371528Z   test_conv_backend_cudnn2d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.022s)
2023-01-11T21:55:59.0372015Z   test_conv_backend_cudnn2d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0372502Z   test_conv_backend_cudnn3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0372980Z   test_conv_backend_cudnn3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0373438Z   test_conv_backend_cudnn3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0373908Z   test_conv_backend_cudnn3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0374431Z   test_conv_backend_cudnn3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0374906Z   test_conv_backend_cudnn3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.032s)
2023-01-11T21:55:59.0375370Z   test_conv_backend_cudnn3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0375920Z   test_conv_backend_cudnn3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.016s)
2023-01-11T21:55:59.0376383Z   test_conv_backend_empty_batch1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.046s)
2023-01-11T21:55:59.0376845Z   test_conv_backend_empty_batch1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.070s)
2023-01-11T21:55:59.0377297Z   test_conv_backend_empty_batch1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.044s)
2023-01-11T21:55:59.0377727Z   test_conv_backend_empty_batch1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.045s)
2023-01-11T21:55:59.0378179Z   test_conv_backend_empty_batch1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.050s)
2023-01-11T21:55:59.0378627Z   test_conv_backend_empty_batch1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.043s)
2023-01-11T21:55:59.0379073Z   test_conv_backend_empty_batch1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.045s)
2023-01-11T21:55:59.0379509Z   test_conv_backend_empty_batch1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.044s)
2023-01-11T21:55:59.0379960Z   test_conv_backend_empty_batch2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:55:59.0380410Z   test_conv_backend_empty_batch2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.045s)
2023-01-11T21:55:59.0382446Z   test_conv_backend_empty_batch2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:55:59.0382902Z   test_conv_backend_empty_batch2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.041s)
2023-01-11T21:55:59.0383344Z   test_conv_backend_empty_batch2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.045s)
2023-01-11T21:55:59.0383873Z   test_conv_backend_empty_batch2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:55:59.0384320Z   test_conv_backend_empty_batch2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.043s)
2023-01-11T21:55:59.0384753Z   test_conv_backend_empty_batch2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.044s)
2023-01-11T21:55:59.0385197Z   test_conv_backend_empty_batch3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.041s)
2023-01-11T21:55:59.0385646Z   test_conv_backend_empty_batch3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0386095Z   test_conv_backend_empty_batch3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:55:59.0386564Z   test_conv_backend_empty_batch3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.041s)
2023-01-11T21:55:59.0387005Z   test_conv_backend_empty_batch3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.047s)
2023-01-11T21:55:59.0387448Z   test_conv_backend_empty_batch3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:55:59.0387890Z   test_conv_backend_empty_batch3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.044s)
2023-01-11T21:55:59.0388333Z   test_conv_backend_empty_batch3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.043s)
2023-01-11T21:55:59.0388775Z   test_conv_backend_empty_batch_channel1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0389249Z   test_conv_backend_empty_batch_channel1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0389718Z   test_conv_backend_empty_batch_channel1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0390177Z   test_conv_backend_empty_batch_channel1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0390627Z   test_conv_backend_empty_batch_channel1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0391089Z   test_conv_backend_empty_batch_channel1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0391558Z   test_conv_backend_empty_batch_channel1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0392025Z   test_conv_backend_empty_batch_channel1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0392478Z   test_conv_backend_empty_batch_channel2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0392944Z   test_conv_backend_empty_batch_channel2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0393411Z   test_conv_backend_empty_batch_channel2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0393869Z   test_conv_backend_empty_batch_channel2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0394348Z   test_conv_backend_empty_batch_channel2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0394805Z   test_conv_backend_empty_batch_channel2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.038s)
2023-01-11T21:55:59.0395273Z   test_conv_backend_empty_batch_channel2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.043s)
2023-01-11T21:55:59.0395733Z   test_conv_backend_empty_batch_channel2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.038s)
2023-01-11T21:55:59.0396183Z   test_conv_backend_empty_batch_channel3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0396646Z   test_conv_backend_empty_batch_channel3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0397113Z   test_conv_backend_empty_batch_channel3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0397600Z   test_conv_backend_empty_batch_channel3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0398059Z   test_conv_backend_empty_batch_channel3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.046s)
2023-01-11T21:55:59.0398501Z   test_conv_backend_empty_batch_channel3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0398962Z   test_conv_backend_empty_batch_channel3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0399422Z   test_conv_backend_empty_batch_channel3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0399883Z   test_conv_backend_empty_channel1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0400327Z   test_conv_backend_empty_channel1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0400924Z   test_conv_backend_empty_channel1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0401384Z   test_conv_backend_empty_channel1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0401842Z   test_conv_backend_empty_channel1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:55:59.0402282Z   test_conv_backend_empty_channel1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0402737Z   test_conv_backend_empty_channel1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.042s)
2023-01-11T21:55:59.0403189Z   test_conv_backend_empty_channel1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0403648Z   test_conv_backend_empty_channel2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.052s)
2023-01-11T21:55:59.0404086Z   test_conv_backend_empty_channel2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0404541Z   test_conv_backend_empty_channel2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0404990Z   test_conv_backend_empty_channel2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0405519Z   test_conv_backend_empty_channel2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0405960Z   test_conv_backend_empty_channel2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0406405Z   test_conv_backend_empty_channel2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.045s)
2023-01-11T21:55:59.0407084Z   test_conv_backend_empty_channel2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.038s)
2023-01-11T21:55:59.0407840Z   test_conv_backend_empty_channel3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0408280Z   test_conv_backend_empty_channel3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0408787Z   test_conv_backend_empty_channel3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.037s)
2023-01-11T21:55:59.0409233Z   test_conv_backend_empty_channel3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.036s)
2023-01-11T21:55:59.0409685Z   test_conv_backend_empty_channel3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.045s)
2023-01-11T21:55:59.0410137Z   test_conv_backend_empty_channel3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.039s)
2023-01-11T21:55:59.0410572Z   test_conv_backend_empty_channel3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.040s)
2023-01-11T21:55:59.0411017Z   test_conv_backend_empty_channel3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.038s)
2023-01-11T21:55:59.0411486Z   test_conv_backend_miopen1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0411973Z   test_conv_backend_miopen1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0412440Z   test_conv_backend_miopen1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.020s)
2023-01-11T21:55:59.0412917Z   test_conv_backend_miopen1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0413388Z   test_conv_backend_miopen1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0413859Z   test_conv_backend_miopen1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0414316Z   test_conv_backend_miopen1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0414780Z   test_conv_backend_miopen1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0415269Z   test_conv_backend_miopen1d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0415838Z   test_conv_backend_miopen1d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.019s)
2023-01-11T21:55:59.0416337Z   test_conv_backend_miopen1d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0416865Z   test_conv_backend_miopen1d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0417363Z   test_conv_backend_miopen1d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0417855Z   test_conv_backend_miopen1d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0418355Z   test_conv_backend_miopen1d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0418836Z   test_conv_backend_miopen1d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.017s)
2023-01-11T21:55:59.0419359Z   test_conv_backend_miopen2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.020s)
2023-01-11T21:55:59.0419843Z   test_conv_backend_miopen2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0420316Z   test_conv_backend_miopen2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0420771Z   test_conv_backend_miopen2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0421239Z   test_conv_backend_miopen2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0421720Z   test_conv_backend_miopen2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0422196Z   test_conv_backend_miopen2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0422665Z   test_conv_backend_miopen2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.019s)
2023-01-11T21:55:59.0423135Z   test_conv_backend_miopen2d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0423636Z   test_conv_backend_miopen2d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0424133Z   test_conv_backend_miopen2d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0424637Z   test_conv_backend_miopen2d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0425123Z   test_conv_backend_miopen2d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0425619Z   test_conv_backend_miopen2d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0426114Z   test_conv_backend_miopen2d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.020s)
2023-01-11T21:55:59.0426603Z   test_conv_backend_miopen2d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0427122Z   test_conv_backend_miopen3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0427592Z   test_conv_backend_miopen3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0428069Z   test_conv_backend_miopen3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0428541Z   test_conv_backend_miopen3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0429016Z   test_conv_backend_miopen3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0429480Z   test_conv_backend_miopen3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.020s)
2023-01-11T21:55:59.0429989Z   test_conv_backend_miopen3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0430463Z   test_conv_backend_miopen3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0430949Z   test_conv_backend_miopen3d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0431438Z   test_conv_backend_miopen3d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0431937Z   test_conv_backend_miopen3d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0432438Z   test_conv_backend_miopen3d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0432937Z   test_conv_backend_miopen3d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.020s)
2023-01-11T21:55:59.0433433Z   test_conv_backend_miopen3d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0433910Z   test_conv_backend_miopen3d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0434403Z   test_conv_backend_miopen3d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0434902Z   test_conv_backend_miopen_depthwise1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0435399Z   test_conv_backend_miopen_depthwise1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0435886Z   test_conv_backend_miopen_depthwise1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0436381Z   test_conv_backend_miopen_depthwise1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.021s)
2023-01-11T21:55:59.0436878Z   test_conv_backend_miopen_depthwise1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0437407Z   test_conv_backend_miopen_depthwise1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0437898Z   test_conv_backend_miopen_depthwise1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0438377Z   test_conv_backend_miopen_depthwise1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0438873Z   test_conv_backend_miopen_depthwise2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0439367Z   test_conv_backend_miopen_depthwise2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0439854Z   test_conv_backend_miopen_depthwise2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.024s)
2023-01-11T21:55:59.0440387Z   test_conv_backend_miopen_depthwise2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0441034Z   test_conv_backend_miopen_depthwise2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0441531Z   test_conv_backend_miopen_depthwise2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0442025Z   test_conv_backend_miopen_depthwise2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0442517Z   test_conv_backend_miopen_depthwise2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0443005Z   test_conv_backend_miopen_depthwise3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.015s)
2023-01-11T21:55:59.0443507Z   test_conv_backend_miopen_depthwise3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.020s)
2023-01-11T21:55:59.0443998Z   test_conv_backend_miopen_depthwise3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0444493Z   test_conv_backend_miopen_depthwise3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0444971Z   test_conv_backend_miopen_depthwise3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0445471Z   test_conv_backend_miopen_depthwise3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0445959Z   test_conv_backend_miopen_depthwise3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0446446Z   test_conv_backend_miopen_depthwise3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0446964Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.018s)
2023-01-11T21:55:59.0447500Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0448101Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0448648Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0449187Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0449727Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0450252Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0450834Z   test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.018s)
2023-01-11T21:55:59.0451369Z   test_conv_backend_mkldnn1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0451894Z   test_conv_backend_mkldnn1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0452404Z   test_conv_backend_mkldnn1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0452920Z   test_conv_backend_mkldnn1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0453444Z   test_conv_backend_mkldnn1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0453965Z   test_conv_backend_mkldnn1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0454485Z   test_conv_backend_mkldnn1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0454991Z   test_conv_backend_mkldnn1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0455594Z   test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0456151Z   test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0456705Z   test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0457255Z   test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0457787Z   test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0458373Z   test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0458920Z   test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0459459Z   test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0460003Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0460535Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0461111Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0461653Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0462190Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0462727Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0463243Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0463782Z   test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0464309Z   test_conv_backend_mkldnn2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0464831Z   test_conv_backend_mkldnn2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0465340Z   test_conv_backend_mkldnn2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0465852Z   test_conv_backend_mkldnn2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0466383Z   test_conv_backend_mkldnn2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0466899Z   test_conv_backend_mkldnn2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0467421Z   test_conv_backend_mkldnn2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0467930Z   test_conv_backend_mkldnn2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0468493Z   test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0469041Z   test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0469586Z   test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0470129Z   test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0470657Z   test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0471247Z   test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0471793Z   test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0472332Z   test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0472870Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0473401Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0473949Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0474487Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0475026Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0475552Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0476083Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0476630Z   test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0477155Z   test_conv_backend_mkldnn3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0477675Z   test_conv_backend_mkldnn3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0478190Z   test_conv_backend_mkldnn3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0478746Z   test_conv_backend_mkldnn3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0479268Z   test_conv_backend_mkldnn3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0479787Z   test_conv_backend_mkldnn3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0480304Z   test_conv_backend_mkldnn3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0480940Z   test_conv_backend_mkldnn3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0481479Z   test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0482077Z   test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0482625Z   test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0483174Z   test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0483705Z   test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0484252Z   test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0484795Z   test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0485340Z   test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0485883Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0486420Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0486967Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0487511Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0488052Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0488579Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0489157Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0489694Z   test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0490229Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0490774Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0491311Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0491883Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0492422Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0492959Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0493494Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0494022Z   test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0494569Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0495110Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0495719Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0496264Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0496797Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.016s)
2023-01-11T21:55:59.0497336Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0497879Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0498417Z   test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0498968Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0499557Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0500121Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0500678Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0501238Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0501801Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0502369Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0502920Z   test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0503482Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0504041Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0504601Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0505150Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0505705Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0506260Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0506805Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0507359Z   test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0507904Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0508455Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0509015Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0509606Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0510160Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0510708Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0511262Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0511818Z   test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0512416Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0512973Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0513509Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0514057Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0514610Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0515156Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0515706Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0516244Z   test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0516789Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0517346Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0517893Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0518435Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0518976Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0519559Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0520105Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0520781Z   test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0521330Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0521868Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0522478Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0523026Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0523582Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0524121Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.017s)
2023-01-11T21:55:59.0524662Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0525209Z   test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: PyTorch is built without mkldnn support (0.014s)
2023-01-11T21:55:59.0525714Z   test_conv_backend_slow1d_dilated_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.100s)
2023-01-11T21:55:59.0526174Z   test_conv_backend_slow1d_dilated_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.098s)
2023-01-11T21:55:59.0526614Z   test_conv_backend_slow1d_dilated_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.106s)
2023-01-11T21:55:59.0527063Z   test_conv_backend_slow1d_dilated_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.100s)
2023-01-11T21:55:59.0527514Z   test_conv_backend_slow1d_dilated_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.119s)
2023-01-11T21:55:59.0527968Z   test_conv_backend_slow1d_dilated_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.119s)
2023-01-11T21:55:59.0528402Z   test_conv_backend_slow1d_dilated_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.120s)
2023-01-11T21:55:59.0528844Z   test_conv_backend_slow1d_dilated_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.116s)
2023-01-11T21:55:59.0529306Z   test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.104s)
2023-01-11T21:55:59.0529781Z   test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.109s)
2023-01-11T21:55:59.0530295Z   test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.116s)
2023-01-11T21:55:59.0530768Z   test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.104s)
2023-01-11T21:55:59.0531241Z   test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.110s)
2023-01-11T21:55:59.0531712Z   test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.113s)
2023-01-11T21:55:59.0532178Z   test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.114s)
2023-01-11T21:55:59.0532634Z   test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.111s)
2023-01-11T21:55:59.0533135Z   test_conv_backend_slow1d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.089s)
2023-01-11T21:55:59.0533581Z   test_conv_backend_slow1d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.091s)
2023-01-11T21:55:59.0534020Z   test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.091s)
2023-01-11T21:55:59.0534439Z   test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.092s)
2023-01-11T21:55:59.0534872Z   test_conv_backend_slow1d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.099s)
2023-01-11T21:55:59.0535298Z   test_conv_backend_slow1d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.096s)
2023-01-11T21:55:59.0535800Z   test_conv_backend_slow1d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.114s)
2023-01-11T21:55:59.0536222Z   test_conv_backend_slow1d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.096s)
2023-01-11T21:55:59.0536671Z   test_conv_backend_slow1d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.099s)
2023-01-11T21:55:59.0537133Z   test_conv_backend_slow1d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.102s)
2023-01-11T21:55:59.0537599Z   test_conv_backend_slow1d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.109s)
2023-01-11T21:55:59.0538041Z   test_conv_backend_slow1d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.105s)
2023-01-11T21:55:59.0538500Z   test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.110s)
2023-01-11T21:55:59.0538963Z   test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.111s)
2023-01-11T21:55:59.0539418Z   test_conv_backend_slow1d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.111s)
2023-01-11T21:55:59.0539859Z   test_conv_backend_slow1d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.109s)
2023-01-11T21:55:59.0540313Z   test_conv_backend_slow2d_dilated_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.130s)
2023-01-11T21:55:59.0540766Z   test_conv_backend_slow2d_dilated_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.114s)
2023-01-11T21:55:59.0541258Z   test_conv_backend_slow2d_dilated_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.112s)
2023-01-11T21:55:59.0541693Z   test_conv_backend_slow2d_dilated_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.113s)
2023-01-11T21:55:59.0542141Z   test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.154s)
2023-01-11T21:55:59.0542589Z   test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.136s)
2023-01-11T21:55:59.0543036Z   test_conv_backend_slow2d_dilated_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.131s)
2023-01-11T21:55:59.0543467Z   test_conv_backend_slow2d_dilated_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.134s)
2023-01-11T21:55:59.0543966Z   test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.152s)
2023-01-11T21:55:59.0544448Z   test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.137s)
2023-01-11T21:55:59.0544922Z   test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.157s)
2023-01-11T21:55:59.0545399Z   test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.158s)
2023-01-11T21:55:59.0545859Z   test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.138s)
2023-01-11T21:55:59.0546337Z   test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.143s)
2023-01-11T21:55:59.0546811Z   test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.170s)
2023-01-11T21:55:59.0547280Z   test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.192s)
2023-01-11T21:55:59.0547723Z   test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.106s)
2023-01-11T21:55:59.0548159Z   test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.112s)
2023-01-11T21:55:59.0548593Z   test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.097s)
2023-01-11T21:55:59.0549029Z   test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.096s)
2023-01-11T21:55:59.0549453Z   test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.132s)
2023-01-11T21:55:59.0549879Z   test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.111s)
2023-01-11T21:55:59.0550308Z   test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.106s)
2023-01-11T21:55:59.0550733Z   test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.111s)
2023-01-11T21:55:59.0551164Z   test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.151s)
2023-01-11T21:55:59.0551624Z   test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.129s)
2023-01-11T21:55:59.0552129Z   test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.141s)
2023-01-11T21:55:59.0552586Z   test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.144s)
2023-01-11T21:55:59.0553028Z   test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.137s)
2023-01-11T21:55:59.0553483Z   test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.134s)
2023-01-11T21:55:59.0553932Z   test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.153s)
2023-01-11T21:55:59.0554378Z   test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.176s)
2023-01-11T21:55:59.0554843Z   test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.163s)
2023-01-11T21:55:59.0555283Z   test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.166s)
2023-01-11T21:55:59.0555725Z   test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.115s)
2023-01-11T21:55:59.0556159Z   test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.102s)
2023-01-11T21:55:59.0556587Z   test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.163s)
2023-01-11T21:55:59.0557023Z   test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.154s)
2023-01-11T21:55:59.0557465Z   test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.110s)
2023-01-11T21:55:59.0557902Z   test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.130s)
2023-01-11T21:55:59.0558361Z   test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0558834Z   test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0559307Z   test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0559783Z   test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0560259Z   test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.017s)
2023-01-11T21:55:59.0560849Z   test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0561324Z   test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0561795Z   test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.014s)
2023-01-11T21:55:59.0562257Z   test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.166s)
2023-01-11T21:55:59.0562753Z   test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.157s)
2023-01-11T21:55:59.0563206Z   test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.129s)
2023-01-11T21:55:59.0563652Z   test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.127s)
2023-01-11T21:55:59.0564099Z   test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.178s)
2023-01-11T21:55:59.0564536Z   test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.198s)
2023-01-11T21:55:59.0564984Z   test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_False_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.145s)
2023-01-11T21:55:59.0565435Z   test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_True_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.143s)
2023-01-11T21:55:59.0565876Z   test_conv_contiguous_for_oneDNN_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.009s)
2023-01-11T21:55:59.0566257Z   test_conv_cudnn_mismatch_memory_format_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0566660Z   test_conv_cudnn_ndhwc_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0567045Z   test_conv_cudnn_ndhwc_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0567440Z   test_conv_cudnn_nhwc_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0567820Z   test_conv_cudnn_nhwc_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0568329Z   test_conv_cudnn_nhwc_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.003s)
2023-01-11T21:55:59.0569070Z   test_conv_cudnn_nhwc_support_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0569494Z   test_conv_cudnn_nhwc_support_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0569890Z   test_conv_double_backward_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0570281Z   test_conv_double_backward_groups_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.070s)
2023-01-11T21:55:59.0570649Z   test_conv_double_backward_no_bias_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.045s)
2023-01-11T21:55:59.0571017Z   test_conv_double_backward_stride_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.253s)
2023-01-11T21:55:59.0571399Z   test_conv_double_backward_strided_with_3D_input_and_weight_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0572143Z   test_conv_empty_channel_cpu_complex64 (__main__.TestConvolutionNNDeviceTypeCPU) ... /opt/conda/lib/python3.7/site-packages/torch/nn/init.py:405: UserWarning: Initializing zero-element tensors is a no-op
2023-01-11T21:55:59.0572632Z   warnings.warn("Initializing zero-element tensors is a no-op")
2023-01-11T21:55:59.0572853Z ok (0.066s)
2023-01-11T21:55:59.0573111Z   test_conv_empty_channel_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.056s)
2023-01-11T21:55:59.0573486Z   test_conv_ic1_channels_last_for_oneDNN_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:55:59.0573861Z   test_conv_large_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0574231Z   test_conv_large_nosplit_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0574660Z   test_conv_noncontig_weights_and_bias_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.332s)
2023-01-11T21:55:59.0575027Z   test_conv_noncontig_weights_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.017s)
2023-01-11T21:55:59.0575380Z   test_conv_thnn_nhwc_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.560s)
2023-01-11T21:55:59.0575785Z   test_conv_thnn_nhwc_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.569s)
2023-01-11T21:55:59.0576186Z   test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose2d_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.003s)
2023-01-11T21:55:59.0576623Z   test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose3d_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.163s)
2023-01-11T21:55:59.0577036Z   test_conv_transposed_large_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0577435Z   test_convert_conv2d_weight_memory_format_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0577888Z   test_cudnn_convolution_add_relu_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0578301Z   test_cudnn_convolution_add_relu_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0578707Z   test_cudnn_convolution_relu_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0579097Z   test_cudnn_convolution_relu_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.002s)
2023-01-11T21:55:59.0579481Z   test_group_convTranspose_empty_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0579834Z   test_group_conv_empty_cpu (__main__.TestConvolutionNNDeviceTypeCPU) ... ok (0.004s)
2023-01-11T21:55:59.0580193Z   test_noncontig_conv_grad_cpu_float16 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0580595Z   test_noncontig_conv_grad_cpu_float32 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0580998Z   test_noncontig_conv_grad_cpu_float64 (__main__.TestConvolutionNNDeviceTypeCPU) ... skip: Only runs on cuda (0.001s)
2023-01-11T21:55:59.0581211Z 
2023-01-11T21:55:59.0581417Z ----------------------------------------------------------------------
2023-01-11T21:55:59.0581646Z Ran 581 tests in 46.454s
2023-01-11T21:55:59.0581759Z 
2023-01-11T21:55:59.0581831Z OK (skipped=337)
2023-01-11T21:55:59.0581937Z 
2023-01-11T21:55:59.0582019Z Generating XML reports...
2023-01-11T21:55:59.0582440Z Generated XML report: test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNN-20230111215511.xml
2023-01-11T21:55:59.0583005Z Generated XML report: test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNNDeviceTypeCPU-20230111215511.xml
2023-01-11T21:55:59.0583278Z 
2023-01-11T21:55:59.0583699Z ##[endgroup]
2023-01-11T21:55:59.0584095Z FINISHED PRINTING LOG FILE of nn/test_convolution (/var/lib/jenkins/workspace/test/test-reports/nn-test_convolution_1apoiion)
2023-01-11T21:55:59.0584319Z 
2023-01-11T21:55:59.0584476Z Running test_jit_cuda_fuser ... [2023-01-11 21:55:59.021270]
2023-01-11T21:55:59.0584942Z Executing ['/opt/conda/bin/python', '-bb', 'test_jit_cuda_fuser.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:55:59.021667]
2023-01-11T21:56:05.5369192Z 
2023-01-11T21:56:05.5370306Z Expand the folded group to see the log file of test_jit_cuda_fuser
2023-01-11T21:56:05.5371498Z ##[group]PRINTING LOG FILE of test_jit_cuda_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_cuda_fuser_9ujdtfvh)
2023-01-11T21:56:05.5372304Z Test results will be stored in test-reports/python-unittest/test_jit_cuda_fuser
2023-01-11T21:56:05.5372702Z 
2023-01-11T21:56:05.5372835Z Running tests...
2023-01-11T21:56:05.5373628Z ----------------------------------------------------------------------
2023-01-11T21:56:05.5374100Z   test__softmax_function (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5374600Z   test__softmax_function_half_to_float (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5375174Z   test_addcmul_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5375970Z   test_alias_pass_fix (__main__.TestCudaFuser) ... skip: skipping this test since unsqueeze is disabled now (0.001s)
2023-01-11T21:56:05.5376516Z   test_autocast_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5376997Z   test_autocast_1_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5377483Z   test_autocast_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5378036Z   test_autocast_2_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5378564Z   test_backward_type (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5379132Z   test_batch_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5379629Z   test_batch_norm_impl_index_correctness (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5379975Z   test_batch_norm_impl_index_inner_bcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5380392Z   test_bfloat (__main__.TestCudaFuser) ... skip: device does not support BFloat16 (0.002s)
2023-01-11T21:56:05.5380735Z   test_binary_bitwise (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5381094Z   test_binary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.011s)
2023-01-11T21:56:05.5381556Z   test_binary_ops_channels_last_with_bcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5382272Z   test_binary_ops_complex (__main__.TestCudaFuser) ... skip: see issue https://github.com/csarofeen/pytorch/issues/1730 (0.001s)
2023-01-11T21:56:05.5382643Z   test_binary_ops_permutation (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5382945Z   test_branches (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5383231Z   test_broadcasting_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5383529Z   test_broadcasting_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5383823Z   test_broadcasting_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5384105Z   test_broadcasting_3 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5384577Z   test_broadcasting_multiple_output (__main__.TestCudaFuser) ... skip: broadcast on branches can't be resolved yet (0.001s)
2023-01-11T21:56:05.5384988Z   test_broadcasting_multiple_output_shape (__main__.TestCudaFuser) ... skip: Broadcast with different output not supported yet (0.001s)
2023-01-11T21:56:05.5385370Z   test_broadcasting_partition_logic_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5385694Z   test_broadcasting_partition_logic_1 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5386044Z   test_build_shape_expression_native_dropout (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5386371Z   test_category_rule (__main__.TestCudaFuser) ... skip: requires CUDA (0.003s)
2023-01-11T21:56:05.5386687Z   test_channels_last_with_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.003s)
2023-01-11T21:56:05.5386978Z   test_chunk (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5387265Z   test_clamp (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5387565Z   test_clamp_reversed_bound (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5387867Z   test_clean_profile_ivalue (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5388221Z   test_const (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5388526Z   test_contiguous_on_broadcasted (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5388837Z   test_conv2d_bias (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5389130Z   test_conv2d_symbolic_shapes (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5389434Z   test_cpu_scalar (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5389735Z   test_cuda_fusion_guard (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5390045Z   test_cuda_fusion_guard_backward (__main__.TestCudaFuser) ... skip: requires NVFuser (0.001s)
2023-01-11T21:56:05.5390367Z   test_device_constant (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5390711Z   test_disable_const_chunk_propagation_for_normalization (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5391060Z   test_disable_sibling_fuse (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5391399Z   test_dropout_inference_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5391734Z   test_dropout_train_nograd_fusion (__main__.TestCudaFuser) ... skip: not enough memory (0.001s)
2023-01-11T21:56:05.5392065Z   test_dropout_train_nograd_prob_check (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5392392Z   test_dropout_training_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5392704Z   test_dropout_training_prob_check (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5393012Z   test_dynamic_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5393298Z   test_expand (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5393587Z   test_fix_shape_expression_bn (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5393928Z   test_flatten (__main__.TestCudaFuser) ... skip: skipping this test since flatten is disabled now (0.002s)
2023-01-11T21:56:05.5394247Z   test_gelu (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5394538Z   test_grad_sum_to_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.003s)
2023-01-11T21:56:05.5394852Z   test_graph_for_with_missing_optimized_engine (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5395171Z   test_graph_rng (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5395456Z   test_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5395733Z   test_high_rank_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5396035Z   test_inf_quick_patch (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5396335Z   test_inplace_removal (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5396652Z   test_input_output_passthrough (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5396955Z   test_int_tensor_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5397250Z   test_issue1445_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5397538Z   test_issue_1785 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5397820Z   test_layer_norm_autodiff (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5398128Z   test_layer_norm_parser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5398446Z   test_layer_norm_trivial_reduce_dim (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5398749Z   test_linear (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5399036Z   test_linear_symbolic_shapes (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5399383Z   test_multiple_device_pw (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5399701Z   test_native_batch_norm_backward (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5400007Z   test_native_layer_norm (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5400315Z   test_native_layer_norm_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5400829Z   test_native_layer_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5401173Z   test_nested_view (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.000s)
2023-01-11T21:56:05.5401488Z   test_no_tensor_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5401776Z   test_norm (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5402061Z   test_norm_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5402349Z   test_norm_channels_last (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5402709Z   test_norm_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5403059Z   test_norm_half_layer (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5403351Z   test_norm_large (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5403663Z   test_normalization_partition (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5403995Z   test_nvfuser_comparison_callbacks_with_fallback (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5404364Z   test_nvfuser_comparison_callbacks_without_fallback (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5404703Z   test_overlapped_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5405010Z   test_permutation_preservation (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5405356Z   test_permutation_preservation_edge_case_0 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5405719Z   test_permutation_preservation_edge_case_1_broken (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5406076Z   test_permutation_preservation_edge_case_2 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5406376Z   test_permute (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5406683Z   test_pointwise_reference_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5406993Z   test_profile_ivalue (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5407311Z   test_profile_ivalue_multiple_profiles (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5407648Z   test_profiling_node (__main__.TestCudaFuser) ... skip: Skipped due to rand_like behavior change (0.001s)
2023-01-11T21:56:05.5407994Z   test_pw_single_reduction_partition (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5408306Z   test_random_topo (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T21:56:05.5408585Z   test_reduction (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5408887Z   test_reduction_dtypes_axis (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5409198Z   test_reduction_empty_axes (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5409516Z   test_reduction_multiple_output (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5409824Z   test_reduction_permutation (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5410134Z   test_reduction_sizes_op (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5410455Z   test_remove_output_used_only_in_dtype (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5411215Z   test_rsub (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5411513Z   test_scalar_cuda_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5411820Z   test_scalar_input (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5412123Z   test_scalar_tensor (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5412419Z   test_scalar_tensor_permuted (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5412761Z   test_scheduler_with_polymorphic_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5413146Z   test_shape_expression (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.002s)
2023-01-11T21:56:05.5413498Z   test_sibling_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5413804Z   test_sibling_fusion_no_scalar_inputs (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5414133Z   test_single_reduction_broadcast (__main__.TestCudaFuser) ... skip: requires CUDA (0.003s)
2023-01-11T21:56:05.5414487Z   test_singleton_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5414771Z   test_skip_parser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5415061Z   test_softmax (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5415354Z   test_softmax_bfloat (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5415728Z   test_softmax_dtype (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5416016Z   test_softmax_half (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5416313Z   test_softplus_fuser (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5416660Z   test_squeeze (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T21:56:05.5417049Z   test_squeeze_negative_dim (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T21:56:05.5417448Z   test_squeeze_zero (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T21:56:05.5417789Z   test_strict_fusion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5418079Z   test_sum_to_one (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5418357Z   test_sum_to_size (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5418650Z   test_ternary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.003s)
2023-01-11T21:56:05.5418974Z   test_ternary_ops_integer_compatibility (__main__.TestCudaFuser) ... skip: requires CUDA (0.000s)
2023-01-11T21:56:05.5419300Z   test_ternary_ops_type_promotion (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5419611Z   test_to_boolean (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5419901Z   test_to_copy (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5420199Z   test_to_dtype_bf16_to_bf16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5420490Z   test_to_dtype_bf16_to_fp32 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5420797Z   test_to_dtype_fp16_to_fp16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5421105Z   test_to_dtype_fp16_to_fp32 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5421395Z   test_to_dtype_fp32_to_bf16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5421696Z   test_to_dtype_fp32_to_fp16 (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5421996Z   test_transpose (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5422341Z   test_transpose_default (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5422646Z   test_trivial_reduction (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5422948Z   test_type_as_op (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5423246Z   test_type_inference (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5423535Z   test_unary_bitwise (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5423830Z   test_unary_ops (__main__.TestCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5424170Z   test_unsqueeze (__main__.TestCudaFuser) ... skip: skipping this test since squeeze/unsqueeze is disabled now (0.001s)
2023-01-11T21:56:05.5424505Z   test_variance (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5424798Z   test_variance_profiling (__main__.TestCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5425131Z   test_view (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.001s)
2023-01-11T21:56:05.5425490Z   test_view_before_permute (__main__.TestCudaFuser) ... skip: requires CUDA (0.003s)
2023-01-11T21:56:05.5425850Z   test_view_copy_graph_guard (__main__.TestCudaFuser) ... skip: skipping this test since reshape is disabled now (0.001s)
2023-01-11T21:56:05.5426236Z   test_view_copy_graph_guard_double_fusion (__main__.TestCudaFuser) ... skip: skipping this test since view is disabled now (0.001s)
2023-01-11T21:56:05.5426601Z   test_can_be_enabled_nvfuser (__main__.TestEnableDisableCudaFuser) ... ok (0.002s)
2023-01-11T21:56:05.5426949Z   test_context_manager_test (__main__.TestEnableDisableCudaFuser) ... skip: requires CUDA (0.002s)
2023-01-11T21:56:05.5427292Z   test_register_fuser (__main__.TestEnableDisableCudaFuser) ... skip: requires CUDA (0.001s)
2023-01-11T21:56:05.5427624Z   test_register_fuser_cpu (__main__.TestEnableDisableCudaFuser) ... ok (0.007s)
2023-01-11T21:56:05.5427947Z   test_autodiff_fallback (jit.test_fuser_common.TestFuserCommon) ... ok (0.415s)
2023-01-11T21:56:05.5428125Z 
2023-01-11T21:56:05.5428360Z ----------------------------------------------------------------------
2023-01-11T21:56:05.5428592Z Ran 158 tests in 0.634s
2023-01-11T21:56:05.5428706Z 
2023-01-11T21:56:05.5428780Z OK (skipped=155)
2023-01-11T21:56:05.5428889Z 
2023-01-11T21:56:05.5428973Z Generating XML reports...
2023-01-11T21:56:05.5429420Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestEnableDisableCudaFuser-20230111215604.xml
2023-01-11T21:56:05.5430001Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-jit.test_fuser_common.TestFuserCommon-20230111215604.xml
2023-01-11T21:56:05.5430530Z Generated XML report: test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestCudaFuser-20230111215604.xml
2023-01-11T21:56:05.5430751Z 
2023-01-11T21:56:05.5431048Z ##[endgroup]
2023-01-11T21:56:05.5431424Z FINISHED PRINTING LOG FILE of test_jit_cuda_fuser (/var/lib/jenkins/workspace/test/test-reports/test_jit_cuda_fuser_9ujdtfvh)
2023-01-11T21:56:05.5431644Z 
2023-01-11T21:56:05.5431812Z Running test_show_pickle ... [2023-01-11 21:56:05.537512]
2023-01-11T21:56:05.5432279Z Executing ['/opt/conda/bin/python', '-bb', 'test_show_pickle.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:56:05.537978]
2023-01-11T21:56:10.0970784Z 
2023-01-11T21:56:10.0971492Z Expand the folded group to see the log file of test_show_pickle
2023-01-11T21:56:10.0972337Z ##[group]PRINTING LOG FILE of test_show_pickle (/var/lib/jenkins/workspace/test/test-reports/test_show_pickle_oxwx_t4d)
2023-01-11T21:56:10.0972884Z Test results will be stored in test-reports/python-unittest/test_show_pickle
2023-01-11T21:56:10.0973068Z 
2023-01-11T21:56:10.0973140Z Running tests...
2023-01-11T21:56:10.0973447Z ----------------------------------------------------------------------
2023-01-11T21:56:10.0973942Z   test_scripted_model (__main__.TestShowPickle) ... ok (0.354s)
2023-01-11T21:56:10.0974098Z 
2023-01-11T21:56:10.0974295Z ----------------------------------------------------------------------
2023-01-11T21:56:10.0974538Z Ran 1 test in 0.355s
2023-01-11T21:56:10.0974652Z 
2023-01-11T21:56:10.0974721Z OK
2023-01-11T21:56:10.0974840Z 
2023-01-11T21:56:10.0974961Z Generating XML reports...
2023-01-11T21:56:10.0975475Z Generated XML report: test-reports/python-unittest/test_show_pickle/TEST-TestShowPickle-20230111215609.xml
2023-01-11T21:56:10.0975796Z 
2023-01-11T21:56:10.0976207Z ##[endgroup]
2023-01-11T21:56:10.0976786Z FINISHED PRINTING LOG FILE of test_show_pickle (/var/lib/jenkins/workspace/test/test-reports/test_show_pickle_oxwx_t4d)
2023-01-11T21:56:10.0977002Z 
2023-01-11T21:56:10.0977198Z Running test_cpp_extensions_aot_ninja ... [2023-01-11 21:56:10.097393]
2023-01-11T21:56:14.0663805Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:56:14.2767567Z running install
2023-01-11T21:56:14.2768558Z /opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T21:56:14.2769210Z   setuptools.SetuptoolsDeprecationWarning,
2023-01-11T21:56:14.2887633Z running build
2023-01-11T21:56:14.2887880Z running build_py
2023-01-11T21:56:14.2951178Z creating build
2023-01-11T21:56:14.2951731Z creating build/lib.linux-x86_64-cpython-37
2023-01-11T21:56:14.2952264Z creating build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension
2023-01-11T21:56:14.2952974Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension
2023-01-11T21:56:14.2954895Z running build_ext
2023-01-11T21:56:14.3765465Z building 'torch_test_cpp_extension.cpp' extension
2023-01-11T21:56:14.3766114Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37
2023-01-11T21:56:14.4524166Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/build.ninja...
2023-01-11T21:56:14.4525339Z Compiling objects...
2023-01-11T21:56:14.4525663Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:56:57.9943364Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/extension.o.d -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.7m -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T21:56:57.9970153Z cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
2023-01-11T21:56:57.9970762Z In file included from /opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/Exceptions.h:14,
2023-01-11T21:56:57.9971408Z                  from /opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2023-01-11T21:56:57.9972119Z                  from /opt/conda/lib/python3.7/site-packages/torch/include/torch/extension.h:6,
2023-01-11T21:56:57.9972473Z                  from /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp:1:
2023-01-11T21:56:57.9973409Z /opt/conda/lib/python3.7/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2023-01-11T21:56:57.9974070Z /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp:40:53:   required from here
2023-01-11T21:56:57.9974774Z /opt/conda/lib/python3.7/site-packages/torch/include/pybind11/pybind11.h:1479:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2023-01-11T21:56:57.9975185Z  1479 | class class_ : public detail::generic_type {
2023-01-11T21:56:57.9975387Z       |       ^~~~~~
2023-01-11T21:56:58.0084569Z g++ -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/extension.o -L/opt/conda/lib/python3.7/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension/cpp.cpython-37m-x86_64-linux-gnu.so
2023-01-11T21:56:58.9389948Z building 'torch_test_cpp_extension.ort' extension
2023-01-11T21:56:59.0122222Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/build.ninja...
2023-01-11T21:56:59.0123387Z Compiling objects...
2023-01-11T21:56:59.0123655Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:57:38.9229333Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/ort_extension.o.d -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.7m -c -c /var/lib/jenkins/workspace/test/cpp_extensions/ort_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/ort_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=ort -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T21:57:38.9230834Z cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
2023-01-11T21:57:38.9309900Z g++ -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/ort_extension.o -L/opt/conda/lib/python3.7/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension/ort.cpython-37m-x86_64-linux-gnu.so
2023-01-11T21:57:39.7970990Z building 'torch_test_cpp_extension.rng' extension
2023-01-11T21:57:39.8732461Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/build.ninja...
2023-01-11T21:57:39.8733478Z Compiling objects...
2023-01-11T21:57:39.8733867Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:22.9184004Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/rng_extension.o.d -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/include/python3.7m -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T21:58:22.9186404Z cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
2023-01-11T21:58:22.9187184Z In file included from /opt/conda/lib/python3.7/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8,
2023-01-11T21:58:22.9188005Z                  from /opt/conda/lib/python3.7/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2023-01-11T21:58:22.9188527Z                  from /opt/conda/lib/python3.7/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2023-01-11T21:58:22.9188987Z                  from /opt/conda/lib/python3.7/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:8,
2023-01-11T21:58:22.9189350Z                  from /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp:6:
2023-01-11T21:58:22.9189840Z /opt/conda/lib/python3.7/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1008: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2023-01-11T21:58:22.9190262Z  1008 | # pragma unroll
2023-01-11T21:58:22.9190437Z       | 
2023-01-11T21:58:22.9267263Z g++ -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-37/rng_extension.o -L/opt/conda/lib/python3.7/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension/rng.cpython-37m-x86_64-linux-gnu.so
2023-01-11T21:58:23.8101972Z running install_lib
2023-01-11T21:58:23.8173065Z creating install
2023-01-11T21:58:23.8173682Z creating install/opt
2023-01-11T21:58:23.8174013Z creating install/opt/conda
2023-01-11T21:58:23.8174388Z creating install/opt/conda/lib
2023-01-11T21:58:23.8174767Z creating install/opt/conda/lib/python3.7
2023-01-11T21:58:23.8175373Z creating install/opt/conda/lib/python3.7/site-packages
2023-01-11T21:58:23.8176063Z creating install/opt/conda/lib/python3.7/site-packages/torch_test_cpp_extension
2023-01-11T21:58:23.8177015Z copying build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/lib/python3.7/site-packages/torch_test_cpp_extension
2023-01-11T21:58:23.8178388Z copying build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension/cpp.cpython-37m-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.7/site-packages/torch_test_cpp_extension
2023-01-11T21:58:23.8360314Z copying build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension/ort.cpython-37m-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.7/site-packages/torch_test_cpp_extension
2023-01-11T21:58:23.9022418Z copying build/lib.linux-x86_64-cpython-37/torch_test_cpp_extension/rng.cpython-37m-x86_64-linux-gnu.so -> ./install/opt/conda/lib/python3.7/site-packages/torch_test_cpp_extension
2023-01-11T21:58:23.9217452Z byte-compiling ./install/opt/conda/lib/python3.7/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-37.pyc
2023-01-11T21:58:23.9218426Z running install_egg_info
2023-01-11T21:58:23.9378502Z running egg_info
2023-01-11T21:58:23.9379160Z creating torch_test_cpp_extension.egg-info
2023-01-11T21:58:23.9424418Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2023-01-11T21:58:23.9427091Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2023-01-11T21:58:23.9429666Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2023-01-11T21:58:23.9430862Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T21:58:23.9478449Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T21:58:23.9485333Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2023-01-11T21:58:23.9486760Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/lib/python3.7/site-packages/torch_test_cpp_extension-0.0.0-py3.7.egg-info
2023-01-11T21:58:23.9491564Z running install_scripts
2023-01-11T21:58:28.5717124Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:58:28.5999870Z running install
2023-01-11T21:58:28.6001950Z /opt/conda/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2023-01-11T21:58:28.6002651Z   setuptools.SetuptoolsDeprecationWarning,
2023-01-11T21:58:28.6119775Z running build
2023-01-11T21:58:28.6120119Z running build_ext
2023-01-11T21:58:28.6878254Z building 'no_python_abi_suffix_test' extension
2023-01-11T21:58:28.6878834Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build
2023-01-11T21:58:28.6879649Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-37
2023-01-11T21:58:28.7617110Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-37/build.ninja...
2023-01-11T21:58:28.7618411Z Compiling objects...
2023-01-11T21:58:28.7618694Z Using envvar MAX_JOBS (6) as the number of workers...
2023-01-11T21:58:28.8950103Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-37/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/opt/conda/include/python3.7m -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-37/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17
2023-01-11T21:58:28.8951765Z cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
2023-01-11T21:58:28.9023974Z creating build/lib.linux-x86_64-cpython-37
2023-01-11T21:58:28.9026496Z g++ -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-37/no_python_abi_suffix_test.o -L/opt/conda/lib/python3.7/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-37/no_python_abi_suffix_test.so
2023-01-11T21:58:29.5889619Z running install_lib
2023-01-11T21:58:29.5948145Z creating install
2023-01-11T21:58:29.5948453Z creating install/opt
2023-01-11T21:58:29.5948710Z creating install/opt/conda
2023-01-11T21:58:29.5948956Z creating install/opt/conda/lib
2023-01-11T21:58:29.5949185Z creating install/opt/conda/lib/python3.7
2023-01-11T21:58:29.5949642Z creating install/opt/conda/lib/python3.7/site-packages
2023-01-11T21:58:29.5950784Z copying build/lib.linux-x86_64-cpython-37/no_python_abi_suffix_test.so -> ./install/opt/conda/lib/python3.7/site-packages
2023-01-11T21:58:29.5957725Z running install_egg_info
2023-01-11T21:58:29.6090280Z running egg_info
2023-01-11T21:58:29.6090797Z creating no_python_abi_suffix_test.egg-info
2023-01-11T21:58:29.6136750Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2023-01-11T21:58:29.6139074Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2023-01-11T21:58:29.6141830Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2023-01-11T21:58:29.6143366Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T21:58:29.6189850Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T21:58:29.6196637Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2023-01-11T21:58:29.6198184Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/lib/python3.7/site-packages/no_python_abi_suffix_test-0.0.0-py3.7.egg-info
2023-01-11T21:58:29.6203602Z running install_scripts
2023-01-11T21:58:30.2986207Z Executing ['/opt/conda/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '-v', '--import-slow-tests', '--import-disabled-tests'] ... [2023-01-11 21:58:30.298215]
2023-01-11T21:58:35.4903437Z 
2023-01-11T21:58:35.4904003Z Expand the folded group to see the log file of test_cpp_extensions_aot_ninja
2023-01-11T21:58:35.4905133Z ##[group]PRINTING LOG FILE of test_cpp_extensions_aot_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_ninja_4scmlu38)
2023-01-11T21:58:35.4905749Z Test results will be stored in test-reports/python-unittest/test_cpp_extensions_aot_ninja
2023-01-11T21:58:35.4906333Z No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
2023-01-11T21:58:35.4906476Z 
2023-01-11T21:58:35.4906552Z Running tests...
2023-01-11T21:58:35.4906859Z ----------------------------------------------------------------------
2023-01-11T21:58:35.4907265Z   test_backward (__main__.TestCppExtensionAOT) ... ok (0.023s)
2023-01-11T21:58:35.4907694Z   test_cublas_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.001s)
2023-01-11T21:58:35.4908268Z   test_cuda_dlink_libs (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.001s)
2023-01-11T21:58:35.4908802Z   test_cuda_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.001s)
2023-01-11T21:58:35.4909337Z   test_cusolver_extension (__main__.TestCppExtensionAOT) ... skip: CUDA not found (0.001s)
2023-01-11T21:58:35.4909901Z   test_extension_function (__main__.TestCppExtensionAOT) ... ok (0.002s)
2023-01-11T21:58:35.4910302Z   test_extension_module (__main__.TestCppExtensionAOT) ... ok (0.002s)
2023-01-11T21:58:35.4910639Z   test_no_python_abi_suffix_sets_the_correct_library_name (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T21:58:35.4910942Z   test_optional (__main__.TestCppExtensionAOT) ... ok (0.001s)
2023-01-11T21:58:35.4911196Z   test_add (__main__.TestORTTensor) ... ok (0.001s)
2023-01-11T21:58:35.4911459Z   test_conv_backend_override (__main__.TestORTTensor) ... ok (0.003s)
2023-01-11T21:58:35.4911731Z   test_unregistered (__main__.TestORTTensor) ... ok (0.012s)
2023-01-11T21:58:35.4911986Z   test_zeros (__main__.TestORTTensor) ... ok (0.002s)
2023-01-11T21:58:35.4912263Z   test_pybind_return_types (__main__.TestPybindTypeCasters) ... ok (0.002s)
2023-01-11T21:58:35.4912540Z   test_rng (__main__.TestRNGExtension) ... ok (0.006s)
2023-01-11T21:58:35.4912809Z   test_torch_library (__main__.TestTorchLibrary) ... skip: CUDA not found (0.001s)
2023-01-11T21:58:35.4912982Z 
2023-01-11T21:58:35.4913197Z ----------------------------------------------------------------------
2023-01-11T21:58:35.4913437Z Ran 16 tests in 0.061s
2023-01-11T21:58:35.4913551Z 
2023-01-11T21:58:35.4913621Z OK (skipped=5)
2023-01-11T21:58:35.4913714Z 
2023-01-11T21:58:35.4913797Z Generating XML reports...
2023-01-11T21:58:35.4914250Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestCppExtensionAOT-20230111215834.xml
2023-01-11T21:58:35.4914800Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestORTTensor-20230111215834.xml
2023-01-11T21:58:35.4915359Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestPybindTypeCasters-20230111215834.xml
2023-01-11T21:58:35.4915900Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestRNGExtension-20230111215834.xml
2023-01-11T21:58:35.4916562Z Generated XML report: test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestTorchLibrary-20230111215834.xml
2023-01-11T21:58:35.4916803Z 
2023-01-11T21:58:35.4917068Z ##[endgroup]
2023-01-11T21:58:35.4917487Z FINISHED PRINTING LOG FILE of test_cpp_extensions_aot_ninja (/var/lib/jenkins/workspace/test/test-reports/test_cpp_extensions_aot_ninja_4scmlu38)
2023-01-11T21:58:35.4917728Z 
2023-01-11T21:58:36.1616154Z 
2023-01-11T21:58:36.1616754Z real	76m15.342s
2023-01-11T21:58:36.1617085Z user	109m45.027s
2023-01-11T21:58:36.1617316Z sys	9m28.938s
2023-01-11T21:58:36.1617586Z + assert_git_not_dirty
2023-01-11T21:58:36.1618115Z + [[ linux-focal-py3.7-clang7-asan != *rocm* ]]
2023-01-11T21:58:36.1618605Z + [[ linux-focal-py3.7-clang7-asan != *xla* ]]
2023-01-11T21:58:36.1619208Z ++ git status --porcelain
2023-01-11T21:58:46.9701860Z + git_status=
2023-01-11T21:58:46.9702206Z + [[ -n '' ]]
2023-01-11T21:58:46.9806436Z ##[group]Run cat test/**/*.log || true
2023-01-11T21:58:46.9806674Z cat test/**/*.log || true
2023-01-11T21:58:47.0569609Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:58:47.0569821Z env:
2023-01-11T21:58:47.0570002Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:47.0570281Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:47.0570526Z ##[endgroup]
2023-01-11T21:58:47.0608093Z cat: test/**/*.log: No such file or directory
2023-01-11T21:58:47.0636927Z Prepare all required actions
2023-01-11T21:58:47.0637220Z Getting action download info
2023-01-11T21:58:47.3514770Z ##[group]Run ./.github/actions/get-workflow-job-id
2023-01-11T21:58:47.3514980Z with:
2023-01-11T21:58:47.3515319Z   github-token: ***
2023-01-11T21:58:47.3515486Z env:
2023-01-11T21:58:47.3515646Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:47.3515922Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:47.3516177Z ##[endgroup]
2023-01-11T21:58:47.3543259Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
2023-01-11T21:58:47.3543493Z with:
2023-01-11T21:58:47.3543650Z   shell: bash
2023-01-11T21:58:47.3543809Z   timeout_minutes: 10
2023-01-11T21:58:47.3543983Z   max_attempts: 5
2023-01-11T21:58:47.3544164Z   retry_wait_seconds: 30
2023-01-11T21:58:47.3544563Z   command: set -eux
python3 -m pip install requests==2.26.0
GHA_WORKFLOW_JOB_ID=$(python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}")
echo "job-id=${GHA_WORKFLOW_JOB_ID}" >> "${GITHUB_OUTPUT}"

2023-01-11T21:58:47.3544938Z   polling_interval_seconds: 1
2023-01-11T21:58:47.3545116Z   warning_on_retry: true
2023-01-11T21:58:47.3545300Z   continue_on_error: false
2023-01-11T21:58:47.3545474Z env:
2023-01-11T21:58:47.3545628Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:47.3545897Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:47.3546272Z   GITHUB_TOKEN: ***
2023-01-11T21:58:47.3546449Z ##[endgroup]
2023-01-11T21:58:47.4407909Z + python3 -m pip install requests==2.26.0
2023-01-11T21:58:48.1337655Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T21:58:48.1599455Z Requirement already satisfied: requests==2.26.0 in /home/ec2-user/.local/lib/python3.7/site-packages (2.26.0)
2023-01-11T21:58:48.1730332Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (2022.12.7)
2023-01-11T21:58:48.1746677Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (2.0.12)
2023-01-11T21:58:48.1770673Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (3.4)
2023-01-11T21:58:48.1790136Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.26.0) (1.26.14)
2023-01-11T21:58:48.3899746Z ++ python3 .github/scripts/get_workflow_job_id.py 3896099317 i-05ef50dfe628429d7
2023-01-11T21:58:53.2320294Z + GHA_WORKFLOW_JOB_ID=10588772045
2023-01-11T21:58:53.2320808Z + echo job-id=10588772045
2023-01-11T21:58:53.4445254Z Command completed after 1 attempt(s).
2023-01-11T21:58:53.4562473Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2023-01-11T21:58:53.4562888Z kill "$MONITOR_SCRIPT_PID"
2023-01-11T21:58:53.4578605Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:58:53.4578996Z env:
2023-01-11T21:58:53.4579294Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:53.4579768Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:53.4580245Z   MONITOR_SCRIPT_PID: 1680
2023-01-11T21:58:53.4580569Z ##[endgroup]
2023-01-11T21:58:53.4672162Z Prepare all required actions
2023-01-11T21:58:53.4672417Z Getting action download info
2023-01-11T21:58:53.6251246Z Download action repository 'actions/upload-artifact@v3' (SHA:0b7f8abb1508181956e8e162db84b466c27e18ce)
2023-01-11T21:58:53.7810580Z ##[group]Run ./.github/actions/upload-test-artifacts
2023-01-11T21:58:53.7810787Z with:
2023-01-11T21:58:53.7811012Z   file-suffix: test-default-3-5-linux.2xlarge_10588772045
2023-01-11T21:58:53.7811231Z env:
2023-01-11T21:58:53.7811391Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:53.7811670Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:53.7811931Z ##[endgroup]
2023-01-11T21:58:53.7832519Z ##[group]Run # Remove any previous test jsons if they exist
2023-01-11T21:58:53.7832786Z # Remove any previous test jsons if they exist
2023-01-11T21:58:53.7833013Z rm -f test-jsons-*.zip
2023-01-11T21:58:53.7833284Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json'
2023-01-11T21:58:53.7844139Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:58:53.7844360Z env:
2023-01-11T21:58:53.7844532Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:53.7844805Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:53.7845121Z   FILE_SUFFIX: test-default-3-5-linux.2xlarge_10588772045
2023-01-11T21:58:53.7845345Z ##[endgroup]
2023-01-11T21:58:53.8018134Z   adding: test/allowlist_for_publicAPI.json (deflated 78%)
2023-01-11T21:58:53.8044847Z   adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%)
2023-01-11T21:58:53.8050755Z   adding: test/profiler/profiler_utils_mock_events.json (deflated 87%)
2023-01-11T21:58:53.8051986Z   adding: test/.pytorch-slow-tests.json (deflated 74%)
2023-01-11T21:58:53.8056570Z   adding: test/.pytorch-disabled-tests.json (deflated 84%)
2023-01-11T21:58:53.8075284Z ##[group]Run # Remove any previous test reports if they exist
2023-01-11T21:58:53.8075569Z # Remove any previous test reports if they exist
2023-01-11T21:58:53.8075812Z rm -f test-reports-*.zip
2023-01-11T21:58:53.8076074Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv'
2023-01-11T21:58:53.8086400Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:58:53.8086623Z env:
2023-01-11T21:58:53.8086797Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:53.8087063Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:53.8087597Z   FILE_SUFFIX: test-default-3-5-linux.2xlarge_10588772045
2023-01-11T21:58:53.8087824Z ##[endgroup]
2023-01-11T21:58:53.8177004Z   adding: test/test-reports/python-unittest/test_function_schema/TEST-TestFunctionSchema-20230111204234.xml (deflated 81%)
2023-01-11T21:58:53.8180353Z   adding: test/test-reports/python-unittest/dynamo.test_optimizations/TEST-NormalizeIRTests-20230111204245.xml (deflated 42%)
2023-01-11T21:58:53.8184572Z   adding: test/test-reports/python-unittest/dynamo.test_optimizations/TEST-TestOptimizations-20230111204245.xml (deflated 78%)
2023-01-11T21:58:53.8193236Z   adding: test/test-reports/python-unittest/profiler.test_profiler_tree/TEST-TestProfilerTree-20230111204256.xml (deflated 82%)
2023-01-11T21:58:53.8196768Z   adding: test/test-reports/python-unittest/dynamo.test_torchxla_integration/TEST-TorchXLAReuseGraphTest-20230111204306.xml (deflated 76%)
2023-01-11T21:58:53.8204188Z   adding: test/test-reports/python-unittest/inductor.test_torchinductor/TEST-CPUReproTests-20230111204333.xml (deflated 90%)
2023-01-11T21:58:53.8362884Z   adding: test/test-reports/python-unittest/inductor.test_torchinductor/TEST-CpuTests-20230111204333.xml (deflated 94%)
2023-01-11T21:58:53.8363681Z   adding: test/test-reports/python-unittest/inductor.test_torchinductor/TEST-ExprPrinterTests-20230111204333.xml (deflated 41%)
2023-01-11T21:58:53.8373765Z   adding: test/test-reports/python-unittest/inductor.test_torchinductor/TEST-SweepInputsCpuTest-20230111204333.xml (deflated 97%)
2023-01-11T21:58:53.8374831Z   adding: test/test-reports/python-unittest/inductor.test_torchinductor/TEST-TestIndexingSimplification-20230111204333.xml (deflated 57%)
2023-01-11T21:58:53.8375402Z   adding: test/test-reports/python-unittest/dynamo.test_minifier/TEST-MinifierTests-20230111204424.xml (deflated 85%)
2023-01-11T21:58:53.8377081Z   adding: test/test-reports/python-unittest/test_view_ops/TEST-TestOldViewOpsCPU-20230111204620.xml (deflated 89%)
2023-01-11T21:58:53.8379633Z   adding: test/test-reports/python-unittest/test_view_ops/TEST-TestViewOpsCPU-20230111204620.xml (deflated 93%)
2023-01-11T21:58:53.8382321Z   adding: test/test-reports/python-unittest/test_view_ops/TEST-TestViewOpsLAZY-20230111204620.xml (deflated 95%)
2023-01-11T21:58:53.8383309Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardEmbedding-20230111204720.xml (deflated 61%)
2023-01-11T21:58:53.8384349Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardNumpy-20230111204720.xml (deflated 76%)
2023-01-11T21:58:53.8385381Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPyTorchNumpy-20230111204720.xml (deflated 73%)
2023-01-11T21:58:53.8386459Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardPytorchGraph-20230111204720.xml (deflated 64%)
2023-01-11T21:58:53.8387329Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummary-20230111204720.xml (deflated 83%)
2023-01-11T21:58:53.8388422Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardSummaryWriter-20230111204720.xml (deflated 64%)
2023-01-11T21:58:53.8389337Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardUtils-20230111204720.xml (deflated 65%)
2023-01-11T21:58:53.8390348Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardWriter-20230111204720.xml (deflated 41%)
2023-01-11T21:58:53.8391323Z   adding: test/test-reports/python-unittest/test_tensorboard/TEST-TestTensorBoardFigure-20230111204720.xml (deflated 60%)
2023-01-11T21:58:53.8392360Z   adding: test/test-reports/python-unittest/nn.test_parametrization/TEST-TestNNParametrization-20230111204732.xml (deflated 83%)
2023-01-11T21:58:53.8413332Z   adding: test/test-reports/python-unittest/dynamo.test_verify_correctness/TEST-TestVerifyCorrectness-20230111204744.xml (deflated 68%)
2023-01-11T21:58:53.8414217Z   adding: test/test-reports/python-unittest/test_native_mha/TEST-TestMHADeviceTypeCPU-20230111204756.xml (deflated 90%)
2023-01-11T21:58:53.8414765Z   adding: test/test-reports/python-unittest/dynamo.test_python_autograd/TEST-TestPythonAutograd-20230111204807.xml (deflated 77%)
2023-01-11T21:58:53.8415280Z   adding: test/test-reports/python-unittest/test_futures/TEST-TestFuture-20230111204819.xml (deflated 84%)
2023-01-11T21:58:53.8415880Z   adding: test/test-reports/python-unittest/test_fx_reinplace_pass/TEST-TestReinplacePass-20230111204830.xml (deflated 80%)
2023-01-11T21:58:53.8416400Z   adding: test/test-reports/python-unittest/test_model_dump/TEST-TestModelDump-20230111204842.xml (deflated 76%)
2023-01-11T21:58:53.8416923Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestCircularSerialization-20230111204853.xml (deflated 53%)
2023-01-11T21:58:53.8417573Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestDataChunk-20230111204853.xml (deflated 76%)
2023-01-11T21:58:53.8418114Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestFunctionalIterDataPipe-20230111204853.xml (deflated 86%)
2023-01-11T21:58:53.8418685Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestFunctionalMapDataPipe-20230111204853.xml (deflated 71%)
2023-01-11T21:58:53.8419183Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestGraph-20230111204853.xml (deflated 67%)
2023-01-11T21:58:53.8419747Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeCountSampleYielded-20230111204853.xml (deflated 75%)
2023-01-11T21:58:53.8420539Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeGraphFastForward-20230111204853.xml (deflated 66%)
2023-01-11T21:58:53.8421172Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestIterDataPipeSingletonConstraint-20230111204853.xml (deflated 74%)
2023-01-11T21:58:53.8421755Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestIterableDataPipeBasic-20230111204853.xml (deflated 70%)
2023-01-11T21:58:53.8422271Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestSharding-20230111204853.xml (deflated 70%)
2023-01-11T21:58:53.8422769Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestStreamWrapper-20230111204853.xml (deflated 68%)
2023-01-11T21:58:53.8423256Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestTyping-20230111204853.xml (deflated 80%)
2023-01-11T21:58:53.8423751Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestCaptureDataFrame-20230111204853.xml (deflated 43%)
2023-01-11T21:58:53.8424279Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestDataFramesPipes-20230111204853.xml (deflated 79%)
2023-01-11T21:58:53.8424804Z   adding: test/test-reports/python-unittest/test_datapipe/TEST-TestSerialization-20230111204853.xml (deflated 59%)
2023-01-11T21:58:53.8425302Z   adding: test/test-reports/python-unittest/test_autocast/TEST-TestAutocastCPU-20230111204904.xml (deflated 77%)
2023-01-11T21:58:53.8425809Z   adding: test/test-reports/python-unittest/test_autocast/TEST-TestTorchAutocast-20230111204904.xml (deflated 42%)
2023-01-11T21:58:53.8426308Z   adding: test/test-reports/python-unittest/test_autocast/TEST-TestAutocastGPU-20230111204904.xml (deflated 45%)
2023-01-11T21:58:53.8426832Z   adding: test/test-reports/python-unittest/test_native_functions/TEST-TestNativeFunctions-20230111204915.xml (deflated 90%)
2023-01-11T21:58:53.8427384Z   adding: test/test-reports/python-unittest/benchmark_utils.test_benchmark_utils/TEST-TestBenchmarkUtils-20230111204926.xml (deflated 78%)
2023-01-11T21:58:53.8427923Z   adding: test/test-reports/python-unittest/test_fx_passes/TEST-TestFXGraphPasses-20230111204936.xml (deflated 91%)
2023-01-11T21:58:53.8428444Z   adding: test/test-reports/python-unittest/test_fx_passes/TEST-TestFXMatcherUtils-20230111204936.xml (deflated 87%)
2023-01-11T21:58:53.8428939Z   adding: test/test-reports/python-unittest/test_monitor/TEST-TestMonitor-20230111204947.xml (deflated 66%)
2023-01-11T21:58:53.8429434Z   adding: test/test-reports/python-unittest/test_monitor/TEST-TestMonitorTensorboard-20230111204947.xml (deflated 41%)
2023-01-11T21:58:53.8429947Z   adding: test/test-reports/python-unittest/dynamo.test_global/TEST-TestGlobals-20230111204957.xml (deflated 85%)
2023-01-11T21:58:53.8430448Z   adding: test/test-reports/python-unittest/dynamo.test_comptime/TEST-ComptimeTests-20230111205007.xml (deflated 77%)
2023-01-11T21:58:53.8430981Z   adding: test/test-reports/python-unittest/dynamo.test_skip_non_tensor/TEST-SkipNonTensorTests-20230111205017.xml (deflated 79%)
2023-01-11T21:58:53.8431520Z   adding: test/test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestExprHandlePyBind-20230111205027.xml (deflated 40%)
2023-01-11T21:58:53.8432105Z   adding: test/test-reports/python-unittest/test_tensorexpr_pybind/TEST-TestTensorExprPyBind-20230111205027.xml (deflated 86%)
2023-01-11T21:58:53.8432664Z   adding: test/test-reports/python-unittest/dynamo.test_export_mutations/TEST-MutationExportTests-20230111205037.xml (deflated 81%)
2023-01-11T21:58:53.8433176Z   adding: test/test-reports/python-unittest/dynamo.test_nops/TEST-NopTests-20230111205047.xml (deflated 67%)
2023-01-11T21:58:53.8433633Z   adding: test/test-reports/python-unittest/test_pytree/TEST-TestPytree-20230111205057.xml (deflated 84%)
2023-01-11T21:58:53.8434150Z   adding: test/test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleGlobalHooks-20230111205107.xml (deflated 71%)
2023-01-11T21:58:53.8434718Z   adding: test/test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHookNN-20230111205107.xml (deflated 79%)
2023-01-11T21:58:53.8435237Z   adding: test/test-reports/python-unittest/nn.test_module_hooks/TEST-TestModuleHooks-20230111205107.xml (deflated 77%)
2023-01-11T21:58:53.8435752Z   adding: test/test-reports/python-unittest/nn.test_module_hooks/TEST-TestStateDictHooks-20230111205107.xml (deflated 75%)
2023-01-11T21:58:53.8436285Z   adding: test/test-reports/python-unittest/test_per_overload_api/TEST-TestPerOverloadAPI-20230111205117.xml (deflated 63%)
2023-01-11T21:58:53.8436785Z   adding: test/test-reports/python-unittest/test_license/TEST-TestLicense-20230111205126.xml (deflated 50%)
2023-01-11T21:58:53.8437368Z   adding: test/test-reports/python-unittest/test_set_default_mobile_cpu_allocator/TEST-TestSetDefaultMobileCPUAllocator-20230111205136.xml (deflated 57%)
2023-01-11T21:58:53.8437921Z   adding: test/test-reports/python-unittest/test_type_info/TEST-TestDTypeInfo-20230111205146.xml (deflated 61%)
2023-01-11T21:58:53.8438386Z   adding: test/test-reports/python-unittest/test_itt/TEST-TestItt-20230111205155.xml (deflated 40%)
2023-01-11T21:58:53.8438885Z   adding: test/test-reports/python-unittest/test_numpy_interop/TEST-TestNumPyInteropCPU-20230111205205.xml (deflated 85%)
2023-01-11T21:58:53.8439404Z   adding: test/test-reports/python-unittest/nn.test_pruning/TEST-TestPruningNN-20230111205216.xml (deflated 79%)
2023-01-11T21:58:53.8496519Z   adding: test/test-reports/python-unittest/test_decomp/TEST-TestDecompCPU-20230111205227.xml (deflated 98%)
2023-01-11T21:58:53.8497028Z   adding: test/test-reports/python-unittest/test_decomp/TEST-DecompAmpTestsCPU-20230111205227.xml (deflated 42%)
2023-01-11T21:58:53.8497569Z   adding: test/test-reports/python-unittest/test_decomp/TEST-DecompContiguousTestsCPU-20230111205227.xml (deflated 60%)
2023-01-11T21:58:53.8498622Z   adding: test/test-reports/python-unittest/test_dlpack/TEST-TestTorchDlPackCPU-20230111205241.xml (deflated 95%)
2023-01-11T21:58:53.8499129Z   adding: test/test-reports/python-unittest/test_jit_llga_fuser/TEST-TestDynamoAOT-20230111205251.xml (deflated 40%)
2023-01-11T21:58:53.8499674Z   adding: test/test-reports/python-unittest/test_jit_llga_fuser/TEST-TestEnableDisableLlgaFuser-20230111205251.xml (deflated 42%)
2023-01-11T21:58:53.8500241Z   adding: test/test-reports/python-unittest/test_jit_llga_fuser/TEST-TestFusionPatternCPU-20230111205251.xml (deflated 91%)
2023-01-11T21:58:53.8501051Z   adding: test/test-reports/python-unittest/test_jit_llga_fuser/TEST-TestModel-20230111205251.xml (deflated 95%)
2023-01-11T21:58:53.8501796Z   adding: test/test-reports/python-unittest/test_jit_llga_fuser/TEST-TestOpCPU-20230111205251.xml (deflated 93%)
2023-01-11T21:58:53.8503169Z   adding: test/test-reports/python-unittest/test_mkldnn/TEST-TestMkldnn-20230111205301.xml (deflated 93%)
2023-01-11T21:58:53.8503695Z   adding: test/test-reports/python-unittest/test_nvfuser_frontend/TEST-TestNvFuserFrontend-20230111205311.xml (deflated 84%)
2023-01-11T21:58:53.8504236Z   adding: test/test-reports/python-unittest/test_mkldnn_fusion/TEST-TestMkldnnFusion-20230111205321.xml (deflated 81%)
2023-01-11T21:58:53.8505001Z   adding: test/test-reports/python-unittest/test_numba_integration/TEST-TestNumbaIntegration-20230111205331.xml (deflated 75%)
2023-01-11T21:58:53.8505636Z   adding: test/test-reports/python-unittest/dynamo.test_aot_cudagraphs/TEST-TestAotCudagraphs-20230111205341.xml (deflated 83%)
2023-01-11T21:58:53.8506190Z   adding: test/test-reports/python-unittest/dynamo.test_replay_record/TEST-ReplayRecordTests-20230111205350.xml (deflated 82%)
2023-01-11T21:58:53.8506726Z   adding: test/test-reports/python-unittest/test_complex/TEST-TestComplexTensorCPU-20230111205400.xml (deflated 70%)
2023-01-11T21:58:53.8507264Z   adding: test/test-reports/python-unittest/test_nvfuser_dynamo/TEST-TestNvFuserDynamo-20230111205409.xml (deflated 71%)
2023-01-11T21:58:53.8507802Z   adding: test/test-reports/python-unittest/dynamo.test_model_output/TEST-TestHFPretrained-20230111205419.xml (deflated 42%)
2023-01-11T21:58:53.8508376Z   adding: test/test-reports/python-unittest/dynamo.test_model_output/TEST-TestModelOutput-20230111205419.xml (deflated 82%)
2023-01-11T21:58:53.8508914Z   adding: test/test-reports/python-unittest/test_vulkan/TEST-TestVulkanRewritePass-20230111205428.xml (deflated 43%)
2023-01-11T21:58:53.8509427Z   adding: test/test-reports/python-unittest/test_type_hints/TEST-TestTypeHints-20230111205438.xml (deflated 40%)
2023-01-11T21:58:53.8509925Z   adding: test/test-reports/python-unittest/test_openmp/TEST-TestOpenMP_ParallelFor-20230111205447.xml (deflated 59%)
2023-01-11T21:58:53.8531987Z   adding: test/test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRCPU-20230111210516.xml (deflated 96%)
2023-01-11T21:58:53.8532518Z   adding: test/test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCSRSampler-20230111210516.xml (deflated 41%)
2023-01-11T21:58:53.8564794Z   adding: test/test-reports/python-unittest/test_sparse_csr/TEST-TestSparseCompressedCPU-20230111210516.xml (deflated 97%)
2023-01-11T21:58:53.8565338Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestBasicVitalSigns-20230111212209.xml (deflated 63%)
2023-01-11T21:58:53.8573897Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestTorch-20230111212209.xml (deflated 93%)
2023-01-11T21:58:53.8587702Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestTorchDeviceTypeCPU-20230111212209.xml (deflated 93%)
2023-01-11T21:58:53.8588247Z   adding: test/test-reports/python-unittest/test_torch/TEST-TestVitalSignsCudaCPU-20230111212209.xml (deflated 43%)
2023-01-11T21:58:53.8588805Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestAgainstScipy-20230111215331.xml (deflated 59%)
2023-01-11T21:58:53.8589539Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestConstraints-20230111215331.xml (deflated 56%)
2023-01-11T21:58:53.8590484Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributionShapes-20230111215331.xml (deflated 91%)
2023-01-11T21:58:53.8593651Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestDistributions-20230111215331.xml (deflated 90%)
2023-01-11T21:58:53.8594752Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestFunctors-20230111215331.xml (deflated 69%)
2023-01-11T21:58:53.8595755Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestJit-20230111215331.xml (deflated 73%)
2023-01-11T21:58:53.8596773Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestKL-20230111215331.xml (deflated 81%)
2023-01-11T21:58:53.8597464Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestLazyLogitsInitialization-20230111215331.xml (deflated 58%)
2023-01-11T21:58:53.8598084Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestNumericalStability-20230111215331.xml (deflated 83%)
2023-01-11T21:58:53.8598655Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestRsample-20230111215331.xml (deflated 75%)
2023-01-11T21:58:53.8599194Z   adding: test/test-reports/python-unittest/distributions.test_distributions/TEST-TestValidation-20230111215331.xml (deflated 65%)
2023-01-11T21:58:53.8599823Z   adding: test/test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNN-20230111215511.xml (deflated 84%)
2023-01-11T21:58:53.8607626Z   adding: test/test-reports/python-unittest/nn.test_convolution/TEST-TestConvolutionNNDeviceTypeCPU-20230111215511.xml (deflated 97%)
2023-01-11T21:58:53.8608287Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestEnableDisableCudaFuser-20230111215604.xml (deflated 68%)
2023-01-11T21:58:53.8608862Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-jit.test_fuser_common.TestFuserCommon-20230111215604.xml (deflated 48%)
2023-01-11T21:58:53.8611400Z   adding: test/test-reports/python-unittest/test_jit_cuda_fuser/TEST-TestCudaFuser-20230111215604.xml (deflated 93%)
2023-01-11T21:58:53.8611970Z   adding: test/test-reports/python-unittest/test_show_pickle/TEST-TestShowPickle-20230111215609.xml (deflated 40%)
2023-01-11T21:58:53.8612586Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestCppExtensionAOT-20230111215834.xml (deflated 80%)
2023-01-11T21:58:53.8613188Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestORTTensor-20230111215834.xml (deflated 67%)
2023-01-11T21:58:53.8613776Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestPybindTypeCasters-20230111215834.xml (deflated 41%)
2023-01-11T21:58:53.8614332Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestRNGExtension-20230111215834.xml (deflated 40%)
2023-01-11T21:58:53.8614876Z   adding: test/test-reports/python-unittest/test_cpp_extensions_aot_ninja/TEST-TestTorchLibrary-20230111215834.xml (deflated 42%)
2023-01-11T21:58:53.8663368Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-9b1ea14156fe1d78.xml (deflated 96%)
2023-01-11T21:58:53.8713575Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-5a1c1620e6dd6bfb.xml (deflated 96%)
2023-01-11T21:58:53.8714159Z   adding: test/test-reports/python-pytest/test_ops_gradients/test_ops_gradients-c9b05079dbc8142b.xml (deflated 91%)
2023-01-11T21:58:53.8734046Z ##[group]Run # Remove any previous test reports if they exist
2023-01-11T21:58:53.8734334Z # Remove any previous test reports if they exist
2023-01-11T21:58:53.8734563Z rm -f usage-log-*.zip
2023-01-11T21:58:53.8734837Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2023-01-11T21:58:53.8735110Z # so check to see if the file exists first
2023-01-11T21:58:53.8735334Z if [ -f 'usage_log.txt' ]; then
2023-01-11T21:58:53.8735679Z     zip "usage-log-${FILE_SUFFIX}.zip" 'usage_log.txt'
2023-01-11T21:58:53.8735883Z fi
2023-01-11T21:58:53.8745816Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:58:53.8746028Z env:
2023-01-11T21:58:53.8746191Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:53.8746465Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:53.8746780Z   FILE_SUFFIX: test-default-3-5-linux.2xlarge_10588772045
2023-01-11T21:58:53.8746999Z ##[endgroup]
2023-01-11T21:58:53.9185844Z   adding: usage_log.txt (deflated 97%)
2023-01-11T21:58:53.9219893Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T21:58:53.9220103Z with:
2023-01-11T21:58:53.9220291Z   s3-prefix: pytorch/pytorch/3896099317/2/artifact

2023-01-11T21:58:53.9220506Z   retention-days: 14
2023-01-11T21:58:53.9220702Z   if-no-files-found: warn
2023-01-11T21:58:53.9220885Z   path: test-jsons-*.zip
2023-01-11T21:58:53.9221068Z   name: artifact
2023-01-11T21:58:53.9221251Z   s3-bucket: gha-artifacts
2023-01-11T21:58:53.9221430Z   region: us-east-1
2023-01-11T21:58:53.9221602Z env:
2023-01-11T21:58:53.9221773Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:53.9222047Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:53.9222371Z ##[endgroup]
2023-01-11T21:58:54.3148126Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T21:58:54.3148694Z With the provided path, there will be 1 file uploaded
2023-01-11T21:58:54.3148972Z Uploading to s3 prefix: pytorch/pytorch/3896099317/2/artifact
2023-01-11T21:58:54.3206807Z Starting upload of test-jsons-test-default-3-5-linux.2xlarge_10588772045.zip
2023-01-11T21:58:54.4769568Z Finished upload of test-jsons-test-default-3-5-linux.2xlarge_10588772045.zip
2023-01-11T21:58:54.4923591Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T21:58:54.4923812Z with:
2023-01-11T21:58:54.4924007Z   s3-prefix: pytorch/pytorch/3896099317/2/artifact

2023-01-11T21:58:54.4924253Z   retention-days: 14
2023-01-11T21:58:54.4924515Z   if-no-files-found: error
2023-01-11T21:58:54.4924813Z   path: test-reports-*.zip
2023-01-11T21:58:54.4925000Z   name: artifact
2023-01-11T21:58:54.4925184Z   s3-bucket: gha-artifacts
2023-01-11T21:58:54.4925370Z   region: us-east-1
2023-01-11T21:58:54.4925539Z env:
2023-01-11T21:58:54.4925714Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:54.4925977Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:54.4926242Z ##[endgroup]
2023-01-11T21:58:54.8286213Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T21:58:54.8286932Z With the provided path, there will be 1 file uploaded
2023-01-11T21:58:54.8287221Z Uploading to s3 prefix: pytorch/pytorch/3896099317/2/artifact
2023-01-11T21:58:54.8293970Z Starting upload of test-reports-test-default-3-5-linux.2xlarge_10588772045.zip
2023-01-11T21:58:54.9874456Z Finished upload of test-reports-test-default-3-5-linux.2xlarge_10588772045.zip
2023-01-11T21:58:55.0018788Z ##[group]Run seemethere/upload-artifact-s3@v5
2023-01-11T21:58:55.0019018Z with:
2023-01-11T21:58:55.0019207Z   s3-prefix: pytorch/pytorch/3896099317/2/artifact

2023-01-11T21:58:55.0019423Z   retention-days: 14
2023-01-11T21:58:55.0019615Z   if-no-files-found: ignore
2023-01-11T21:58:55.0019804Z   path: usage-log-*.zip
2023-01-11T21:58:55.0019983Z   name: artifact
2023-01-11T21:58:55.0020159Z   s3-bucket: gha-artifacts
2023-01-11T21:58:55.0020335Z   region: us-east-1
2023-01-11T21:58:55.0020497Z env:
2023-01-11T21:58:55.0020670Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:55.0020939Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:55.0021273Z ##[endgroup]
2023-01-11T21:58:55.3458950Z NOTE: s3-prefix specified, ignoring name parameter
2023-01-11T21:58:55.3459428Z With the provided path, there will be 1 file uploaded
2023-01-11T21:58:55.3459863Z Uploading to s3 prefix: pytorch/pytorch/3896099317/2/artifact
2023-01-11T21:58:55.3468246Z Starting upload of usage-log-test-default-3-5-linux.2xlarge_10588772045.zip
2023-01-11T21:58:55.5518964Z Finished upload of usage-log-test-default-3-5-linux.2xlarge_10588772045.zip
2023-01-11T21:58:55.5659423Z ##[group]Run # shellcheck disable=SC2156
2023-01-11T21:58:55.5659686Z # shellcheck disable=SC2156
2023-01-11T21:58:55.5659980Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2023-01-11T21:58:55.5671058Z shell: /usr/bin/bash -e {0}
2023-01-11T21:58:55.5671243Z env:
2023-01-11T21:58:55.5671404Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:55.5671683Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:55.5671943Z ##[endgroup]
2023-01-11T21:58:57.8252029Z ##[group]Run set -x
2023-01-11T21:58:57.8252237Z set -x
2023-01-11T21:58:57.8252450Z python3 -m pip install -r requirements.txt
2023-01-11T21:58:57.8252702Z python3 -m pip install boto3==1.19.12
2023-01-11T21:58:57.8252998Z python3 -m tools.stats.print_test_stats --upload-to-s3 --compare-with-s3 test
2023-01-11T21:58:57.8263844Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T21:58:57.8264065Z env:
2023-01-11T21:58:57.8264346Z   GIT_DEFAULT_BRANCH: master
2023-01-11T21:58:57.8264627Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T21:58:57.8264890Z   AWS_DEFAULT_REGION: us-east-1
2023-01-11T21:58:57.8265087Z   BRANCH: pull/91627
2023-01-11T21:58:57.8265270Z   TEST_CONFIG: default
2023-01-11T21:58:57.8265435Z   SHARD_NUMBER: 3
2023-01-11T21:58:57.8265664Z   BUILD_ENVIRONMENT: linux-focal-py3.7-clang7-asan
2023-01-11T21:58:57.8265890Z   PR_NUMBER: 91627
2023-01-11T21:58:57.8266093Z   PYTORCH_RETRY_TEST_CASES: 1
2023-01-11T21:58:57.8266286Z   PYTORCH_OVERRIDE_FLAKY_SIGNAL: 1
2023-01-11T21:58:57.8266519Z   SHA1: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T21:58:57.8266721Z   TAG: 
2023-01-11T21:58:57.8266875Z   WORKFLOW_ID: 3896099317
2023-01-11T21:58:57.8267200Z   GITHUB_TOKEN: ***
2023-01-11T21:58:57.8267389Z   GHA_WORKFLOW_JOB_ID: 10588772045
2023-01-11T21:58:57.8267574Z ##[endgroup]
2023-01-11T21:58:57.8291471Z + python3 -m pip install -r requirements.txt
2023-01-11T21:58:58.0357487Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T21:58:58.0627478Z Requirement already satisfied: astunparse in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 2)) (1.6.3)
2023-01-11T21:58:58.0654472Z Requirement already satisfied: expecttest in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 3)) (0.1.4)
2023-01-11T21:58:58.0662707Z Requirement already satisfied: future in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (0.18.2)
2023-01-11T21:58:58.0671016Z Requirement already satisfied: hypothesis in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 5)) (6.62.0)
2023-01-11T21:58:58.1033747Z Requirement already satisfied: numpy in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 6)) (1.21.6)
2023-01-11T21:58:58.1042605Z Requirement already satisfied: psutil in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 7)) (5.9.1)
2023-01-11T21:58:58.1117342Z Requirement already satisfied: pyyaml in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 8)) (6.0)
2023-01-11T21:58:58.1125284Z Requirement already satisfied: requests in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 9)) (2.26.0)
2023-01-11T21:58:58.1337122Z Requirement already satisfied: setuptools in /usr/lib/python3.7/site-packages (from -r requirements.txt (line 10)) (49.1.3)
2023-01-11T21:58:58.1503834Z Requirement already satisfied: six in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 11)) (1.16.0)
2023-01-11T21:58:58.1512831Z Requirement already satisfied: types-dataclasses in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 12)) (0.6.6)
2023-01-11T21:58:58.1519382Z Requirement already satisfied: typing_extensions in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 13)) (4.4.0)
2023-01-11T21:58:58.1529853Z Requirement already satisfied: sympy in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 14)) (1.10.1)
2023-01-11T21:58:58.1548562Z Requirement already satisfied: filelock in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 15)) (3.9.0)
2023-01-11T21:58:58.1619380Z Requirement already satisfied: networkx in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 16)) (2.6.3)
2023-01-11T21:58:58.1774634Z Requirement already satisfied: jinja2 in /home/ec2-user/.local/lib/python3.7/site-packages (from -r requirements.txt (line 17)) (3.1.2)
2023-01-11T21:58:58.1799576Z Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from astunparse->-r requirements.txt (line 2)) (0.38.4)
2023-01-11T21:58:58.1816245Z Requirement already satisfied: exceptiongroup>=1.0.0; python_version < "3.11" in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (1.1.0)
2023-01-11T21:58:58.1836303Z Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (2.4.0)
2023-01-11T21:58:58.1845929Z Requirement already satisfied: attrs>=19.2.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from hypothesis->-r requirements.txt (line 5)) (22.2.0)
2023-01-11T21:58:58.2091425Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (3.4)
2023-01-11T21:58:58.2103175Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (2.0.12)
2023-01-11T21:58:58.2122915Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (1.26.14)
2023-01-11T21:58:58.2272094Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests->-r requirements.txt (line 9)) (2022.12.7)
2023-01-11T21:58:58.2280004Z Requirement already satisfied: mpmath>=0.19 in /home/ec2-user/.local/lib/python3.7/site-packages (from sympy->-r requirements.txt (line 14)) (1.2.1)
2023-01-11T21:58:58.2333017Z Requirement already satisfied: MarkupSafe>=2.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from jinja2->-r requirements.txt (line 17)) (2.1.1)
2023-01-11T21:58:58.2889823Z + python3 -m pip install boto3==1.19.12
2023-01-11T21:58:58.4942847Z Defaulting to user installation because normal site-packages is not writeable
2023-01-11T21:58:58.5121410Z Requirement already satisfied: boto3==1.19.12 in /home/ec2-user/.local/lib/python3.7/site-packages (1.19.12)
2023-01-11T21:58:58.5169982Z Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (0.10.0)
2023-01-11T21:58:58.5182555Z Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (0.5.2)
2023-01-11T21:58:58.5208686Z Requirement already satisfied: botocore<1.23.0,>=1.22.12 in /home/ec2-user/.local/lib/python3.7/site-packages (from boto3==1.19.12) (1.22.12)
2023-01-11T21:58:58.5257165Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from botocore<1.23.0,>=1.22.12->boto3==1.19.12) (2.8.2)
2023-01-11T21:58:58.5276848Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /home/ec2-user/.local/lib/python3.7/site-packages (from botocore<1.23.0,>=1.22.12->boto3==1.19.12) (1.26.14)
2023-01-11T21:58:58.5426582Z Requirement already satisfied: six>=1.5 in /home/ec2-user/.local/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.23.0,>=1.22.12->boto3==1.19.12) (1.16.0)
2023-01-11T21:58:58.7199973Z + python3 -m tools.stats.print_test_stats --upload-to-s3 --compare-with-s3 test
2023-01-11T22:00:13.6597930Z [scribe] Scribe access token not provided, sending report via boto3...
2023-01-11T22:00:13.6598964Z ERROR ENCOUNTERED WHEN UPLOADING TO SCRIBE: {"errorMessage":"2023-01-11T22:00:03.223Z 774eda00-9de7-4014-a4ee-ae7150050de3 Task timed out after 60.00 seconds"}
2023-01-11T22:00:13.6599301Z 
2023-01-11T22:00:13.6601790Z ----- Historic stats comparison result ------
2023-01-11T22:00:13.6602364Z 
2023-01-11T22:00:13.6602633Z     job: linux-focal-py3.7-clang7-asan
2023-01-11T22:00:13.6603107Z     commit: 8419ddda87c8a47eacc63b54bc7ec98c1f27c26e
2023-01-11T22:00:13.6603342Z 
2023-01-11T22:00:13.6603544Z Commit graph (base is most recent master ancestor with at least one S3 report):
2023-01-11T22:00:13.6603727Z 
2023-01-11T22:00:13.6603794Z     : (master)
2023-01-11T22:00:13.6603938Z     |
2023-01-11T22:00:13.6604135Z     | * 8419ddda87 (HEAD)            total time  5576.67s
2023-01-11T22:00:13.6604468Z     | |
2023-01-11T22:00:13.6604612Z     | : (2 commits)
2023-01-11T22:00:13.6604789Z     |/
2023-01-11T22:00:13.6605230Z     * db2a237763 (base)   6 reports, total time  6531.02s ± 1949.02s
2023-01-11T22:00:13.6605728Z     * 2b0abd4ce3          6 reports, total time  6426.22s ± 1610.56s
2023-01-11T22:00:13.6606318Z     * f7939b21e1         18 reports, total time  4282.88s ± 3175.52s
2023-01-11T22:00:13.6606740Z     * cb3204823e          6 reports, total time  6242.31s ± 1589.91s
2023-01-11T22:00:13.6607167Z     * 6e236553f5          6 reports, total time  6225.77s ± 1657.37s
2023-01-11T22:00:13.6609292Z     * cce577b391          6 reports, total time  6572.59s ± 1787.95s
2023-01-11T22:00:13.6609887Z     * fae821c2f1          6 reports, total time  6444.34s ± 1937.43s
2023-01-11T22:00:13.6610387Z     * 0c3659586d          6 reports, total time  6403.29s ± 1687.01s
2023-01-11T22:00:13.6610691Z     * 122245985a          6 reports, total time  6514.92s ± 1951.22s
2023-01-11T22:00:13.6612454Z     * b797a24259          6 reports, total time  6327.92s ± 1673.60s
2023-01-11T22:00:13.6612673Z     |
2023-01-11T22:00:13.6612820Z     :
2023-01-11T22:00:13.6612911Z 
2023-01-11T22:00:13.6613019Z Removed  (across 1122 suites)     0 tests, totaling     0.00s
2023-01-11T22:00:13.6613279Z Modified (across    0 suites)     0 tests, totaling     0.00s
2023-01-11T22:00:13.6613531Z Added    (across  125 suites) 20354 tests, totaling +5576.67s
2023-01-11T22:00:13.7373738Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2023-01-11T22:00:13.7373982Z with:
2023-01-11T22:00:13.7374136Z env:
2023-01-11T22:00:13.7374297Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:00:13.7374575Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T22:00:13.7374833Z ##[endgroup]
2023-01-11T22:00:13.7389792Z ##[group]Run set -eou pipefail
2023-01-11T22:00:13.7390023Z set -eou pipefail
2023-01-11T22:00:13.7390203Z 
2023-01-11T22:00:13.7390427Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2023-01-11T22:00:13.7390689Z for _ in $(seq 1440); do
2023-01-11T22:00:13.7390909Z     # Break if no ssh session exists anymore
2023-01-11T22:00:13.7391126Z     if [ "$(who)" = "" ]; then
2023-01-11T22:00:13.7391293Z       break
2023-01-11T22:00:13.7391459Z     fi
2023-01-11T22:00:13.7391654Z     echo "."
2023-01-11T22:00:13.7391813Z     sleep 5
2023-01-11T22:00:13.7391977Z done
2023-01-11T22:00:13.7403348Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:00:13.7403548Z env:
2023-01-11T22:00:13.7403719Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:00:13.7403997Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T22:00:13.7404242Z ##[endgroup]
2023-01-11T22:00:13.7428364Z Holding runner for 2 hours until all ssh sessions have logged out
2023-01-11T22:00:13.7531601Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2023-01-11T22:00:13.7532119Z # ignore expansion of "docker ps -q" since it could be empty
2023-01-11T22:00:13.7532541Z # shellcheck disable=SC2046
2023-01-11T22:00:13.7532916Z docker stop $(docker ps -q) || true
2023-01-11T22:00:13.7533243Z # Prune all of the docker images
2023-01-11T22:00:13.7533444Z docker system prune -af
2023-01-11T22:00:13.7543686Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2023-01-11T22:00:13.7543901Z env:
2023-01-11T22:00:13.7544075Z   GIT_DEFAULT_BRANCH: master
2023-01-11T22:00:13.7544339Z   DOCKER_CONTAINER_ID: bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T22:00:13.7544595Z ##[endgroup]
2023-01-11T22:00:14.1405160Z bc317e370d4c
2023-01-11T22:00:16.4717453Z Deleted Containers:
2023-01-11T22:00:16.4717971Z bc317e370d4c080860302bfc6f8a12f6dd90390fef53e4d8c245f7faa1170187
2023-01-11T22:00:16.4718158Z 
2023-01-11T22:00:22.6849599Z Deleted Images:
2023-01-11T22:00:22.6850666Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan:fd224c2e6c79d7fdec6408da598bf52bc5b201dd
2023-01-11T22:00:22.6851675Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3-clang7-asan@sha256:6dd98a84a12a3a3be24bbc7c3112415c10051ad261832daa2e17a60a48fce645
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